Rate limiter decomposition
You need to add a new rate-limiting layer to a high-traffic Go API gateway: a token-bucket per API key backed by Redis, with graceful degradation if Redis is unreachable, plus config, metrics, and tests. How do you decompose this for an AI agent — which pieces do you delegate, which do you keep, and where exactly do the human checkpoints sit?
Implement
decide_rate_limit(request_times_ms: list[int], redis_up: list[bool], capacity: int, refill_per_sec: int) → list[bool]Examples
in
[[0,0,0],[true,true,true],2,1]out[true,true,false]in
[[0,0,0,1000],[true,true,true,true],2,1]out[true,true,false,true]in
[[0,0,0,500],[true,true,false,true],2,1]out[true,true,true,false]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 20 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.