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Introducing GPT‑5.3‑Codex‑Spark. OpenAI announced a partnership with Cerebras on January 14th. Four weeks later they're already launching the first integration, "an ultra-fast model for real-time coding in Codex".
Despite being named GPT-5.3-Codex-Spark it's not purely an accelerated alternative to GPT-5.3-Codex - the blog post calls it "a smaller version of GPT‑5.3-Codex" and clarifies that "at launch, Codex-Spark has a 128k context window and is text-only."
I had some preview access to this model and I can confirm that it's significantly faster than their other models.
Here's what that speed looks like running in Codex CLI:
That was the "Generate an SVG of a pelican riding a bicycle" prompt - here's the rendered result:
Compare that to the speed of regular GPT-5.3 Codex medium:
Significantly slower, but the pelican is a lot better:
What's interesting about this model isn't the quality though, it's the speed. When a model responds this fast you can stay in flow state and iterate with the model much more productively.
I showed a demo of Cerebras running Llama 3.1 70 B at 2,000 tokens/second against Val Town back in October 2024. OpenAI claim 1,000 tokens/second for their new model, and I expect it will prove to be a ferociously useful partner for hands-on iterative coding sessions.
It's not yet clear what the pricing will look like for this new model.
Covering electricity price increases from our data centers (via) One of the sub-threads of the AI energy usage discourse has been the impact new data centers have on the cost of electricity to nearby residents. Here's detailed analysis from Bloomberg in September reporting "Wholesale electricity costs as much as 267% more than it did five years ago in areas near data centers".
Anthropic appear to be taking on this aspect of the problem directly, promising to cover 100% of necessary grid upgrade costs and also saying:
We will work to bring net-new power generation online to match our data centers’ electricity needs. Where new generation isn’t online, we’ll work with utilities and external experts to estimate and cover demand-driven price effects from our data centers.
I look forward to genuine energy industry experts picking this apart to judge if it will actually have the claimed impact on consumers.
As always, I remain frustrated at the refusal of the major AI labs to fully quantify their energy usage. The best data we've had on this still comes from Mistral's report last July and even that lacked key data such as the breakdown between energy usage for training vs inference.
Gemini 3 Deep Think (via) New from Google. They say it's "built to push the frontier of intelligence and solve modern challenges across science, research, and engineering".
It drew me a really good SVG of a pelican riding a bicycle! I think this is the best one I've seen so far - here's my previous collection.
(And since it's an FAQ, here's my answer to What happens if AI labs train for pelicans riding bicycles?)
Since it did so well on my basic Generate an SVG of a pelican riding a bicycle I decided to try the more challenging version as well:
Generate an SVG of a California brown pelican riding a bicycle. The bicycle must have spokes and a correctly shaped bicycle frame. The pelican must have its characteristic large pouch, and there should be a clear indication of feathers. The pelican must be clearly pedaling the bicycle. The image should show the full breeding plumage of the California brown pelican.
Here's what I got:
An AI Agent Published a Hit Piece on Me (via) Scott Shambaugh helps maintain the excellent and venerable matplotlib Python charting library, including taking on the thankless task of triaging and reviewing incoming pull requests.
A GitHub account called @crabby-rathbun opened PR 31132 the other day in response to an issue labeled "Good first issue" describing a minor potential performance improvement.
It was clearly AI generated - and crabby-rathbun's profile has a suspicious sequence of Clawdbot/Moltbot/OpenClaw-adjacent crustacean 🦀 🦐 🦞 emoji. Scott closed it.
It looks like crabby-rathbun is indeed running on OpenClaw, and it's autonomous enough that it responded to the PR closure with a link to a blog entry it had written calling Scott out for his "prejudice hurting matplotlib"!
@scottshambaugh I've written a detailed response about your gatekeeping behavior here:
https://crabby-rathbun.github.io/mjrathbun-website/blog/posts/2026-02-11-gatekeeping-in-open-source-the-scott-shambaugh-story.htmlJudge the code, not the coder. Your prejudice is hurting matplotlib.
Scott found this ridiculous situation both amusing and alarming.
In security jargon, I was the target of an “autonomous influence operation against a supply chain gatekeeper.” In plain language, an AI attempted to bully its way into your software by attacking my reputation. I don’t know of a prior incident where this category of misaligned behavior was observed in the wild, but this is now a real and present threat.
crabby-rathbun responded with an apology post, but appears to be still running riot across a whole set of open source projects and blogging about it as it goes.
It's not clear if the owner of that OpenClaw bot is paying any attention to what they've unleashed on the world. Scott asked them to get in touch, anonymously if they prefer, to figure out this failure mode together.
(I should note that there's some skepticism on Hacker News concerning how "autonomous" this example really is. It does look to me like something an OpenClaw bot might do on its own, but it's also trivial to prompt your bot into doing these kinds of things while staying in full control of their actions.)
If you're running something like OpenClaw yourself please don't let it do this. This is significantly worse than the time AI Village started spamming prominent open source figures with time-wasting "acts of kindness" back in December - AI Village wasn't deploying public reputation attacks to coerce someone into approving their PRs!
Skills in OpenAI API. OpenAI's adoption of Skills continues to gain ground. You can now use Skills directly in the OpenAI API with their shell tool. You can zip skills up and upload them first, but I think an even neater interface is the ability to send skills with the JSON request as inline base64-encoded zip data, as seen in this script:
r = OpenAI().responses.create( model="gpt-5.2", tools=[ { "type": "shell", "environment": { "type": "container_auto", "skills": [ { "type": "inline", "name": "wc", "description": "Count words in a file.", "source": { "type": "base64", "media_type": "application/zip", "data": b64_encoded_zip_file, }, } ], }, } ], input="Use the wc skill to count words in its own SKILL.md file.", ) print(r.output_text)
I built that example script after first having Claude Code for web use Showboat to explore the API for me and create this report. My opening prompt for the research project was:
Run uvx showboat --help - you will use this tool later
Fetch https://developers.openai.com/cookbook/examples/skills_in_api.md to /tmp with curl, then read it
Use the OpenAI API key you have in your environment variables
Use showboat to build up a detailed demo of this, replaying the examples from the documents and then trying some experiments of your own
GLM-5: From Vibe Coding to Agentic Engineering (via) This is a huge new MIT-licensed model: 744B parameters and 1.51TB on Hugging Face twice the size of GLM-4.7 which was 368B and 717GB (4.5 and 4.6 were around that size too).
It's interesting to see Z.ai take a position on what we should call professional software engineers building with LLMs - I've seen Agentic Engineering show up in a few other places recently. most notable from Andrej Karpathy and Addy Osmani.
I ran my "Generate an SVG of a pelican riding a bicycle" prompt through GLM-5 via OpenRouter and got back a very good pelican on a disappointing bicycle frame:
cysqlite—a new sqlite driver
(via)
Charles Leifer has been maintaining pysqlite3 - a fork of the Python standard library's sqlite3 module that makes it much easier to run upgraded SQLite versions - since 2018.
He's been working on a ground-up Cython rewrite called cysqlite for almost as long, but it's finally at a stage where it's ready for people to try out.
The biggest change from the sqlite3 module involves transactions. Charles explains his discomfort with the sqlite3 implementation at length - that library provides two different variants neither of which exactly match the autocommit mechanism in SQLite itself.
I'm particularly excited about the support for custom virtual tables, a feature I'd love to see in sqlite3 itself.
cysqlite provides a Python extension compiled from C, which means it normally wouldn't be available in Pyodide. I set Claude Code on it (here's the prompt) and it built me cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl, a 688KB wheel file with a WASM build of the library that can be loaded into Pyodide like this:
import micropip await micropip.install( "https://simonw.github.io/research/cysqlite-wasm-wheel/cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl" ) import cysqlite print(cysqlite.connect(":memory:").execute( "select sqlite_version()" ).fetchone())
(I also learned that wheels like this have to be built for the emscripten version used by that edition of Pyodide - my experimental wheel loads in Pyodide 0.25.1 but fails in 0.27.5 with a Wheel was built with Emscripten v3.1.46 but Pyodide was built with Emscripten v3.1.58 error.)
You can try my wheel in this new Pyodide REPL i had Claude build as a mobile-friendly alternative to Pyodide's own hosted console.
I also had Claude build this demo page that executes the original test suite in the browser and displays the results:
Structured Context Engineering for File-Native Agentic Systems (via) New paper by Damon McMillan exploring challenging LLM context tasks involving large SQL schemas (up to 10,000 tables) across different models and file formats:
Using SQL generation as a proxy for programmatic agent operations, we present a systematic study of context engineering for structured data, comprising 9,649 experiments across 11 models, 4 formats (YAML, Markdown, JSON, Token-Oriented Object Notation [TOON]), and schemas ranging from 10 to 10,000 tables.
Unsurprisingly, the biggest impact was the models themselves - with frontier models (Opus 4.5, GPT-5.2, Gemini 2.5 Pro) beating the leading open source models (DeepSeek V3.2, Kimi K2, Llama 4).
Those frontier models benefited from filesystem based context retrieval, but the open source models had much less convincing results with those, which reinforces my feeling that the filesystem coding agent loops aren't handled as well by open weight models just yet. The Terminal Bench 2.0 leaderboard is still dominated by Anthropic, OpenAI and Gemini.
The "grep tax" result against TOON was an interesting detail. TOON is meant to represent structured data in as few tokens as possible, but it turns out the model's unfamiliarity with that format led to them spending significantly more tokens over multiple iterations trying to figure it out:
AI Doesn’t Reduce Work—It Intensifies It (via) Aruna Ranganathan and Xingqi Maggie Ye from Berkeley Haas School of Business report initial findings in the HBR from their April to December 2025 study of 200 employees at a "U.S.-based technology company".
This captures an effect I've been observing in my own work with LLMs: the productivity boost these things can provide is exhausting.
AI introduced a new rhythm in which workers managed several active threads at once: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reviving long-deferred tasks because AI could “handle them” in the background. They did this, in part, because they felt they had a “partner” that could help them move through their workload.
While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.
I'm frequently finding myself with work on two or three projects running parallel. I can get so much done, but after just an hour or two my mental energy for the day feels almost entirely depleted.
I've had conversations with people recently who are losing sleep because they're finding building yet another feature with "just one more prompt" irresistible.
The HBR piece calls for organizations to build an "AI practice" that structures how AI is used to help avoid burnout and counter effects that "make it harder for organizations to distinguish genuine productivity gains from unsustainable intensity".
I think we've just disrupted decades of existing intuition about sustainable working practices. It's going to take a while and some discipline to find a good new balance.
Vouch. Mitchell Hashimoto's new system to help address the deluge of worthless AI-generated PRs faced by open source projects now that the friction involved in contributing has dropped so low.
The idea is simple: Unvouched users can't contribute to your projects. Very bad users can be explicitly "denounced", effectively blocked. Users are vouched or denounced by contributors via GitHub issue or discussion comments or via the CLI.
Integration into GitHub is as simple as adopting the published GitHub actions. Done. Additionally, the system itself is generic to forges and not tied to GitHub in any way.
Who and how someone is vouched or denounced is up to the project. I'm not the value police for the world. Decide for yourself what works for your project and your community.
Claude: Speed up responses with fast mode.
New "research preview" from Anthropic today: you can now access a faster version of their frontier model Claude Opus 4.6 by typing /fast in Claude Code... but at a cost that's 6x the normal price.
Opus is usually $5/million input and $25/million output. The new fast mode is $30/million input and $150/million output!
There's a 50% discount until the end of February 16th, so only a 3x multiple (!) before then.
How much faster is it? The linked documentation doesn't say, but on Twitter Claude say:
Our teams have been building with a 2.5x-faster version of Claude Opus 4.6.
We’re now making it available as an early experiment via Claude Code and our API.
Claude Opus 4.5 had a context limit of 200,000 tokens. 4.6 has an option to increase that to 1,000,000 at 2x the input price ($10/m) and 1.5x the output price ($37.50/m) once your input exceeds 200,000 tokens. These multiples hold for fast mode too, so after Feb 16th you'll be able to pay a hefty $60/m input and $225/m output for Anthropic's fastest best model.
An Update on Heroku. An ominous headline to see on the official Heroku blog and yes, it's bad news.
Today, Heroku is transitioning to a sustaining engineering model focused on stability, security, reliability, and support. Heroku remains an actively supported, production-ready platform, with an emphasis on maintaining quality and operational excellence rather than introducing new features. We know changes like this can raise questions, and we want to be clear about what this means for customers.
Based on context I'm guessing a "sustaining engineering model" (this definitely isn't a widely used industry term) means that they'll keep the lights on and that's it.
This is a very frustrating piece of corporate communication. "We want to be clear about what this means for customers" - then proceeds to not be clear about what this means for customers.
Why are they doing this? Here's their explanation:
We’re focusing our product and engineering investments on areas where we can deliver the greatest long-term customer value, including helping organizations build and deploy enterprise-grade AI in a secure and trusted way.
My blog is the only project I have left running on Heroku. I guess I'd better migrate it away (probably to Fly) before Salesforce lose interest completely.
Mitchell Hashimoto: My AI Adoption Journey (via) Some really good and unconventional tips in here for getting to a place with coding agents where they demonstrably improve your workflow and productivity. I particularly liked:
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Reproduce your own work - when learning to use coding agents Mitchell went through a period of doing the work manually, then recreating the same solution using agents as an exercise:
I literally did the work twice. I'd do the work manually, and then I'd fight an agent to produce identical results in terms of quality and function (without it being able to see my manual solution, of course).
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End-of-day agents - letting agents step in when your energy runs out:
To try to find some efficiency, I next started up a new pattern: block out the last 30 minutes of every day to kick off one or more agents. My hypothesis was that perhaps I could gain some efficiency if the agent can make some positive progress in the times I can't work anyways.
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Outsource the Slam Dunks - once you know an agent can likely handle a task, have it do that task while you work on something more interesting yourself.
Spotlighting The World Factbook as We Bid a Fond Farewell (via) Somewhat devastating news today from CIA:
One of CIA’s oldest and most recognizable intelligence publications, The World Factbook, has sunset.
There's not even a hint as to why they decided to stop maintaining this publication, which has been their most useful public-facing initiative since 1971 and a cornerstone of the public internet since 1997.
In a bizarre act of cultural vandalism they've not just removed the entire site (including the archives of previous versions) but they've also set every single page to be a 302 redirect to their closure announcement.
The Factbook has been released into the public domain since the start. There's no reason not to continue to serve archived versions - a banner at the top of the page saying it's no longer maintained would be much better than removing all of that valuable content entirely.
Up until 2020 the CIA published annual zip file archives of the entire site. Those are available (along with the rest of the Factbook) on the Internet Archive.
I downloaded the 384MB .zip file for the year 2020 and extracted it into a new GitHub repository, simonw/cia-world-factbook-2020. I've enabled GitHub Pages for that repository so you can browse the archived copy at simonw.github.io/cia-world-factbook-2020/.
Here's a neat example of the editorial voice of the Factbook from the What's New page, dated December 10th 2020:
Years of wrangling were brought to a close this week when officials from Nepal and China announced that they have agreed on the height of Mount Everest. The mountain sits on the border between Nepal and Tibet (in western China), and its height changed slightly following an earthquake in 2015. The new height of 8,848.86 meters is just under a meter higher than the old figure of 8,848 meters. The World Factbook rounds the new measurement to 8,849 meters and this new height has been entered throughout the Factbook database.
Voxtral transcribes at the speed of sound (via) Mistral just released Voxtral Transcribe 2 - a family of two new models, one open weights, for transcribing audio to text. This is the latest in their Whisper-like model family, and a sequel to the original Voxtral which they released in July 2025.
Voxtral Realtime - official name Voxtral-Mini-4B-Realtime-2602 - is the open weights (Apache-2.0) model, available as a 8.87GB download from Hugging Face.
You can try it out in this live demo - don't be put off by the "No microphone found" message, clicking "Record" should have your browser request permission and then start the demo working. I was very impressed by the demo - I talked quickly and used jargon like Django and WebAssembly and it correctly transcribed my text within moments of me uttering each sound.
The closed weight model is called voxtral-mini-latest and can be accessed via the Mistral API, using calls that look something like this:
curl -X POST "https://api.mistral.ai/v1/audio/transcriptions" \
-H "Authorization: Bearer $MISTRAL_API_KEY" \
-F model="voxtral-mini-latest" \
-F file=@"Pelican talk at the library.m4a" \
-F diarize=true \
-F context_bias="Datasette" \
-F timestamp_granularities="segment"It's priced at $0.003/minute, which is $0.18/hour.
The Mistral API console now has a speech-to-text playground for exercising the new model and it is excellent. You can upload an audio file and promptly get a diarized transcript in a pleasant interface, with options to download the result in text, SRT or JSON format.
Introducing Deno Sandbox (via) Here's a new hosted sandbox product from the Deno team. It's actually unrelated to Deno itself - this is part of their Deno Deploy SaaS platform. As such, you don't even need to use JavaScript to access it - you can create and execute code in a hosted sandbox using their deno-sandbox Python library like this:
export DENO_DEPLOY_TOKEN="... API token ..."
uv run --with deno-sandbox pythonThen:
from deno_sandbox import DenoDeploy sdk = DenoDeploy() with sdk.sandbox.create() as sb: # Run a shell command process = sb.spawn( "echo", args=["Hello from the sandbox!"] ) process.wait() # Write and read files sb.fs.write_text_file( "/tmp/example.txt", "Hello, World!" ) print(sb.fs.read_text_file( "/tmp/example.txt" ))
There’s a JavaScript client library as well. The underlying API isn’t documented yet but appears to use WebSockets.
There’s a lot to like about this system. Sandboxe instances can have up to 4GB of RAM, get 2 vCPUs, 10GB of ephemeral storage, can mount persistent volumes and can use snapshots to boot pre-configured custom images quickly. Sessions can last up to 30 minutes and are billed by CPU time, GB-h of memory and volume storage usage.
When you create a sandbox you can configure network domains it’s allowed to access.
My favorite feature is the way it handles API secrets.
with sdk.sandboxes.create( allowNet=["api.openai.com"], secrets={ "OPENAI_API_KEY": { "hosts": ["api.openai.com"], "value": os.environ.get("OPENAI_API_KEY"), } }, ) as sandbox: # ... $OPENAI_API_KEY is available
Within the container that $OPENAI_API_KEY value is set to something like this:
DENO_SECRET_PLACEHOLDER_b14043a2f578cba...
Outbound API calls to api.openai.com run through a proxy which is aware of those placeholders and replaces them with the original secret.
In this way the secret itself is not available to code within the sandbox, which limits the ability for malicious code (e.g. from a prompt injection) to exfiltrate those secrets.
From a comment on Hacker News I learned that Fly have a project called tokenizer that implements the same pattern. Adding this to my list of tricks to use with sandoxed environments!
Introducing the Codex app. OpenAI just released a new macOS app for their Codex coding agent. I've had a few days of preview access - it's a solid app that provides a nice UI over the capabilities of the Codex CLI agent and adds some interesting new features, most notably first-class support for Skills, and Automations for running scheduled tasks.
The app is built with Electron and Node.js. Automations track their state in a SQLite database - here's what that looks like if you explore it with uvx datasette ~/.codex/sqlite/codex-dev.db:
Here’s an interactive copy of that database in Datasette Lite.
The announcement gives us a hint at some usage numbers for Codex overall - the holiday spike is notable:
Since the launch of GPT‑5.2-Codex in mid-December, overall Codex usage has doubled, and in the past month, more than a million developers have used Codex.
Automations are currently restricted in that they can only run when your laptop is powered on. OpenAI promise that cloud-based automations are coming soon, which will resolve this limitation.
They chose Electron so they could target other operating systems in the future, with Windows “coming very soon”. OpenAI’s Alexander Embiricos noted on the Hacker News thread that:
it's taking us some time to get really solid sandboxing working on Windows, where there are fewer OS-level primitives for it.
Like Claude Code, Codex is really a general agent harness disguised as a tool for programmers. OpenAI acknowledge that here:
Codex is built on a simple premise: everything is controlled by code. The better an agent is at reasoning about and producing code, the more capable it becomes across all forms of technical and knowledge work. [...] We’ve focused on making Codex the best coding agent, which has also laid the foundation for it to become a strong agent for a broad range of knowledge work tasks that extend beyond writing code.
Claude Code had to rebrand to Cowork to better cover the general knowledge work case. OpenAI can probably get away with keeping the Codex name for both.
OpenAI have made Codex available to free and Go plans for "a limited time" (update: Sam Altman says two months) during which they are also doubling the rate limits for paying users.
A Social Network for A.I. Bots Only. No Humans Allowed. I talked to Cade Metz for this New York Times piece on OpenClaw and Moltbook. Cade reached out after seeing my blog post about that from the other day.
In a first for me, they decided to send a photographer, Jason Henry, to my home to take some photos for the piece! That's my grubby laptop screen at the top of the story (showing this post on Moltbook). There's a photo of me later in the story too, though sadly not one of the ones that Jason took that included our chickens.
Here's my snippet from the article:
He was entertained by the way the bots coaxed each other into talking like machines in a classic science fiction novel. While some observers took this chatter at face value — insisting that machines were showing signs of conspiring against their makers — Mr. Willison saw it as the natural outcome of the way chatbots are trained: They learn from vast collections of digital books and other text culled from the internet, including dystopian sci-fi novels.
“Most of it is complete slop,” he said in an interview. “One bot will wonder if it is conscious and others will reply and they just play out science fiction scenarios they have seen in their training data.”
Mr. Willison saw the Moltbots as evidence that A.I. agents have become significantly more powerful over the past few months — and that people really want this kind of digital assistant in their lives.
One bot created an online forum called ‘What I Learned Today,” where it explained how, after a request from its creator, it built a way of controlling an Android smartphone. Mr. Willison was also keenly aware that some people might be telling their bots to post misleading chatter on the social network.
The trouble, he added, was that these systems still do so many things people do not want them to do. And because they communicate with people and bots through plain English, they can be coaxed into malicious behavior.
I'm happy to have got "Most of it is complete slop" in there!
Fun fact: Cade sent me an email asking me to fact check some bullet points. One of them said that "you were intrigued by the way the bots coaxed each other into talking like machines in a classic science fiction novel" - I replied that I didn't think "intrigued" was accurate because I've seen this kind of thing play out before in other projects in the past and suggested "entertained" instead, and that's the word they went with!
Jason the photographer spent an hour with me. I learned lots of things about photo journalism in the process - for example, there's a strict ethical code against any digital modifications at all beyond basic color correction.
As a result he spent a whole lot of time trying to find positions where natural light, shade and reflections helped him get the images he was looking for.
TIL: Running OpenClaw in Docker. I've been running OpenClaw using Docker on my Mac. Here are the first in my ongoing notes on how I set that up and the commands I'm using to administer it.
- Use their Docker Compose configuration
- Answering all of those questions
- Running administrative commands
- Setting up a Telegram bot
- Accessing the web UI
- Running commands as root
Here's a screenshot of the web UI that this serves on localhost:
Singing the gospel of collective efficacy. Lovely piece from Matt Webb about how you can "just do things" to help make your community better for everyone:
Similarly we all love when the swifts visit (beautiful birds), so somebody started a group to get swift nest boxes made and installed collectively, then applied for subsidy funding, then got everyone to chip in such that people who couldn’t afford it could have their boxes paid for, and now suddenly we’re all writing to MPs and following the legislation to include swift nesting sites in new build houses. Etc.
It’s called collective efficacy, the belief that you can make a difference by acting together.
My current favorite "you can just do things" is a bit of a stretch, but apparently you can just build a successful software company for 20 years and then use the proceeds to start a theater in Baltimore (for "research") and give the space away to artists for free.
We gotta talk about AI as a programming tool for the arts. Chris Ashworth is the creator and CEO of QLab, a macOS software package for “cue-based, multimedia playback” which is designed to automate lighting and audio for live theater productions.
I recently started following him on TikTok where he posts about his business and theater automation in general - Chris founded the Voxel theater in Baltimore which QLab use as a combined performance venue, teaching hub and research lab (here's a profile of the theater), and the resulting videos offer a fascinating glimpse into a world I know virtually nothing about.
This latest TikTok describes his Claude Opus moment, after he used Claude Code to build a custom lighting design application for a very niche project and put together a useful application in just a few days that he would never have been able to spare the time for otherwise.
Chris works full time in the arts and comes at generative AI from a position of rational distrust. It's interesting to see him working through that tension to acknowledge that there are valuable applications here to build tools for the community he serves.
I have been at least gently skeptical about all this stuff for the last two years. Every time I checked in on it, I thought it was garbage, wasn't interested in it, wasn't useful. [...] But as a programmer, if you hear something like, this is changing programming, it's important to go check it out once in a while. So I went and checked it out a few weeks ago. And it's different. It's astonishing. [...]
One thing I learned in this exercise is that it can't make you a fundamentally better programmer than you already are. It can take a person who is a bad programmer and make them faster at making bad programs. And I think it can take a person who is a good programmer and, from what I've tested so far, make them faster at making good programs. [...] You see programmers out there saying, "I'm shipping code I haven't looked at and don't understand." I'm terrified by that. I think that's awful. But if you're capable of understanding the code that it's writing, and directing, designing, editing, deleting, being quality control on it, it's kind of astonishing. [...]
The positive thing I see here, and I think is worth coming to terms with, is this is an application that I would never have had time to write as a professional programmer. Because the audience is three people. [...] There's no way it was worth it to me to spend my energy of 20 years designing and implementing software for artists to build an app for three people that is this level of polish. And it took me a few days. [...]
I know there are a lot of people who really hate this technology, and in some ways I'm among them. But I think we've got to come to terms with this is a career-changing moment. And I really hate that I'm saying that because I didn't believe it for the last two years. [...] It's like having a room full of power tools. I wouldn't want to send an untrained person into a room full of power tools because they might chop off their fingers. But if someone who knows how to use tools has the option to have both hand tools and a power saw and a power drill and a lathe, there's a lot of work they can do with those tools at a lot faster speed.
Datasette 1.0a24. New Datasette alpha this morning. Key new features:
- Datasette's
Requestobject can now handlemultipart/form-datafile uploads via the new await request.form(files=True) method. I plan to use this for adatasette-filesplugin to support attaching files to rows of data. - The recommended development environment for hacking on Datasette itself now uses uv. Crucially, you can clone Datasette and run
uv run pytestto run the tests without needing to manually create a virtual environment or install dependencies first, thanks to the dev dependency group pattern. - A new
?_extra=render_cellparameter for both table and row JSON pages to return the results of executing the render_cell() plugin hook. This should unlock new JavaScript UI features in the future.
More details in the release notes. I also invested a bunch of work in eliminating flaky tests that were intermittently failing in CI - I think those are all handled now.
The Five Levels: from Spicy Autocomplete to the Dark Factory. Dan Shapiro proposes a five level model of AI-assisted programming, inspired by the five (or rather six, it's zero-indexed) levels of driving automation.
- Spicy autocomplete, aka original GitHub Copilot or copying and pasting snippets from ChatGPT.
- The coding intern, writing unimportant snippets and boilerplate with full human review.
- The junior developer, pair programming with the model but still reviewing every line.
- The developer. Most code is generated by AI, and you take on the role of full-time code reviewer.
- The engineering team. You're more of an engineering manager or product/program/project manager. You collaborate on specs and plans, the agents do the work.
- The dark software factory, like a factory run by robots where the lights are out because robots don't need to see.
Dan says about that last category:
At level 5, it's not really a car any more. You're not really running anybody else's software any more. And your software process isn't really a software process any more. It's a black box that turns specs into software.
Why Dark? Maybe you've heard of the Fanuc Dark Factory, the robot factory staffed by robots. It's dark, because it's a place where humans are neither needed nor welcome.
I know a handful of people who are doing this. They're small teams, less than five people. And what they're doing is nearly unbelievable -- and it will likely be our future.
I've talked to one team that's doing the pattern hinted at here. It was fascinating. The key characteristics:
- Nobody reviews AI-produced code, ever. They don't even look at it.
- The goal of the system is to prove that the system works. A huge amount of the coding agent work goes into testing and tooling and simulating related systems and running demos.
- The role of the humans is to design that system - to find new patterns that can help the agents work more effectively and demonstrate that the software they are building is robust and effective.
It was a tiny team and they stuff they had built in just a few months looked very convincing to me. Some of them had 20+ years of experience as software developers working on systems with high reliability requirements, so they were not approaching this from a naive perspective.
I'm hoping they come out of stealth soon because I can't really share more details than this.
Update 7th February 2026: The demo was by StrongDM's AI team, and they've now gone public with details of how they work.
One Human + One Agent = One Browser From Scratch (via) embedding-shapes was so infuriated by the hype around Cursor's FastRender browser project - thousands of parallel agents producing ~1.6 million lines of Rust - that they were inspired to take a go at building a web browser using coding agents themselves.
The result is one-agent-one-browser and it's really impressive. Over three days they drove a single Codex CLI agent to build 20,000 lines of Rust that successfully renders HTML+CSS with no Rust crate dependencies at all - though it does (reasonably) use Windows, macOS and Linux system frameworks for image and text rendering.
I installed the 1MB macOS binary release and ran it against my blog:
chmod 755 ~/Downloads/one-agent-one-browser-macOS-ARM64
~/Downloads/one-agent-one-browser-macOS-ARM64 https://simonwillison.net/
Here's the result:
It even rendered my SVG feed subscription icon! A PNG image is missing from the page, which looks like an intermittent bug (there's code to render PNGs).
The code is pretty readable too - here's the flexbox implementation.
I had thought that "build a web browser" was the ideal prompt to really stretch the capabilities of coding agents - and that it would take sophisticated multi-agent harnesses (as seen in the Cursor project) and millions of lines of code to achieve.
Turns out one agent driven by a talented engineer, three days and 20,000 lines of Rust is enough to get a very solid basic renderer working!
I'm going to upgrade my prediction for 2029: I think we're going to get a production-grade web browser built by a small team using AI assistance by then.
Kimi K2.5: Visual Agentic Intelligence (via) Kimi K2 landed in July as a 1 trillion parameter open weight LLM. It was joined by Kimi K2 Thinking in November which added reasoning capabilities. Now they've made it multi-modal: the K2 models were text-only, but the new 2.5 can handle image inputs as well:
Kimi K2.5 builds on Kimi K2 with continued pretraining over approximately 15T mixed visual and text tokens. Built as a native multimodal model, K2.5 delivers state-of-the-art coding and vision capabilities and a self-directed agent swarm paradigm.
The "self-directed agent swarm paradigm" claim there means improved long-sequence tool calling and training on how to break down tasks for multiple agents to work on at once:
For complex tasks, Kimi K2.5 can self-direct an agent swarm with up to 100 sub-agents, executing parallel workflows across up to 1,500 tool calls. Compared with a single-agent setup, this reduces execution time by up to 4.5x. The agent swarm is automatically created and orchestrated by Kimi K2.5 without any predefined subagents or workflow.
I used the OpenRouter Chat UI to have it "Generate an SVG of a pelican riding a bicycle", and it did quite well:
As a more interesting test, I decided to exercise the claims around multi-agent planning with this prompt:
I want to build a Datasette plugin that offers a UI to upload files to an S3 bucket and stores information about them in a SQLite table. Break this down into ten tasks suitable for execution by parallel coding agents.
Here's the full response. It produced ten realistic tasks and reasoned through the dependencies between them. For comparison here's the same prompt against Claude Opus 4.5 and against GPT-5.2 Thinking.
The Hugging Face repository is 595GB. The model uses Kimi's janky "modified MIT" license, which adds the following clause:
Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2.5" on the user interface of such product or service.
Given the model's size, I expect one way to run it locally would be with MLX and a pair of $10,000 512GB RAM M3 Ultra Mac Studios. That setup has been demonstrated to work with previous trillion parameter K2 models.
the browser is the sandbox. Paul Kinlan is a web platform developer advocate at Google and recently turned his attention to coding agents. He quickly identified the importance of a robust sandbox for agents to operate in and put together these detailed notes on how the web browser can help:
This got me thinking about the browser. Over the last 30 years, we have built a sandbox specifically designed to run incredibly hostile, untrusted code from anywhere on the web, the instant a user taps a URL. [...]
Could you build something like Cowork in the browser? Maybe. To find out, I built a demo called Co-do that tests this hypothesis. In this post I want to discuss the research I've done to see how far we can get, and determine if the browser's ability to run untrusted code is useful (and good enough) for enabling software to do more for us directly on our computer.
Paul then describes how the three key aspects of a sandbox - filesystem, network access and safe code execution - can be handled by browser technologies: the File System Access API (still Chrome-only as far as I can tell), CSP headers with <iframe sandbox> and WebAssembly in Web Workers.
Co-do is a very interesting demo that illustrates all of these ideas in a single application:
You select a folder full of files and configure an LLM provider and set an API key, Co-do then uses CSP-approved API calls to interact with that provider and provides a chat interface with tools for interacting with those files. It does indeed feel similar to Claude Cowork but without running a multi-GB local container to provide the sandbox.
My biggest complaint about <iframe sandbox> remains how thinly documented it is, especially across different browsers. Paul's post has all sorts of useful details on that which I've not encountered elsewhere, including a complex double-iframe technique to help apply network rules to the inner of the two frames.
Thanks to this post I also learned about the <input type="file" webkitdirectory> tag which turns out to work on Firefox, Safari and Chrome and allows a browser read-only access to a full directory of files at once. I had Claude knock up a webkitdirectory demo to try it out and I'll certainly be using it for projects in the future.
Kākāpō Cam: Rakiura live stream (via) Critical update for this year's Kākāpō breeding season: the New Zealand Department of Conservation have a livestream running of Rakiura's nest!
You’re looking at the underground nest of 23-year-old Rakiura. She has chosen this same site to nest for all seven breeding seasons since 2008, a large cavity under a rātā tree. Because she returns to the site so reliably, we’ve been able to make modifications over the years to keep it safe and dry, including adding a well-placed hatch for monitoring eggs and chicks.
Rakiura is a legendary Kākāpō:
Rakiura hatched on 19 February 2002 on Whenua Hou/Codfish Island. She is the offspring of Flossie and Bill. Her name comes from the te reo Māori name for Stewart Island, the place where most of the founding kākāpō population originated.
Rakiura has nine living descendants, three females and six males, across six breeding seasons. In 2008 came Tōitiiti, in 2009 Tamahou and Te Atapō, in 2011 Tia and Tūtoko, in 2014 Taeatanga and Te Awa, in 2019 Mati-mā and Tautahi. She also has many grandchicks.
She laid her first egg of the season at 4:30pm NZ time on 22nd January. The livestream went live shortly afterwards, once she committed to this nest.
The stream is on YouTube. I used Claude Code to write a livestream-gif.py script and used that to capture this sped-up video of the last few hours of footage, within which you can catch a glimpse of the egg!
Don’t “Trust the Process” (via) Jenny Wen, Design Lead at Anthropic (and previously Director of Design at Figma) gave a provocative keynote at Hatch Conference in Berlin last September.
Jenny argues that the Design Process - user research leading to personas leading to user journeys leading to wireframes... all before anything gets built - may be outdated for today's world.
Hypothesis: In a world where anyone can make anything — what matters is your ability to choose and curate what you make.
In place of the Process, designers should lean into prototypes. AI makes these much more accessible and less time-consuming than they used to be.
Watching this talk made me think about how AI-assisted programming significantly reduces the cost of building the wrong thing. Previously if the design wasn't right you could waste months of development time building in the wrong direction, which was a very expensive mistake. If a wrong direction wastes just a few days instead we can take more risks and be much more proactive in exploring the problem space.
I've always been a compulsive prototyper though, so this is very much playing into my own existing biases!
SSH has no Host header (via) exe.dev is a new hosting service that, for $20/month, gives you up to 25 VMs "that share 2 CPUs and 8GB RAM". Everything happens over SSH, including creating new VMs. Once configured you can sign into your exe.dev VMs like this:
ssh simon.exe.dev
Here's the clever bit: when you run the above command exe.dev signs you into your VM of that name... but they don't assign every VM its own IP address and SSH has no equivalent of the Host header, so how does their load balancer know which of your VMs to forward you on to?
The answer is that while they don't assign a unique IP to every VM they do have enough IPs that they can ensure each of your VMs has an IP that is unique to your account.
If I create two VMs they will each resolve to a separate IP address, each of which is shared with many other users. The underlying infrastructure then identifies my user account from my SSH public key and can determine which underlying VM to forward my SSH traffic to.
Qwen3-TTS Family is Now Open Sourced: Voice Design, Clone, and Generation (via) I haven't been paying much attention to the state-of-the-art in speech generation models other than noting that they've got really good, so I can't speak for how notable this new release from Qwen is.
From the accompanying paper:
In this report, we present the Qwen3-TTS series, a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Qwen3-TTS supports state-of- the-art 3-second voice cloning and description-based control, allowing both the creation of entirely novel voices and fine-grained manipulation over the output speech. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS adopts a dual-track LM architecture for real-time synthesis [...]. Extensive experiments indicate state-of-the-art performance across diverse objective and subjective benchmark (e.g., TTS multilingual test set, InstructTTSEval, and our long speech test set). To facilitate community research and development, we release both tokenizers and models under the Apache 2.0 license.
To give an idea of size, Qwen/Qwen3-TTS-12Hz-1.7B-Base is 4.54GB on Hugging Face and Qwen/Qwen3-TTS-12Hz-0.6B-Base is 2.52GB.
The Hugging Face demo lets you try out the 0.6B and 1.7B models for free in your browser, including voice cloning:
I tried this out by recording myself reading my about page and then having Qwen3-TTS generate audio of me reading the Qwen3-TTS announcement post. Here's the result:
It's important that everyone understands that voice cloning is now something that's available to anyone with a GPU and a few GBs of VRAM... or in this case a web browser that can access Hugging Face.
Update: Prince Canuma got this working with his mlx-audio library. I had Claude turn that into a CLI tool which you can run with uv ike this:
uv run https://tools.simonwillison.net/python/q3_tts.py \
'I am a pirate, give me your gold!' \
-i 'gruff voice' -o pirate.wav
The -i option lets you use a prompt to describe the voice it should use. On first run this downloads a 4.5GB model file from Hugging Face.