
Require Evidence for AI-Generated Code
Use explicit acceptance criteria, test results, risk-based review and production feedback before accepting AI-generated changes.

Use explicit acceptance criteria, test results, risk-based review and production feedback before accepting AI-generated changes.

Why many apparent multi-agent gains are really test-time compute gains, and when extra agents are still worth the complexity.

When machines master the ordinary, humans are freed to pursue the extraordinary. From photography to AI music to vibe coding, the same pattern repeats.

AI coding tools and autonomous agents are shipping faster than the guardrails meant to govern them. Here is where the risks are and what thoughtful adoption looks like.

A practical guide to running AI coding agents safely and efficiently in a monorepo, including the wins, the configuration changes, and the common failure modes.

Engineers judge AI coding differently because its value depends on tests, review capacity, code ownership and how teams develop technical skill.

Why many teams use agile tooling and ceremonies but still deliver in large, slow, phase-gated batches, and how to get back to real fast-feedback delivery.

Why fix-forward is usually safer than rolling back when deployments touch databases, background jobs, and other non-idempotent state.