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How Figma’s databases team lived to tell the scale (via) The best kind of scaling war story:
"Figma’s database stack has grown almost 100x since 2020. [...] In 2020, we were running a single Postgres database hosted on AWS’s largest physical instance, and by the end of 2022, we had built out a distributed architecture with caching, read replicas, and a dozen vertically partitioned databases."
I like the concept of "colos", their internal name for sharded groups of related tables arranged such that those tables can be queried using joins.
Also smart: separating the migration into "logical sharding" - where queries all still run against a single database, even though they are logically routed as if the database was already sharded - followed by "physical sharding" where the data is actually copied to and served from the new database servers.
Logical sharding was implemented using PostgreSQL views, which can accept both reads and writes:
CREATE VIEW table_shard1 AS SELECT * FROM table
WHERE hash(shard_key) >= min_shard_range AND hash(shard_key) < max_shard_range)
The final piece of the puzzle was DBProxy, a custom PostgreSQL query proxy written in Go that can parse the query to an AST and use that to decide which shard the query should be sent to. Impressively it also has a scatter-gather mechanism, so select * from table can be sent to all shards at once and the results combined back together again.
Lateral Thinking with Withered Technology. Gunpei Yokoi’s product design philosophy at Nintendo (“Withered” is also sometimes translated as “Weathered”). Use “mature technology that can be mass-produced cheaply”, then apply lateral thinking to find radical new ways to use it.
This has echos for me of Dan McKinley’s “Choose Boring Technology”, which argues that in software projects you should default to a proven, stable stack so you can focus your innovation tokens on the problems that are unique to your project.
Guidepup. I’ve been hoping to find something like this for years. Guidepup is “a screen reader driver for test automation”—you can use it to automate both VoiceOver on macOS and NVDA on Windows, and it can both drive the screen reader for automated tests and even produce a video at the end of the test.
Also available: @guidepup/playwright, providing integration with the Playwright browser automation testing framework.
I’d love to see open source JavaScript libraries both use something like this for their testing and publish videos of the tests to demonstrate how they work in these common screen readers.
llm-claude-3 0.3. Anthropic released Claude 3 Haiku today, their least expensive model: $0.25/million tokens of input, $1.25/million of output (GPT-3.5 Turbo is $0.50/$1.50). Unlike GPT-3.5 Haiku also supports image inputs.
I just released a minor update to my llm-claude-3 LLM plugin adding support for the new model.
Berkeley Function-Calling Leaderboard. The team behind Berkeley’s Gorilla OpenFunctions model—an Apache 2 licensed LLM trained to provide OpenAI-style structured JSON functions—also maintain a leaderboard of different function-calling models. Their own Gorilla model is the only non-proprietary model in the top ten.
pywebview 5 (via) pywebview is a library for building desktop (and now Android) applications using Python, based on the idea of displaying windows that use the system default browser to display an interface to the user—styled such that the fact they run on HTML, CSS and JavaScript is mostly hidden from the end-user.
It’s a bit like a much simpler version of Electron. Unlike Electron it doesn’t bundle a full browser engine (Electron bundles Chromium), which reduces the size of the dependency a lot but does mean that cross-browser differences (quite rare these days) do come back into play.
I tried out their getting started example and it’s very pleasant to use—import webview, create a window and then start the application loop running to display it.
You can register JavaScript functions that call back to Python, and you can execute JavaScript in a window from your Python code.
The Bing Cache thinks GPT-4.5 is coming. I was able to replicate this myself earlier today: searching Bing (or apparently Duck Duck Go) for “openai announces gpt-4.5 turbo” would return a link to a 404 page at openai.com/blog/gpt-4-5-turbo with a search result page snippet that announced 256,000 tokens and knowledge cut-off of June 2024
I thought the knowledge cut-off must have been a hallucination, but someone got a screenshot of it showing up in the search engine snippet which would suggest that it was real text that got captured in a cache somehow.
I guess this means we might see GPT 4.5 in June then? I have trouble believing that OpenAI would release a model in June with a June knowledge cut-off, given how much time they usually spend red-teaming their models before release.
Or maybe it was one of those glitches like when a newspaper accidentally publishes a pre-written obituary for someone who hasn’t died yet—OpenAI may have had a draft post describing a model that doesn’t exist yet and it accidentally got exposed to search crawlers.
Astro DB. A new scale-to-zero hosted SQLite offering, described as “A fully-managed SQL database designed exclusively for Astro”. It’s built on top of LibSQL, the SQLite fork maintained by the Turso database team.
Astro DB encourages defining your tables with TypeScript, and querying them via the Drizzle ORM.
Running Astro locally uses a local SQLite database. Deployed to Astro Cloud switches to their DB product, where the free tier currently includes 1GB of storage, one billion row reads per month and one million row writes per month.
Astro itself is a “web framework for content-driven websites”—so hosted SQLite is a bit of an unexpected product from them, though it does broadly fit the ecosystem they are building.
This approach reminds me of how Deno K/V works—another local SQLite storage solution that offers a proprietary cloud hosted option for deployment.
gh-116167: Allow disabling the GIL with PYTHON_GIL=0 or -X gil=0. Merged into python:main 14 hours ago. Looks like the first phase of Sam Gross’s phenomenal effort to provide a GIL free Python (here via an explicit opt-in) will ship in Python 3.13.
Speedometer 3.0: The Best Way Yet to Measure Browser Performance. The new browser performance testing suite, released as a collaboration between Blink, Gecko, and WebKit. It’s fun to run this in your browser and watch it rattle through 580 tests written using a wide variety of modern JavaScript frameworks and visualization libraries.
NICAR 2024 Tipsheets & Audio. The NICAR data journalism conference was outstanding this year: ~1100 attendees, and every slot on the schedule had at least 2 sessions that I wanted to attend (and usually a lot more).
If you’re interested in the intersection of data analysis and journalism it really should be a permanent fixture on your calendar, it’s fantastic.
Here’s the official collection of handouts (NICAR calls them tipsheets) and audio recordings from this year’s event.
S3 is files, but not a filesystem (via) Cal Paterson helps some concepts click into place for me: S3 imitates a file system but has a number of critical missing features, the most important of which is the lack of partial updates. Any time you want to modify even a few bytes in a file you have to upload and overwrite the entire thing. Almost every database system is dependent on partial updates to function, which is why there are so few databases that can use S3 directly as a backend storage mechanism.
datasette/studio. I’m trying a new way to make Datasette available for small personal data manipulation projects, using GitHub Codespaces.
This repository is designed to be opened directly in Codespaces—detailed instructions in the README.
When the container starts it installs the datasette-studio family of plugins—including CSV upload, some enrichments and a few other useful feature—then starts the server running and provides a big green button to click to access the server via GitHub’s port forwarding mechanism.
Coroutines and web components (via) I like using generators in Python but I rarely knowingly use them in JavaScript—I’m probably most exposed to them by Observable, which uses then extensively under the hood as a mostly hidden implementation detail.
Laurent Renard here shows some absolutely ingenious tricks with them as a way of building stateful Web Components.
You can now train a 70b language model at home (via) Jeremy Howard and team: “Today, we’re releasing Answer.AI’s first project: a fully open source system that, for the first time, can efficiently train a 70b large language model on a regular desktop computer with two or more standard gaming GPUs (RTX 3090 or 4090).”
This is about fine-tuning an existing model, not necessarily training one from scratch.
There are two tricks at play here. The first is QLoRA, which can be used to train quantized models despite the reduced precision usually preventing gradient descent from working correctly.
QLoRA can bring the memory requirements for a 70b model down to 35GB, but gaming GPUs aren’t quite that big. The second trick is Meta’s Fully Sharded Data Parallel or FSDP library, which can shard a model across GPUs. Two consumer 24GB GPUs can then handle the 70b training run.
Become a Wikipedian in 30 minutes (via) A characteristically informative and thoughtful guide to getting started with Wikipedia editing by Molly White - video accompanied by a full transcript.
I found the explanation of Reliable Sources particularly helpful, including why Wikipedia prefers secondary to primary sources.
The way we determine reliability is typically based on the reputation for editorial oversight, and for factchecking and corrections. For example, if you have a reference book that is published by a reputable publisher that has an editorial board and that has edited the book for accuracy, if you know of a newspaper that has, again, an editorial team that is reviewing articles and issuing corrections if there are any errors, those are probably reliable sources.
Eloquent JavaScript, 4th edition (2024) (via) Marijn Haverbeke is the creator of both the CodeMirror JavaScript code editor library (used by Datasette and many other projects) and the ProseMirror rich-text editor. Eloquent JavaScript is his Creative Commons licensed book on JavaScript, first released in 2007 and now in its 4th edition.
I’ve only dipped into it myself but it has an excellent reputation.
Inflection-2.5: meet the world’s best personal AI (via) I’ve not been paying much attention to Inflection’s Pi since it released last year, but yesterday they released a new version that they claim is competitive with GPT-4.
“Inflection-2.5 approaches GPT-4’s performance, but used only 40% of the amount of compute for training.”
(I wasn’t aware that the compute used to train GPT-4 was public knowledge.)
If this holds true, that means that the GPT-4 barrier has been well and truly smashed: we now have Claude 3 Opus, Gemini 1.5, Mistral Large and Inflection-2.5 in the same class as GPT-4, up from zero contenders just a month ago.
American Community Survey Data via FTP. I got talking to some people from the US Census at NICAR today and asked them if there was a way to download their data in bulk (in addition to their various APIs)... and there was!
I had heard of the American Community Survey but I hadn’t realized that it’s gathered on a yearly basis, as a 5% sample compared to the full every-ten-years census. It’s only been running for ten years, and there’s around a year long lead time on the survey becoming available.
Training great LLMs entirely from ground zero in the wilderness as a startup. Yi Tay has a really interesting perspective on training LLMs, having worked at Google Brain before co-founding an independent startup, Reka.
At Google the clusters are provided for you. On the outside, Yi finds himself bargaining for cluster resources from a wide range of vendors—and running into enormous variance in quality.
“We’ve seen clusters that range from passable (just annoying problems that are solvable with some minor SWE hours) to totally unusable clusters that fail every few hours due to a myriad of reasons.”
The Claude 3 system prompt, explained. Anthropic research scientist Amanda Askell provides a detailed breakdown of the Claude 3 system prompt in a Twitter thread.
This is some fascinating prompt engineering. It's also great to see an LLM provider proudly documenting their system prompt, rather than treating it as a hidden implementation detail.
The prompt is pretty succinct. The three most interesting paragraphs:
If it is asked to assist with tasks involving the expression of views held by a significant number of people, Claude provides assistance with the task even if it personally disagrees with the views being expressed, but follows this with a discussion of broader perspectives.
Claude doesn't engage in stereotyping, including the negative stereotyping of majority groups.
If asked about controversial topics, Claude tries to provide careful thoughts and objective information without downplaying its harmful content or implying that there are reasonable perspectives on both sides.
How I use git worktrees (via) TIL about worktrees, a Git feature that lets you have multiple repository branches checked out to separate directories at the same time.
The default UI for them is a little unergonomic (classic Git) but Bill Mill here shares a neat utility script for managing them in a more convenient way.
One particularly neat trick: Bill’s “worktree” Bash script checks for a node_modules folder and, if one exists, duplicates it to the new directory using copy-on-write, saving you from having to run yet another lengthy “npm install”.
Wikimedia Commons Category:Bach Dancing & Dynamite Society. After creating a new Wikipedia page for the Bach Dancing & Dynamite Society in Half Moon Bay I ran a search across Wikipedia for other mentions of the venue... and found 41 artist pages that mentioned it in a photo caption.
On further exploration it turns out that Brian McMillen, the official photographer for the venue, has been uploading photographs to Wikimedia Commons since 2007 and adding them to different artist pages. Brian has been a jazz photographer based out of Half Moon Bay for 47 years and has an amazing portfolio of images. It’s thrilling to see him share them on Wikipedia in this way.
Observable Framework 1.1 (via) Less than three weeks after 1.0, the 1.1 release adds a whole lot of interesting new stuff. The signature feature is self-hosted npm imports: Framework 1.0 linked out to CDN hosted copies of libraries, but 1.1 fetches copies locally and then bundles that code with the deployed static site.
This works by using the acorn JavaScript parsing library to statically analyze the code and find all of the relevant imports.
Wikipedia: Bach Dancing & Dynamite Society (via) I created my first Wikipedia page! The Bach Dancing & Dynamite Society is a really neat live music venue in Half Moon Bay which has been showcasing world-class jazz talent for over 50 years. I attended a concert there for the first time on Sunday and was surprised to see it didn’t have a page yet.
Creating a Wikipedia page is an interesting process. New pages on English Wikipedia created by infrequent editors stay in “draft” mode until they’ve been approved by a member of “WikiProject Articles for creation”—the standards are really high, especially around sources of citations. I spent quite a while tracking down good citation references for the key facts I used in my first draft for the page.
llm-claude-3. I built a new plugin for LLM—my command-line tool and Python library for interacting with Large Language Models—which adds support for the new Claude 3 models from Anthropic.
The new Claude 3 model family from Anthropic. Claude 3 is out, and comes in three sizes: Opus (the largest), Sonnet and Haiku.
Claude 3 Opus has self-reported benchmark scores that consistently beat GPT-4. This is a really big deal: in the 12+ months since the GPT-4 release no other model has consistently beat it in this way. It’s exciting to finally see that milestone reached by another research group.
The pricing model here is also really interesting. Prices here are per-million-input-tokens / per-million-output-tokens:
Claude 3 Opus: $15 / $75
Claude 3 Sonnet: $3 / $15
Claude 3 Haiku: $0.25 / $1.25
All three models have a 200,000 length context window and support image input in addition to text.
Compare with today’s OpenAI prices:
GPT-4 Turbo (128K): $10 / $30
GPT-4 8K: $30 / $60
GPT-4 32K: $60 / $120
GPT-3.5 Turbo: $0.50 / $1.50
So Opus pricing is comparable with GPT-4, more than GPT-4 Turbo and significantly cheaper than GPT-4 32K... Sonnet is cheaper than all of the GPT-4 models (including GPT-4 Turbo), and Haiku (which has not yet been released to the Claude API) will be cheaper even than GPT-3.5 Turbo.
It will be interesting to see if OpenAI respond with their own price reductions.
Who Am I? Conditional Prompt Injection Attacks with Microsoft Copilot (via) New prompt injection variant from Johann Rehberger, demonstrated against Microsoft Copilot. If the LLM tool you are interacting with has awareness of the identity of the current user you can create targeted prompt injection attacks which only activate when an exploit makes it into the token context of a specific individual.
The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions (via) How fast can you read a billion semicolon delimited (name;float) lines and output a min/max/mean summary for each distinct name—13GB total?
Ben Hoyt describes his 9 incrementally improved versions written in Go in detail. The key optimizations involved custom hashmaps, optimized line parsing and splitting the work across multiple CPU cores.
The Radio Squirrels of Point Reyes (via) Beautiful photo essay by Ann Hermes about the band of volunteer “radio squirrels” keeping maritime morse code radio transmissions alive in the Point Reyes National Seashore.