The adoption of AI-assisted coding has dramatically increased development velocity, with teams reporting 10x more pull requests. This shift has forced organizations to rethink their development workflows, particularly around code review and merge processes. While generating initial code has become significantly easier, the challenge now lies in efficiently moving code from PR to production.
- Think a little, and get the ball rolling.
- Be specific about what you want. Vague prompts get vague code.
- But don't overthink it — ready, fire, aim.
- If you're copy-pasting code blocks for the fifth time, stop.
- Pull the branch, commit directly.
- AI is your intern, not your boss.
- You're responsible for what ships — act like it.
- AI writes scaffolding, you handle the gnarly bits.
- AI refactors, you write integration tests.
- Back and forth.
- Get something working first.
- It's easier to reason about a solution with concrete code.
- AI excels at "make it work", you excel at "make it right."
- Automate bug reports, and error + other alerts to generate PRs.
- If the AI has to wait for you to initiate, or your laptop must stay open for work to continue, you're a bottleneck.
- Small commits from AI, small commits from you.
- Git history should tell a story, not be a novel.
- It's easier than ever to have test suites—AI can draft tests well.
- But you need to decide what actually needs testing, and why.
- With this principle alone, your software quality can go way up… without costing you much, if any, time.
- Because it did.
- AI writes confident code that might be confidently wrong.
- When in doubt, your judgment wins.
- AI suggests, you decide.
- AI knows syntax and patterns, but you know why the business logic exists and what could go wrong in production.
- That context gap is where human judgment becomes irreplaceable.
(PR improvements to this manifesto, if you have any)
(PR your signature to bottom of this list, if you're in)
(PR additional tools to bottom of this list, if you love them)