Notes

來源原標題

Notes

作者

Simon Willison

原始標籤

blog

原始連結

https://simonwillison.net/notes/

摘要原文

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 . 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! # 3rd October 2026 , 10 pm / newsletter 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. # 30th September 2026 , 9:54 pm / museums , new-york The more time I spend working with coding agents, the more convinced I am that they make software engineering even harder. We can do amazing things with them, but unlocking their full potential requires extraordinary discipline and knowledge. # 24th September 2026 , 11:31 pm / coding-agents , ai , llms Being a computer scientist who refuses to find anything about LLMs interesting right now is a bit like being a geneticist who refuses to find anything interesting about the recently opened Jurassic Park. Skeptical geneticist: "pfft, it's just frog DNA. And they deliberately let them eat people for the marketing." # 18th September 2026 , 7:21 pm / llms , ai , generative-ai Graham Dumpleton's new monkey patching package wrapture is shaping up to be an indispensable tool for Python developers. I'm not sure why I've seen so little buzz about it! Graham has been posting new tutorials for it almost daily since the initial release on August 31st. Here's everything he's published so far: Graham also has a set of interactive workshops for wrapture, implemented as JupyterLab notebooks. Wrapture is still alpha software but it's already very usable - especially given you can configure and try it out with a TOML file without modifying any Python code at all. This feels like one of those Swiss Army Knife packages that, once mastered, will provide value against all sorts of problems for years to come. # 11th September 2026 , 1:51 pm / graham-dumpleton , open-source , testing , python , observability , monkey-patching The August 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 . Here's a copy of the July newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy! # 4th September 2026 , 5:54 am / newsletter I was poking around in my ~/.cache/ folder using OmniDiskSweeper when I spotted something interesting. The OpenAI Codex desktop app (since rebranded to just ChatGPT) has 1.7GB of stuff in there in a folder called codex-primary-runtime , including a full Python installation, a full Node.js installation, and native binaries for Poppler , git, and the LibreOffice open source office suite (which forked from OpenOffice.org in 2010): The ~/.cache/codex-runtimes/codex-primary-runtime/plugins/openai-primary-runtime/plugins/documents folder includes skills which tell Codex how to find and use those binaries. # 1st September 2026 , 7:03 pm / codex , generative-ai , openai , ai , llms , openoffice , open-source The key skill required to make productive use of coding agents is being able to confidently instruct them on how to make changes and then confidently verify that those changes have been applied in the correct way. Sometimes this involves reviewing every line of code they have written, but there are other ways to achieve that goal. Eyeballing every line of code has never been the most effective way to validate a change to a piece of software. # 22nd August 2026 , 3:56 pm / code-review , coding-agents , generative-ai , agentic-engineering , ai , llms I started building my markdown-svg-renderer tool in May , but I've since added enough features to it that it's worth talking about here again. It's evolved into my ideal tool for sharing Markdown transcripts that include SVG documents. Given my proclivity for drawing pelicans riding bicycles this is a problem that I needed to solve! The tool is very simple. Navigate to markdown-svg-renderer in your browser and paste in some Markdown to see it rendered... or save that Markdown to a CORS-friendly URL or a GitHub Gist and paste in a URL to that document. The URL option will give you a bookmarkable page, for example https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F6f9e48293be5c916652d29f0dc0b0657 - which bakes in the URL to this Gist . If you visit the Gist you'll see raw SVG: In the rendered tool that looks like this instead: As you can see, that SVG block in the Markdown has been transformed into a rendered SVG (in this case animated) plus several tabs. The tabs are the really fun bit. The PNG and JPEG tabs render that SVG to those image formats in the browser and lets you copy or download them - useful for sharing on platforms that don't support SVG directly. The MP4 tab is new today - it examines the SVG to see if it contains any animations, attempts to guess how long the looped video should be, then renders a whole bunch of frames of the animation and loads 30+MB of ffmpeg.wasm so it can compile those frames into an MP4 video using the full power of FFMPEG compiled to WebAssembly and running in the browser. Being able to turn an animated SVG into a MP4 again makes it easy to share on platforms that can't support SVG animation natively. It's a neat trick! # 16th August 2026 , 11:59 pm / svg , markdown , tools I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and American AI Leadership was shepherded by Microsoft, dated July 24th, and signed by 235 AI-adjacent companies including NVIDIA (see Jensen's first ever tweet ), Amazon, Y Combinator, The Linux Foundation, and (a later signer) OpenAI. It's clearly an argument designed to counter any instincts by the current US government to ban or limit open weight models over "safety" concerns - a reasonable consideration given what happened to Claude Fable 5 ! Relying solely on closed models is not inherently safe: they can be breached, misused, or fail in ways that outsiders cannot detect. And concentrating advanced AI capabilities behind a small number of closed models compounds that risk. It results in a small number of single points of failure, weakens competition, and leaves critical technology in the hands of a few providers. Open weight models, on the other hand, allow a broad community of researchers and developers to examine their behavior, identify vulnerabilities, develop safeguards, and improve them over time. The one surprising note in the letter is that it comes out in support of distillation, where models train on output from other models: In shaping this ecosystem, policymakers should be careful not to conflate legitimate model-development techniques with misappropriation. Distillation, or the practice of using one model’s outputs to help train or improve another, is a widely used technique for model improvement, evaluation, and validation. It reflects a long tradition of learning from, building upon, and improving existing technologies, a tradition that has helped drive innovation since the rise of the open-source software movement. Notably absent from the signatures: Anthropic, who published their own response Our position on open-weights models three days later. CEO Dario Amodei doubled down on the risk of authoritarian governments building "AI models that are more powerful than those built by the US", and models being "misused to carry out cyberattacks or biological attacks", and called for "a crack down on industrial-scale distillation operations ", while also stating that "Anthropic has never advocated for a ban on open-weights models".