Tagged ai-product
11 essays
- Shutting down hey anna An ablation test proved the product worked. A benchmark pilot backed it up. A real GTM effort still couldn't move traffic. What a good product without distribution actually looks like, and what I'm keeping from it.
- 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.
- 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.
- AI is an interface Every system makes you learn its menus and its query language before it will help you. The useful thing AI does is remove that step: you say what you want, and it works out which buttons to press.
- 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.
- The unit economics of a one-person AI product About 40% of what it costs me to run hey anna is the Claude API bill. Most SaaS advice assumes serving one more customer is free, and it stops working once that isn't true. Free trials get expensive and going viral can hurt.
- Make every AI claim clickable People will use an AI answer they can check and quietly abandon one they can't. Linking every number back to the rows it came from sounds like a nice-to-have. It's usually the reason the feature gets used twice.
- The trust-calibration tax Getting a model to produce a good answer is the cheap part now. The expensive part is teaching someone when to believe it, and what to do the first time it's confidently wrong. Nobody puts that on a roadmap.
- Not every AI feature should be a chat Companies happily let AI sort a million records unsupervised, then refuse to let it nudge a box five pixels because you have to ask for it in a sentence. Typing a request to do something you could have done by dragging it is worse.
- 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.
- 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.