Engineering — practical AI-native guides.
Coding agents, code review, test generation, incidents, tech debt, weekly shipping cadence.
Shipping product features weekly with AI Dev AI Agents Teams
The cadence that lets a 10-person startup ship product like a 50-person team — with AI Dev AI Agents Teams handling the non-core work.
Read more →AI coding agents: Claude Code, Cursor, GitHub Copilot for production
Honest comparison of the major AI coding tools for production code in 2026. What ships, what doesn't, pricing, code review fit.
Read more →When to hire a Dev AI Agents Team vs build in-house engineering
Cost, speed, IP ownership, scaling. A founder's framework for deciding between a managed AI dev team and bringing engineering in-house.
Read more →AI tech debt reduction: refactor faster, safer, continuously
Pattern detection, refactor planning, test generation. Agents do the discovery; engineers approve the change.
Read more →AI database migration planning: safe schema changes at scale
Analysis, rollback planning, performance simulation. Agents do the homework; senior engineers approve the migration.
Read more →AI incident response: first 15 minutes when production breaks
Detection, summary, runbook lookup, comms drafting. Agents handle the mechanical; on-call engineers handle the judgement.
Read more →AI pull request summaries: reviewers say yes to merging again
What changed, why, and what to test. Agents generate the summary engineers skip writing.
Read more →AI documentation generation: README, API docs, runbooks
Docs that match code reality, refreshed continuously. Engineers stop avoiding documentation tasks.
Read more →AI test generation: cover the surface area you've been skipping
Unit, integration, regression. Agents draft tests; engineers review. Coverage improves where it was thin.
Read more →AI bug triage: from chaos to ranked queue without standups
New issues classified by severity, ownership, duplicate detection. The queue stays sane without manual triage meetings.
Read more →AI code review in 2026: what to automate, what to keep human
Linting, security, complexity, style — automated. Architecture, business context, edge cases — human.
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