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Build a mini data loader with sharding for distributed training: split data across workers without overlap.

A from-scratch test of distributed input pipelines. The signal is partitioning data across workers with no overlap and no gaps, epoch-consistent shuffling with a shared seed, and handling the uneven-last-batch problem. Here is the implementation.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

A from-scratch test of distributed input pipelines. The signal is partitioning data across workers with no overlap and no gaps, epoch-consistent shuffling with a shared seed, and handling the uneven-last-batch problem. Here is the implementation.

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Next in this trackWrite a JSON parser from scratch. Now make it handle the partial JSON an LLM streams mid-generation.Next in this trackWrite an async batch caller for an LLM API: N requests, a concurrency cap, timeouts, and retries with backoff.Next in this trackImplement a minimal RAG pipeline end to end: embed and index a corpus, retrieve for a query, answer with citations.
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