Simon Willison’s Weblog

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 hand-on iterative coding sessions.

# 9:16 pm / ai, openai, generative-ai, llms, cerebras, pelican-riding-a-bicycle, llm-release, codex-cli

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.

# 8:01 pm / ai, anthropic, ai-ethics, ai-energy-usage

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:

# 6:12 pm / google, ai, generative-ai, llms, gemini, pelican-riding-a-bicycle, llm-reasoning, llm-release

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.html

Judge 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!

# 5:45 pm / open-source, ai, generative-ai, llms, ai-agents, ai-ethics, openclaw

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

# 7:19 pm / ai, openai, generative-ai, llms, ai-assisted-programming, skills, showboat

GLM-5: From Vibe Coding to Agentic Engineering (via) This is a huge new MIT-licensed model: 754B 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:

# 6:56 pm / definitions, ai, generative-ai, llms, ai-assisted-programming, pelican-riding-a-bicycle, llm-release, vibe-coding, openrouter, ai-in-china, glm

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:

# 5:34 pm / python, sqlite, charles-leifer, webassembly, pyodide, ai-assisted-programming, claude-code

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:

# 11:56 pm / ai, prompt-engineering, generative-ai, llms, paper-review, context-engineering

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.

# 4:43 pm / careers, ai, generative-ai, llms, ai-assisted-programming, ai-ethics

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.

He says:

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.

# 11:57 pm / open-source, ai, github-actions, generative-ai, mitchell-hashimoto, ai-ethics

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.

# 11:10 pm / ai, generative-ai, llms, anthropic, claude, llm-pricing, claude-code

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.

# 6:44 pm / salesforce, heroku, fly

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:

• 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).

• 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.

• 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.

# 11:39 pm / ai, generative-ai, llms, ai-assisted-programming, mitchell-hashimoto, coding-agents

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.

# 12:23 am / cia, github, internet-archive

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.

# 10:42 pm / ai, generative-ai, llms, hugging-face, mistral, speech-to-text

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 python

Then:

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!

# 10:44 pm / python, sandboxing, security, deno, fly