Field notes · 224 articles
Practical notes on AI, business software, and the decisions behind a production build.
AI, software, and business
A pre-launch checklist for AI features: evals, guardrails, cost controls, monitoring, fallbacks, and the failure modes that embarrass you in production.
Most AI MVPs die because they were scoped wrong, not built wrong. A founder's guide to picking the first use case, setting a quality bar, and avoiding the demo trap.
Why AI projects blow past estimates, and a framework for scoping timeline and budget that accounts for evals, data work, and the iteration AI actually needs.
How to measure AI feature quality in numbers leadership can act on, covering what to measure, how to avoid vanity metrics, and how to report quality over time.