Simon Willison’s Weblog

Python 3.15.0 candidate 2 is here! (via) Hugo van Kemenade (release manager for Python 3.14 and 3.15) announces the final release candidate for Python 3.15, scheduled for release in October:

Entering the release candidate phase, only reviewed code changes which are clear bug fixes are allowed between this release candidate and the final release. [...]

We strongly encourage maintainers of third-party Python projects to prepare their projects for 3.15 during this phase, and publish Python 3.15 wheels on PyPI to be ready for the final release of 3.15.0, and to help other projects do their own testing. Any binary wheels built against Python 3.15.0 release candidates will work with future versions of Python 3.15.

Back in 2021 I found a bug in Python 3.10 by running my test suites against it... but I hadn't done this during the RC period, so that bug had already shipped! Since then I've always paid much closer attention to these RCs.

The new RC isn't available for GitHub Actions just yet - keep an eye on actions/python-versions for that. For the moment though you can add this to a testing matrix:

strategy:
  matrix:
    python-version: ["3.14", "3.15"]

steps:
  - uses: actions/setup-python@v7
    with:
      python-version: ${{ matrix.python-version }}
      allow-prereleases: true
      check-latest: true

The allow-prereleases and check-latest flags mean that today this will test against RC1, and when RC2 lands it will automatically switch to that version (and then the stable version once that comes out.)

Update: Datasette passes, sqlite-utils passes, LLM is currently blocked waiting for a 3.15 wheel for scikit-learn, which is optionally used in the test suite.

# 2:59 pm / open-source, python, github-actions

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

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

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

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

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

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