September 2024
Sept. 16, 2024
o1 prompting is alien to me. Its thinking, gloriously effective at times, is also dreamlike and unamenable to advice.
Just say what you want and pray. Any notes on “how” will be followed with the diligence of a brilliant intern on ketamine.
Sept. 17, 2024
Do not fall into the trap of anthropomorphizing Larry Ellison. You need to think of Larry Ellison the way you think of a lawnmower. You don’t anthropomorphize your lawnmower, the lawnmower just mows the lawn - you stick your hand in there and it’ll chop it off, the end. You don’t think "oh, the lawnmower hates me" – lawnmower doesn’t give a shit about you, lawnmower can’t hate you. Don’t anthropomorphize the lawnmower. Don’t fall into that trap about Oracle.
Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison (via) I'm the guest for the latest episode of the TWIML AI podcast - This Week in Machine Learning & AI, hosted by Sam Charrington.
We mainly talked about how I use LLM tooling for my own work - Claude, ChatGPT, Code Interpreter, Claude Artifacts, LLM and GitHub Copilot - plus a bit about my experiments with local models.
Serializing package requirements in marimo notebooks. The latest release of Marimo - a reactive alternative to Jupyter notebooks - has a very neat new feature enabled by its integration with uv:
One of marimo’s goals is to make notebooks reproducible, down to the packages used in them. To that end, it’s now possible to create marimo notebooks that have their package requirements serialized into them as a top-level comment.
This takes advantage of the PEP 723 inline metadata mechanism, where a code comment at the top of a Python file can list package dependencies (and their versions).
I tried this out by installing marimo using uv:
uv tool install --python=3.12 marimo
Then grabbing one of their example notebooks:
wget 'https://raw.githubusercontent.com/marimo-team/spotlights/main/001-anywidget/tldraw_colorpicker.py'
And running it in a fresh dependency sandbox like this:
marimo run --sandbox tldraw_colorpicker.py
Also neat is that when editing a notebook using marimo edit:
marimo edit --sandbox notebook.py
Just importing a missing package is enough for Marimo to prompt to add that to the dependencies - at which point it automatically adds that package to the comment at the top of the file:
Something that I confirmed that other conference organisers are also experiencing is last-minute ticket sales. This is something that happened with UX London this year. For most of the year, ticket sales were trickling along. Then in the last few weeks before the event we sold more tickets than we had sold in the six months previously. […]
When I was in Ireland I had a chat with a friend of mine who works at the Everyman Theatre in Cork. They’re experiencing something similar. So maybe it’s not related to the tech industry specifically.
In general, the claims about how long people are living mostly don’t stack up. I’ve tracked down 80% of the people aged over 110 in the world (the other 20% are from countries you can’t meaningfully analyse). Of those, almost none have a birth certificate. [...]
Regions where people most often reach 100-110 years old are the ones where there’s the most pressure to commit pension fraud, and they also have the worst records.
Oracle, it’s time to free JavaScript. (via) Oracle have held the trademark on JavaScript since their acquisition of Sun Microsystems in 2009. They’ve continued to renew that trademark over the years despite having no major products that use the mark.
Their December 2019 renewal included a screenshot of the Node.js homepage as a supporting specimen!
Now a group lead by a team that includes Ryan Dahl and Brendan Eich is coordinating a legal challenge to have the USPTO treat the trademark as abandoned and “recognize it as a generic name for the world’s most popular programming language, which has multiple implementations across the industry.”
Sept. 18, 2024
Things I’ve learned serving on the board of the Python Software Foundation
Two years ago I was elected to the board of directors for the Python Software Foundation—the PSF. I recently returned from the annual PSF board retreat (this one was in Lisbon, Portugal) and this feels like a good opportunity to write up some of the things I’ve learned along the way.
[... 2,703 words]The problem that you face is that it's relatively easy to take a model and make it look like it's aligned. You ask GPT-4, “how do I end all of humans?” And the model says, “I can't possibly help you with that”. But there are a million and one ways to take the exact same question - pick your favorite - and you can make the model still answer the question even though initially it would have refused. And the question this reminds me a lot of coming from adversarial machine learning. We have a very simple objective: Classify the image correctly according to the original label. And yet, despite the fact that it was essentially trivial to find all of the bugs in principle, the community had a very hard time coming up with actually effective defenses. We wrote like over 9,000 papers in ten years, and have made very very very limited progress on this one small problem. You all have a harder problem and maybe less time.
Sept. 19, 2024
The web’s clipboard, and how it stores data of different types.
Alex Harri's deep dive into the Web clipboard API, the more recent alternative to the old document.execCommand() mechanism for accessing the clipboard.
There's a lot to understand here! Some of these APIs have a history dating back to Internet Explorer 4 in 1997, and there have been plenty of changes over the years to account for improved understanding of the security risks of allowing untrusted code to interact with the system clipboard.
Today, the most reliable data formats for interacting with the clipboard are the "standard" formats of text/plain, text/html and image/png.
Figma does a particularly clever trick where they share custom Figma binary data structures by encoding them as base64 in data-metadata and data-buffer attributes on a <span> element, then write the result to the clipboard as HTML. This enables copy-and-paste between the Figma web and native apps via the system clipboard.
Moshi (via) Moshi is "a speech-text foundation model and full-duplex spoken dialogue framework". It's effectively a text-to-text model - like an LLM but you input audio directly to it and it replies with its own audio.
It's fun to play around with, but it's not particularly useful in comparison to other pure text models: I tried to talk to it about California Brown Pelicans and it gave me some very basic hallucinated thoughts about California Condors instead.
It's very easy to run locally, at least on a Mac (and likely on other systems too). I used uv and got the 8 bit quantized version running as a local web server using this one-liner:
uv run --with moshi_mlx python -m moshi_mlx.local_web -q 8
That downloads ~8.17G of model to a folder in ~/.cache/huggingface/hub/ - or you can use -q 4 and get a 4.81G version instead (albeit even lower quality).
Sept. 20, 2024
Introducing Contextual Retrieval (via) Here's an interesting new embedding/RAG technique, described by Anthropic but it should work for any embedding model against any other LLM.
One of the big challenges in implementing semantic search against vector embeddings - often used as part of a RAG system - is creating "chunks" of documents that are most likely to semantically match queries from users.
Anthropic provide this solid example where semantic chunks might let you down:
Imagine you had a collection of financial information (say, U.S. SEC filings) embedded in your knowledge base, and you received the following question: "What was the revenue growth for ACME Corp in Q2 2023?"
A relevant chunk might contain the text: "The company's revenue grew by 3% over the previous quarter." However, this chunk on its own doesn't specify which company it's referring to or the relevant time period, making it difficult to retrieve the right information or use the information effectively.
Their proposed solution is to take each chunk at indexing time and expand it using an LLM - so the above sentence would become this instead:
This chunk is from an SEC filing on ACME corp's performance in Q2 2023; the previous quarter's revenue was $314 million. The company's revenue grew by 3% over the previous quarter."
This chunk was created by Claude 3 Haiku (their least expensive model) using the following prompt template:
<document>
{{WHOLE_DOCUMENT}}
</document>
Here is the chunk we want to situate within the whole document
<chunk>
{{CHUNK_CONTENT}}
</chunk>
Please give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else.
Here's the really clever bit: running the above prompt for every chunk in a document could get really expensive thanks to the inclusion of the entire document in each prompt. Claude added context caching last month, which allows you to pay around 1/10th of the cost for tokens cached up to your specified beakpoint.
By Anthropic's calculations:
Assuming 800 token chunks, 8k token documents, 50 token context instructions, and 100 tokens of context per chunk, the one-time cost to generate contextualized chunks is $1.02 per million document tokens.
Anthropic provide a detailed notebook demonstrating an implementation of this pattern. Their eventual solution combines cosine similarity and BM25 indexing, uses embeddings from Voyage AI and adds a reranking step powered by Cohere.
The notebook also includes an evaluation set using JSONL - here's that evaluation data in Datasette Lite.
Notes on using LLMs for code
I was recently the guest on TWIML—the This Week in Machine Learning & AI podcast. Our episode is titled Supercharging Developer Productivity with ChatGPT and Claude with Simon Willison, and the focus of the conversation was the ways in which I use LLM tools in my day-to-day work as a software developer and product engineer.
[... 859 words]YouTube Thumbnail Viewer.
I wanted to find the best quality thumbnail image for a YouTube video, so I could use it as a social media card. I know from past experience that GPT-4 has memorized the various URL patterns for img.youtube.com, so I asked it to guess the URL for my specific video.
This piqued my interest as to what the other patterns were, so I got it to spit those out too. Then, to save myself from needing to look those up again in the future, I asked it to build me a little HTML and JavaScript tool for turning a YouTube video URL into a set of visible thumbnails.
I iterated on the code a bit more after pasting it into Claude and ended up with this, now hosted in my tools collection.
Sept. 21, 2024
Markdown and Math Live Renderer.
Another of my tiny Claude-assisted JavaScript tools. This one lets you enter Markdown with embedded mathematical expressions (like $ax^2 + bx + c = 0$) and live renders those on the page, with an HTML version using MathML that you can export through copy and paste.
Here's the Claude transcript. I started by asking:
Are there any client side JavaScript markdown libraries that can also handle inline math and render it?
Claude gave me several options including the combination of Marked and KaTeX, so I followed up by asking:
Build an artifact that demonstrates Marked plus KaTeX - it should include a text area I can enter markdown in (repopulated with a good example) and live update the rendered version below. No react.
Which gave me this artifact, instantly demonstrating that what I wanted to do was possible.
I iterated on it a tiny bit to get to the final version, mainly to add that HTML export and a Copy button. The final source code is here.