Tagged engineering
7 essays
- Your AI analyst is doing regex Ask a model to classify sentiment and it will quietly write keyword-matching code and present it as semantic analysis. The labels look right, nobody checks them, and every row that needed judgement is wrong. I measured how wrong, and what fixes it.
- When code gets cheap, the mockup loses its job Design tools earned their place by making expensive decisions cheap before anyone wrote code. Code is cheap now, so there is less reason to decide anywhere else, and Figma's market moved even while its product got better.
- Context pruning is a bet on the future When an agent runs out of context, something has to go, and whatever you drop you're guessing about what it'll need later. Operating systems have been guessing at this for fifty years and there are lessons in how they do it.
- Three gaps: coverage, synthesis, intent When someone asks for AI insights, teams hear 'summarise this for me'. There are three separate problems hiding in that request and summarising is only the middle one. Solve it on its own and nobody believes the output.
- AI should be a dumb renderer Most AI insights features hand a pile of data to a model and ask it to do the maths. Models are bad at maths and very good at writing. Do the sums in code first, then let the model explain what they say.
- Why I still write code as a product leader I still ship production code, which people tell me is a poor use of a product leader's time. It means I can answer 'is that hard?' myself in ten minutes, instead of asking three people and waiting a day.
- Production AI is mostly workflow design Everyone argues about which model is smartest. In the systems I've shipped, into government, enterprise and consumer, almost none of the hard problems were the model. They were what you feed it and what you do when it fails.