1 min lesson
Have a researcher's opinion on the tool
Work through the cases in "Have a researcher's opinion on the tool", pairing each signal with the move that fits.
Step 1 of 2
Have a researcher's opinion on the tool
You are interviewing to improve these models, so a vague “it's amazing” wastes the question. Hold a defensible view on where AI coding help wins, where it hurts and what you would push on as a user turned researcher.
Mechanical edits and scaffolding where the cost of a wrong guess is a cheap revert.
Holding context across a large codebase so you spend attention on the hard call, not on lookups.
Confident wrong edits in subtle logic, where the failure is silent until production.
Long-horizon agent runs where a bad early step compounds, exactly the credit-assignment problem your RL work targets.
Lead with a complaint, then connect it to your research. “Where Agent loses me is multi-step tasks: one wrong tool-call early and the rest of the trajectory is garbage. That is the sparse-reward credit-assignment problem and it is the thing I would want to work on.” A papercut that becomes a research direction reads as a daily user and a future teammate at once.