Applied intuition·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Applied Intuition had me do a systems-style coding round where the whole point was handling I/O constraints on a massive grid, not cleverness. Felt more like an infrastructure problem than a typical leetcode session, which was a nice change of pace.

Questions Asked (1)

You're given starter code for a Conway's Game of Life-style cellular automaton on a 2D grid. The grid is too large to fit in memory (think over a million rows and columns). How do you implement a single generation step without loading the whole grid at once?

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

The core of the problem is basically: stop thinking about the grid as a 2D array you can index freely, and start thinking about it as a stream.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the constraints: grid size, memory limit, and whether the grid is stored on disk or generated on the fly. Then propose a streaming approach that processes the grid in overlapping row blocks, keeping only a few rows in memory at a time, and writing each new row to output as soon as it's computed. Discuss trade-offs like I/O overhead, block size tuning, and handling boundaries.

Pro tip: Mention that you can avoid storing the entire grid by using a sliding window of rows and that you can further optimize by only recomputing cells that could change (e.g., using a sparse representation if the grid is mostly dead).

1. Clarify requirements and constraints

Ask about grid size, memory limit, whether the grid is stored on disk or can be generated, and if random access is available. Confirm that we only need one generation step and that output can be written to disk.

2. Choose a streaming strategy

Propose processing the grid in horizontal strips (blocks of rows) that overlap by one row to compute the next generation correctly. Keep only the current block and the previous row in memory.

3. Design the algorithm for a block

For each block, read the rows from disk, compute the next generation for the interior rows, and write them out. Handle the top and bottom boundaries by reading one extra row above and below the block.

4. Address I/O and performance trade-offs

Discuss block size tuning: larger blocks reduce I/O but increase memory; smaller blocks do the opposite. Mention using buffered I/O and possibly parallelizing across blocks if dependencies allow.

5. Consider edge cases and optimizations

Handle grid boundaries (wrap-around or fixed), and mention optimizations like sparse representation for mostly dead grids or bit-packing to reduce memory and I/O.

Key Points to Mention

Sliding window of rows: only need current row and previous row to compute next row.
Block processing with overlapping rows to handle dependencies between blocks.
I/O efficiency: use buffered reads/writes, and consider compression or bit-packing.
Trade-off between block size and memory usage; tune based on available memory and disk speed.
Boundary conditions: handle edges correctly, especially if the grid wraps around.
Sparse optimization: if the grid is mostly empty, store only live cells and their neighbors.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.