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Build a tiny autograd engine from scratch: a scalar Value with backprop over a computation graph.

A from-scratch test of how PyTorch actually works under the hood. The signal is building a computation graph during the forward pass, local derivatives per op, and a topological-order backward pass that accumulates gradients. Here is a minimal engine.

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

A from-scratch test of how PyTorch actually works under the hood. The signal is building a computation graph during the forward pass, local derivatives per op, and a topological-order backward pass that accumulates gradients. Here is a minimal engine.

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Next in this trackImplement a WordPiece tokenizer from scratch: greedy longest-match-first subword segmentation.Next in this trackBuild a mini data loader with sharding for distributed training: split data across workers without overlap.Next in this trackWrite a JSON parser from scratch. Now make it handle the partial JSON an LLM streams mid-generation.
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