Memoization decorator with TTL
You want an in-memory memoization decorator in Python, `@memoize(maxsize, ttl)`, that caches a pure function's results with a size cap and a time-to-live. You'll ask an AI agent to write it. Draft the prompt/spec — constraints, edge cases, acceptance criteria — for a first-try-correct result. What does a lazy prompt ("add a caching decorator with a TTL") get wrong?
Implement
simulate_ttl_lru_cache(max_size: int, ttl: int, call_keys: list[str], call_times: list[int]) → list[str]Examples
in
[2,10,["a","a","b"],[0,3,4]]out["miss","hit","miss"]in
[1,10,["a","b","a"],[0,1,2]]out["miss","miss","miss"]in
[2,5,["a","a"],[0,5]]out["miss","miss"]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 12 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.