Notes on AI engineering, agent workflows, evaluation and shipping AI products that actually work.
Context engineering—not prompt engineering—determines AI system quality. A practical guide to CLAUDE.md, instruction budgets, and production patterns.
Stop vibe coding. The plan-execute-clear loop is the workflow that separates teams shipping real AI-assisted code from those generating throwaway snippets.
Agent skills turn ad-hoc AI prompting into repeatable engineering processes. Here's how to build skills that actually improve code quality.
Cut through AI hype. What CTOs and engineering leaders need to know about AI implementation, team structure, and infrastructure decisions.
Test-driven development makes AI-generated code dramatically better. Here's how to apply red-green-refactor cycles when working with coding agents.
Most AI products fail because teams build technology before validating the problem. Here's the validation framework that took us from pre-revenue to Series B.
Latency, cost, observability, and trust all break differently as AI products scale. Lessons from scaling an AI platform from pre-revenue to Series B.
Most RAG implementations fail silently. Here are the retrieval, chunking, and architecture mistakes teams make and how to fix them.
Traditional testing assumes determinism. AI systems break that assumption. Here's how to build evaluation pipelines that actually catch regressions.