How to give AI coding agents useful context in real repositories: task boundaries, file evidence, project constraints, and acceptance checks that make the result reviewable.
A practical model-selection guide for Codex, Claude Code, and other coding agents, organized by task risk, context size, tool use, latency, cost, and validation strategy.
A practical guide to where AI coding tools help most: reading code, adding tests, moving boilerplate, tracing call chains, and making small verified changes, without handing over architecture decisions too early.
A practical look at why vibe coding became popular, how natural language became a software-building interface, and where AI-generated code can fail without clear requirements, review, testing, and production discipline.