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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.

Recent articles

Claude Haiku 5.5 - 7th October 2026

We're going to need default hard budget caps on pretty much everything - 3rd October 2026

OpenAI DevDay 2026 live blog - 29th September 2026

This is a beat by Simon Willison, posted on 9th October 2026 .

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By the time I got out of bed yesterday, I had three people ask me if I'd seen the OpenAI announcement that the Partition Principle does not imply the Axiom of Choice. The number kept increasing during the day, and I can see why. I am famously interested in this problem.

Indeed, at least two people asked if I plan on sending Sam Altman a bottle of whisky, as promised in my Problems page. The answer to that is no. And let me explain to you why, and why you should be very angry at OpenAI.

Let me clarify something first. I am not entirely against the use of AI. I understand that it is a tool, and many people find it useful, and I am happy to agree that it is merits. I think that we need to better understand how this technology changes our field and how we want to use it before we rush to throw our lot with it. This is why I generally avoid using AI for mathematics (I am happy to ask LLMs to consolidate information for me, or to generate a useful infographic, or to proof read an email, etc.), it's just the framework of using it that is currently missing in my view. Still, we are in the stage of discovery, people use it and we see the consequences unfold and we can decide as a community to adopt it further or restrict its use. For example, arXiv introduced rate limiting as a consequence of these tools, and almost all papers should include some AI statement at this point (even to state that no AI was used, I guess). We need to decide how we judge the contribution of the author, e.g., do we require the chat log be made public, or at least available to the reviewers and the editor? These are questions that we need to contend with and see where they take us.

But, back to the Partition Principle. I took a brief look at the preprint released by OpenAI (not at the Lean code, s...

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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.

Recent articles

Claude Haiku 5.5 - 7th October 2026

We're going to need default hard budget caps on pretty much everything - 3rd October 2026

OpenAI DevDay 2026 live blog - 29th September 2026

This is a quotation collected by Simon Willison, posted on 8th October 2026 .

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REDWOOD CITY, CA, October 7, 2026 — Biohub, the U.S. Department of Energy, the National Institutes of Health, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.

As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.

In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life.

These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will...

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