Engineering notes on AI, ML, custom apps, and the messy bits of shipping production software.
Hype-free engineering principles for AI products that serve users, not nudge them. Grounding, refusal, evals, cost-bounding — the boring decisions that actually ship.
A practical AI development guide — when retrieval-augmented generation beats fine-tuning, when prompt engineering wins, and the cost equation most teams skip.
What you can realistically ship in a 4-week custom app MVP — features, tradeoffs, and the timeline traps that kill startups.
A working model is not a working product. The full production ML stack — data pipelines, deployment patterns, monitoring, drift detection.