Simon Willison’s Weblog

Chromium Docs: The Rule Of 2. Alex Russell pointed me to this principle in the Chromium security documentation as similar to my description of the lethal trifecta. First added in 2019, the Chromium guideline states:

When you write code to parse, evaluate, or otherwise handle untrustworthy inputs from the Internet — which is almost everything we do in a web browser! — we like to follow a simple rule to make sure it's safe enough to do so. The Rule Of 2 is: Pick no more than 2 of

• untrustworthy inputs;
• unsafe implementation language; and
• high privilege.

Chromium uses this design pattern to help try to avoid the high severity memory safety bugs that come when untrustworthy inputs are handled by code running at high privilege.

Chrome Security Team will generally not approve landing a CL or new feature that involves all 3 of untrustworthy inputs, unsafe language, and high privilege. To solve this problem, you need to get rid of at least 1 of those 3 things.

# 4:02 am / alex-russell, browsers, chrome, security, lethal-trifecta

AI for data engineers with Simon Willison. I recorded an episode last week with Claire Giordano for the Talking Postgres podcast. The topic was "AI for data engineers" but we ended up covering an enjoyable range of different topics.

• How I got started programming with a Commodore 64 - the tape drive for which inspired the name Datasette
• Selfish motivations for TILs (force me to write up my notes) and open source (help me never have to solve the same problem twice)
• LLMs have been good at SQL for a couple of years now. Here's how I used them for a complex PostgreSQL query that extracted alt text from my blog's images using regular expressions
• Structured data extraction as the most economically valuable application of LLMs for data work
• 2025 has been the year of tool calling a loop ("agentic" if you like)
• Thoughts on running MCPs securely - read-only database access, think about sandboxes, use PostgreSQL permissions, watch out for the lethal trifecta
• Jargon guide: Agents, MCP, RAG, Tokens
• How to get started learning to prompt: play with the models and "bring AI to the table" even for tasks that you don't think it can handle
• "It's always a good day if you see a pelican"

# 5:16 am / postgresql, ai, generative-ai, llms, podcast-appearances

qwen-image-mps (via) Ivan Fioravanti built this Python CLI script for running the Qwen/Qwen-Image image generation model on an Apple silicon Mac, optionally using the Qwen-Image-Lightning LoRA to dramatically speed up generation.

Ivan has tested it this on 512GB and 128GB machines and it ran really fast - 42 seconds on his M3 Ultra. I've run it on my 64GB M2 MacBook Pro - after quitting almost everything else - and it just about manages to output images after pegging my GPU (fans whirring, keyboard heating up) and occupying 60GB of my available RAM. With the LoRA option running the script to generate an image took 9m7s on my machine.

Ivan merged my PR adding inline script dependencies for uv which means you can now run it like this:

uv run https://raw.githubusercontent.com/ivanfioravanti/qwen-image-mps/refs/heads/main/qwen-image-mps.py \
-p 'A vintage coffee shop full of raccoons, in a neon cyberpunk city' -f

The first time I ran this it downloaded the 57.7GB model from Hugging Face and stored it in my ~/.cache/huggingface/hub/models--Qwen--Qwen-Image directory. The -f option fetched an extra 1.7GB Qwen-Image-Lightning-8steps-V1.0.safetensors file to my working directory that sped up the generation.

Here's the resulting image:

# 6:19 am / macos, python, ai, generative-ai, uv, qwen, text-to-image, ai-in-china, ivan-fioravanti

Reddit will block the Internet Archive. Well this sucks. Jay Peters for the Verge:

Reddit says that it has caught AI companies scraping its data from the Internet Archive’s Wayback Machine, so it’s going to start blocking the Internet Archive from indexing the vast majority of Reddit. The Wayback Machine will no longer be able to crawl post detail pages, comments, or profiles; instead, it will only be able to index the Reddit.com homepage, which effectively means Internet Archive will only be able to archive insights into which news headlines and posts were most popular on a given day.

# 6:11 pm / internet-archive, reddit, scraping, ai, training-data, ai-ethics

Claude Sonnet 4 now supports 1M tokens of context (via) Gemini and OpenAI both have million token models, so it's good to see Anthropic catching up. This is 5x the previous 200,000 context length limit of the various Claude Sonnet models.

Anthropic have previously made 1 million tokens available to select customers. From the Claude 3 announcement in March 2024:

The Claude 3 family of models will initially offer a 200K context window upon launch. However, all three models are capable of accepting inputs exceeding 1 million tokens and we may make this available to select customers who need enhanced processing power.

This is also the first time I've seen Anthropic use prices that vary depending on context length:

• Prompts ≤ 200K: $3/million input, $15/million output
• Prompts > 200K: $6/million input, $22.50/million output

Gemini have been doing this for a while: Gemini 2.5 Pro is $1.25/$10 below 200,000 tokens and $2.50/$15 above 200,000.

Here's Anthropic's full documentation on the 1m token context window. You need to send a context-1m-2025-08-07 beta header in your request to enable it.

Note that this is currently restricted to "tier 4" users who have purchased at least $400 in API credits:

Long context support for Sonnet 4 is now in public beta on the Anthropic API for customers with Tier 4 and custom rate limits, with broader availability rolling out over the coming weeks.

# 6:14 pm / ai, generative-ai, llms, anthropic, claude, llm-pricing, long-context

simonw/codespaces-llm. GitHub Codespaces provides full development environments in your browser, and is free to use with anyone with a GitHub account. Each environment has a full Linux container and a browser-based UI using VS Code.

I found out today that GitHub Codespaces come with a GITHUB_TOKEN environment variable... and that token works as an API key for accessing LLMs in the GitHub Models collection, which includes dozens of models from OpenAI, Microsoft, Mistral, xAI, DeepSeek, Meta and more.

Anthony Shaw's llm-github-models plugin for my LLM tool allows it to talk directly to GitHub Models. I filed a suggestion that it could pick up that GITHUB_TOKEN variable automatically and Anthony shipped v0.18.0 with that feature a few hours later.

... which means you can now run the following in any Python-enabled Codespaces container and get a working llm command:

pip install llm
llm install llm-github-models
llm install llm-github-models
llm models default github/gpt-4.1
llm "Fun facts about pelicans"

Setting the default model to github/gpt-4.1 means you get free (albeit rate-limited) access to that OpenAI model.

To save you from needing to even run that sequence of commands I've created a new GitHub repository, simonw/codespaces-llm, which pre-installs and runs those commands for you.

Anyone with a GitHub account can use this URL to launch a new Codespaces instance with a configured llm terminal command ready to use:

codespaces.new/simonw/codespaces-llm?quickstart=1

While putting this together I wrote up what I've learned about devcontainers so far as a TIL: Configuring GitHub Codespaces using devcontainers.

# 5:39 am / github, projects, ai, til, openai, generative-ai, llms, llm, github-codespaces, anthony-shaw