Tagged ai-product
9 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.
- AI is an interface The interface was always the tax you paid to use a system: its menus, its query language, its API. AI's most durable job is to take that tax off, by turning plain language into the task you meant and the systems that carry it out.
- 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.
- The unit economics of a one-person AI product hey anna's variable cost is about 40% Claude API. That single fact rewrites the pricing, the acquisition maths, and whether viral growth is load-bearing. The SaaS playbook assumed COGS was a rounding error; it isn't anymore.
- Make every AI claim clickable Customers don't want AI that hands them the answer. They want to verify it. Deep-research-style citations are the line between an AI feature people adopt and one they paste into a doc and never open again.
- The trust-calibration tax The hard part of AI UX isn't generating answers. It's teaching users when to trust the system, when not to, and how to recover when it's wrong. That work is the real cost, and most teams never budget for it.
- Not every AI feature should be a chat Enterprises trust AI for invisible categorisation and distrust it for reversible work behind a chat box. 'Chat, move this five pixels' is all-or-nothing and risky. Sometimes the right surface for an AI feature is a button.
- 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.
- 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.