Architecture Case Studies

Deep Dives

Problem, architecture, data flow, performance decisions, and results — how these systems were actually built.

A multi-tab recommendation surface that stays fast under changing filters and personalization signals.

Keeping the app usable and correct when the network isn't — with predictable retries and no silent data loss.

Retrieval-augmented generation with grounded, cited answers instead of confident hallucinations.