We dive into Andrej Karpathy’s NanoChat project—a compact, hackable end-to-end LLM pipeline built on a tight budget. From 560M-parameter pretraining to supervised fine-tuning, tool use, and a sub-$100 cloud run, we unpack the philosophy of cognitive accessibility, the lean 8,300-line codebase, and what this teaches about readability, accessibility, and scalable AI on a budget.
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