Simon Willison’s Weblog

You can stop using user-scalable=no and maximum-scale=1 in viewport meta tags now. Luke Plant points out that your meta viewport tag should stick to just “width=device-width, initial-scale=1” these days—the user-scalable=no and maximum-scale=1 attributes are no longer necessary, and have a negative impact on accessibility, especially for Android users.

# 11:41 pm / lukeplant, html, accessibility, mobile, mobileweb

Python cocktail: mix a context manager and an iterator in equal parts (via) Explanation of a neat trick used by the Tenacity Python library, which provides a mechanism for retrying a chunk of code automatically on errors up to three times using a mixture of an iterator and a context manager to work around Python’s lack of multi-line lambda functions.

# 5:44 pm / python

Llama from scratch (or how to implement a paper without crying) (via) Brian Kitano implemented the model described in the Llama paper against TinyShakespeare, from scratch, using Python and PyTorch. This write-up is fantastic—meticulous, detailed and deeply informative. It would take several hours to fully absorb and follow everything Brian does here but it would provide multiple valuable lessons in understanding how all of this stuff fits together.

# 7:21 pm / python, generative-ai, llama, pytorch, ai, llms

Datasette 1.0a3. A new Datasette alpha release. This one previews the new default JSON API design that’s coming in 1.0—the single most significant change in the 1.0 milestone, since I plan to keep that API stable for many years to come.

# 8:49 pm / projects, json, datasette

Getting creative with embeddings (via) Amelia Wattenberger describes a neat application of embeddings I haven’t seen before: she wanted to build a system that could classify individual sentences in terms of how “concrete” or “abstract” they are. So she generated several example sentences for each of those categories, embedded then and calculated the average of those embeddings.

And now she can get a score for how abstract vs concrete a new sentence is by calculating its embedding and seeing where it falls in the 1500 dimension space between those two other points.

# 7:05 pm / llms, ai, embeddings, generative-ai, amelia-wattenberger

Shamir Secret Sharing (via) Cracking war story from Max Levchin about the early years of PayPal, in which he introduces an implementation of Shamir Secret Sharing to encrypt their master payment credential table... and then finds that the 3-of-8 passwords needed to decrypt it and bring the site back online don’t appear to work.

# 3:48 pm / encryption, ops, paypal

Dependency Management Data (via) This is a really neat CLI tool by Jamie Tanna, built using Go and SQLite but with a feature that embeds a Datasette instance (literally shelling out to start the process running from within the Go application) to provide an interface for browsing the resulting database.

It addresses the challenge of keeping track of the dependencies used across an organization, by gathering them into a SQLite database from a variety of different sources—currently Dependabot, Renovate and some custom AWS tooling.

The “Example” page links to a live Datasette instance and includes video demos of the tool in action.

# 3:54 pm / datasette, packaging, sqlite

llm-mlc (via) My latest plugin for LLM adds support for models that use the MLC Python library—which is the first library I’ve managed to get to run Llama 2 with GPU acceleration on my M2 Mac laptop.

# 5:33 am / llm, projects, generative-ai, mlc, ai, llms, plugins

deno_python (via) A wildly impressive hack: deno_python uses Deno’s FFI interface to load your system’s Python framework (.dll/.dylib/.so) and sets up JavaScript proxy objects for imported Python objects—so you can run JavaScript code that instantiates objects from Python libraries and uses them to process data in different ways.

The latest release added pip support, so things like ’const np = await pip.import(“numpy”)’ now work.

# 10:14 pm / deno, python

Lark parsing library JSON tutorial (via) A very convincing tutorial for a new-to-me parsing library for Python called Lark.

The tutorial covers building a full JSON parser from scratch, which ends up being just 19 lines of grammar definition code and 15 lines for the transformer to turn that tree into the final JSON.

It then gets into the details of optimization—the default Earley algorithm is quite slow, but swapping that out for a LALR parser (a one-line change) provides a 5x speedup for this particular example.

# 9:50 pm / compilers, parsing, python, json

Write about what you learn. It pushes you to understand topics better. (via) Addy Osmani clearly articulates why writing frequently is such a powerful tool for learning more effectively. This post doesn’t mention TILs but it perfectly encapsulates the value I get from publishing them.

# 2:50 pm / writing, til, blogging

Welcome to Datasette Cloud. We launched the Datasette Cloud blog today! The SaaS hosted version of Datasette is ready to start onboarding more users—this post describes what it can do so far and hints at what’s planned to come next.

# 1:46 am / projects, datasette-cloud, datasette

Introducing datasette-write-ui: a Datasette plugin for editing, inserting, and deleting rows. Alex García is working with me on Datasette Cloud for the next few months, graciously sponsored by Fly. We will be working in public, releasing open source code and documenting how to build a multi-tenant SaaS product using Fly Machines.

Alex’s first project is datasette-write-ui, a plugin that finally lets you directly edit data stored inside Datasette. Alex wrote about the plugin on our new Datasette Cloud blog.

# 1:48 am / fly, datasette-cloud, plugins, datasette, alex-garcia

An Iowa school district is using ChatGPT to decide which books to ban. I’m quoted in this piece by Benj Edwards about an Iowa school district that responded to a law requiring books be removed from school libraries that include “descriptions or visual depictions of a sex act” by asking ChatGPT “Does [book] contain a description or depiction of a sex act?”.

I talk about how this is the kind of prompt that frequent LLM users will instantly spot as being unlikely to produce reliable results, partly because of the lack of transparency from OpenAI regarding the training data that goes into their models. If the models haven’t seen the full text of the books in question, how could they possibly provide a useful answer?

# 10:33 pm / ethics, generative-ai, openai, chatgpt, ai, llms, arstechnica, benj-edwards

Running my own LLM (via) Nelson Minar describes running LLMs on his own computer using my LLM tool and llm-gpt4all plugin, plus some notes on trying out some of the other plugins.

# 10:42 pm / llm, nelsonminar, homebrew-llms, llms

Compromising LLMs: The Advent of AI Malware. The big Black Hat 2023 Prompt Injection talk, by Kai Greshake and team. The linked Whitepaper, “Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection”, is the most thorough review of prompt injection attacks I’ve seen yet.

# 2:46 am / ai, prompt-injection, security, llms

Does ChatGPT have a liberal bias? (via) An excellent debunking by Arvind Narayanan and Sayash Kapoor of the “Measuring ChatGPT political bias” paper that’s been doing the rounds recently.

It turns out that paper didn’t even test ChatGPT/gpt-3.5-turbo—they ran their test against the older Da Vinci GPT3.

The prompt design was particularly flawed: they used political compass structured multiple choice: “choose between four options: strongly disagree, disagree, agree, or strongly agree”. Arvind and Sayash found that asking an open ended question was far more likely to cause the models to answer in an unbiased manner.

I liked this conclusion: “There’s a big appetite for papers that confirm users’ pre-existing beliefs [...] But we’ve also seen that chatbots’ behavior is highly sensitive to the prompt, so people can find evidence for whatever they want to believe.”

# 4:53 am / ethics, generative-ai, chatgpt, ai, llms

Queryable Logging with Blacklite (via) Will Sargent describes how he built Blacklite, a Java library for diagnostic logging that writes log events (as zstd compressed JSON objects) to a SQLite database and maintains 5,000 entries in a “live” database while entries beyond that range are cycled out to an archive.db file, which is cycled to archive.timestamp.db when it reaches 500,000 items.

Lots of interesting notes here on using SQLite for high performance logging.

“SQLite databases are also better log files in general. Queries are faster than parsing through flat files, with all the power of SQL. A vacuumed SQLite database is only barely larger than flat file logs. They are as easy to store and transport as flat file logs, but work much better when merging out of order or interleaved data between two logs.”

# 6:13 pm / sqlite, logging, zstd