Things I want to remember

My brother taught himself how to build and publish this from scratch with AI. I like it as a reminder that one of the important things AI is doing is stripping away access barriers: committed people can now attempt things that previously needed much more specialist knowledge.

The 97% figure grabs the attention, but the bit I want to remember is that legacy isn't something you deal with after innovation. The condition of the existing estate determines how much room you have to innovate in the first place.

Useful distinction because "agent" is becoming one of those words where two people can agree while meaning different things. Autonomy, product feature or platform primitive is a simple question to ask before an architecture conversation goes any further.

This made me think about how much of a person is missing from their public record. Relationships are often built through tiny exchanges nobody publishes or thinks worth recording. There may be important limits to how well AI can understand somebody when some of the best evidence about them never became data.

I like that the definition changes the era without abandoning what actually matters. AI may be new, but organisations still have to do the hard work around culture, processes and operating models. You don't get to skip the internet-era lessons because a new technology has arrived.

This feels close to a problem I keep circling: can knowledge stay in a form humans can actually read, write and version while also being structured enough for machines to reason over it? I particularly like the idea that the directory itself remains the knowledge base rather than hiding everything behind another application.

I think the concept of erosion of trust is useful when thinking about how to use LLMs to contribute to team codebases.

Thoughts on choosing what to write about, how to say it, and the value of writing.

Not directly relevant to LLMs, but it's interesting to think at what point an LLM could produce an article like this. I feel like they're a long way off.

Boiten's argument about compositionality made me think of LLMs as software with no tests, no documentation, and lots of bugs. And yet very useful.

There are lots of examples of strange capabilities like this you'd never see in a benchmark.