Tagged engineering
5 essays
- Context pruning is a bet on the future When an agent's window fills, the obvious move is to drop the oldest, biggest tool results. That's a cache-eviction bet you can't make optimally without seeing the future, and the right one depends entirely on your workload.
- Three gaps: coverage, synthesis, intent Most AI insights requests get treated as a synthesis problem. That's the wrong reframe. There are three stacked gaps - coverage, synthesis, intent - and you can't skip a layer without trust collapsing underneath you.
- AI should be a dumb renderer The default pattern for AI insights dumps data into a model and asks it to count, compare, and conclude. That's the wrong order. Precompute the numbers deterministically; let the model render and narrate them.
- Why I still write code as a product leader Writing production code as a product leader compresses decision latency: faster iteration, feasibility you can check yourself, AI behaviour you can debug directly, and no organisational telephone.
- Production AI is mostly workflow design The model-intelligence obsession misreads where production AI actually succeeds or fails. Across government, enterprise, and consumer, the wins came from orchestration, retrieval, evaluation, and fallback handling, not a smarter model.