Simon Willison’s Weblog

I released ttok 0.4, ran uv tool upgrade ttok, piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead!

I figured switching the default was a reasonable excuse to finally ship a 1.0.

OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same:

All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus.

ttok is my CLI tool for counting tokens, using OpenAI's open source tiktoken library.

It hasn't been in updated in a couple of years, but I finally fixed a Click warning, updated CI, and added a --list-models command to list available models.

It works with uvx, so you can count tokens in anything like this:

cat file.txt | uvx ttok

OpenAI released their new Jev-style Decisions API, as previously announced at last week's DevDay.

Since I already have an llm-typesafe plugin for talking to Jev, I had GPT-6 Astra read the new OpenAI API documentation and build an llm-openai-decisions plugin inspired by llm-typesafe.

Unlike Jev, the new gpt-6-luna decision model supports image input in addition to text. Both models charge for input it and not for output: OpenAI's is 10 cents per million input tokens, Jev's is 4.2 cents per million.

Otherwise the API shape is very similar to Jev, at least conceptually. Jev supports three question types for yes/no, choices, or scores. OpenAI Decisions supports the same three types.

Install the plugin like this:

llm install llm-openai-decisions

Here's an example query against an image attachment:

llm -m openai-decisions/gpt-6-luna \
  -a https://static.simonwillison.net/static/2025/two-pelicans.jpg \
  -s 'Does this image contain any mammals?'

And example output:

{"type": "predicate", "name": "evaluation", "probability": 0.0}

Consult the README for full details of how to run the other types of questions.

Adds support for reasoning models, such as the newly released Mistral Large 4.

A minor fix for compatibility with the latest Datasette alphas. This meant we could upgrade the datasette.io site to Datasette 1.0a41.

I wanted to see if Claude Opus 5.5 could compose music, so I tried this:

I want you to write some computer game music for me. First design simple text based format for the music and build an artifact that can play it out loud - include some example tracks in that artifact

I am looking for music of the quality of the original secret of Monkey Island

It leaned a lot harder into the Monkey Island theme than I had intended, but the results are surprisingly good.

I wonder if the ability to compose competent music is similar to the 3D graphics thing - a new capability for text models that emerged in the past few months?

Would need some careful experiments with other recent and not-so-recent models to confirm if this is new or if they've been able to do this for a while.

Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results:

I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment.

I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf. Here's the result for a run of 30 attempts per combination with reasoning disabled:

Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%:

It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here.

Here's a version of the report that includes the reasoning traces from some of those larger calculations, which include text like this:

Wait, let me redo this more carefully.

4,299,366,105,622
6,088,794,067,970

Let me align them:
4 2 9 9 3 6 6 1 0 5 6 2 2
6 0 8 8 7 9 4 0 6 7 9 7 0

Adding from right to left:
Position 1 (units): 2 + 0 = 2
Position 2 (tens): 2 + 7 = 9
Position 3 (hundreds): 6 + 9 = 15, write 5, carry 1

pwasm is one of my folly projects - an entirely vibe-coded pure Python WebAssembly engine that I built in January during my first bout of AI mania.

I hadn't touched it since January, so I decided to let Claude Opus 5.5 loose on it and see if it could make any significant improvements:

Evaluate current state of pwasm - then consider what it would take to get the MicroPython and micro JavaScript experiments from the research repo working under it - and what it would take to speed it up

42 commits later (with minimal follow-up prompting) it now handles almost all of the WASM specification, and the wheel from PyPI bundles working WASM builds of MicroPython, QuickJS and Micro QuickJS.

I wouldn't trust this thing at all - hence the alpha version tag - but it's interesting seeing how today's models can improve on the work of models from 10 months ago.

I took a photograph of some protesters, then thought about how I don't like sharing photographs of strangers with identifiable faces. I had GPT-6 Astra build this experimental tool that would identify faces and automatically blur them out.

It uses Google's MediaPipe C++ library, compiled to WebAssembly via @mediapipe/tasks-vision, plus the BlazeFace face detection model.

In addition to Claude Sonnet 5.5, this release adds the ability to run llm anthropic refresh to refresh the list of Anthropic models directly from their API - which means I don't need to push a new release just to add support for a newly released model.

I also added an llm anthropic count command which can use their free token counting API to return a count of tokens that will be used by any prompt, before you send that prompt.

Automated reply bots on Twitter are a scourge - as someone with a decent number of followers I attract a swarm of these, such that anything I post there attracts dozens of mindless automated replies.

They've started manifesting on Bluesky as well.

Unlike Twitter, Bluesky still has a freely available and useful API. The lack of such a thing doesn't slow down the bots, but it does make investigating them a lot more frustrating.

So I had Opus 5.5 vibe code this tool, which examines any Bluesky profile for evidence of a likely reply bot.

It looks for signals like replies posted within seconds of other posts from the same account, or accounts that never post their own content (or images or links) but instead consistently reply to messages from other, higher-follower users.

It also looks for question marks, because I'm extra infuriated by reply bots that trick me into wasting my time answering a question that no human ever posed.

I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026.

For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt:

Here are some photos of kakapo parrots just to remind you what they look like

I need you to make an animation in animated pixel art on HTML 5 canvas of obviously pixel art kakapo jumping up and down having a party with confetti and suchlike - there should be at least 20 of them

Here's the transcript, and this is the resulting page. It's pretty great!

I wanted to embed it in a Keynote presentation file, so I downloaded the HTML and told a local Claude Code session:

Make me a video of file:///Users/simon/Downloads/kakapo-party.html - you need to load it in a browser and click on it a few times to get the confetti effect, the video should be 15s long

don't start clicking until 3s in

make sure several clicks are spread around the clickable area

Claude Code used Playwright (transcript here) and produced this video, which was exactly what I needed for my final slide:

Here's the full Playwright script it used, which was pleasingly short:

# /// script
# dependencies = ["playwright"]
# ///
import time
from playwright.sync_api import sync_playwright
W, H = 1280, 720
# Canvas fills the viewport; spread clicks across corners, edges and centre
clicks = [
    (3.0, 640, 360),   # centre
    (4.2, 160, 120),   # top-left
    (5.4, 1120, 120),  # top-right
    (6.6, 180, 600),   # bottom-left
    (7.8, 1100, 600),  # bottom-right
    (9.0, 640, 100),   # top-centre
    (10.0, 380, 380),  # mid-left
    (11.0, 900, 380),  # mid-right
    (12.2, 640, 620),  # bottom-centre
    (13.2, 640, 300),  # finale centre
]
with sync_playwright() as p:
    b = p.chromium.launch()
    ctx = b.new_context(viewport={"width":W,"height":H}, record_video_dir="vids", record_video_size={"width":W,"height":H})
    page = ctx.new_page()
    t0 = time.time()
    page.goto("file:///Users/simon/Downloads/kakapo-party.html")
    for t,x,y in clicks:
        time.sleep(max(0, t-(time.time()-t0)))
        page.mouse.click(x,y)
    time.sleep(max(0, 16.0-(time.time()-t0)))
    ctx.close(); b.close()

Support for branches other than the default branch. Use uvx commit-rewriter --branch other to run against another branch. #3

Alec Garcia added support for OpenTelemetry to Datasette in this release.

I've also refactored all of Datasette's modal dialogs to a single Web Component, which is now documented for other plugins to use.

Google released two new Gemini text-to-speech models today - gemini-3.8-flash-tts and gemini-3.8-flash-lite-tts.

They come with a library of over 2,000 voices, plus the ability to create a custom voice with "just a 30-second audio sample of your voice or a voice you have the rights to use".

I vibe coded this bring-your-own-key playground interface with GPT-6 Astra, taking advantage of the open CORS policy of the underlying Gemini API.

A notable feature of the API is that it makes it easy to define a full conversation between multiple characters, each with different voices and voice style instructions.

Here's a short demo clip of a conversation between two pelicans debating if they should move to the Pacifica Pier. I had Claude 4.5 Opus write the script and generate a URL to render it using the tool.

It took ~20 seconds to generate 1m 18s of audio using Gemini 3.8 Flash TTS (not the cheaper Flash-Lite), at a cost of 2.74 cents.