I knew the rules going in but the implementation details are where it gets messy.
Start by clarifying the problem scope (e.g., infinite grid, performance constraints) and then present a clean, modular solution that separates the simulation logic from the grid representation. Implement the core algorithm using an in-place update with boundary checks, and discuss trade-offs between time/space complexity and readability.
Pro tip: Demonstrate awareness of edge cases like infinite grids or large-scale simulations by mentioning optimizations such as using a hash set for live cells or parallelizing updates, which shows you think beyond the basic implementation.
Ask about grid size, boundary conditions, performance expectations, and whether the grid is finite or infinite. This ensures you address the actual problem and avoid assumptions.
Decide on a representation for the grid (e.g., 2D array, set of live cells) and explain your choice based on trade-offs like memory usage and access speed.
Write a function to compute the next state by counting live neighbors for each cell and applying the Game of Life rules. Use in-place updates with a copy or a separate buffer to avoid overwriting.
Mention potential optimizations (e.g., only iterating over live cells and their neighbors) and discuss time/space complexity, scalability, and readability trade-offs.
Walk through a small example (e.g., blinker) to verify correctness, and consider edge cases like empty grid or all dead cells.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.