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Discord·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Discord coding interview where they handed me a massive open-ended project: build a TCP chat server from scratch, Discord-style. The scope was intentionally huge and they explicitly said AI tools were fair game, which was a twist I wasn't expecting. Evaluating how well you use AI is apparently part of the rubric.

Questions Asked (1)

Q1

Build a TCP-based chat server with slash commands (/help, /nick, /users, /quit), chat rooms with broadcast messaging, reconnection support, message history (last 10 messages on join), data persistence across restarts, and scalability in mind. You can use AI tools.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

The scope of this thing is genuinely absurd for a single interview session.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level architecture that separates concerns: connection handling, command parsing, room management, and persistence. Walk through the core components and data flow, emphasizing trade-offs and scalability considerations. Finally, discuss how you would leverage AI tools to accelerate development while ensuring correctness.

Pro tip: Demonstrate awareness of real-world challenges like handling partial TCP messages, graceful shutdowns, and idempotent reconnection. Mention that you'd use AI to generate boilerplate but rigorously test edge cases like message ordering and persistence consistency.

1. Clarify Requirements and Constraints

Ask about expected scale (concurrent users, rooms), persistence guarantees (e.g., durability vs. performance), and reconnection semantics (e.g., session resumption vs. new session). This shows you think before coding.

2. Design High-Level Architecture

Propose a modular design: a TCP server (e.g., using asyncio or Netty) that accepts connections, a command parser, a room manager, and a persistence layer (e.g., Redis for history, database for user data). Emphasize separation of concerns for scalability.

3. Detail Core Features and Data Flow

Explain how slash commands are parsed and executed, how broadcast messaging works within rooms, and how reconnection is handled (e.g., via unique client IDs and session tokens). Describe how message history is stored and retrieved (e.g., Redis lists with capped length).

4. Address Scalability and Persistence

Discuss scaling horizontally with multiple server instances behind a load balancer, using a pub/sub system (e.g., Redis Pub/Sub) for cross-instance broadcasts. Explain persistence strategies: write-through caching for history, periodic snapshots, or append-only logs.

5. Leverage AI Tools and Discuss Trade-offs

Mention using AI to generate boilerplate (e.g., TCP server setup, command parsing) and to suggest optimizations. Highlight trade-offs: e.g., in-memory vs. persistent storage, consistency vs. latency, and complexity of reconnection logic.

Key Points to Mention

  • Handling TCP stream framing (e.g., length-prefixed messages or delimiters) to avoid partial reads.
  • Using a pub/sub mechanism (like Redis) for scalable broadcast across multiple server instances.
  • Storing message history in a capped data structure (e.g., Redis list with LTRIM) for efficient retrieval of last 10 messages.
  • Reconnection support via session tokens or client IDs, with idempotent message delivery to avoid duplicates.
  • Persistence layer choices: Redis for ephemeral data (history, sessions) and a database (e.g., PostgreSQL) for durable user/room metadata.
  • Graceful shutdown and error handling: closing connections cleanly, flushing pending writes, and notifying clients.

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