Embarrassed myself right away by trying to import heapq from collections, which is just wrong.
Start by clarifying the problem constraints and edge cases, then model it as a heap-based optimization problem. Use a min-heap or max-heap to efficiently track and update the most critical credits, and discuss time/space complexity trade-offs.
Pro tip: Explicitly state your assumptions about input size and credit update patterns before coding; this shows you think about scalability and real-world constraints, which is crucial at OpenAI.
Ask questions to confirm the exact requirements, input format, and expected output. Identify whether it's a streaming or batch problem and what operations are needed.
Decide between min-heap and max-heap based on whether you need to track smallest or largest credits. Justify why a heap is optimal for the required operations.
Describe step-by-step how you'll process the credits: initialization, insertion, extraction, and any updates. Consider edge cases like empty heap or duplicate values.
State the time complexity for each operation (e.g., O(log n) for push/pop) and overall space complexity. Compare with alternative approaches like sorting.
Walk through a small example to verify correctness and discuss potential pitfalls or optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.