Blogmarks
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Why We Replaced Firecracker with QEMU (via) Hocus are building a self-hosted alternative to cloud development environment tools like GitPod and Codespaces. They moved away from Firecracker because it’s optimized for short-running (AWS Lambda style) functions—which means it never releases allocated RAM or storage volume space back to the host machine unless the container is entirely restarted. It also lacks GPU support.
Tech debt metaphor maximalism (via) I’ve long been a fan of the metaphor of technical debt, because it implies that taking on some debt is OK provided you’re strategic about how much you take on and how quickly you pay it off. Avery Pennarun provides the definitive guide to thinking about technical debt, including an extremely worthwhile explanation of how financial debt works as well.
Stamina: tutorial (via) Stamina is Hynek’s new Python library that implements an opinionated wrapper on top of Tenacity, providing a decorator for easily implementing exponential backoff retries. This tutorial includes a concise, clear explanation as to why this is such an important concept in building distributed systems.
Data analysis with SQLite and Python. I turned my 2hr45m workshop from PyCon into the latest official tutorial on the Datasette website. It includes an extensive handout which should be useful independently of the video itself.
Databricks Signs Definitive Agreement to Acquire MosaicML, a Leading Generative AI Platform. MosaicML are the team behind MPT-7B and MPT-30B, two of the most impressive openly licensed LLMs. They just got acquired by Databricks for $1.3 billion dollars.
abacaj/mpt-30B-inference. MPT-30B, released last week, is an extremely capable Apache 2 licensed open source language model. This repo shows how it can be run on a CPU, using the ctransformers Python library based on GGML. Following the instructions in the README got me a working MPT-30B model on my M2 MacBook Pro. The model is a 19GB download and it takes a few seconds to start spitting out tokens, but it works as advertised.
Status of Python Versions (via) Very clear and useful page showing the exact status of different Python versions. 3.7 reaches end of life today (no more security updates), while 3.11 will continue to be supported until October 2027.
Building Search DSLs with Django (via) Neat tutorial by Dan Lamanna: how to build a GitHub-style search feature—supporting modifiers like “is:open author:danlamanna”—using PyParsing and the Django ORM.
LLM 0.4. I released a major update to my LLM CLI tool today—version 0.4, which adds conversation mode and prompt templates so you can store and re-use interesting prompts, plus a whole bunch of other large and small improvements.
I also released 0.4.1 with some minor fixes and the ability to install the tool using Hombrew: brew install simonw/llm/llm
sqlean.py: Python’s sqlite3 with extensions. Anton Zhiyanov built a new Python package which bundles a fresh, compiled copy of SQLite with his SQLean family of C extensions built right in. Installing it gets you the latest SQLite—3.42.0—with nearly 200 additional functions, including things like define() and eval(), fileio_read() and fileio_write(), percentile_95() and uuid4() and many more. “import sqlean as sqlite3” works as a drop-in replacement for the module from the standard library.
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.