How much of a manager are you?
Calvin French-Owen, who ran engineering at Segment, argues that engineers split on LLMs depending on how much they care about the 'what' versus the 'how'. Using AI coding tools has made him feel more like a manager than an engineer. He says success comes from specifying clearly, iterating in small steps, and knowing where models are weak.
We link to the original and never republish it. All credit to Calvin French-Owen.
Key takeaways
- Write a spec before you prompt: pick the framework, define the data types, and sketch the API or pages, so the model has somewhere to start instead of a blank canvas.
- Tighten your feedback loop. Instant page reloads, unit tests the model can re-run, and cheap automated checks like lint or CI let you judge the output instead of reading every line of code.
- Build bottoms-up in small steps: database types and methods first, then API routes, then frontend pages, then critique the styling. Models rarely nail a one-shot build.
- Sort work into cheap tasks (boilerplate, unit tests, small regressions, docs, one-off scripts) and expensive ones (big refactors, mission-critical APIs, core data models, database migrations), and hand off the cheap ones first.
- Keep system design and complex work like race conditions and performance-critical code for yourself. Nudge the model to refactor too, since it tends to keep adding code rather than consolidating it.
Who it's for
Engineers and technical founders deciding how much of their codebase to hand to AI coding tools, especially for side projects and early products.
Why it's worth your time
It offers a practical way to judge when to trust an LLM and when not to, and French-Owen notes the cheap-versus-expensive line keeps moving as models improve.
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