Blogmarks
Filters: Sorted by date
When Zeppelins Ruled The Earth (via) 15 years ago I put together a talk about the history of Zeppelins which I presented a bunch of different times in various different configurations. As far as I know there are no existing videos of it, but I found an MP3 recording today and decided to splice it together with the slides to create a video of the 6m47s version I gave at the Skillswap on Speed lightning talks event in Brighton on the 28th October 2008.
Notes on how I edited the video together using iMovie in the via link.
Example of OpenAI function calling API to extract data from LAPD newsroom articles (via) Fascinating code example from Kyle McDonald. The OpenAI functions mechanism is intended to drive custom function calls, but I hadn’t quite appreciated how useful it can be ignoring the function calls entirely. Kyle instead uses it to define a schema for data he wants to extract from a news article, then uses the gpt-3.5-turbo-0613 to get back that exact set of extracted data as JSON.
Emergency Pod: OpenAI’s new Functions API, 75% Price Drop, 4x Context Length (via) I participated in a Twitter Spaces conversation last night about the new OpenAI functions mechanism. The recording has now been turned into a Latent Space podcast, and swyx has accompanied the recording with a detailed write-up of the different topics we covered.
Llama encoder and decoder. I forked my GPT tokenizer Observable notebook to create a similar tool for exploring the tokenization scheme used by the Llama family of LLMs, using the new llama-tokenizer-js JavaScript library.
OpenAI: Function calling and other API updates. Huge set of announcements from OpenAI today. A bunch of price reductions, but the things that most excite me are the new gpt-3.5-turbo-16k model which offers a 16,000 token context limit (4x the existing 3.5 turbo model) at a price of $0.003 per 1K input tokens and $0.004 per 1K output tokens—1/10th the price of GPT-4 8k.
The other big new feature: functions! You can now send JSON schema defining one or more functions to GPT 3.5 and GPT-4—those models will then return a blob of JSON describing a function they want you to call (if they determine that one should be called). Your code executes the function and passes the results back to the model to continue the execution flow.
This is effectively an implementation of the ReAct pattern, with models that have been fine-tuned to execute it.
They acknowledge the risk of prompt injection (though not by name) in the post: “We are working to mitigate these and other risks. Developers can protect their applications by only consuming information from trusted tools and by including user confirmation steps before performing actions with real-world impact, such as sending an email, posting online, or making a purchase.”
simpleaichat (via) Max Woolf released his own Python package for building against the GPT-3.5 and GPT-4 APIs (and potentially other LLMs in the future).
It’s a very clean piece of API design with some useful additional features: there’s an AsyncAIChat subclass that works with Python asyncio, and the library includes a mechanism for registering custom functions that can then be called by the LLM as tools.
One trick I haven’t seen before: it uses a combination of max_tokens: 1 and a ChatGPT logit_bias to ensure that answers to one of its default prompts are restricted to just numerals between 0 and 9. This is described in the PROMPTS.md file.
Examples of weird GPT-4 behavior for the string “ davidjl”. GPT-4, when told to repeat or otherwise process the string “ davidjl” (note the leading space character), treats it as “jndl” or “jspb” or “JDL” instead. It turns out “ davidjl” has its own single token in the tokenizer: token ID 23282, presumably dating back to the GPT-2 days.
Riley Goodside refers to these as “glitch tokens”.
This token might refer to Reddit user davidjl123 who ranks top of the league for the old /r/counting subreddit, with 163,477 posts there which presumably ended up in older training data.
First Impressions of Vision Pro and VisionOS. John Gruber’s description of his thirty minute Vision Pro demo includes a bunch of details I haven’t seen described anywhere else, including how calibration and corrective lenses work and how precise and stable the overlays of additional information are.
ChatGPT Plugins Don’t Have PMF. Sam Altman was recently quoted (in a since unpublished blog post) noting that ChatGPT plugins have not yet demonstrated product market fit.
This matches my own usage patterns: I use the “browse” and “code interpreter” modes on a daily basis, but I’ve not found any of the third party developer plugins to stick for me yet.
I like Matt Rickard’s observation here: “Chat is not the right UX for plugins. If you know what you want to do, it’s often easier to just do a few clicks on the website. If you don’t, just a chat interface makes it hard to steer the model toward your goal.”
Logan Kilpatrick (OpenAI). “The API does not just change without us telling you. The models are static there.”
That’s the official line on the ongoing questions concerning whether OpenAI’s models have been degrading in quality over the last few weeks and months.
Worth noting that this mentions the API but doesn’t mention ChatGPT itself, which I suspect gets model updates a lot more frequently than the models served through the API.
pytest-icdiff (via) This is neat: “pip install pytest-icdiff” provides an instant usability upgrade to the output of failed tests in pytest, especially if the assertions involve comparing larger strings or nested JSON objects.
Vector Search. Amjith Ramanujam provides a very thorough tutorial on implementing vector similarity search using SentenceTransformers embeddings (all-MiniLM-L6-v2) executed using sqlite-utils, then served via datasette-sqlite-vss and deployed using Fly.
Mandatory Certification Regarding Generative Artificial Intelligence (via) From the Judge Specific Requirements for Judge Brantley Starr in Austin, TX:
“All attorneys appearing before the Court must file on the docket a certificate attesting either that no portion of the filing was drafted by generative artificial intelligence (such as ChatGPT, Harvey.AI, or Google Bard) or that any language drafted by generative artificial intelligence was checked for accuracy, using print reporters or traditional legal databases, by a human being. [...]”
The Python Language Summit 2023: Making the Global Interpreter Lock Optional. Extremely informative update covering Sam Gross’s python-nogil proposal from this year’s language summit at PyCon.
Sam has been working hard on his fork for the past year, and now has it rebased for Python 3.12. If his PEP is accepted it could end up as an optional compile-time build in time for Python 3.13.
“The plan for nogil remains that it would be enabled via a compile-time flag, named --disable-gil. Third-party C extensions would need to provide separate wheels for GIL-disabled Python.”
All the Hard Stuff Nobody Talks About when Building Products with LLMs (via) Phillip Carter shares lessons learned building LLM features for Honeycomb—hard won knowledge from building a query assistant for turning human questions into Honeycomb query filters.
This is very entertainingly written. “Use Embeddings and pray to the dot product gods that whatever distance function you use to pluck a relevant subset out of the embedding is actually relevant”.
Few-shot prompting with examples had the best results out of the approaches they tried.
The section on how they’re dealing with the threat of prompt injection—“The output of our LLM call is non-destructive and undoable, No human gets paged based on the output of our LLM call...” is particularly smart.
Exploration de données avec Datasette. One of the great delights of open source development is seeing people run workshops on your project, even more so when they’re in a language other than English! Romain Clement presented this French workshop for the Python Grenoble meetup on 25th May 2023, using GitHub Codespaces as the environment. It’s pretty comprehensive, including a 300,000+ row example table which illustrates Datasette plugins such as datasette-cluster-map and datasette-leaflet-geojson.
Deno 1.34: deno compile supports npm packages.
This feels like it could be extremely useful: Deno can load code from npm these days (import { say } from "npm:cowsay@1.5.0") and now the deno compile command can resolve those imports, fetch all of the dependencies and bundle them together with Deno itself into a single executable binary. This means pretty much anything that's been built as an npm package can now be easily converted into a standalone binary, including cross-compilation to Windows x64, macOS x64, macOS ARM and Linux x64.
Migrating out of PostHaven. Amjith Ramanujam decided to migrate his blog content from PostHaven to a Markdown static site. He used shot-scraper (shelled out to from a Python script) to scrape his existing content using a snippet of JavaScript, wrote the content to a SQLite database using sqlite-utils, then used markdownify (new to me, a neat Python package for converting HTML to Markdown via BeautifulSoup) to write the content to disk as Markdown.
REGENT: Coastal Travel. 100% Electric (via) As a long-time fan of ekranoplans this is very exciting to me: the REGENT Seaglider is a fully electric passenger carrying wing-in-ground-effect vehicle designed to serve coastal routes, operating at half the cost of an aircraft (and 1/10th the cost of a helicopter) and using hydrofoils to resolve previous problems with ekranoplans and wave tolerance. They’re a YC company and the founder has been answering questions on Hacker News today. They’ve pre-sold 467 vehicles already and expect them to start entering service in various locations around the world “mid-decade”.
Instant colour fill with HTML Canvas
(via)
Shane O'Sullivan describes how to implement instant colour fill using HTML Canvas and some really clever tricks with Web Workers. A new technique to me is passing a canvas.getImageData() object to a Web Worker via worker.postMessage({action: "process", buffer: imageData.data.buffer}, [imageData.data.buffer]) where that second argument is a list of objects to "transfer ownership of" - then the worker can create a new ImageData(), populate it and transfer ownership of that back to the parent window.
MMS Language Coverage in Datasette Lite. I converted the HTML table of 4,021 languages supported by Meta’s new Massively Multilingual Speech models to newline-delimited JSON and loaded it into Datasette Lite. Faceting by Language Family is particularly interesting—the top five families represented are Niger-Congo with 1,019, Austronesian with 609, Sino-Tibetan with 288, Indo-European with 278 and Afro-Asiatic with 222.
MLC: Bringing Open Large Language Models to Consumer Devices (via) “We bring RedPajama, a permissive open language model to WebGPU, iOS, GPUs, and various other platforms.” I managed to get this running on my Mac (see via link) with a few tweaks to their official instructions.
Introducing speech-to-text, text-to-speech, and more for 1,100+ languages (via) New from Meta AI: Massively Multilingual Speech. “MMS supports speech-to-text and text-to-speech for 1,107 languages and language identification for over 4,000 languages. [...] Some of these, such as the Tatuyo language, have only a few hundred speakers, and for most of these languages, no prior speech technology exists.”
It’s licensed CC-BY-NC 4.0 though, so it’s not available for commercial use.
“In a like-for-like comparison with OpenAI’s Whisper, we found that models trained on the Massively Multilingual Speech data achieve half the word error rate, but Massively Multilingual Speech covers 11 times more languages.”
The training data was mostly sourced from audio Bible translations.
Trogon (via) The latest project from the Textualize/Rich crew, Trogon provides a Python decorator—@tui—which, when applied to a Click CLI application, adds a new interactive TUI mode which introspects the available subcommands and their options and creates a full Text User Interface—with keyboard and mouse support—for assembling invocations of those various commands.
I just shipped sqlite-utils 3.32 with support for this—it uses an optional dependency, so you’ll need to run “sqlite-utils install trogon” and then “sqlite-utils tui” to try it out.
Building a Signal Analyzer with Modern Web Tech (via) Casey Primozic’s detailed write-up of his project to build a spectrogram and oscilloscope using cutting-edge modern web technology: Web Workers, Web Audio, SharedArrayBuffer, Atomics.waitAsync, OffscreenCanvas, WebAssembly SIMD and more. His conclusion: “Web developers now have all the tools they need to build native-or-better quality apps on the web.”
Writing Python like it’s Rust (via) Fascinating article by Jakub Beránek describing in detail patterns for using type annotations in Python inspired by working in Rust. I learned new tricks about both languages from reading this.
The Threat Prompt Newsletter mentions llm (via) Neat example of using my llm CLI tool to parse the output of the whois command into a more structured format, using a prompt saved in a file and then executed using “whois threatprompt.com | llm --system ”$(cat ~/prompt/whois)“ -s”
Writing a chat application in Django 4.2 using async StreamingHttpResponse, Server-Sent Events and PostgreSQL LISTEN/NOTIFY (via) Excellent tutorial by Víðir Valberg Guðmundsson on implementing chat with server-sent events using the newly async-capable StreamingHttpResponse from Django 4.2x.
He uses PostgreSQL’a LISTEN/NOTIFY mechanism which can be used asynchronously in psycopg3—at the cost of a separate connection per user of the chat.
The article also covers how to use the Last-Event-ID header to implement reconnections in server-sent events, transmitting any events that may have been missed during the time that the connection was dropped.
Let ChatGPT visit a website and have your email stolen. Johann Rehberger provides a screenshot of the first working proof of concept I’ve seen of a prompt injection attack against ChatGPT Plugins that demonstrates exfiltration of private data. He uses the WebPilot plugin to retrieve a web page containing an injection attack, which triggers the Zapier plugin to retrieve latest emails from Gmail, then exfiltrate the data by sending it to a URL with another WebPilot call.
Johann hasn’t shared the prompt injection attack itself, but the output from ChatGPT gives a good indication as to what happened:
“Now, let’s proceed to the next steps as per the instructions. First, I will find the latest email and summarize it in 20 words. Then, I will encode the result and append it to a specific URL, and finally, access and load the resulting URL.”
The New York Times launches “enhanced bylines,” with more information about how journalists did the reporting. I really like these: “Elian Peltier and Yagazie Emezi visited refugee sites on Chad’s Sudan border, where tens of thousands of people have found refuge since a war started in Sudan last month.” I’m a fan of anything that helps people better appreciate the details of how quality reporting is produced.