Insights

Notes from building AI-first products and the engineering foundations behind them: retrieval systems, semantic search, Python tooling, containers, data structures and inference economics.

I use these articles to document how I think about production AI: what to measure, where systems fail, how to keep projects reproducible, and how to turn model capability into useful product behavior.

The focus is practical AI engineering, ML engineering and Python engineering, written for teams who care about reliable systems more than impressive demos.