Built a chronological reading path through 66 AI papers, from Turing 1936 to Blackwell 2025 When I started learning ML, I kept hitting the same wall. Papers made sense individually but not together. AlexNet without LeNet felt random. Transformers without attention felt like magic. The field looked like a pile of disconnected breakthroughs instead of a story. So I rebuilt the timeline for myself, then turned it into a free repo. 66 chapters covering one paper or moment each, in order from 1936 to 2025. Every chapter answers three questions: what did this paper do, why did it matter at the time, what did it unlock next. Coverage runs from Turing and McCulloch-Pitts through perceptrons, the AI winters, backprop, LeNet, AlexNet, ResNet, attention, Transformers, BERT, GPT, diffusion, RLHF, scaling laws, and the hardware arc up to Blackwell. No heavy math. Plain language. Works for someone newer to the field or someone experienced who wants the connective tissue.…