#architecture

Every recommendation system that works well is fusing multiple signal types. The ones that don't understand this ship vibes-based retrieval and wonder why users leave. A taxonomy of signals, how they combine, and what the SOTA ecosystem gets right and wrong.

Start with one function call. Add skills when the prompt gets too long. A no-framework guide to building agents that actually ship.

Not all behavioral signals deserve the same compute budget. Genre affinity changes over weeks; session mood changes in seconds. Classify by stability, infer by tier, and stop pretending daily batch is the answer to everything.

Two users search 'sleep' and get different results — with no LLM at query time. How pre-computed, theme-keyed expansion terms turn a flat search into something that actually knows you.

Why your personalization agent should never run at request time. The LLM does its heavy lifting on a schedule; your product serves the artifacts. Zero latency, infinite scale.

One LLM call, multiple output shapes for multiple consumers. Design your schema like a protocol, not an afterthought.