Simon Willison’s Weblog

Soft-deprecating re.match() (via) Python has a concept of soft deprecation, where APIs are marked as "should no longer be used to write new code" without any promise/threat to remove them in the future.

Python 3.15 release manager Hugo van Kemenade describes how in the upcoming 3.15 release soft deprecation has come for the venerable but deeply confusing re.match() function. It's now available with the much clearer alternative re.prefixmatch() name - reflecting how it anchors at the beginning of the string but not the end.

Most of the time you probably want re.search() (match this pattern anywhere in the string) or re.fullmatch() (match the entire string) instead.

# 2:47 pm / python, regular-expressions

Datasette 1.0a39 and 0.65.4 security releases. Today we're releasing two new security patch versions of Datasette: 1.0a39 and 0.65.4 - one for the current alpha series and one for the stable 0.65.x family.

These are security fixes which you should apply if you are running a Datasette instance on the public web - in particular if that instance mixes both public and private tables.

Following issues reported by Sevban Dönmez, Alex Garcia and I ran an extensive audit of Datasette using Claude Fable 5.1, GPT-5.6, and GPT-6 Astra. We then spent almost a week collaborating on and reviewing the fixes.

They helped find some very subtle bugs. We'll be incorporating security audits by frontier models into all of our development work going forward.

Alex came up with a way of splitting the work which I found extremely productive:

Alex Garcia and I worked together running and then responding to the audit, working in a shared private repository. For most of the issues we split the work: one of us would create the automated tests highlighting the issue, then the other would implement the fix. This ensured that two separate humans had eyes on each of the issues, in addition to our coding agents running different models.

# 3:27 am / releases, security, ai, datasette, generative-ai, llms, agentic-engineering, ai-security-research

Any Nix package, live in your browser (via) Farid Zakaria calls this his "magnum opus of Nix work", and I can see why.

trynix.dev provides a qemu-wasm powered x86_64 Linux virtual machine running entirely in your browser through WebAssembly. That VM can then be booted with any Nix package from the past 13 years. They are URL addressable, so you can navigate to this page:

https://trynix.dev/?pkg=python3%403.6.2

Then click "Load" and get an interactive shell against a virtual machine running Python 3.6.2 from 2017.

Farid is building all sorts of neat things on top of this. One recent example: Review a pull request by booting it introduces trynix-preview, described like this:

GitHub action that comments a link on a pull request which lets you boot the PR’s build in the browser using https://trynix.dev. No servers, just browsers.

# 11:44 pm / code-review, linux, webassembly, github-actions

Native is now the future of mobile at Shopify (via) Shopify are moving from React Native back to separate Swift and Kotlin codebases for their native apps, for the exact reason you would expect:

We decided to switch from native to React Native in 2020 for three reasons:

• Stop building the same features twice
• Allow developers to work across the stack
• Spend less time chasing feature parity and more time shipping value

[...]

Native still means building and maintaining software on two platforms, that cost has not disappeared. What changed is that agents can now do enough of the implementation, translation, testing, and review work that it’s no longer the deciding factor it was in 2020.

It's a well-written post, which gives full credit to React Native as a great platform for the six years they were using it.

Shopify are the maintainers of three significant React Native libraries: react-native-skia, flash-list, and restyle. The first two are finding new homes; the third "has a smaller user base than our other libraries" and will be archived at the end of 2026.

# 9:11 pm / android, mobile, open-source, ios, ai, react, generative-ai, llms, ai-assisted-search, coding-agents, swift, shopify

Introducing ChatGPT Images 2.5. OpenAI's image generation models are apparently used "more than 3 billion images across ChatGPT Images and the GPT‑Image models in the API". This latest release improves their instruction-following ability across multiple turns, responds faster, and "is better at preserving the subjects in your reference photos".

There are two new model IDs in the API: gpt-image-2.5-sunburst and gpt-image-2.5-flare. Based on this I think Sunburst is the stronger option:

Choose Sunburst for workflows where editing precision matters most, and Flare for fast, high-quality everyday image generation.

I upgraded my openai_image.py CLI tool to support passing in one or more reference images, so now this works:

uv run https://tools.simonwillison.net/python/openai_image.py \
  'add a raccoon scientist studying the chart thoughtfully' \
  -i https://static.simonwillison.net/static/2026/openai-agent-usage.webp \
  -m gpt-image-2.5-sunburst

This is the original image, and here's what I got back from that prompt to "add a raccoon scientist studying the chart thoughtfully":

# 10:46 pm / tools, ai, openai, generative-ai, uv, text-to-image

Creepy crawlies (via) Konstantin Ryabitsev discusses how bad the "background radiation" of abusive crawlers has become from the perspective of git.kernel.org, the official Git repository for the Linux kernel:

TL;DR: we spend more CPU cycles rendering commits for scrapers than we spend on all other kinds of legitimate access, including git clones. At any one time, across 5 geo-distributed nodes, there are 14 CPU cores doing nothing but rendering git commits as html.

I worry about this a lot from the perspective of Datasette, which serves a huge number of crawlable web pages.

# 11:08 pm / crawling, git, linux, datasette, ai-ethics

Research acceleration: The view inside OpenAI. Apparently today is RSI day at OpenAI, for Recursive Self-Improvement - I think it's their new AGI. Both this piece and the new essay An Alien Mind (by Chief Scientist Jakub Pachocki) talk about it, and this one doesn't even bother to expand the acronym.

Included are details on how OpenAI's own research team are using coding agents. Like pretty much everyone else 2026 has been the year that agentic engineering really took off at OpenAI, best illustrated by this chart:

I'm intrigued at what caused that significant acceleration in AI spend per researcher in late July - my best guess is that's when internal employees gained access to the model later released as GPT-6 Astra.

# 11:57 pm / ai, openai, generative-ai, chatgpt, llms, coding-agents, november-2025-inflection, recursive-self-improvement

The purpose of DNS is to spread scams. Terence Eden shares some daunting statistics in support of his take that "the Domain Name System's purpose seems to be a vector for criminals to run scams on people at a terrifyingly high rate".

On this Interisle report (via Andrew Campling), Terence says:

It says 85 million new registrations of gTLDs were made in 2025. Of those 8.5 million were added to blocklists by May 2025. It reckons that a 10% abuse rate is the likely floor for these numbers and it's probably closer to 20%. One in five newly registered domains with a gTLD are scams. That's a bloody crisis.

I had no idea. Apparently ICANN have been discussing this problem for years.

# 2:40 pm / dns, scams, terence-eden

Introducing GPT-6 Astra for developers (via) Blink and you'll miss it, but there's a familiar creature at 1m59s:

Across the board, Astra has more attention to detail, better understanding of the user's prompt, and can build more sophisticated outputs. In particular, it excels at building 3D models. I've seen it make incredible renderings of gardens, shipyards, animals, cityscapes, even Dyson spheres.

Astra really does believe in putting a red neckerchief on a pelican riding a bicycle.

# 11:27 pm / ai, openai, generative-ai, llms, pelican-riding-a-bicycle, gpt-6-astra

GPT‑6 Astra (via) GPT-6 Astra is "rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS" - I've not tried it yet myself, so I don't have a great deal to say about it yet.

It's going to be API priced at the same rate as Claude Fable 5 and 5.1: $10/million input and $50/million output. This is clearly OpenAI's Fable competitor, and appears to score higher than Fable on most of OpenAI's self-reported benchmarks.

Most impressively, Astra scores 99.9% on the recent (released in March) ARC-AGI 3 benchmark - though notably Fable 5 does not yet have a published result, and the ARC-AGI blog notes that the 99.9% score was achieved for $19K using OpenAI's custom "Provider Adapter harness", while the default ARC-AGI harness scored 62.7% for $26K.

The Provider Adapter harness preserves opaque reasoning state between requests and uses compaction for longer conversations, allowing the model to reuse prior work.

Unsurprisingly, given the recent Hugging Face incident, Astra is a beast at security tasks. It scores 100% on ExploitBench (GPT-5.6 Sol got 78.5%), 42.4% on ExploitGym (Sol got 30.3%), and 99.2% within four attempts on SRE-Bench binary reverse engineering compared to Sol's 68.7%.

It's also better at long context: on OpenAI's eight-needle benchmark it got 100% at 256K–512K tokens and 96.3% at 512K–1M tokens. OpenAI may have vanquished one of the ongoing challenges with long context processing.

It doesn't win at everything though. Artificial Analysis note that Astra is still beaten by Fable on their Intelligence Index:

Sits beside GPT-5.6 Sol in Intelligence: GPT-6 Astra scores equal to GPT-5.6 Sol in the Index at 61. This is 5 points lower than Claude Fable 5.1 (max with fallback). The model also trails Meta’s newly released Muse Spark 1.3 (max).

It did better on their Coding Agent Index:

Leads Coding Agent Index cost efficiency frontier: At max effort, GPT-6 Astra costs about the same as GPT-5.6 Sol (max) while scoring 2 points higher on the Index. Per task, the model is less than half the cost of Claude Fable 5, for the same score.

I'll write more about Astra once I get access to it. The API model label once it rolls out will be gpt-6-astra.

# 8:18 pm / ai, openai, generative-ai, llms, llm-release, gpt-6-astra