Recap
Six reminders before you go. Skim on the way out, or come back when something feels off.
We're three years into a five-hundred-year skill. About 1200 ELO. When the model lets you down, the closed-minded blame the AI; the open-minded ask what they could have done differently. The second is the faster path — by a lot.
When the output disappoints, walk the four levers. Better tool. Better model. Better prompt. Better context. The biggest lever is almost always context.
Eight moves worth practicing —
- What questions should you ask me before starting?
- What's the smallest version of this that ships?
- Give me 3 options, rank them, name the trade-offs.
- Argue against your last suggestion.
- What did you skip?
- How would a senior engineer review this?
- Match the style of
components/PostCard.tsx. - Update CLAUDE.md with what you just learned.
Every change lives somewhere on a 2D map: breadth (which area of code) × depth (how zoomed in). At any node, three moves: ask, plan, delegate. Most non-trivial work moves through all three.
Treat the agent as a colleague — a fast, knowledgeable junior senior who only sees what you've shown them. The discipline gets more important, not less, as the models get smarter.
One agent makes you faster — but now you have access to a whole team. The bottleneck stops being model speed and becomes you: how well you can brief them, unblock them, and manage several at once. Optimize the hour, not the message.
One more thing
We're all early in this. Three years in. Maybe 1200 ELO. The next 1200 is wide open — and the most useful move is paying attention while everyone else assumes they've figured it out.
Thanks for reading. Go talk to a machine.