AI code review should not stop at style. Before merge, it should look for behavior regressions, permission mistakes, cache invalidation bugs, missing tests, and unreviewable scope.
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.
Practical guardrails for letting AI agents work in real repositories: branches, worktrees, validation, command approval, and dirty working tree discipline.
A practical guide to using Codex, Claude Code, and other coding agents with Git branches, diff review, validation, PR descriptions, command approval, and sane task scope.
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 explanation of Codex and Claude Code, how coding agents differ from normal AI chat, who they fit, and what risks developers should watch for.
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.