Simon Willison's Weblog: Notes

Claude Code uses Bun written in Rust now

2026-07-19T03:54:09+00:00

In Rewriting Bun in Rust Jarred Sumner made the following claim:

Claude Code v2.1.181 (released June 17th) and later use the Rust port of Bun. Startup got 10% faster on Linux but otherwise, barely anyone noticed. Boring is good.

I decided to have a poke at my own Claude Code installation to see if I could find evidence that it was using Bun written in Rust.

I found these two commands convincing:

strings ~/.local/bin/claude | grep -m1 'Bun v1'

For me this outputs Bun v1.4.0 (macOS arm64). The most recent release of Bun on GitHub is currently v1.3.14 from May 12th, so that v1.4.0 version number in Claude supports them shipping a preview of a not-yet-released Bun version.

strings ~/.local/bin/claude | grep -Eo 'src/[[:alnum:]_./-]+\.rs'

This outputs a list of 563 filenames, starting with these:

src/runtime/bake/dev_server/mod.rs
src/runtime/bake/production.rs
src/bundler/bundle_v2.rs

It looks like Bun in Rust is indeed being run in production across millions of different devices. Like Jarred said, "Boring is good".

Tags: bun, rust, anthropic, claude-code, jarred-sumner

Spot birds not golf

2026-07-17T02:58:07+00:00

Suggestion for hyperscalers feeling pressure over data center water use:

Buy up a few exclusive country clubs, convert the golf courses into public parks, pay for guides and binoculars to get the previous members into birdwatching - help them embrace a more sustainable hobby!

Google used 10.9 billion gallons in 2025, so about 30 million gallons per day.

The Coachella Valley has 120 golf courses each using ~800 acre-feet per year, which is ~750,000 gallons per day.

So Google buying up 40 of those courses (1/3) should do the trick.

Tags: ai-energy-usage, ai

Fable gets another bump

2026-07-12T21:20:07+00:00

One of the consequences of GPT-5.6 Sol being clearly a Fable/Mythos class model is that Anthropic have, once again, bumped the date that Fable stops being available in their Claude Max plans:

We're extending Claude Fable 5 access on all paid plans, as well as keeping Claude Code’s weekly rate limits 50% higher, through July 19.

As before, you can use up to half of your weekly usage limit on Fable 5. After that, you can continue using Fable 5 with usage credits, or switch to another model to keep working within your remaining limits.

Anthropic's original rationale for this was compute constraints - they wanted a better idea of both demand and compute availability before committing to keeping the new model cheap for subscribers.

OpenAI appear confident that they won't need to restrict access to GPT-5.6 in the same way. Here's Thibault Sottiaux this morning:

The last 48 hours of Codex and ChatGPT Work have been intense! Three important updates:

Temporarily removing the 5 hour usage limit restriction for all Plus, Business and Pro plans

Rolling out changes that will make GPT 5.6 Sol more efficient across the board and that will be reflected in less usage being used so that it can take you further. Exact impact to be quantified and shared

We hit 6M active users, and are landing a usage reset in the next hour

At this point I think Anthropic should change track and keep Fable permanently available on those plans. OpenAI are winning users simply due to the uncertainty that surrounds Fable access.

Tags: anthropic, generative-ai, gpt, openai, claude-mythos-fable, ai, llms, llm-pricing

Fable's judgement

2026-07-03T18:51:06+00:00

One of the most interesting tips I got from the Fireside Chat I hosted with Cat Wu and Thariq Shihipar from the Claude Code team at AIE on Wednesday was to let Fable (and to a certain extent Opus) use their own judgement rather than dictating how they should work.

The example they gave was testing. You can tell Fable "only use automated testing for larger features, don't update and run tests for small copy or design changes" - but it's better to just tell Fable to use its own judgement when deciding to write tests instead.

Jesse Vincent just gave me a related tip to help avoid burning too many of those valuable Fable tokens in the few days we have left before the prices go up. Tell Fable to use other models for smaller tasks, applying its own judgement about which model to use.

I prompted Claude Code just now with:

For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent

Claude saved this memory file in ~/.claude/projects/name-of-project/memory/delegate-coding-to-subagents.md:

---
name: delegate-coding-to-subagents
description: Simon wants coding tasks delegated to subagents running an appropriately lower-power model
metadata: 
  node_type: memory
  type: feedback
  originSessionId: 30068d78-43a9-4fb1-bb29-9799e18c526a
---
Stated by Simon on 2026-07-03: "For all coding tasks use your judgement to decide an appropriate lower power model and run that in a subagent."

Why: cost/efficiency — implementation work rarely needs the top-tier model; judgment, review, and synthesis stay with the main loop.

How to apply: when a task in this project is primarily writing/editing code, spawn an Agent with a model override (sonnet for substantive implementation, haiku for trivial/mechanical edits) and a self-contained prompt; review the result in the main loop before committing. Design, auditing, data synthesis, and anything judgment-heavy stays in the main model. See also [[project-goals]].

So far it seems to be working well. I'm getting a ton of work done and my Fable allowance is shrinking less quickly than before.

Tags: claude, ai, claude-code, llms, prompt-engineering, coding-agents, generative-ai, claude-mythos-fable, anthropic

June 2026 newsletter

2026-07-03T14:50:50+00:00

The June edition of my sponsors-only monthly newsletter is out. If you are a sponsor (or if you start a sponsorship now) you can access it here.

This month:

Claude Fable 5, GPT-5.6, and US export restrictions
GLM-5.2 is the new best open weights model
Tokenmaxxing is so over
Datasette Apps
sqlite-utils and shot-scraper and Datasette
Miscellaneous WASM projects
Other model releases
What I'm using

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

Tags: newsletter

Understand to participate

2026-07-02T17:07:14+00:00

I saw Geoffrey Litt speak at AIE yesterday, and one framing he used particularly resonated with me:

Understand to participate

Geoffrey was talking about the challenge of collaborating with coding agents as they construct increasingly large and sophisticated changes, and the need to avoid taking on cognitive debt as your understanding drifts from how the code actually works.

His argument is that you need to understand the code to a depth that enables you to participate further with the model:

You can learn what the agent is doing to make sure you can be an active participant in the creative process. [...]

You need a rich set of concepts in your mind to think creatively and fluently about how to move something forward. If you're lacking that fluency, your ability to participate in the project is meaningfully limited.

The AIE talks are all recorded - all 300+ of them! - and should be trickling out over the next three weeks. Geoffrey's is one that I recommend catching on YouTube.

Update 10th July: here's Geoffrey's talk on YouTube.

Geoffrey also published a thread version of his talk on Twitter.

Tags: geoffrey-litt, coding-agents, cognitive-debt, generative-ai, ai, llms

Count the number of Safari tabs

2026-06-29T18:36:18+00:00

Tiniest TIL, using AppleScript to count the number of open browser tabs in Safari:

osascript -e 'tell application "Safari" to count tabs of every window'

Tags: safari, til, applescript

Siri AI at WWDC 2026

2026-06-08T23:58:04+00:00

Given how badly burned anyone who took Apple's 2024 WWDC Apple Intelligence announcements at face value was, I'm holding to a strict "I'll believe it when I see it" policy for everything they announced today.

The new Siri AI features do at least look feasible with today's technology, especially since Apple are licensing a custom Gemini-derived model that they can run on their own Private Cloud Compute.

It sounds like they'll be taking advantage of vision LLMs to extract information from the user's screen, which neatly sidesteps the need for every existing application to ship custom code in order to integrate with Apple Intelligence. Vision LLMs were a much less mature category in June 2024.

The new Core AI library looks like a good step in enabling developers to finally take full advantage of Apple's hardware for running their own models. It integrates with Meta's open source PyTorch ecosystem, using these Core AI PyTorch extensions:

Core AI PyTorch Extensions (coreai-torch) is a Python package that bridges PyTorch and Core AI. You can use it to bring up an existing PyTorch model — exported as a torch.export.ExportedProgram — into a Core AI AIProgram ready to run on Apple hardware, traversing the FX graph node-by-node and mapping ATen operators to Core AI operations.

You can install an iOS 27 Developer Beta today, which supposedly has the new features - but you then have to make it through a waiting list for access to the new Siri AI. Aaron Perris from MacRumors reports having made it off the waitlist so we may start seeing credible reports on how well Siri AI works in the very near future.

Update: These Private Cloud Compute Gemini models are running in Google Cloud, and using NVIDIA hardware. According to Expanding Private Cloud Compute on Apple's Security Research blog:

For the most demanding tasks, including agentic tool-use and complex reasoning, we worked with Google and NVIDIA to extend our PCC infrastructure to Google Cloud systems using NVIDIA GPUs, while maintaining Apple's powerful security and privacy protections. [...]

PCC on Google Cloud leverages many of the same architectural security patterns as PCC on Apple silicon to implement these layered protections: initial network data parsing for each request happens in a dedicated process within its own namespace, shared inference software is recycled with a short time-to-live duration, and attested keys are held in a separate, dedicated confidential VM isolated from external inputs. [...]

As with PCC on Apple silicon, all binaries will be published for public inspection.

Tags: vision-llms, apple, generative-ai, ai, llms, gemini, nvidia, google

Microsoft's new MAI models

2026-06-02T22:21:52+00:00

Microsoft announced two new text LLMs this morning - MAI-Thinking-1 (reasoning, 1T parameters, 35B active, available to "select early partners") and MAI-Code-1-Flash (137B Parameters, 5B active, "purpose-built for GitHub Copilot and VS Code to deliver high performance and lower cost [...] rolling out to GitHub Copilot individual users in Visual Studio Code"). I've not been able to try either of them just yet.

It's very interesting to see Microsoft releasing models with such low parameter counts, especially given how expensive larger models are to access right now. They claim MAI-Thinking-1 "is preferred to Sonnet 4.6 in our blind human side-by-side evaluations", which is impressive for a 35B model seeing as I frequently run models larger than that on my own laptop. (UPDATE: I got this entirely wrong, see note below.)

Also of note:

We trained [MAI-Thinking-1] from the ground up on enterprise grade, clean and commercially licensed data, without distillation from third-party models.

And for MAI-Code-1-Flash as well:

It is built end-to-end by Microsoft using clean and appropriately licensed data.

I would very much like to learn more about this "appropriately licensed" data! Could these be the first generally useful code-specialist models that didn't train on an unlicensed dump of the web? (Update: the answer is no, see note below.)

Update: My initial published notes got the size of the models wrong. I misread Microsoft's announcements and interpreted the MoE active parameter count as the total parameter count, but the model card for MAI-Code-1-Flash lists it as 137B with 5B active and the MAI-Thinking-1 technical paper reveals it to be a 1T model with 35B active.

I deeply regret this error.

Update 2: That technical paper describes the training data in some detail from page 80 onwards. It has the same licensing problems as all of the other major LLMs: it's trained on a crawl of the public web:

The majority of our web HTML corpus comes from a proprietary crawl. After initial page discovery and selection, approximately 1.2 trillion pages are crawled and parsed. [...] In addition to Microsoft standard policy Sec. 2.4, we apply UT1 block list (Prigent, 2026) to remove adult content and piracy-related domains. In all, this filtering reduces the corpus from 1.2 trillion pages to 794 billion pages. Given the prevalence of AI-generated content on the web, we also score pages with a proprietary AI-content detection model and use manual inspection to identify domains with extensive AI-generated content; those domains are filtered out of the training corpus.

[...]

We process Common Crawl with the same pipeline. [...] After filtering, deduplication, merging with the proprietary web corpus, and a final round of exact-URL and content-level fuzzy deduplication, the Common Crawl portion contains 24.2 billion pages.

I did not cover this one at all well, which is somewhat ironic since I was at the Microsoft Build conference when I wrote this up! I'm sorry for not digging deeper before publishing my initial notes.

Tags: llm-release, generative-ai, ai, microsoft, llms, training-data

May 2026 newsletter

2026-06-01T04:45:00+00:00

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

This month:

Al got expensive, and Anthropic had a really good month
The model releases were a little disappointing
Conferences and podcasts
I launched Datasette Agent and made a lot of progress on Datasette
What I'm using, May 2026 edition
Miscellaneous extras

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

Tags: newsletter

Anthropic's run-rate revenue hits $47 billion

2026-05-29T01:23:08+00:00

The most interesting thing about Anthropic's $65B Series H announcement is this line (emphasis mine):

Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month.

Anthropic have made a bit of a habit of sharing their "run-rate revenue" in this kind of announcement, which is an annualized projection of their current revenue - typically calculated by taking the most recent month and multiplying by 12. Update: here's a leaked description of their run-rate formula.

Earlier this year:

Apr 6, 2026 in Anthropic expands partnership with Google and Broadcom: "Our run-rate revenue has now surpassed $30 billion—up from approximately $9 billion at the end of 2025."
Feb 12, 2026 in Anthropic raises $30 billion in Series G: "Today, our run-rate revenue is $14 billion, with this figure growing over 10x annually in each of those past three years."

I had Claude Opus 4.8 make me this chart using Matplotlib (Claude: "a data line chart is more straightforward matplotlib work—not really a design piece"):

Back in April Axios CEO Jim VandeHei wrote that he could not find "any company — in any industry, in any era — that has scaled organic revenue this quickly at this level as Anthropic" - and that was when they were at a paltry $30 billion.

(Also in Axios today is an anonymously sourced note that "An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees" - times that by 12 and you get an extra $6 billion in annualized run-rate!)

Ed Zitron was extremely skeptical of that $30 billion number - I wonder if his skepticism will update for the new $47 billion figure.

I've seen a few people dismiss this as untrustworthy, because the numbers come from Anthropic. That doesn't hold up: these numbers were included in announcements of their fundraises, and lying to investors who just put in $65 billion would be securities fraud. They're even less likely to lie given that the real numbers will no doubt come out in their S-1 when they file for their IPO.

Tags: anthropic, ai

Google I/O, Gemini Spark, Antigravity

2026-05-20T15:32:17+00:00

It's hard to find much to write about Google I/O this year because I have a policy of not writing about anything that I can't try out myself, and a lot of the big announcements are "coming soon".

I actually prefer to write about things that are in general availability, because I've had instances in the past where the previews didn't match what was released to the general public later on.

Aside from Gemini 3.5 Flash the most interesting announcement looks to be Google's upcoming OpenClaw competitor Gemini Spark, described as "your personal AI agent" which can "connect natively with your favorite Google apps like Gmail, Calendar, Drive, Docs, Sheets, Slides, YouTube, and Google Maps". The FAQ for that also includes this confusing detail:

What Gemini model does Gemini Spark run on?

Gemini Spark runs on Gemini 3.5 Flash and Antigravity.

The antigravity.google website currently lists Antigravity as a desktop app, a CLI agent tool (written in Go), the Antigravity SDK (an open source Python wrapper around a bundled closed source Go binary), and the original Antigravity IDE (a VS Code fork).

I guess Gemini Spark, the user-facing hosted agent product, might be running on that Go binary, but I'm not sure why that's worth mentioning in the FAQ!

Naturally I went looking for notes on how Gemini Spark intends to handle the risk of prompt injection. The best information I could find on that was in the Everything Google Cloud customers need to know coming out of Google I/O post aimed at enterprise customers, which includes:

Spark operates in a fully managed, secure runtime on Google Cloud, meaning you get enterprise-grade security without ever having to manage the underlying infrastructure. Every task executes in a fresh, strictly isolated, ephemeral VM to help ensure data never overlaps between sessions. To protect your enterprise, all traffic routes through our secure Agent Gateway that enforces Data Loss Prevention (DLP) policies, while user credentials remain fully encrypted and are never exposed directly to the agent.

Given how many people are going to be piping very sensitive data through Gemini Spark in the near future I hope they've made this bullet-proof, or this could be a top candidate for the agent security challenger disaster that we still haven't seen.

Also of note: in Transitioning Gemini CLI to Antigravity CLI Google announce that the open source Gemini CLI tool (Apache 2.0 licensed TypeScript) will stop working with their AI subscription plans on June 18th, replaced by the new closed source Antigravity CLI.

Tags: gemini, google, generative-ai, ai, google-io, llms, prompt-injection

Warelay -> OpenClaw

2026-05-16T20:23:30+00:00

In preparation for a lightning talk I'm giving at PyCon US this afternoon I decided to figure out how many names OpenClaw has actually had since that first commit back in November.

Thanks to this first_line_history.py tool (code here) the answer, according to the Git history of the OpenClaw README, is:

Warelay → CLAWDIS → CLAWDBOT → Clawdbot → Moltbot →🦞 OpenClaw

Or in detail (the output from the tool):

2025-11-24T11:23:15+01:00 16dfc1a # Warelay — WhatsApp Relay CLI (Twilio)
2025-11-24T11:41:37+01:00 d4153da # 📡 Warelay — WhatsApp Relay CLI (Twilio)
2025-11-24T17:47:57+01:00 343ef9b # 📡 warelay — WhatsApp Relay CLI (Twilio)
2025-11-25T04:44:10+01:00 14b3c6f # 📡 warelay — WhatsApp Relay CLI
2025-11-25T12:48:40+01:00 4814021 # 📡 warelay — Send, receive, and auto-reply on WhatsApp—Twilio-backed or QR-linked.
2025-11-25T13:50:18+01:00 d51a3e9 # warelay 📡 - Send, receive, and auto-reply on WhatsApp via Twilio or QR-linked WhatsApp Web; webhook setup in one command
2025-11-25T13:51:13+01:00 4d2a8a8 # 📡 warelay — Send, receive, and auto-reply on WhatsApp—Twilio-backed or QR-linked.
2025-11-25T14:52:43+01:00 1ef7f4d # 📡 warelay — Send, receive, and auto-reply on WhatsApp.
2025-12-03T15:45:32+00:00 a27ee23 # 🦞 CLAWDIS — WhatsApp Gateway for AI Agents
2025-12-08T12:43:13+01:00 17fa2f4 # 🦞 CLAWDIS — WhatsApp & Telegram Gateway for AI Agents
2025-12-19T18:41:17+01:00 7710439 # 🦞 CLAWDIS — Personal AI Assistant
2026-01-04T14:32:47+00:00 246adaa # 🦞 CLAWDBOT — Personal AI Assistant
2026-01-10T05:14:09+01:00 cdb915d # 🦞 Clawdbot — Personal AI Assistant
2026-01-27T13:37:47-05:00 3fe4b25 # 🦞 Moltbot — Personal AI Assistant
2026-01-30T03:15:10+01:00 9a71607 # 🦞 OpenClaw — Personal AI Assistant

Tags: openclaw, git, tools

Not so locked in any more

2026-05-14T22:53:49+00:00

This Mitchell Hashimoto quote about Bun migrating from Zig to Rust reminded me of a similar conversation I had at a conference last week.

I was talking to someone who worked for a medium sized technology company with a pair of legacy/legendary iPhone and Android apps.

They told me they had just completed a coding-agent driven rewrite of both apps to React Native.

I asked why they chose that, given that coding agents presumably drive down the cost of maintaining separate iPhone and Android apps.

They said that React Native has improved a lot over the past few years and covered everything their apps needed to do.

And... if it turned out to be the wrong decision, they could just port back to native in the future.

Like Mitchell said:

Programming languages used to be LOCK IN, and they're increasingly not so.

Tags: react, coding-agents, ai-assisted-programming, generative-ai, ai, llms

April 2026 newsletter

2026-05-04T22:38:36+00:00

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

In this month's newsletter:

Opus 4.7 and GPT-5.5, both with price increases
Claude Mythos and LLM security research
ChatGPT Images 2.0
More model releases
Other highlights from my blog
What I'm using, April 2026 edition

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

Tags: newsletter