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MEDIUMArray, Binary Search, Sliding Window, Prefix SumThis problem sounds like a bit-flipping question, but the useful reframe is simpler:
find the longest contiguous subarray that contains at most k zeroes.
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MEDIUMArray, Dynamic ProgrammingThis problem asks for the contiguous subarray whose product is as large as possible.
That sounds close to Maximum Subarray, but products behave very differently from sums. With sums, a very negative running total is usually bad news. With products, a very negative running product can suddenly become the best thing to keep if the next number is also negative.
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MEDIUMArray, Dynamic Programming, MatrixThe input is a grid of "0" and "1" values. The job is to find the area of the largest square made entirely of 1s.
The part that usually traps people is the word “square.” A brute-force approach tends to ask:
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MEDIUMArray, Breadth-First Search, MatrixThe grid has three kinds of cells: empty (0), fresh oranges (1), and rotten oranges (2). Every minute, any fresh orange touching a rotten orange in the four main directions also becomes rotten.
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Phil Plait opens by dismantling one of science fiction’s most persistent visual lies: the idea that an asteroid belt is a dense obstacle course of tumbling rocks packed close enough to force constant evasive maneuvers. In reality, the main belt between Mars and Jupiter is vast, and most asteroids are separated by enormous distances. A spacecraft could pass through it without facing anything like the cinematic hazard field people imagine.
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Selected problem: Distributed File Storage
Scope: Design a cloud file storage and sync service that stores large files durably, keeps file and folder metadata consistent, and propagates changes across devices and shared workspaces.
This is the classic Dropbox / Google Drive interview problem. Grokking and Alex Xu both push the same core split early: separate the metadata and synchronization plane from the binary file storage plane. DDIA adds the more durable framing: the namespace, versions, and permissions are the system of record, while notifications, sync cursors, search indexes, thumbnails, and audit views are derived data built from change streams.
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Selected problem: Distributed Job Scheduler
Scope: Design a multi-tenant scheduler that supports one-off and recurring jobs, dispatches them reliably to worker fleets, and gives operators clear control over retries, overlap, and execution history.
This interview problem sits between a cron service and a workload orchestrator. Grokking and Alex Xu repeatedly lean on the same building blocks across crawler, notification, and media-processing systems: stateless control planes, durable metadata, queues, workers, and explicit retry paths. DDIA adds the harder part: lease-based coordination, partition ownership, hot-spot avoidance, and clear delivery semantics.
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Selected problem: Metrics/Logging Pipeline
Scope: Design a centralized telemetry pipeline that ingests high-volume metrics and logs from many services, powers near-real-time dashboards and alerts, and retains searchable history without letting the write path collapse under bursts.
Grokking and Alex Xu both push the same interview habit first: clarify scope before naming technology. Here the system is not “the dashboard product”; it is the backend write and query path behind centralized observability.
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Selected problem: Ride-Sharing Dispatch
Scope: Design the real-time backend that ingests rider requests and driver location updates, finds good matches within seconds, assigns exactly one driver to a rider, and keeps the trip state and live map updated until completion.
This is the classic “design Uber/Lyft dispatch” interview problem: a city-scale marketplace where the hard parts are not just finding a nearby driver, but doing so with fresh-enough location data, realistic ETAs, strong assignment correctness, and graceful handling of retries, timeouts, cancellations, and partial failures.
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Techmeme surfaced this April 24, 2026 story in its Techmeme item, and the direct source used here is DeepSeek’s DeepSeek-V4 technical report.
DeepSeek released preview versions of DeepSeek-V4-Pro and DeepSeek-V4-Flash, two Mixture-of-Experts models aimed at a specific claim: open models can push much farther into long-context and agentic work without the usual explosion in cost. Pro is a 1.6T-parameter model with 49B activated parameters, while Flash is 284B total with 13B activated. Both support one-million-token contexts and were trained on more than 32T tokens.
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