Clarify the physics model and boundary conditions first, then implement a pure function that applies gravity and velocity to update position, and finally return the new state with a boolean indicating if the bird is within bounds. Discuss trade-offs between simple Euler integration and more accurate methods, and consider edge cases like boundary collisions.
Pro tip: Explicitly state your assumptions about units, gravity, and boundary behavior, and mention that you'd write unit tests for edge cases like hitting the top/bottom or zero velocity. This shows production-level thinking.
Ask about the physics model (e.g., gravity constant, velocity update order), boundary definitions, and what 'within playable area' means (inclusive/exclusive). Confirm input/output types and whether the function should be pure.
Decide the order of operations: typically update velocity by adding gravity, then update position by adding velocity. Consider if any damping or terminal velocity applies.
Write a clean function that takes current position, velocity, and possibly other parameters, and returns new position, new velocity, and a boolean for in-bounds. Use clear variable names and avoid side effects.
Check if the new position is outside the playable area (e.g., y < 0 or y > height). Decide if the bird should be clamped or if the boolean simply indicates out-of-bounds. Consider if velocity should reset on collision.
Walk through example scenarios (e.g., bird at top with upward velocity, bird at bottom with downward velocity). Mention potential improvements like using delta time for frame-rate independence or more accurate integration methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and defining the objective (e.g., maximize total fees). Then outline a greedy algorithm that respects dependencies, handles cycles, and optimizes for large mempools using caching and efficient data structures. Finally, analyze time complexity and discuss trade-offs.
Pro tip: Emphasize that dependency cycles are invalid and must be detected and excluded early, and that caching topological order or dependency counts can drastically improve performance for large mempools.
Ask questions to confirm the goal (e.g., maximize fees), transaction properties (fee, size, dependencies), and constraints (block size limit, mempool size).
Propose a greedy strategy: sort transactions by fee rate (fee/size) descending, then iterate, adding transactions whose dependencies are already included, skipping those with unmet dependencies or cycles.
Detect cycles using DFS or topological sort; exclude transactions involved in cycles from consideration.
Use caching (e.g., memoize dependency counts, topological order) and efficient data structures (priority queue, hash maps) to avoid recomputation.
Discuss time and space complexity (e.g., O(n log n) for sorting, O(n + e) for cycle detection), and trade-offs between optimality and speed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.