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Implement Real-time Data Ingestion with Kafka/Pulsar

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Linked challenge: Real-time AI Sports Content Generation & Moderation with DSPy

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Linked challenge
Real-time AI Sports Content Generation & Moderation with DSPy

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Set up a local Kafka or Pulsar instance (e.g., using Docker Compose) to simulate a live stream of sports event data. Implement a Python producer that pushes simulated `event_transcript` and `highlight_type` messages to a topic. Then, implement a consumer that feeds these messages into your DSPy content generation pipeline in real-time.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.