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But once we got that and got this aviation grade testing in place, the number of bugs just dropped to a trickle. Now we still do have bugs but the aviation grade testing allows us to move fast, which is important because in this business you either move fast or you're disrupted. So, we're able to make major changes to the structure of the code that we deliver and be confident that we're not breaking things because we had these intense tests. Probably half the time we spend is actually writing new tests, we're constantly writing new tests. And over the 17-year history, we have amassed a huge suite of tests which we run constantly.

Other database engines don't do this; don't have this level of testing. But they're still high quality, I mean, I noticed in particular, PostgreSQL is a very high-quality database engine, they don't have many bugs. I went to the PostgreSQL and ask them “how do you prevent the bugs”? We talked about this for a while. What I came away with was they've got a very elaborate peer review process, and if they've got code that has worked for 10 years they just don't mess with it, leave it alone, it works. Whereas we change our code fearlessly, and we have a much smaller team and we don't have the peer review process.

The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.

That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python.

The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.

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.

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.

• <article contenteditable="plaintext-only"> - I did not know about the plaintext-only value, supported across all the modern browsers.

• It uses new CompressionStream('deflate-raw') to compress the editor state so it can fit in a shorter fragment URL.

• It has a neat custom save option which triggers if you hit ((e.metaKey || e.ctrlKey) && e.key === 's') - on browsers that support it (mainly Chrome variants) this uses window.showSaveFilePicker(), other browsers get a straight download - in both cases generated using URL.createObjectURL(new Blob([html], {type: 'text/html'}))

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.

• MicroQuickJS is very well suited to the sandbox problem. It has robust near and time limits baked in, it doesn't expose any dangerous primitive like filesystem of network access and even has a regular expression engine that protects against exhaustion attacks (provided you configure a time limit).

• Claude span up and tested a Python library that calls a MicroQuickJS shared library (involving a little bit of extra C), a compiled a Python binding and a library that uses the original MicroQuickJS CLI tool. All of those approaches work well.

• Compiling to WebAssembly was a little harder. It got a version working in Node.js and Deno and Pyodide, but the Python libraries wasmer and wasmtime proved harder, apparently because "mquickjs uses setjmp/longjmp for error handling". It managed to get to a working wasmtime version with a gross hack.

Cooking with Claude

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.

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).

Your job is to deliver code you have proven to work