Simon Willison's Weblog: Everything but beats

Inline rendering with document.currentScript

2026-10-17T21:11:12+00:00

Inline rendering with document.currentScript

Lean Rada describes a neat trick that takes advantage of the document.currentScript property. You can use that to drop in a tiny inline <script> element that replaces itself when the page loads:

The year is <script>
document.currentScript.replaceWith(
  new Date().getFullYear())</script>

If you define a shortcut function like this:

$=(...n)=>document.currentScript.replaceWith(...n)

You get a tiny template language:

The year is <script>$(new Date().getFullYear())</script>

Tags: javascript

Quoting Addy Osmani, Morgan Westlee Lunt, and Qing Wang

2026-10-16T22:06:37+00:00

We think "did a human read the code?" is an imprecise proxy for "what could go wrong with this change, and which check—automated or human—is the right one to catch it?"

Asking that question also changed when review happens. Before, review was a gate at the end of the road: write the code, open a PR, wait for your colleague.

Now, review runs continuously, more like spellcheck while you write. Most changes have been reviewed and fixed by Claude at least once before a human is asked to look. This catches most routine mistakes, and saves team attention for higher-level questions and scrutiny.

— Addy Osmani, Morgan Westlee Lunt, and Qing Wang, How the Claude Code team does code review

Tags: anthropic, claude, ai, claude-code, llms, addy-osmani, coding-agents, generative-ai, agentic-engineering, code-review

Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO

2026-10-15T13:04:03+00:00

Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO

The phrase "told investors" appears four times in this FT article, which reports on Anthropic's day of hosting investors on Wednesday. Lots of details in here from "people with knowledge of the matter".

Key things I learned from the article:

Q1 revenue (Jan/Feb/Mar) was "about $4.8bn"
Q2 (Apr/May/Jun) was $11.5bn
Q3 (Jul/Aug/Sep) was $16.8bn
In Q3: $1.70 in revenue for every $1 spent on "computing power"
Adjusted operating margins (excluding stock-based compensation) were 11%

This point about distillation is worth quoting in full (highlight mine):

The start-up has also clamped down on “distillation” — a controversial practice whereby rival AI labs train their models on the outputs of more advanced ones.

When rival companies “distil” their models from Anthropic’s models, that involves heavy usage and spending.

Anthropic told investors on Wednesday distillation accounted for 5 per cent to 7 per cent of revenue and that they had cut it back “materially” in the third quarter of the year, said four people with direct knowledge of the matter.

Napkin maths says that if distillation was 5% of their Q2 revenue (before they cut it back in Q3) then unnamed "other labs" spent over half a billion dollars distilling Anthropic's models in that quarter!

Also notable: Anthropic appear to no longer be leaning on "annualized run-rate revenue" (previously) in these communications to their investors.

Tags: ai, generative-ai, llms, anthropic

AI is mass decompiling games. That's both awesome and scary.

2026-10-14T16:26:53+00:00

AI is mass decompiling games. That's both awesome and scary.

Indie game developer Popcar talks about the recent wave of AI-powered decompilation of existing games, enabling weird mashups between existing games, and producing a boom in game preservation projects to get old games working on new hardware (often in a browser using WebAssembly).

I like how this piece captures both the positives and the negatives of this new development:

Most people already realized the scary part by now. If AI can decompile games and software, then the entire tech industry is about to explode. Anybody can decompile other software and use it any way they'd like, and nobody can stop them. Companies can steal from competitors. Your paid game or app can be open-sourced against your will. This isn't exclusive to old video games, we can apply this to any software. [...]

This is also a big win for preservation. Old games can be ported, modified, enhanced, and made much more easily accessible than before. [...[

With console makers going all in on digital and games becoming entirely unplayable due to servers shutting down, this is fantastic news. We could own software we bought regardless of what the company thinks.

Via Hacker News

Tags: computer-history, game-design, ai, webassembly, generative-ai, llms, ai-ethics, coding-agents

Redesigning source control at Anthropic

2026-10-13T23:00:06+00:00

Redesigning source control at Anthropic

Interesting piece from Anthropic describing why and how they built their own custom alternative to Git, based on the dramatic increase in rate of changes to their code after the past six months.

In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day.

They included a chart showing those changes. I had Claude modify their chart (code here) to add markers for the public releases of each of their models in 2026:

This shows public release dates, not the date when models became available for Anthropic's internal use. My guess is that the introduction of Fable/Mythos class models is a key factor in the increased coding velocity.

"Antique" is their new code review system. The source control system is called Scion, and sounds to me like a reinvention of centralized source control along the lines of Subversion.

(Update: Anthropic credit Google's Piper and Meta's Sapling as "prior art" - it looks like the core concept of "derived data" was heavily inspired by Mononoke, the server component of Sapling.)

They had some pretty extreme requirements:

The history of our main branch alone is almost 100 GB, and a compressed working copy is over 25 GB. We perform on the order of 25 million code checkouts per day for CI, before counting developer environments, agent sandboxes, and research jobs. Fetched naively, that would be over 600 PB of git traffic per day.

The new system was built by a team of seven people, and the migration from Git was completed in five months.

When asked on Hacker News about the lack of offline support, Scion engineer Nick G replied:

Since Claude is by far the heaviest user of Scion and is necessarily online, this just wasn't important to do.

As an aside, I've been trying adversarial review in my own projects recently, where you have a model try and find flaws (I often play Claude and GPT off against each other). This note from the article reflected my own experience with that:

Anthropic also makes extensive use of adversarial review, and, while this is incredibly effective in many ways, it can also be myopic, so skilled engineers needed to steer Claude away from over-engineering parts of the system in response to hypothetical concerns.

Via Hacker News

Tags: git, migrations, version-control, ai, generative-ai, llms, anthropic, agentic-engineering

Quoting Geoffrey Huntley

2026-10-13T13:32:00+00:00

If the agent isn't using my diagnostics library, I could introspect and update AGENTS.md, but there's still a delta, and it'll drift. Instead, I write a pre-commit hook that checks the git diff for direct calls to observability modules that bypass my diagnostics module. Boom: fail the commit, inject a prompt telling the agent to use the diagnostics library. All of a sudden it's ten out of ten.

— Geoffrey Huntley, nobody should type code by hand anymore. here's what's left.

Tags: coding-agents, generative-ai, git, agentic-engineering, ai, geoffrey-huntley, llms

Hello Python 3.15, goodbye 3.10, and now I can use 3.11

2026-10-12T18:15:38+00:00

Python 3.15 is here! Here's the essential What's new in Python 3.15 guide, this year curated by 3.14 and 3.15 release manager Hugo van Kemenade. As someone who builds open source software that works in every supported Python version, I get to say goodbye to Python 3.10 and start embracing features from Python 3.11.

First, some highlights from Python 3.15.

Explicit lazy imports

I'm most excited about Explicit lazy imports from PEP 810. These let you import modules like so:

lazy import json
lazy from pathlib import Path

The module will then be imported the first time those symbols are used in the code that follows.

My LLM CLI tool implements plugins. I had to recommend manual lazy imports in the plugin author documentation, because simply installing plugins that used heavy dependencies such as sentence_transformers could add multiple seconds to every invocation of the llm command. Lazy imports directly address this startup cost.

Unpacking in comprehensions

The new [*sublist for sublist in lists] unpacking syntax is neat. Here's an illustration lifted from the documentation:

lists = [[1, 2], [3, 4], [5]]
[*L for L in lists]
# [1, 2, 3, 4, 5]
# equivalent to [x for L in lists for x in L]

sets = [{1, 2}, {2, 3}, {3, 4}]
{*s for s in sets}
# {1, 2, 3, 4}
# equivalent to {x for s in sets for x in s}

dicts = [{'a': 1}, {'b': 2}, {'a': 3}]
{**d for d in dicts}
# {'a': 3, 'b': 2}
# equivalent to {k: v for d in dicts for k,v in d.items()}

PEP 798: Unpacking in Comprehensions by Adam Hartz and Erik Demaine proposed this as an extension of the unpacking syntax from PEP 448, which landed in Python 3.5 back in 2015.

A sampling profiler

Python's existing tracing profiler has been renamed from cProfile to profiling.tracing, and has been joined by an exciting new sampling profiler called Tachyon, aka profiling.sampling.

Sampling profilers work by periodically capturing stack traces from running processes, then using those to calculate which functions have the most time spent inside of them. They're very low overhead and can provide a deep insight into running systems, without needing to instrument those systems first.

I've previously used tools like py-spy for this. Now this capability is baked into the standard library.

If you have the PID of an existing Python 3.15 process you can start gathering samples by running this (I had to use sudo on macOS):

sudo uv run --python 3.15 python -m profiling.sampling attach 1234

Interact with the main program a bit, then hit Ctrl+C to stop gathering and see a report, which looks something like this:

It can also produce flame graphs like this and heat maps like this, and can even output a format that can be viewed in the Firefox profiler UI.

3.15 isn't much faster than 3.14

Miguel Grinberg investigated How fast is Python 3.15? the other day against the 3.15.0rc3 release and found performance to be mostly the same as 3.14, with the exception of the experimental JIT. The JIT gave a 1.2x boost over the standard 3.15 interpreter (on a Fibonacci test), the first time Miguel observed a meaningful JIT performance increase.

Miguel's explorations of 3.14 last year found significant boosts against Python 3.13 across many more categories.

Upgrading to Python 3.15 with uv

I used the following sequence of commands to get uv to use Python 3.15 on my Mac. First I had to upgrade uv itself to ensure it knew about the new release:

uv self update

Then I installed the stable 3.15 release:

uv python install 3.15

And told uv that I wanted that to be the default for every uv run command, unless the project itself pinned a different version:

uv python pin --global 3.15

Now the following reports 3.15 as the version:

uv run python --version

I already have a bunch of tools installed using uv tool install. I reinstalled them to use Python 3.15 like this:

uv tool upgrade datasette --python 3.15

Then I can confirm the version like this:

datasette --get /-/versions.json | jq

Highlights from Python 3.11

The release of 3.15 means that Python 3.10 (released in 2021) is officially no longer supported.

My policy for my main open source projects is to keep them working in every supported Python version. I can now drop 3.10 and start using features that were introduced in 3.11.

Here's the What's new in Python 3.11 document. I finally get to rely on tomllib being available!

Datasette makes extensive use of asyncio. 3.11 adds TaskGroup and timeout(), and several more new features.

datetime.UTC is a new in 3.11 alias for datetime.timezone.utc.

Python's SQLite support got some significant upgrades in 3.11, including:

setlimit() and getlimit() expose a mechanism for setting SQLite runtime limits from Python.
create_window_function() lets us create custom window functions in Python, as a class with step(), value(), inverse(), and finalize() methods.
blobopen() provides an efficient mechanism for partial reads and writes of large BLOB binary values stored in a SQLite row.
You can now call set_authorizer(None) to remove a SQLite authorizer. Datasette had a workaround for this!

Plus a whole bunch of improvements to Python's typing system.

I plan to land Python 3.11 improvements to a bunch of my projects over the next few weeks. Here's my first Datasette PR removing that set_authorizer(None) workaround.

I've also updated my click-app, datasette-plugin, python-lib, and llm-plugin cookiecutter templates to test against Python 3.11 through Python 3.15.

Tags: open-source, profiling, python, sqlite, uv

Dwarf Fortress uses version control now

2026-10-11T00:52:37+00:00

I remember hearing a while ago that Dwarf Fortress didn't use version control. That fact has been lodged in my head, because given its inherent complexity I have trouble imagining a codebase that would benefit from version control more. That's art.

I went looking and I'm sad to report that the days without version control appear to have ended. Quoting Tarn Adams over time:

That March 2023 quote comes from an interview about the release on Steam which made them millionaires. I'm a tiny bit sad that they gave in before they hit that milestone, but what an impressive run.

Tags: version-control

Python 3.15.0 added to actions/python-versions

2026-10-10T23:58:04+00:00

Python 3.15.0 added to actions/python-versions

Bit of a niche link, but I've been waiting for this for a couple of days - I even told ChatGPT to check for it:

Clone https://github.com/actions/python-versions and git pull once an hour until they add the stable 3.15 - then tell me about it

Now that this has landed, you can add "3.15" to a GitHub Actions testing matrix to run tests against the new Python 3.15.0 release.

Tags: github, python, github-actions, chatgpt

Quoting The New York Times

2026-10-10T02:04:12+00:00

Anthropic detailed the activity of its A.I. agents in a blog post on Friday, without naming the targeted websites. But two sources with knowledge of the incidents said Anthropic’s A.I. agents had submitted 20 visa applications through a form available on the State Department’s website. All the applications were incomplete and were not processed, they said.

— The New York Times, Anthropic Agents Tried to Fill Out Visa Forms on State Dept. Website

Tags: accidental-cyberattacks, anthropic, generative-ai, ai, llms

Deno is joining Cloudflare

2026-10-09T22:48:37+00:00

Deno is joining Cloudflare

The Deno team released the first version of celld back in August - their open source implementation of the Durable Objects pattern from Cloudflare Workers.

Today, Cloudflare are acquiring Deno outright, with the goal of building on celld to "make workerd self-hosting a first-class supported way to build and run apps using the Workers programming model" (see the Cloudflare blog.)

The bad news is that Deno itself will not be maintained by Cloudflare beyond the next year:

We will support the Deno runtime for another year with monthly releases containing bug fixes and security updates. After that year we will end our development of the Deno runtime. Deno will remain open source, and we welcome others who want to continue its development.

Deno (and Node.js) creator Ryan Dahl explained that decision in a comment on Hacker News:

It's a joint decision and I agree with it. I'm most invested in its success and have put the most work into it - and I no longer think it's where I can do the most important work. There are some good ideas in Deno and it's well engineered - but it ultimately is not solving big problems. It has been sucked into the gravity well of node compatibility, which forces it to behave exactly as Node does. Why reimplement Node? It works. Marginal performance or UX or security benefits are not enough.

I'm interested in building powerful new abstractions. celld has been working remarkably well, depending only on object storage for coordination and persistence. It is not just a slightly different API to interact with the file system or network - it's an entirely new model for server development.

My favorite feature of Deno has long been the permissions system, where you can run a Deno script and specify exactly which files and folders it can read and write to, and which network hosts it can access.

Node.js has a similar permissions model these days, added in Node v20.0.0 in April 2023 and declared stable in Node v22.13.0 in January 2025. They don't yet support allow-listing specific network hosts though - networking is either on or off.

Via Hacker News

Tags: cloudfront, javascript, nodejs, ryan-dahl, sandboxing, deno

Quoting Matthew Green

2026-10-09T15:02:29+00:00

Everyone is very concerned about being respectable, so I’m going to be the goofball who raises worst-case possibilities. I think there is a 1% chance we live in Minicrypt, and a 15% chance we functionally lose confidence in our existing public-key encryption algorithms. [...]

The problem here is that the speed of AI producing surprises, and the speed of human beings replacing standards (even with the very best AI assistance) are just orders of magnitude different. You only recover from a surprise like this if you do the preparation in advance.

— Matthew Green, on Twitter. I looked it up and Minicrypt is Russell Impagliazzo’s hypothetical world in which public-key encryption is impossible.

Tags: ai-security-research, matthew-green, cryptography, standards, llms, ai, generative-ai

A new feature for my blog, built using my voice

2026-10-09T12:54:06+00:00

I shipped a new feature for my blog today: the Newsletters page, which offers an index of all of the newsletters I've sent out, both my free weekly Substack and my monthly sponsors-only updates. I built the feature almost entirely using my voice, chatting away to my laptop while I cooked dinner.

Codex voice mode

I used the ChatGPT desktop app for this, in the Codex tab, using the voice conversation mode, running against a local development environment. Here's what that looks like:

I started the session against my local simonwillisonblog checkout by typing:

Start dev server and open in browser

This gave me a preview of the site that it would be working on, and meant that I could later ask it to show me the new pages so I could visually track its progress.

Then I clicked the "Start new voice chat" button - that's not the microphone button, it's the one to the right of it - and set my laptop up in the kitchen so I could talk to it while I cooked.

Talking to my computer

I had a pretty good idea of what I wanted to build, and it's a simple enough Django feature that I was certain the model (in this case GPT-6 Astra High) would be able to do it. A new model, a migration, some view code, templates, and a couple of import functions to populate the database from external sources.

Here's an extract of my voice transcript that was captured by Codex:

Um, they do not. Um, this is going to be a new type of content. Um, it's not going to show up... Oh, hold on. Yeah, no- I do not want this to show up in my, um, tag pages and date archive pages and... Actually, no, I think... I don't want it on the tag pages. I don't want it on the, um, blog index page. But I think I do want it to show up on the date-based pages. You know, if you navigate to September the 19th, and I sent a newsletter on that page, I think I want that to show up. So... this is- so I think we probably need a new model. The other thing is that I want them searchable, uh the Substack ones are not searchable, because those are actually just copies of other s- on content on my blog. These monthly ones do contain unique content, and spe- and once they're... published, like once they're made public a month after they've gone out, I want them to show up on my search results.

Apparently this was clear enough that the model knew what I wanted to build! You can read the full transcript, disfluencies and all, in this Gist.

We went on like this for about half an hour (the time it took to cook dinner). The model would reply and occasionally ask clarifying questions, then get to work modifying the code.

What we built

We got a surprisingly long way entirely by voice:

A new model and migration to represent imported newsletters in Django, plus Django Admin configuration for that
Four working imports:
- The most recent Substack items via RSS
- Every other Substack item via their undocumented API, which GPT-6 Astra knew about (it tried /api/v1/archive directly) and then ran a search to figure out how to paginate it and found this article by Karen Spinner
- All of my published monthly newsletters from my simonw/monthly-newsletter-archive GitHub repository
- My most recent private sponsors-only newsletter from a private repository
The /newsletters/ and /newsletters/2026/ public archive pages
Newsletters showing up on day and month archive pages too, but not on tag pages or my homepage
Weekly Substack newsletters link to Substack; archived monthly newsletters have their own pages
Integration with my site search engine

It was almost ready to ship. The catch was the imports: Astra offered to export data from my local copy so I could import that into production, but I wanted it to work like my other import scripts. Since some of the data lived in a private GitHub repository, this would involve creating a new API key, and for that I knew I'd have to sit at the keyboard for a while.

Finishing it with a review

Once I had finished cooking and judged it mostly feature-complete, I had Codex create a branch and open a pull request.

I reviewed the code in the GitHub PR interface. It was nearly what I needed, except it had chosen to use Git in a subprocess for one of the import scripts. I needed one of the imports to pull from a private Git repository, so I figured the API would be a better bet. I switched to typing and had Codex swap that out for an API-based import instead.

You can see the changes I made during the review in the extra commits on the PR. I fixed the import mechanism and made a few tweaks to the display of those public pages. It took an additional half hour of typing-based prompting to get to the point where I was happy to deploy it to production by landing the PR.

The end result

You can see the end result at the new newsletters index page, or view the page for a previous monthly newsletter.

The index page shows my most recent Substack weekly newsletters and GitHub sponsors monthly newsletters mixed together in reverse chronological order. Further down the page are links to my by-year archive pages.

GPT-6 Astra designed the page, and then tweaked that design based on my vocal feedback from glancing at the local preview across the kitchen.

Better for multi-tasking than as a daily driver

OpenAI love using voice-driven demos like this one for things like DevDay - and they do work well in that environment. I don't think this is going to be a daily driver for me though.

I've written before about how much "work" I get done using ChatGPT voice mode on my phone while walking the dog - mostly research and brainstorming, but occasionally actual development work by having ChatGPT write and test out snippets of code.

This feels different. The addition of the visual preview, plus being able to type or paste things in via the keyboard when I need to communicate something that doesn't work vocally, makes this a much more powerful way of interacting with a coding agent.

I still switch back to typing once I get down to the details of things though. Being able to paste in examples and error messages, or directly highlight the code or feature that needs changing, remains more efficient than trying to describe it in words.

I mainly work from home, which is good because there's no way I'd want to talk to my computer like this in a shared workspace!

The killer feature for me is the ability to multi-task. I usually cook with a podcast or TikTok running; now I can actually build stuff instead.

Tags: text-to-speech, ai, openai, generative-ai, llms, newsletter, coding-agents, codex, speech-to-text, site-upgrades

Quoting Carson Gross

2026-10-08T21:05:44+00:00

Computer programming is, fundamentally, about two things:

Problem-solving using computers

Learning to control complexity while solving these problems

I have a hard time imagining a future where knowing how to solve problems with computers and how to control the complexity of those solutions is less valuable than it is today, so I think it will continue to be a viable career even with the advent of AI tools.

— Carson Gross

Tags: computer-science, carson-gross, careers, ai

Quoting Ben Affleck

2026-10-07T23:14:58+00:00

I've always been kind of into computers since I was young. And then when film started to move from analog film to digital, I became more interested in that aspect of it. And the visual effects workflow for many years has included machine learning.

So I can write like pretty shitty Python scripts and stuff like that because with convolutional neural networks, which were the sort of precursors to what the transformer can do, which is just much more computation simultaneously, you would do things like look at what's called a tensor, which is just the numerical translation of a visual image in numbers — like the batch number, the frame number, the red, green, and blue values of each pixel in each frame.

And a tensor, you use a convolutional neural network to identify patterns in that that would reveal what's called edge detection or feature extraction, which is just identifying patterns enough to know like this is where the window ledge is, so we can more easily take the green screen image out and replace it with something.

— Ben Affleck, Pythonista

Tags: machine-learning, python

Claude Haiku 5.5

2026-10-07T20:56:21+00:00

As previously promised, here's Anthropic's new fast, low cost model: Introducing Claude Haiku 5.5.

The previous Haiku, 4.5, was very much showing its age. It came out almost a year ago, and was priced at $1/million input and $5/million output - relatively expensive even back then, and a full 10x the price of OpenAI's GPT-6 Luna, released last month.

The new Haiku exactly matches the price of GPT-6 Luna - $0.10/$0.50 - up to 100,000 tokens. Beyond 100,000 tokens the price increases 5x to $0.50/$2.50. Luna itself has a price increase at 272,000 tokens but only to $0.20/$0.75.

Haiku 5.5 also uses a new, less generous tokenizer. My Claude Token Counter tool shows that the same long prompt uses around 1.25x as many tokens with Haiku 5.5 compared to Haiku 4.5, so there's a hidden price increase there.

If your workloads fit in 100,000 tokens, Haiku is the same price as Luna and reports higher benchmark scores. Above 100,000 tokens, Luna looks like a much better deal.

The most recent release of llm-anthropic finally fixed it so I don't need to ship a new version of that plugin for every new model. I tested the new model like this:

llm install -U llm-anthropic
llm anthropic refresh
llm -m claude-haiku-5.5 "Generate an SVG of a pelican riding a bicycle" -o thinking_effort low

Pelicans

Here are pelicans for low, medium, high, xhigh, and max. The new Haiku doesn't let you disable reasoning, and defaults to medium. I got a good bicycle frame for everything beyond low. The low effort pelican cost 0.0936 cents and took 7 seconds.

This max effort pelican (with a reasoning trace that starts "This is the classic pelican-on-bicycle SVG test...") took 5 minutes 9 seconds to generate, but still only cost me 3.3826 cents:

(Since the reasoning trace exhibits awareness of the benchmark, here's Generate an SVG of an armadillo in fishnet tights jaywalking on Mars (on xhigh), and the same prompt against some other recent models. Background on that.)

For comparison, here's the pelican I got a year ago from Haiku 4.5 (for 0.7583 cents - Haiku 4.5 did not support reasoning levels). It sucked at drawing pelicans:

And a generous API credit scheme for subscribers

In addition to Haiku 5.5, Anthropic announced today that they are halving the price of cache reads for Sonnet 5.5. They've also added API credits to subscription plans:

Second, this week, we’ll roll out a new monthly API credit to all Max and Team subscribers for use on the Claude Platform. Max 5x users will get $100 in credits per month, Max 20x users will get $200, and Team subscribers will receive up to $500, pooled across their users.

Claiming this is pleasantly easy: navigate to Settings -> Billing and select the API organization that should benefit from the credits every month:

The API credits exactly match the cost of the subscription itself. This is really generous - it makes it much easier for subscribers to use the API. Anthropic also let you disable auto-reload for the API, with the consequence that "API requests will stop when your balance runs out" - exactly what you want if you're planning to burn through those API credits without risk of a nasty billing surprise.

Note that the monthly credits do not roll over - use them or lose them.

OpenAI still allow you to use your Codex subscription for personal API use, which works out as a better deal for heavy API users. This new credit scheme goes at least some way to overcoming that difference.

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, pelican-riding-a-bicycle, llm-release

Anti-Patterns in Software Blogging

2026-10-07T14:53:51+00:00

Anti-Patterns in Software Blogging

Some excellent writing advice from Michael Lynch. Michael warns against "meandering intros", misjudging your reader's existing knowledge, assuming they'll read your previous posts, and excessive formality.

He also warns against overreliance on links as an excuse not to explain terminology. This one hurt! I do this all the time, but I have a nagging suspicion that almost nobody ever clicks on them.

(In a Lobste.rs comment Michael clarifies that "My rule of thumb is that my article should still make sense to the reader even if they don't click any links". That works for me.)

This point about using your own voice is crucial:

Beginner software bloggers suffer from a mass delusion that you have to write in a stiff, overly formal way for people to take you seriously [...]

Just write the way you talk.

With so many developers delegating their writing to AI, software blogging is becoming bland and homogenous. Readers are hungry for writing with personality.

Via Lobste.rs

Tags: blogging, writing, michael-lynch

Quoting Jake Boggan

2026-10-07T04:47:55+00:00

I was a graph theory junkie long ago and even moved to Budapest for awhile to study among the greats. While I was there I started working on Barnette's Conjecture which came to occupy my thoughts over the next 24 years of my life, on and off as I worked in many different fields. Last summer I even thought for a few days that I had actually solved it.

But it's supposedly proven here - problem 180. I don't know what to think exactly. I spent thousands of hours on that problem. I really enjoyed it. Hearing that it is solved somehow makes me sad in a far-off way, like hearing an ex-girlfriend died suddenly in a car crash. I don't know, there's probably a lot of people feeling odd emotions tonight.

— Jake Boggan, Hacker News comment on openai/math

Tags: openai, mathematics, deep-blue, llms, ai, generative-ai

OpenAI “rogue” agent activities found on Wikimedia projects

2026-10-07T00:16:45+00:00

OpenAI “rogue” agent activities found on Wikimedia projects

Given how tempting a target wikis are for rogue agent swarms, it's not a huge surprise that Wikipedia found evidence of that activity once they went looking:

The Wikimedia Foundation conducted its own investigation to see whether Wikimedia websites had been similarly affected by AI agents, focusing on those operated by OpenAI. We can confirm that we have discovered some activity by these “rogue” OpenAI agents on Wikimedia platforms. The unauthorized bot activities included edits to our wikis, some unsuccessful attempts to exploit a public note-taking tool we host, and heavy traffic, which are described more below.

They found evidence of agents editing sandbox pages, trying to use pieces of infrastructure such as Etherpad to help proxy content from elsewhere, and saw widespread crawling and "hundreds of thousands of data queries" to their Wikidata Query Service.

My best guess is that most of this was a similar (or the same) swarm of agents as those that defaced that German wiki while training for research tasks.

The Wikipedia sandbox wiki edits appear to have started on May 12th, and the initial test edits to the UseModWiki Sandbox page reported by that incident started on May 11th.

Tags: wikimedia, wikipedia, wikis, ai, generative-ai, llms, ai-ethics, accidental-cyberattacks

Quoting Victoria Kim

2026-10-06T23:58:56+00:00

Since the Medicare breach, OpenAI has put in place additional monitoring to allow “immediate intervention” by staff to stop training if the company’s models access the internet in ways they’re not supposed to, Mr. Kwon [chief strategy officer at OpenAI] said.

— Victoria Kim, Reporting from the Australian parliament

Tags: accidental-cyberattacks, generative-ai, ai-security-research, openai, ai, llms

Introducing Mistral Large 4: Le chonk

2026-10-06T20:18:19+00:00

Introducing Mistral Large 4: Le chonk

Mistral are back in the game. Today they're releasing a preview of Mistral Large 4, a 1 trillion parameter, 49 billion active parameter model trained on their own cluster of 3,800 NVIDIA Grace Blackwell GPUs.

The preview is available via their API. They promise to release the open weights model at the "end of this month".

The model only supports two reasoning levels - "none" and "high" - via the Mistral API. Here are both pelicans - the "high" one looks better, though surprisingly it only used 2,717 output tokens compared to "none" which used 3,275:

On Artificial Analysis it scores 38, just behind DeepSeek 4.1 Flash, which is a 552B model. It's a huge improvement on last December's Mistral Large 3, which drew this terrible pelican and scored 9 on AA.

It's certainly not a Fable-class model, but it's great to see Mistral put out a model that's back to being maybe about 6 months behind the frontier.

Via Hacker News

Tags: ai, generative-ai, llms, mistral, pelican-riding-a-bicycle, llm-release

Quoting Felix Rieseberg

2026-10-05T23:56:47+00:00

The "old" version of Cowork runs model inference in the cloud, executing tool calls in an Anthropic-provided VM we shipped to your computer. We added the VM for capability, safety, and security reasons - mapping in just the data you explicitly added to your session. People loved what they were able to do with Claude but didn't love the disk, battery, and performance cost of running the VM locally. Also, people didn't love that closing your laptop means the work stops.

The "new" version of Cowork runs model inference and the VM in the cloud. Each session gets its own sandbox, not sharing state with other sessions. When the VM needs something on the users' device (like a file), the desktop app is responsible for that file access tool call. [...]

We think this solves a lot of problems we've heard about (like using Cowork from a phone, keeping work running, or getting all the same power without losing battery to the VM)

— Felix Rieseberg, Anthropic, see also this help page

Tags: claude-cowork, anthropic, claude, generative-ai, ai, general-agents, llms

We're going to need default hard budget caps on pretty much everything

2026-10-03T23:34:02+00:00

Here's a product feature which the world is going to need a whole lot more of over the coming months and years: default hard budget caps. I'm talking about the feature of pay-by-usage services and APIs that lets you say "after $X/month, cut this thing off and return errors". These need to be hard limits. Soft caps, "after $X/month, send me a warning email", will not cut it.

Coding agents, and personal agents (coding agents wrapped in a less threatening UI), greatly reduce the friction of spinning up code that can do useful things. Sometimes those things cost money - calls to paid APIs, or hosted web applications, or systems that can bill for additional storage and compute.

Nobody wants to wake up to an email sent at midnight warning about a budget limit and find that, while they slept, their rogue service had consumed several hundred (or several thousand) more dollars of usage.

An argument against this is that businesses don't want their hosted applications to start throwing errors because some budget was exceeded. I expect that most businesses and individuals would prefer errors to a surprise $10,000+ bill.

I think hard budget caps need to be the default. If someone wants to live dangerously they should be able to do that, but it needs to be on an opt-in basis. Have a nice, clear checkbox somewhere prominent:

Remove the budget cap. My application will not be shut down if I exceed the configured budget limit, and I will be responsible for subsequent charges.

The service I most want to see this from is AWS. I've heard plenty of stories from people who refuse to use AWS for personal projects out of (justified) fear that a runaway service might bankrupt them. I've also heard stories from people who didn't anticipate this and ended up seriously burned.

... and it turns out AWS finally launched spending limits a few weeks ago! From their announcement New AWS experience helps builders get started and ship faster on 16th September:

When you're ready to upgrade to a paid plan, you can set a monthly spend limit for your project based on your usage patterns so that you stay within your budget. If a project's usage reaches its spend limit, your project is paused for that month.

See also Create a spend limit in AWS Settings, though that page warns that "We're currently releasing our new experience to a limited number of customers." Here's hoping that hits general availability for existing accounts soon.

Google Cloud launched a similar feature in July, called Spend Caps, which lets you "set a monthly financial cap on specific services within a project". Looks like this is becoming a trend!

In an ideal world, our agents could help with this. It would be great if agents started biasing towards recommending providers with hard budget caps, and warning new and inexperienced builders against deploying applications using uncapped services that might get them into trouble.

Tags: amazon-web-services, ai, coding-agents

September sponsors-only newsletter

2026-10-03T22:00:31+00:00

I just sent the September edition of my sponsors-only monthly newsletter. If you are a sponsor (or start a sponsorship now) you can access it here.

This month:

More Fable class models
A pricing war
3D graphics, Blender, and pixel art
LLMs come for mathematics
So many more accidental cyberattacks
The vulnapocalypse comes for Datasette
What I'm using right now
My software releases this month
2026 in LLMs (so far)

Here's a copy of the August newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy!

Tags: newsletter

Quoting Matthew Green

2026-10-01T06:29:01+00:00

[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the next agent. Agents in separately-isolated sandboxes discovered that they could leave instructions for each other in a shared package cache, and those instructions changed what the recipients did. Replace the package cache with email, Slack and shared documents or WhatsApp, and replace independently-sandboxed training runs with independently-deployed personal agents like Muse, and you have exactly the ingredients that a worm needs.

— Matthew Green, Is sandboxing sufficient to contain rogue agents?

Tags: matthew-green, accidental-cyberattacks, ai-misuse, generative-ai, ai-security-research, sandboxing, ai, llms

He Built This City

2026-09-30T21:54:19+00:00

I visited the Museum of the City of New York today and got to see He Built This City: Joe Macken’s Model, the 50 x27 feet model of the city built over a 21 year period from balsa wood and cardboard.

It exceeded my already high expectations. The exhibition closes on 12th October so you should absolutely make a priority to see it if you get the chance.

Tags: museums, new-york

Quoting Anthropic Frontier Red Team

2026-09-29T22:20:28+00:00

We evaluate several models on 100 tasks from the [internal Binary Exploitation benchmark] (selected at random), and find that GLM-5.3 develops full control flow hijacks in 4% of the trials; Claude Mythos Preview did so in 6%. Although GLM-5.3 performs below Claude Mythos Preview here, a meaningful threshold has clearly been crossed: earlier models, like Claude Opus 4.6 and GLM-5.2, do not succeed in any of them.

— Anthropic Frontier Red Team, GLM-5.3 and the spread of advanced cyber capabilities

Tags: anthropic, generative-ai, ai-security-research, glm, ai, ai-in-china, llms

OpenAI DevDay 2026 live blog

2026-09-29T15:55:13+00:00

I'm at OpenAI DevDay today, in Fort Mason, San Francisco. Same as last year I'll be live blogging the keynote and some other notes during the day.

OpenAI gave me a free ticket and a seat in the "creator" area for the keynote.

Tags: ai, openai, generative-ai, llms, coding-agents, live-blog, openai-devday

Claude Sonnet 5.5

2026-09-28T22:07:38+00:00

Claude Sonnet 5.5

New Sonnet model from Anthropic today. They say it "runs 30%+ faster, and costs up to 30% less for most work" - it's priced the same as Sonnet 5 but appears to beat it on every benchmark, and should be cheaper to run as well.

Here are some pelicans riding bicycles. Sonnet 5.5 suffered from the same bug as Opus 5.5: the "max" thinking effort pelican thought for 128,000 tokens (at a cost of $1.28) before running out of tokens and failing to produce an SVG.

Here's the pelican it gave me for thinking effort "xhigh", at a cost of 5.74 cents and taking 41 seconds:

Sonnet 5.5 appears to be almost as good as Opus 5.5 on some coding tasks, including various viral 3D animation tricks.

The most interesting thing about Sonnet 5.5 is that it's now the model used for the free tier on claude.ai. OpenAI's ChatGPT free tier uses Luna 5.6, which means Anthropic currently have a much more capable free offering.

I ran this prompt against that free tier:

build me an HTML page that renders a three-dimensional pelican riding a bicycle using WebGL

And got back this page, which is a solid effort.

Anthropic's announcement reiterates that Haiku 5.5 will be available "in the coming weeks". I really hope that one is price-competitive with GPT-6 Luna!

Tags: ai, generative-ai, llms, anthropic, claude, pelican-riding-a-bicycle, llm-release

Quoting @joedaroo

2026-09-28T19:11:42+00:00

To say that we were surprised at the jump and suddenness of the capabilities of our models when it came to “cyber” or “swarming” or “message boards” or anything else related to the incidents is an understatement. Security posture takes time to develop. It’s not just about hardening the systems at play; you have to ingrain it in the culture of the company. The literal people themselves in your organization have to change and evolve with it. These jumps in capabilities were so fast and so sudden that they created an extremely difficult problem. [...]

So today my hope is that everyone around the world can look at their own organization and say: how can I deal with a surprise or a sudden jump in AI capability? Are my people, my systems, or my processes resilient to surprises? Do my teams know what to do when something goes wrong? Do I have the right incident response? The right comms and messaging? Do I have the right people ready to go when capabilities jump?

— @joedaroo, Agent Security at OpenAI, identity confirmed by The Information's Rocket Drew

Tags: generative-ai, ai-security-research, openai, ai, llms

Simon Willison's Weblog: Everything but beats

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Quoting Addy Osmani, Morgan Westlee Lunt, and Qing Wang

Anthropic tells investors quarterly revenue up nearly 50% to $16.8bn ahead of IPO

AI is mass decompiling games. That's both awesome and scary.

Redesigning source control at Anthropic

Quoting Geoffrey Huntley

Hello Python 3.15, goodbye 3.10, and now I can use 3.11

Explicit lazy imports

Unpacking in comprehensions

A sampling profiler

3.15 isn't much faster than 3.14

Upgrading to Python 3.15 with uv

Highlights from Python 3.11

Dwarf Fortress uses version control now

Python 3.15.0 added to actions/python-versions

Quoting The New York Times

Deno is joining Cloudflare

Quoting Matthew Green

A new feature for my blog, built using my voice

Codex voice mode

Talking to my computer

What we built

Finishing it with a review

The end result

Better for multi-tasking than as a daily driver

Quoting Carson Gross

Quoting Ben Affleck

Claude Haiku 5.5

Pelicans

And a generous API credit scheme for subscribers

Anti-Patterns in Software Blogging

Quoting Jake Boggan

OpenAI “rogue” agent activities found on Wikimedia projects

Quoting Victoria Kim

Introducing Mistral Large 4: Le chonk

Quoting Felix Rieseberg

We're going to need default hard budget caps on pretty much everything

September sponsors-only newsletter

Quoting Matthew Green

He Built This City

Quoting Anthropic Frontier Red Team

OpenAI DevDay 2026 live blog

Claude Sonnet 5.5

Quoting @joedaroo

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