A deep dive into Karpathy's Atomic GPT—a fully functional transformer implemented in roughly 200 lines of pure Python, with no libraries. We trace how a value class records computation history, how backpropagation unfolds from receipts, and how architectural choices like squared ReLU and RMSNorm shape learning. We explore the minimalist attention loop, manual KV cache management, and a from-scratch Adam optimizer, all while reflecting on what this teaches about intelligence, scalability, and the role of production-grade tools in real-world AI projects.
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