5 sessions with Copilot: the agent failed at least once in 0, and 5 finished without a human stepping in.
Takeaway: I'd ask for the full entity list and relationship diagram upfront rather than letting the agent build tables incrementally, since that step-by-step approach worked fine here but wastes turns on straightforward schema design.
Takeaway: I'd start by scoping this to the actual decisions I needed to make for my project instead of asking the assistant to cover the entire ML ecosystem in one sitting — most of these answers never got applied to code.
Takeaway: I'd start by scoping the session to the actual project instead of letting it sprawl into general ML trivia — most of the value came once I anchored questions to my actual stack (Next.js, Drizzle, the Anthropic SDK already installed) rather than asking abstract 'best X' questions.
Takeaway: I'd scope this kind of session differently next time — if the goal is to seed a dashboard's query database, I'd say that upfront instead of letting the assistant infer it from a stray comment at turn 59.
Takeaway: I'd probably batch these into themed sessions next time instead of firing 260+ one-off questions in a row — it's hard to tell from a flat transcript whether the assistant actually stayed accurate as topics drifted.