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Jevons paradox is coming to knowledge work. By making it far cheaper to take on any type of task that we can possibly imagine, we’re ultimately going to be doing far more. The vast majority of AI tokens in the future will be used on things we don't even do today as workers: they will be used on the software projects that wouldn't have been started, the contracts that wouldn't have been reviewed, the medical research that wouldn't have been discovered, and the marketing campaign that wouldn't have been launched otherwise.
— Aaron Levie, Jevons Paradox for Knowledge Work
Tags: ai-ethics, careers, ai, llms, generative-ai, jevons-paradox
actions/setup-python@v4 when the latest is actions/setup-python@v6 for example.
I couldn't find a good single place listing those latest versions, so I had Claude Code for web (via my phone, I'm out on errands) build a Git scraper to publish those versions in one place:
https://simonw.github.io/actions-latest/versions.txt
Tell your coding agent of choice to fetch that any time it wants to write a new GitHub Actions workflows.
(I may well bake this into a Skill.)
Here's the first and second transcript I used to build this, shared using my claude-code-transcripts tool (which just gained a search feature.)
Tags: github, ai, github-actions, git-scraping, generative-ai, llms, coding-agents, claude-code
I just sent out the latest edition of the newsletter version of this blog. It's a long one! Turns out I wrote a lot of stuff in the past 10 days.
The newsletter is out two days later than I had planned because I kept running into an infuriating issue with Substack: it would refuse to save my content with a "Network error" and "Not saved" and I couldn't figure out why.
So I asked ChatGPT to dig into it, which dug up this Hacker News post about the string /etc/hosts triggering an error.
And yeah, it turns out my newsletter included this post describing a SQL injection attack against ClickHouse and PostgreSQL which included the full exploit that was used.
Deleting that annotated example exploit allowed me to send the letter!
Tags: sql-injection, security, newsletter, substack
In advocating for LLMs as useful and important technology despite how they're trained I'm beginning to feel a little bit like John Cena in Pluribus.
Given our druthers, would we choose to consume HDP? No. Throughout history, most cultures, though not all, have taken a dim view of anthropophagy. Honestly, we're not that keen on it ourselves. But we're left with little choice.
Tags: ai-ethics, generative-ai, tv, training-data, ai, llms
A year ago, Claude struggled to generate bash commands without escaping issues. It worked for seconds or minutes at a time. We saw early signs that it may become broadly useful for coding one day.
Fast forward to today. In the last thirty days, I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5.
— Boris Cherny, creator of Claude Code
Tags: anthropic, claude, ai, claude-code, llms, coding-agents, ai-assisted-programming, generative-ai
uv has a useful uv init command for setting up new Python projects, but it comes with a bunch of different options like --app and --package and --lib and I wasn't sure how they differed.
So I created this GitHub repository which demonstrates all of those options, generated using this update-projects.sh script (thanks, Claude) which will run on a schedule via GitHub Actions to capture any changes made by future releases of uv.
Tags: projects, python, github-actions, git-scraping, uv
If this [MicroQuickJS] had been available in 2010, Redis scripting would have been JavaScript and not Lua. Lua was chosen based on the implementation requirements, not on the language ones... (small, fast, ANSI-C). I appreciate certain ideas in Lua, and people love it, but I was never able to like Lua, because it departs from a more Algol-like syntax and semantics without good reasons, for my taste. This creates friction for newcomers. I love friction when it opens new useful ideas and abstractions that are worth it, if you learn SmallTalk or FORTH and for some time you are lost, it's part of how the languages are different. But I think for Lua this is not true enough: it feels like it departs from what people know without good reasons.
— Salvatore Sanfilippo, Hacker News comment on MicroQuickJS
Tags: salvatore-sanfilippo, lua, redis, javascript
Every time you are inclined to use the word “teach”, replace it with “learn”. That is, instead of saying, “I teach”, say “They learn”. It’s very easy to determine what you teach; you can just fill slides with text and claim to have taught. Shift your focus to determining how you know whether they learned what you claim to have taught (or indeed anything at all!). That is much harder, but that is also the real objective of any educator.
— Shriram Krishnamurthi, Pedagogy Recommendations
Tags: teaching
In 2025, Reinforcement Learning from Verifiable Rewards (RLVR) emerged as the de facto new major stage to add to this mix. By training LLMs against automatically verifiable rewards across a number of environments (e.g. think math/code puzzles), the LLMs spontaneously develop strategies that look like "reasoning" to humans - they learn to break down problem solving into intermediate calculations and they learn a number of problem solving strategies for going back and forth to figure things out (see DeepSeek R1 paper for examples).
— Andrej Karpathy, 2025 LLM Year in Review
Tags: andrej-karpathy, llm, generative-ai, llm-reasoning, definitions, ai, llms, deepseek
Sam Rose explains how LLMs work with a visual essay
Sam Rose is one of my favorite authors of explorable interactive explanations - here's his previous collection.Sam joined ngrok in September as a developer educator. Here's his first big visual explainer for them, ostensibly about how prompt caching works but it quickly expands to cover tokenization, embeddings, and the basics of the transformer architecture.
The result is one of the clearest and most accessible introductions to LLM internals I've seen anywhere.
Tags: ai, explorables, generative-ai, llms, sam-rose, tokenization
Kyle ran some benchmarks to compare the different implementations:
- Rust (html5ever) total parse time: 303 ms
- Swift total parse time: 1313 ms
- JavaScript total parse time: 1035 ms
- Python total parse time: 4189 ms
Tags: html5, ai, generative-ai, llms, ai-assisted-programming, vibe-coding, swift