Simon Willison’s Weblog


Weeknotes: PyCon US 2024

28th May 2024

Earlier this month I attended PyCon US 2024 in Pittsburgh, Pennsylvania. I gave an invited keynote on the Saturday morning titled “Imitation intelligence”, tying together much of what I’ve learned about Large Language Models over the past couple of years and making the case that the Python community has a unique opportunity and responsibility to help try to nudge this technology in a positive direction.

The video isn’t out yet but I’ll publish detailed notes to accompany my talk (using my annotated presentation format) as soon as it goes live on YouTube.

PyCon was a really great conference. Pittsburgh is a fantastic city, and I’m delighted that PyCon will be in the same venue next year so I can really take advantage of the opportunity to explore in more detail.

I also realized that it’s about time Datasette participated in the PyCon sprints—the project is mature enough for that to be a really valuable opportunity now. I’m looking forward to leaning into that next year.

I’m on a family-visiting trip back to the UK at the moment, so taking a bit of time off from my various projects.

LLM support for new models

The big new language model releases from May were OpenAI GPT-4o and Google’s Gemini Flash. I released LLM 0.14, datasette-extract 0.1a7 and datasette-enrichments-gpt 0.5 with support for GPT-4o, and llm-gemini 0.1a4 adding support for the new inexpensive Gemini 1.5 Flash.

Gemini 1.5 Flash is a particularly interesting model: it’s now ranked 9th on the LMSYS leaderboard, beating Llama 3 70b. It’s inexpensive, priced close to Claude 3 Haiku, and can handle up to a million tokens of context.

I’m also excited about GPT-4o—half the price of GPT-4 Turbo, around twice as fast and it appears to be slightly more capable too. I’ve been getting particularly good results from it for structured data extraction using datasette-extract—it seems to be able to more reliably produce a longer sequence of extracted rows from a given input.

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