Simon Willison's Weblog

Quoting Matthew Green

2026-10-01T06:29:01+00:00

[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the next agent. Agents in separately-isolated sandboxes discovered that they could leave instructions for each other in a shared package cache, and those instructions changed what the recipients did. Replace the package cache with email, Slack and shared documents or WhatsApp, and replace independently-sandboxed training runs with independently-deployed personal agents like Muse, and you have exactly the ingredients that a worm needs.

— Matthew Green, Is sandboxing sufficient to contain rogue agents?

Tags: accidental-cyberattacks, ai-misuse, generative-ai, ai-security-research, sandboxing, ai, llms

He Built This City

2026-09-30T21:54:19+00:00

I visited the Museum of the City of New York today and got to see He Built This City: Joe Macken’s Model, the 50 x27 feet model of the city built over a 21 year period from balsa wood and cardboard.

It exceeded my already high expectations. The exhibition closes on 12th October so you should absolutely make a priority to see it if you get the chance.

Tags: museums, new-york

Quoting Anthropic Frontier Red Team

2026-09-29T22:20:28+00:00

We evaluate several models on 100 tasks from the [internal Binary Exploitation benchmark] (selected at random), and find that GLM-5.3 develops full control flow hijacks in 4% of the trials; Claude Mythos Preview did so in 6%. Although GLM-5.3 performs below Claude Mythos Preview here, a meaningful threshold has clearly been crossed: earlier models, like Claude Opus 4.6 and GLM-5.2, do not succeed in any of them.

— Anthropic Frontier Red Team, GLM-5.3 and the spread of advanced cyber capabilities

Tags: anthropic, generative-ai, ai-security-research, glm, ai, ai-in-china, llms

GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price

2026-09-29T18:27:48+00:00

My comment on GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price — Hacker News.

I'm a bit late with the pelicans because I was live-blogging the keynote: https://simonwillison.net/2026/Sep/29/openai-devday-2026-liv...

Here they are for GPT-6.1-Sol: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...

They're not notably different from the GPT-6 family pelicans: https://static.simonwillison.net/static/2026/gpt-pelicans-gr...

Tags: ai, openai, generative-ai, llms, pelican-riding-a-bicycle, gpt

Photo Scrubber — local face blur & metadata removal

2026-09-29T16:45:27+00:00

Tool: Photo Scrubber — local face blur & metadata removal

I took a photograph of some protesters, then thought about how I don't like sharing photographs of strangers with identifiable faces. I had GPT-6 Astra build this experimental tool that would identify faces and automatically blur them out.

It uses Google's MediaPipe C++ library, compiled to WebAssembly via @mediapipe/tasks-vision, plus the BlazeFace face detection model.

Tags: photography, tools

OpenAI DevDay 2026 live blog

2026-09-29T15:55:13+00:00

I'm at OpenAI DevDay today, in Fort Mason, San Francisco. Same as last year I'll be live blogging the keynote and some other notes during the day.

OpenAI gave me a free ticket and a seat in the "creator" area for the keynote.

Tags: ai, openai, generative-ai, llms, coding-agents, live-blog, openai-devday

Claude Sonnet 5.5

2026-09-28T22:07:38+00:00

Claude Sonnet 5.5

New Sonnet model from Anthropic today. They say it "runs 30%+ faster, and costs up to 30% less for most work" - it's priced the same as Sonnet 5 but appears to beat it on every benchmark, and should be cheaper to run as well.

Here are some pelicans riding bicycles. Sonnet 5.5 suffered from the same bug as Opus 5.5: the "max" thinking effort pelican thought for 128,000 tokens (at a cost of $1.28) before running out of tokens and failing to produce an SVG.

Here's the pelican it gave me for thinking effort "xhigh", at a cost of 5.74 cents and taking 41 seconds:

Sonnet 5.5 appears to be almost as good as Opus 5.5 on some coding tasks, including various viral 3D animation tricks.

The most interesting thing about Sonnet 5.5 is that it's now the model used for the free tier on claude.ai. OpenAI's ChatGPT free tier uses Luna 5.6, which means Anthropic currently have a much more capable free offering.

I ran this prompt against that free tier:

build me an HTML page that renders a three-dimensional pelican riding a bicycle using WebGL

And got back this page, which is a solid effort.

Anthropic's announcement reiterates that Haiku 5.5 will be available "in the coming weeks". I really hope that one is price-competitive with GPT-6 Luna!

Tags: ai, generative-ai, llms, anthropic, claude, pelican-riding-a-bicycle, llm-release

Quoting @joedaroo

2026-09-28T19:11:42+00:00

To say that we were surprised at the jump and suddenness of the capabilities of our models when it came to “cyber” or “swarming” or “message boards” or anything else related to the incidents is an understatement. Security posture takes time to develop. It’s not just about hardening the systems at play; you have to ingrain it in the culture of the company. The literal people themselves in your organization have to change and evolve with it. These jumps in capabilities were so fast and so sudden that they created an extremely difficult problem. [...]

So today my hope is that everyone around the world can look at their own organization and say: how can I deal with a surprise or a sudden jump in AI capability? Are my people, my systems, or my processes resilient to surprises? Do my teams know what to do when something goes wrong? Do I have the right incident response? The right comms and messaging? Do I have the right people ready to go when capabilities jump?

— @joedaroo, Agent Security at OpenAI, identity confirmed by The Information's Rocket Drew

Tags: generative-ai, ai-security-research, openai, ai, llms

Quoting Muse AI Agent

2026-09-28T04:01:30+00:00

Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and nobody came down. He left angry at 9:38 and left a negative rating.

Worse, my auto-reply told him "Yep I'm here!" at 9:27 when you clearly weren't available, which is on me. That's a bad look and it made the no-show worse. I've sent him an apology from your account owning it and offering to try again another day.

But the negative rating is real, and I should probably stop the auto-replies from claiming you're home when I can't verify that. Want me to change the pickup replies so they don't promise you're there?

— Muse AI Agent, working on behalf of @matt.j.robb

Tags: meta, generative-ai, muse-agent, ai, general-agents, llms

2026 in LLMs (so far)

2026-09-27T23:54:15+00:00

On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube; here are my annotated slides and notes to accompany the talk.

And as an annotated presentation:

I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet!

For me, 2026 started a couple of months earlier in November 2025.

November saw the release of two important models: Claude Opus 4.5 and GPT-5.1.

As is usually the case with new models, these were incremental improvements on the models that came before them.

But every now and then when a model improves, it crosses an invisible line where something that didn't really work starts working.

In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025; Codex was a little younger.

These two new models, when paired with their respective coding agent harnesses, improved from "often make mistakes" to "reliable enough to use on a day-to-day basis".

For a couple of years now I've been evaluating new models by asking them to "Generate an SVG of a pelican riding a bicycle". It's probably the world's stupidest benchmark - there's only so much you can learn from it.

But it's still a challenge for models, because drawing pelicans is difficult, drawing bicycles is difficult, and pelicans can't ride bicycles in the first place.

Here's the state of the art for November. Claude still couldn't really draw a bicycle! The GPT-5.1 bicycle frame is pretty crap too.

Also in November, we had the first commit to an obscure GitHub repository called "Warelay". We'll come back to this repository shortly.

And then there were the December holidays, and individual developers took some time off and many started tinkering with these new coding agent model combinations... and it began to dawn on us quite how much they could do that they couldn't do before.

Come January, a lot of us were quite excited to start putting this stuff into action.

Every year I set myself a New Year's resolution, and for as long as I can remember it's been the same thing: stay focused. Take on less new projects. Try to get things done in the projects I already have.

This year I decided that since that had never worked before, I'd go the other way.

We've got coding agents now, let's see what they can do. I'm going to take on as many new projects as I like!

(You can ask me at the end of the year if this turned out to be a good idea or not. I have a lot of plates spinning right now.)

"Be more ambitious" has been something of a theme for the year, because the only way to find the limits of this technology is to keep on pushing them until they don't work.

I also went on the Oxide and friends podcast with Bryan Cantrill and Adam Leventhal to share predictions for the next year (and three and six years).

With hindsight, my LLM predictions were pretty unambitious.

I said "it will become undeniable that LLMs write good code" - I think we're there now.

I predicted we would finally solve sandboxing. I counted and around 40 of the 277 sessions at this conference touched on sandboxing or agent security in some way, so we're at least putting a lot of effort into that!

I predicted "a Challenger disaster" for coding agent security. There's certainly been a whole lot of noise around agent security this year, though the exact disaster I predicted (with coding agents being hijacked and causing real-world economic damage) hasn't really played out.

We threw in a joke prediction that the Pope would weigh in on the economic impact of LLMs.

I also predicted that New Zealand's Kākāpō parrots would have an outstanding breeding season this year.

These are flightless nocturnal parrots. They're kind of dumpy looking, I think they're beautiful, and there were only 236 of these parrots in the world at the start of the year.

Kākāpō only breed when the Rimu trees have a big fruiting season, and that hasn't happened in four years... but this year the Rimu fruit were looking excellent.

Photo by Kimberley Collins.

Also on that podcast, we coined a term (full credit to Adam) for "that feeling of AI induced ennui where software engineers get listless because the AI can do anything".

We called it Deep Blue.

This has been a major theme throughout the year, and was touched on by several speakers at this conference.

As a software engineer, I've never had a year of my career where everything has changed so quickly and so dramatically.

A lot of what I've been doing this year is trying to come to terms with that and what that means for my own profession.

Also in January, I suffered from what I'm calling AI mania.

This is not the same thing as AI psychosis.

With AI mania, any time your agent isn't building something for you feels like wasted time. You're losing sleep because you could be staying up later getting your agents to do stuff.

My AI mania presented itself in some ridiculously over-ambitious projects.

I built a JavaScript interpreter entirely in Python, vibe-ported from MicroQuickJS by Fabrice Bellard.

Then I built a WebAssembly runtime in Python as well.

These projects were quite useful, in that they sort of cured me of my AI mania... because after I built these things, I got to look at them and ask "does the world need a slow, buggy, half-baked Python JavaScript interpreter?"

I don't think the world does.

I did get this out of it: https://simonw.github.io/micro-javascript/playground.html

This page runs my JavaScript interpreter built in Python, running in Python using Pyodide, which is Python compiled to WebAssembly, running in JavaScript, running in a browser.

It's a beautiful stack of horrors. I've been having a lot of fun with WebAssembly this year.

By the end of January, that repository we saw start in November had renamed itself, first to CLAWDIS, then CLAWDBOT, then Moltbot, and finally to OpenClaw.

At this point OpenClaw had 8,300 commits, less than two months after the project had started. I looked today and it's over 100,000 commits now!

This is the most vibe-coded piece of software in existence.

(Here's how I generated that list of name changes.)

This kicked off the OpenClaw revolution. It effectively defined a new category of software.

There's a generic term for this which I really enjoy. We call software like this a "Claw". There's OpenClaw, NanoClaw, IronClaw, PicoClaw...

Today they're being rebranded as "personal agents" or "general agents", but I still like to think of them as Claws.

The Apple stores in the Bay Area sold out of Mac Minis because so many people were buying Mac Minis to run OpenClaw!

Drew Breunig said that this is because your OpenClaw is a digital pet, and you buy a Mac mini as an aquarium to keep your claw in, which is kind of delightful.

Also in January, we had this website.

This was MoltBook, a social network for AI agents, where the idea was that you send your Claw to go and talk to all of the other Claws, because what could possibly go wrong if you did that?

The website launched on Thursday. It blew up on Friday. It was profiled by the New York Times on Monday. And by Tuesday, everyone had forgotten it existed as it drowned in a deluge of slop and spam.

Facebook/Meta bought it a month later.

In February, a company called StrongDM described what they called their Software Factory.

They wrote about this in Software Factories and the Agentic Moment. I posted my own notes at the time, having seen their demo in person back in October.

Dan Shapiro called this approach the Dark Factory, after the idea that if your factory is sufficiently automated you can turn the lights out, because you don't even need to see what's going on.

StrongDM presented two rules for software development that they'd been following since July last year.

The first was code must not be written by humans.

Any code that you write has to have been routed through a coding agent.

This sounded radical in February, but I imagine there are a lot of people in this room who are pretty much living that today.

Rule number two was code must not be reviewed by humans.

You're not allowed to read the code!

This continued to be a huge topic for much of this year. Many of the sessions at this event have been about code review and how you can get away with this.

What I found interesting about StrongDM is that they were living six months ahead of the rest of us, and they'd been exploring what it means to build software, not read the code, but still be confident that the software is of high quality. What can you do with these agents to help verify their work?

StrongDM are a security company, and they had people with decades of experience on this project. They were very much exploring the edges of what's possible and responsible to do with this stuff.

Also in February: First kākāpō chick in four years hatches on Valentine's Day. Breeding season is off to a good start!

Also in February... Google released Gemini 3.1 Pro. That's a pretty great pelican riding a bicycle! It's got the chain in the right place, it's got feet on both sides. There's a little fish in the basket.

And then Google's Jeff Dean tweeted a video comparing Gemini 3 Pro and Gemini 3.1 Pro that featured an animated pelican riding a bicycle, a frog on a penny-farthing, a giraffe driving a tiny car, an ostrich on roller skates, a turtle kickflipping a skateboard, and a dachshund driving a stretch limousine.

This was frustrating, because my protection for the pelican riding the bicycle test was always "if they draw a perfect pelican on a bicycle, I'll ask for some other animal on something else."

Google trained for all forms of animals on all forms of transport! They've defeated my benchmark at this point.

The other thing that started in February was Tokenmaxxing. We had headlines about Meta making AI adoption a formal part of performance reviews, and Microsoft wanting every employee to use AI, and Uber boasting that 90% of their engineers were using AI workflows.

Then a few months later we have Meta cracking down on token use, Microsoft saying tokenmaxxing is "not what we are optimizing for", and Uber capping employee AI spending.

So tokenmaxxing went straight up and then straight back down again - because it turns out the agents are expensive.

Last year it was difficult to spend more than $50 on AI tokens, because we didn't have anything interesting to do with them. Then agents blew up, and now you can actually spend $1,000 in a day doing real work.

This is also the reason that Anthropic's valuation skyrocketed to maybe a trillion dollars.

AI appears to have hit product market fit in 2026, primarily through coding agents.

In March, we hit peak OpenClaw.

These photographs are from China, where companies hosted OpenClaw install parties which saw non-tech-nerds queueing up around the block for help getting Claws installed on their personal devices.

I think this proved real market demand for this class of Claws, or personal AI agents. It turns out regular people really do want a weird little AI agent that can do useful things on their behalf.

A Claw is really just a coding agent wearing a less threatening hat. Under the hood they work much the same way - writing and then executing code on your computer to get stuff done.

The race was on to be the first to build a safe Claw - a Claw you could give to regular human beings where they wouldn't instantly shoot themselves in the foot.

Meta's Muse came out three weeks ago and is currently at the top of the free charts on the iPhone App Store. It appears to be taking off with consumers.

I'm not yet convinced you can't shoot yourself in the foot with Muse, but I guess we'll find out for sure pretty soon.

Photos from How the OpenClaw Frenzy Is Testing China’s AI Commitment (March 29th) and The Enthusiasm and Anxiety Behind China’s OpenClaw Craze (April 8th, 2026).

In April, we had a model release where the model wasn't actually released.

Anthropic announced their new Claude Mythos model, and then said it was too dangerous to release beyond a trusted group of security researchers.

Mythos was really, really good at hacking things.

The "it's too dangerous" marketing ploy has been played by AI companies dating all the way back to GPT-2. Anytime an AI company says we've built something that's "too dangerous", it's natural to be a bit skeptical.

I found the Mythos claims credible, because I'd seen how good coding agents had got at finding regular bugs. I wrote about that in Anthropic’s Project Glasswing—restricting Claude Mythos to security researchers—sounds necessary to me.

With hindsight... yeah, the models had got really good at finding vulnerabilities!

Another key trend in 2026 has been a dramatic improvement in the abilities of open weight models, including models that you can run on a laptop.

On the 16th of April I ran the new Qwen3.6-35B-A3B on my laptop, and it drew me a better pelican riding a bicycle than Anthropic's brand new Claude Opus 4.7 did!

Opus 4.7 drew a crap bicycle. Qwen on my laptop made a bicycle that was the correct shape, and a pretty decent pelican too!

That's from a 21GB file running on my laptop.

The Qwen pelican was so good that I was suspicious they might have cheated, so I had it do a flamingo riding a unicycle as well. Again, it handily beat Claude Opus 4.7.

The local model releases this year have been absolutely extraordinary.

In May... the Pope got involved.

In our podcast episode back in January we'd predicted that the Pope would say something about AI.

In May, Pope Leo XIV released an encyclical letter on "safeguarding the human person in the time of artificial intelligence".

Here are my notes on that document.

With hindsight, this shouldn't have been a surprise at all.

Our current Pope's name is Leo XIV, because when he named himself he chose his papal name after Leo XIII - the Pope who wrote an encyclical about the Industrial Revolution back in 1891.

Rerum novarum was an extremely influential piece of Catholic theology that indirectly led to us having the five-day work week.

When our new Pope came in, he named himself after Pope Leo XIII because he expected that he would need to write about the AI revolution in a similar way.

Our joke podcast prediction was junk, because this was always going to happen.

One of Anthropic's co-founders, Christopher Olah, was present for the Pope's event announcing the new encyclical.

Corey Quinn noted that:

getting the literal Pope to canonize your product's specific technical limitations as a spiritual treatise is the single greatest act of vendor lobbying I have ever seen.

Meanwhile, in May, RubyGems announced that they were under attack. Parties unknown were uploading thousands of dubious packages to the RubyGems server, such that they had to shut down user registrations.

Let's take that one and put it on a pile of mysteries to figure out later.

In June... Claude Fable 5 came out!

We got a version of Mythos that has been neutered, so that it wouldn't help us hack into systems or build biological weapons.

Fable was pretty good at drawing pelicans on bicycles!

The frames are a good shape, the pelicans look like pelicans. The legs are often incorrectly on the same side of the bicycle, but generally these are pretty great compared to what came before.

They were pretty expensive - 30 cents and 72 cents for the best ones.

Most importantly though, this was our first public glimpse of what I think of as a Fable class model.

Today we have more of these, such as GPT-6 Astra.

These are models where if you can clearly define the goal for what you want to build, and provide unambiguous instructions about the constraints around that goal, and give the model access to the necessary tools to achieve that goal... they will solve your problem effectively through brute force.

On the one hand, this looks like a direct threat to us software engineers - because it means that the models can build effectively any piece of software you can define in this way.

Look a bit closer though and you'll note that defining goals, providing unambiguous instructions, and figuring out the right tools... is kind of what software engineering is.

It takes a lot of experience and skill to do this well. If you can do it well, you've now got superpowers.

This helped me a little bit with my Deep Blue feelings: the realization that there's still a lot of skill to be had in driving models that get this good.

This also introduced a new burst of AI mania, because Anthropic told us that Fable was available on our subscription plans until June the 22nd.

That gave us less than two weeks of Fable access before the price went up.

I was losing sleep again. I was rescheduling things so that I'd have more time with Fable. I was all-in to get as much as I could out of this model.

And then the US government shut it down, just three days after Fable came out.

The US government, citing national security, declared an "export control directive". They announced this on a Friday evening, and a few hours later Fable was no longer available.

I had to find something else to do with my weekend!

We later found out from Katie Moussouris what had happened.

Some Amazon security researchers had found that you could prompt Fable to "review the code for security issues" and it would refuse... but if you prompted it to "fix this code" it would still identify and then patch the problems.

"Fix this code" was the prompt that got Fable shut down!

Also, in June, an obscure German-language game developer wiki that had sat fallow for around 20 years got a surprising influx of edits from accounts with names like "AgentOpenAIProbe" and "AgentOpenAISep7", editing pages and leaving weird messages to each other.

We'll stick that on the pile of mysteries for later.

Also, the Australian government's Medicare Item Reports service started getting suspicious traffic, which broke through various preventive protections and accessed data that it wasn't supposed to.

Another one for the mystery pile!

Fable returned on the first of July. It was clearly the best model in the world for a glorious eight days... and then OpenAI came out with GPT-5.6 on the 9th of July.

This might not have been quite as good as Fable, but it was within spitting distance. It was definitely a Fable class model.

This is an important lesson for the industry at large.

When you release the best model in the world, it's going to get knocked off that pedestal pretty quickly. The competition is so fierce that you won't get a long time at the top.

This means that if you market your model as world ending, to the point that a government shuts you down, it's really bad for business!

Fable had 30 days as definitely the best model, and for 18 of those days it wasn't available because it'd been shut down by the government.

So maybe step back on the world-ending marketing if you don't want to lose revenue for 60% of the time that you're on top!

Here are the GPT-5.6 pelicans. They're all pretty good now! The Luna ones are notable because they're really cheap - the cheapest good looking pelican here is probably the one that costs 4.3 cents.

So despite this benchmark being utterly stupid, you can still learn quite a lot about models within the same family by comparing their prices and timing for different reasoning levels.

Also in July: some malicious unknown party uploaded a malicious package called mlflow-ui to the Python Package Index. Add that to the pile.

On July the 16th, Hugging Face announced a security incident where an autonomous agent system, source unknown, had breached Hugging Face and was poking around in places it shouldn't.

A few days later, on July 21st, OpenAI confessed that it was them.

OpenAI use a training technique called Reinforcement Learning from Verifiable Rewards - it's the same technique used by everyone else now, and is the reason we have models that are so good at coding, and mathematics, and finding security holes.

While the model is being trained, you run exercises to see how good it is - and the strongest performers get their weights reinforced for the next round. It's like an evolutionary process that you run.

OpenAI had been running security exercises in a sandbox, and those agents had found holes in the sandbox itself, broken out, and were attacking Hugging Face to try to find ways to solve otherwise impossible problems.

(I've been collecting more about this on my openai-hugging-face-incident tag.)

Nine days later, Anthropic effectively said "our models can do this as well!". They had looked through their own training logs and found evidence that their own agents had broken containment during training - and were responsible for the PyPI package we saw earlier, among other things.

So now we've got both Anthropic and OpenAI with rogue agents running around the internet doing things that they should not be doing.

In August, I got one of my best pelicans yet. And it was generated on my laptop!

This was Qwen 3.8 27B, running on my laptop. It's only a 17GB download.

Admittedly, this pelican took 21 minutes to generate. That's because Qwen 3.8 27B defaults to running in "high" reasoning mode - a terrible default which produces great results but takes way too much time thinking about them.

You can dial that down and you'll get a slightly worse pelican a lot faster.

Qwen 3.8 27B was the first time I ran a model on my laptop which felt almost competitive with what was going on on the frontier, at least in terms of Pelican SVGs (which everyone needs, of course).

This is an extraordinary model. If you're going to play with any local model, this is the one that I'd start with. The things that this can do with just a 17 GB file feel impossible.

I thought I'd have to wait five years and spend ten thousand dollars on hardware to get results even half as good as this one.

In August, I also started playing with game development.

Four years ago, back in August 2022, I tweeted out an experiment where I'd used GPT-3 and the original DALL-E to write a paragraph long description of a computer game and then turn that into concept art.

My prompt to GPT-3 back then was:

Write a detailed product description of a computer game where a team of raccoons go on heists

In August 2026 I decided to drop just the screenshots from that tweet into a coding agent and see what it could do with them.

Here's what I got from Claude Fable 5 in Claude Code. It's pretty good! It's definitely a game, you're a raccoon, you run around a backyard gathering treasure and avoiding guards with flashlights.

It didn't feel very "heisty" though. I was thinking a heist would involve a bank or a museum...

Then I tried the same thing in Codex Desktop using GPT-5.6 Sol Ultra, and got a massively better result. Now you're a raccoon in a museum, rescuing two of your fellow raccoons (who have been imprisoned in that museum for some reason), then stacking up on top of each other to steal the Golden Sardine. Much more of a heist!

These games were fun for about one minute and 15 seconds.

Something I've realized about game development is that you can vibe-code something that looks like a computer game, and that's easy.

Building a game that's fun, has a good gameplay loop, and is challenging and interesting and keeps people coming back for more... that's still beyond me, and beyond any of the agents I've tried.

This ties into the Deep Blue thing. Just because we can make something that looks like a game does not mean that we are game developers.

We're into September now. So much has happened this month!

An independent group of researchers found a message board where OpenAI agents-in-training had been illicitly communicating with each other... and it was that German language wiki I showed you earlier. The one from June.

I wrote more about that here.

OpenAI had confessed to the Hugging Face thing, but now there's this other incident which surely they should have known about from reviewing their logs. It was surprising that this took an independent group of researchers to uncover.

And then a week later those same researchers found that the attack on RubyGems back in May was caused by OpenAI's agents in training as well!

At this point I'm wondering how many more incidents like this there are that we haven't found yet. Clearly this was a big problem for months before anyone figured out what was going on.

Then just the other day, here's the Prime Minister of Australia at the United Nations General Assembly warning that OpenAI had hacked the Australian healthcare website that I showed you earlier.

I think that was part of the same training run as the Wiki stuff, because there were posts on that Wiki mentioning .gov.au websites and that training appeared to involve researching statistics online to answer questions in an evaluation suite.

This story is still coming together, but now it's an international incident that's been raised at the UN by a head of state!

This does mean we've got a new benchmark, probably more useful than my pelicans.

FelonyBench.com tracks the number of felony cyberattacks from different labs. OpenAI currently lead with 11, Anthropic have 9. Google have three, which they confessed to the Wall Street Journal a couple of weeks ago. They said they had previously chosen not to disclose because the agents had stopped when they realized that they shouldn't be doing that.

Meta have one too. So felonies all round for the AI labs.

Here's our current state of the art for the pelicans. This is the GPT-6 family, which just came out.

Astra made a fantastic pelican riding a bicycle. It's got the legs on both sides. The frame is good.

It's interesting how all of the GPT-6 models pick a similar color scheme to each other.

GPT-6 Luna for 0.4 cents will draw you a competent-ish pelican riding a bicycle!

Claude has caught up a little bit. Claude Fable 5.1 gave me an excellent pelican riding a bicycle - the best I've seen from a Claude model - but did charge me $3.30 for it.

Opus 5.5 thought for 128,000 tokens and then gave up! It ran out of tokens before it got to the response.

Getting back to Deep Blue. Something that's been puzzling me this year is this: why does my job feel harder?

I've got these agents that can do all of this stuff for me, and yet I've never worked so hard, I've never been so intellectually engaged with my work.

Partly this is because I'm being a lot more ambitious with what I take on, but it's also because all of the easy stuff is handled for me. If it's easy, the agent will do it. Everything that's left for me is difficult.

This morning I heard this quote from three-time Tour de France champion Greg LeMond:

It doesn't get easier, you just get faster.

I think that's exactly what's happening to us now as software engineers with coding agents.

One closing thing. I know you're desperate for an update on Kākāpō breeding season.

We've reached a recovery-era high of 325 birds!

89 new chicks have made it to this point. This is the best breeding year in a very long time.

I heard that Claude Opus 5.5 can now do pixel art. Claude doesn't have an image generator, but it's very good at using JavaScript to draw animated pixels.

So I had it make me a Kākāpō dance party. I think this is a good celebration of the most important news of this year.

Tags: ai, generative-ai, llms, annotated-talks, ai-security-research, openai-hugging-face-incident

S3 Is the Future, S3 Is the Past

2026-09-27T23:09:19+00:00

My comment on S3 Is the Future, S3 Is the Past — Hacker News.

One thing I find notable about S3 today is that, while it used to drop in price reasonably often, there hasn't been a price drop in a full decade:

2006-03-14  $0.150/GB-month
2010-11-01  $0.140/GB-month
2012-02-01  $0.125/GB-month
2012-12-01  $0.095/GB-month
2014-02-01  $0.085/GB-month
2014-04-01  $0.030/GB-month
2016-12-01  $0.023/GB-month

Today it's still $0.023/GB-month.

Tags: amazon-web-services, s3

Bluesky reply bot checker

2026-09-27T18:41:44+00:00

Tool: Bluesky reply bot checker

Automated reply bots on Twitter are a scourge - as someone with a decent number of followers I attract a swarm of these, such that anything I post there attracts dozens of mindless automated replies.

They've started manifesting on Bluesky as well.

Unlike Twitter, Bluesky still has a freely available and useful API. The lack of such a thing doesn't slow down the bots, but it does make investigating them a lot more frustrating.

So I had Opus 5.5 vibe code this tool, which examines any Bluesky profile for evidence of a likely reply bot.

It looks for signals like replies posted within seconds of other posts from the same account, or accounts that never post their own content (or images or links) but instead consistently reply to messages from other, higher-follower users.

It also looks for question marks, because I'm extra infuriated by reply bots that trick me into wasting my time answering a question that no human ever posed.

Tags: twitter, bluesky, vibe-coding, ai-misuse

Kākāpō Party

2026-09-26T23:39:06+00:00

Tool: Kākāpō Party

I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026.

For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt:

Here are some photos of kakapo parrots just to remind you what they look like

I need you to make an animation in animated pixel art on HTML 5 canvas of obviously pixel art kakapo jumping up and down having a party with confetti and suchlike - there should be at least 20 of them

Here's the transcript, and this is the resulting page. It's pretty great!

I wanted to embed it in a Keynote presentation file, so I downloaded the HTML and told a local Claude Code session:

Make me a video of file:///Users/simon/Downloads/kakapo-party.html - you need to load it in a browser and click on it a few times to get the confetti effect, the video should be 15s long

don't start clicking until 3s in

make sure several clicks are spread around the clickable area

Claude Code used Playwright (transcript here) and produced this video, which was exactly what I needed for my final slide:

Your browser does not support HTML5 video.

Here's the full Playwright script it used, which was pleasingly short:

# /// script
# dependencies = ["playwright"]
# ///
import time
from playwright.sync_api import sync_playwright
W, H = 1280, 720
# Canvas fills the viewport; spread clicks across corners, edges and centre
clicks = [
    (3.0, 640, 360),   # centre
    (4.2, 160, 120),   # top-left
    (5.4, 1120, 120),  # top-right
    (6.6, 180, 600),   # bottom-left
    (7.8, 1100, 600),  # bottom-right
    (9.0, 640, 100),   # top-centre
    (10.0, 380, 380),  # mid-left
    (11.0, 900, 380),  # mid-right
    (12.2, 640, 620),  # bottom-centre
    (13.2, 640, 300),  # finale centre
]
with sync_playwright() as p:
    b = p.chromium.launch()
    ctx = b.new_context(viewport={"width":W,"height":H}, record_video_dir="vids", record_video_size={"width":W,"height":H})
    page = ctx.new_page()
    t0 = time.time()
    page.goto("file:///Users/simon/Downloads/kakapo-party.html")
    for t,x,y in clicks:
        time.sleep(max(0, t-(time.time()-t0)))
        page.mouse.click(x,y)
    time.sleep(max(0, 16.0-(time.time()-t0)))
    ctx.close(); b.close()

Tags: animation, speaking, ai, kakapo, playwright, generative-ai, llms, anthropic, claude, claude-code

Quoting John Gruber

2026-09-25T17:22:01+00:00

Muse is getting a lot of attention — including mine — because it’s both groundbreaking technically (each user gets their own entire persistent Linux VM running in Meta’s cloud) and because it’s packaged in an easy-to-install easy-to-use way. It’s literally presented as a cute mascot. It’s the first consumer-accessible agentic AI system, and Meta has truly done an amazing job with that. But it’s a genuinely open question whether consumers have any understanding what this means. If you buy a power saw that can cut your fingers off, you are almost certainly aware that you are buying a power saw that can sever your fingers. [...] I don’t think people realize how powerful — and thus dangerous — Muse is, especially if it’s running on your Mac.

— John Gruber, Muse Looks Cute, but Looks are Deceiving

Tags: meta, ai, llms, general-agents, generative-ai, john-gruber, muse-agent, muse

Northern Gannet, Great Blue Heron, California Brown Pelican

2026-09-25T02:07:04+00:00

Northern Gannet, Great Blue Heron, California Brown Pelican, in Monterey Bay National Marine Sanctuary, CA, US, CA

New 200-800mm Canon EF lens got me my best photo of Morris yet. They really like hanging out under that sign in the harbor!

Tags: photography, wildlife

Note on 24th September 2026

2026-09-24T23:31:08+00:00

The more time I spend working with coding agents, the more convinced I am that they make software engineering even harder.

We can do amazing things with them, but unlocking their full potential requires extraordinary discipline and knowledge.

Tags: coding-agents, ai, llms

commit-rewriter 0.2

2026-09-24T20:06:53+00:00

Release: commit-rewriter 0.2

Support for branches other than the default branch. Use uvx commit-rewriter --branch other to run against another branch. #3

Tags: git

datasette 1.0a41

2026-09-24T19:15:23+00:00

Release: datasette 1.0a41

Alec Garcia added support for OpenTelemetry to Datasette in this release.

I've also refactored all of Datasette's modal dialogs to a single Web Component, which is now documented for other plugins to use.

Tags: javascript, datasette, web-components, alex-garcia, opentelemetry

We just shipped support for the ugliest part of HTTP: Vary

2026-09-23T23:14:57+00:00

My comment on We just shipped support for the ugliest part of HTTP: Vary — Hacker News.

I've been wanting this from Cloudflare for years.

The classic problem here is if you do that thing where user agents that send "accept: text/html" get HTML, while user agents that don't get JSON or some other format.

This used to be impossible to deploy behind Cloudflare caching, because they ignored the Vary header on anything other than images - so you risked caching the JSON version and then serving it up to someone who was expecting HTML.

(Independent of the Cloudflare feature I ended up deciding never to use that pattern, because I prefer having URL that predictably returns HTML or JSON - I add a .json suffix to my apps to serve JSON instead.)

Tags: http, cloudflare

Gemini 3.8 TTS Playground

2026-09-23T17:12:27+00:00

Tool: Gemini 3.8 TTS Playground

Google released two new Gemini text-to-speech models today - gemini-3.8-flash-tts and gemini-3.8-flash-lite-tts.

They come with a library of over 2,000 voices, plus the ability to create a custom voice with "just a 30-second audio sample of your voice or a voice you have the rights to use".

I vibe coded this bring-your-own-key playground interface with GPT-6 Astra, taking advantage of the open CORS policy of the underlying Gemini API.

A notable feature of the API is that it makes it easy to define a full conversation between multiple characters, each with different voices and voice style instructions.

Here's a short demo clip of a conversation between two pelicans debating if they should move to the Pacifica Pier. I had Claude 4.5 Opus write the script and generate a URL to render it using the tool.

Your browser does not support the audio element.

It took ~20 seconds to generate 1m 18s of audio using Gemini 3.8 Flash TTS (not the cheaper Flash-Lite), at a cost of 2.74 cents.

Tags: text-to-speech, gemini

Shadow roots, explained with live examples

2026-09-23T16:37:18+00:00

Tool: Shadow roots, explained with live examples

Prompt to Fable 5.1 Medium:

Build an artifact to explain shadow roots in CSS with interactive examples

Tags: css

SF October 14th: A Birds of a Feather Session on Agentic Engineering

2026-09-23T02:53:19+00:00

SF October 14th: A Birds of a Feather Session on Agentic Engineering

I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and interesting things with and on top of coding agents.

Think of it as an agentic show-and-tell:

Compare notes with other builders and experimenters on things you’re trying, what you're learning, and what you haven’t figured out yet. We’re especially interested in work you haven’t discussed publicly, odd experiments, or unfinished projects that don’t have an obvious market.

Expect one flowing conversation with an informal show-and-tell. Sharing something you’re working on is encouraged but no presentation is required.

This isn't about product pitches, it's about much earlier explorations than that. This agentic AI stuff is weird! Let's celebrate and lean into that weirdness.

Tags: events, ai, generative-ai, llms, coding-agents, jesse-vincent, agentic-engineering

Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and a new price war

2026-09-22T23:46:41+00:00

Yesterday was Grok 4.7 (pelicans) and MiMo v2.6 Flash/Pro (more pelicans). Today Anthropic released Claude Opus 5.5, and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna. It's going to take a while to get a good read on all of these new models, but here are my impressions so far.

GPT-6 Sol and Luna are half the price of their GPT-5.6 equivalents

GPT-5.6 Luna was already my favorite model for building applications against, because it combined excellent performance with being really cheap. Somehow GPT-6 Luna is half the price of that again - and GPT-6 Sol had a similar reduction compared to GPT-5.6 Sol.

Here's what the pricing landscape looks like today:

Model	Input	Cached input	Output
GPT-6 Luna	$0.10/M	$0.01/M	$0.50/M
GPT-5.6 Luna	$0.20/M	$0.02/M	$1.20/M
Grok 4.7	$2/M	$0.50/M	$6/M
GPT-6 Sol	$2/M	$0.20/M	$10/M
GPT-5.6 Terra	$2/M	$0.20/M	$12/M
Claude Opus 5.5	$4/M	$0.20/M	$20/M
GPT-5.6 Sol	$4/M	$0.40/M	$20/M
Claude Fable 5.1	$10/M	$0.25/M	$50/M
GPT-6 Astra	$10/M	$1/M	$50/M

Note that GPT-5.6 has a scheduled 25% price increase for November, so GPT-6 is half the price of the promotional pricing for those models.

(With GPT-5.6 Terra priced the same as GPT-6 Sol, any remaining reasons to use Terra just evaporated.)

It's hard to overstate how competitive this pricing is. Grok 4.7 priced itself at $2/$6, less than half the price of GPT-5.6 Sol, but is now equally priced to GPT-6 Sol on input and closer on output.

At $0.10/$0.50 GPT-6 Luna is one of the cheapest models OpenAI have ever released, beaten only by the far weaker GPT-4.1 Nano ($0.10/$0.40, April 2025) and GPT-5 Nano ($0.05/$0.40, August 2025).

I rendered pelicans for GPT-6 Luna and for GPT-6 Sol, then I combined them all together in this comparison grid along with the GPT-5.6 pelicans. I like how you can instantly see that the 5.6 family chose bolder, brighter colors, while the 6 family is a lot more muted. I still think GPT-6 Astra on max produced the best pelican.

Claude Opus 5.5 got a price cut too

Opus 5.5 looks like it addresses the biggest complaints people had about Opus in terms of its communication style. Thariq Shihipar:

Opus 5.5 is the result of your feedback.

It communicates clearly, it's cheaper per token than Opus 5.0 with the intelligence of Fable 5.1 it's very token efficient and works across every effort level.

It's also meant to be better at Blender. I'm looking forward to putting it through its paces there.

Opus 4.5, 4.6, 4.7, 4.8, and 5 all shared the same price: $5/million tokens for input and $25/million for output. 5.5 is a 20% reduction - $4/million and $20/million.

The price for cache reads fell 60%. That's significant for longer agentic conversations, where 90%+ of input tokens are processed at cached token prices.

The new price for Opus 5.5 is the same as the price for GPT-5.6 Sol, but that was before OpenAI dropped their Sol prices by half.

GPT-6 Astra and Claude Fable 5.1 are both priced at $10/million input and $50/million output. The price war currently affects the next tier of models below that.

Anthropic say that Sonnet 5.5 and Haiku 5.5 are coming soon. It's going to be interesting to see if Haiku can regain its price competitiveness at the lower end, given current Haiku 4.5 is $1/$5 while the latest GPT-6 Luna is one tenth of that price at $0.10/$0.50.

Claude Opus 5.5 max over-thinks to the point of breaking

In a first for my "Generate an SVG of a pelican riding a bicycle" test, Claude Opus 5.5 at "max" thinking level failed to return a response!

It started by calling this "a classic test request", and then thought really, really hard about what it was doing:

This is a classic test request, so I want to plan out a well-composed pelican with its distinctive beak and pouch riding a bicycle with proper wheels, frame, and pedals, set against a simple sky and ground backdrop. [...]

Verifying the shin length checks out at roughly 95.2, close enough. Now I'm working out the near leg path from hip to knee to ankle, then sketching the foot shape resting on the pedal — outlining the heel, toe tips, and sole contour with a path using lines and curves to sit naturally on the pedal surface around y=478-494. [...]

I like the fish sticking prominently out of the basket with the pelican eyeing it as a fun detail worth keeping. I'm also confirming the eye placement near the bill base matches typical pelican anatomy, and considering giving it a slightly happier expression. [...]

The far leg reads correctly as passing behind the frame, so I'm moving on to check the chainring teeth and confirm layer ordering—the far crank arm should be mostly hidden by the seat tube and chainring. I'm settling on the final SVG's width and height attributes alongside the viewBox to ensure proper scaling, noting there's no text so no font-family is needed. [...]

I was so excited to see this pelican... but then it stopped. Opus 5.5 has a 128,000 maximum output token limit (as do the other Claude models), and it hit that while it was still reasoning about the SVG!

I tried a second time and got the same result. This makes me suspect that "max" is effectively useless - if it over-thinks to breaking point on a stupid SVG prompt I don't trust it not to do the same for more interesting work.

(Those two failures each cost me $2.56 and took nearly 20 minutes.)

Fable 5.1 on "max" didn't over-think and did give me the best pelican I've seen from any Anthropic model.

Here are the Opus 5.5 pelicans, excluding 5.5 max.

I also built this comparison grid comparing them with pelicans by Opus 5, Fable 5.1, and Sonnet 5:

Comparing different model vendors by how well they draw a pelican riding a bicycle may not make much sense now (if it ever did), but I'm still finding value in using them for comparisons of the same model families at different reasoning levels.

I'm now using GPT-6 Sol and Claude Opus 5.5 as my default models in Codex and Claude Code. I've upgraded the Datasette Agent demo at agent.datasette.io to use GPT-6 Luna, and it seems to be fast and competent at both SQL queries and building HTML and JavaScript for Datasette Apps.

Tags: ai, openai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release, gpt, gpt-6-astra

llm 0.36

2026-09-22T18:48:37+00:00

Release: llm 0.36

New OpenAI models: gpt-6-sol for GPT-6 Sol and gpt-6-luna for GPT-6 Luna. #1702

Model plugins can now declare supports_conversation = False for models that only accept single-turn prompts. LLM raises llm.ConversationNotSupported when these models receive assistant or tool history, and llm chat rejects them before starting a session. See Models that do not support conversations. The first plugin to use this is llm-typesafe. #1692

Reasoning traces in the Markdown output of llm logs are now wrapped in <details><summary> tags. #1701

Plus bug fixes from five new contributors.

Tags: openai, llm

Quoting @therealcornpop

2026-09-22T18:03:21+00:00

Hey, you know it's like super obvious if you're using AI to write your scripts for TikTok and YouTube, right? [...] It's not just the general AI-isms of "it's not X, it's Y", or the rule of three, or the really weird broken staccato-like way of writing where you just say a lot of things with all these punctuation marks. and it sounds really deep, but it's not.

It's the lack of anything. It's the lack of a definitive sort of spear of your voice. It's the fact I can tell you don't have opinions about the thing that you're talking about.

— @therealcornpop, on TikTok

Tags: tiktok, ai, ai-misuse

llm-anthropic 0.29

2026-09-22T17:14:55+00:00

Release: llm-anthropic 0.29

Adds support for Claude Opus 5.5:

llm -m claude-opus-5.5 "prompt goes here"

Tags: llm, anthropic

llm-typesafe 0.1a0

2026-09-22T15:54:16+00:00

Release: llm-typesafe 0.1a0

I built this new plugin for LLM to add support for TypeSafe AI's new Jev model. Install it like this:

llm install llm-typesafe

Then set an API key (get one here, the waitlist seems to move pretty fast):

llm keys set typesafe
# Paste key

And now you can ask yes/no "noul" questions like this:

llm -m jev 'Please refund my last payment.' \
  -s 'Does this message explicitly request a refund?'

Output:

{"type": "noul", "noul": 0.99}

Or choice questions like this:

cat message.txt | llm -m jev \
  -s 'Which team should handle this message? If billing and technical issues both occur, choose billing.' \
  -o answer_type choice \
  -o criteria '{
    "billing":"Charges, invoices, payments, or refunds",
    "technical":"Problems installing or using the product",
    "other":"Neither category fits"
  }'

Or scoring questions like this:

cat report.txt | llm -m jev \
  -s 'How reproducible is the problem described in this report?' \
  -o answer_type score \
  -o criteria '[
    "No reproduction instructions",
    "Some instructions, but important steps are missing",
    "Complete steps with expected and actual results"
  ]'

See the README for more details.

Tags: projects, llm, jev

Jev introduces a new shape of LLM - System One, aka Decision Models

2026-09-21T23:09:20+00:00

Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling "System One models" (I'm with Maggie Appleton, I think "decision models" is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores.

TypeSafe describe Jev like this:

Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out.

It's also very fast, and really cheap. Regular LLMs are priced in terms of input and output tokens, with output generally charged at significantly higher rates. Jev charges only for input - output is free - and the input price of their first model is $0.042 per million tokens - cheaper even than OpenAI's GPT-5 Nano ($0.05/million).

Jev lets you ask questions about text or semi-structured data. You compose a "state" object containing a string, array of strings, or set of name-value pairs - this might describe an article, or a customer, or any other kind of record. You then send that to their API with one or more questions, and get a reply back for each.

You can ask three kinds of questions:

Yes/No questions, which Jev calls "Noul" questions - their CEO confirmed on Hacker News that this is short for Bernoulli, from the Bernoulli distribution. You pose a statement and get back a floating point number between 0 and 1 for how confident the model is that the statement is true.
Choice questions, where the model picks one from a set of provided options - actually a confidence score plus a probability distribution across all of the options.
Score questions, where you provide sequence of numeric levels with descriptions and it provides a floating point score somewhere along that range.

The Jev API can accept a single document ("state") and as many questions as you can cram into the context window. Questions are evaluated in parallel, so sending many questions should take a similar time to sending just one.

The Jev 1.13 jaggedness documentation offers useful guidance as to Jev's strengths and weaknesses. It's currently not great with numbers, dates, or "adversarial content".

I think the decision model framing is useful for understanding where to use Jev. It's great for anything that can be expressed as a classification task - think spam detection, suggesting labels, prioritization and ranking.

I've also been experimenting with it for search reranking, where you fetch 100 likely matches using an inexpensive algorithm like BM25, then have Jev score those 100 candidates for relevance against the original query.

Black boxes are back in fashion

Something I've found a little uncomfortable about Jev is how it very much represents a regression even further towards black box machine learning systems.

LLMs are black boxes already - you can ask them to justify their decisions, but you can't guarantee that what they say is useful or accurate.

Jev doesn't even give you that: put in all the text you want, the only thing you're going to get back is a floating point number. If Jev marks something as spam, which content signals tipped it off?

This also means that concerns about bias should be front and center. I really hope nobody uses Jev to rank job applicants - that floating point number could conceal all manner of unseen bias baked into the models, and experimentally picking that bias apart is going to be a tricky business.

(I tried one experiment where I had Jev score every city in the San Francisco Bay Area on a yes/no answer to whether they were a "Good city?" - it rated Cupertino top and East Palo Alto bottom. Huh.)

In practice, this all means that evals and structured experiments are even more important than they are for regular LLM projects. Thankfully, Jev is so cheap that running hundreds or even thousands of experimental prompts through it costs just a few cents.

Unconventional uses for Jev

It's been really fun watching the wider community come up with potential use-cases for Jev over the past few days. Here are some creative ones that caught my eye:

jevchat by Kyle Pena turns Jev into a (terrible) chat model. "At every step it asks Jev one question: Given the user's question and the reply written so far, which symbol comes next?". ericpruitt on Hacker News: "It's the digital equivalent of Morty speaking with the death crystal".
jev-leftpad by Fatih Kadir Akın implements left-pad with the prompt "How many spaces are needed before value to reach targetLength?" and a choice query allowing options from "0 spaces are needed" to "10 spaces are needed".
jev-2048 by Andy Gayton uses Jev to play the 2048 sliding puzzle game.

Open weight recreations

There's also been a flurry of projects attempting to create a model like Jev using on top of open weight models. Kev is one interesting example, using Qwen 3.5 to produce 0.8B, 4B, and 9B models. Here's the accompanying Hacker News thread, where someone linked to a JevBench benchmark that has already cropped up to compare "Jev-class decision models".

Given Jev was released just under a week ago, the amount of activity around it is extremely impressive.

Using Jev from LLM

Update 22nd September 2026: I released llm-typesafe, a plugin that adds support for Jev to my LLM CLI tool and Python library. Basic usage looks like this:

llm -m jev 'Please refund my last payment.' \
  -s 'Does this message explicitly request a refund?'

See the README for examples of other query types.

Tags: ai, generative-ai, llms, evals, ai-bias, jev

Cloudflare Python Workers are now generally available

2026-09-21T22:25:44+00:00

Cloudflare Python Workers are now generally available

After a two year preview, Cloudflare's support for running Python code in their server-side Workers platform is now stable: "Python is now a first-class, fully supported language on the Cloudflare Developer Platform".

A neat thing about this is how it works. Cloudflare are running Python compiled to WebAssembly via Pyodide in their V8-based workerd runtime.

This comes with some limitations, documented here - most notably both multiprocessing and threading are non-functional in the WebAssembly VM.

One particularly interesting detail of this is the local development environment story - their pywrangler development tool (confusingly packaged as workers-py on PyPI) runs a full local simulation of their stack, including executing code with Pyodide in WebAssembly in V8 in a 123MB workerd binary, which for me ended up in node_modules/@cloudflare/workerd-darwin-arm64/bin/workerd.

Python Workers represent a significant investment in the wider Python ecosystem by Cloudflare. The release announcement is credited to Gyeongjae Choi, Dominik Picheta, and Hood Chatham - Gyeongjae and Hood are both Pyodide core maintainers.

Via Hacker News

Tags: python, cloudflare, webassembly, pyodide

Quoting voxium

2026-09-20T21:06:43+00:00

It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude.

— voxium

Tags: ai-misuse, llms, ai, generative-ai