4 articles.
How text becomes numbers a model can train on: building a vocabulary, why byte pair encoding never needs an unknown token, the embedding layer as a lookup that is provably one-hot times a matrix, and why position has to be added back in by hand.
Assembling a GPT from attention: multi-head projections, layer normalization worked by hand, why shortcut connections rescue the gradient, the 4x feed-forward expansion, and a parameter count that reproduces GPT-2 small at 124 million exactly.
How next-word prediction turns unlabelled text into supervision, why cross entropy is just negative average log probability, what perplexity really measures, and why a model that completes text fluently still cannot follow an instruction.
Queries, keys, and values built from the ground up: why attention exists, how scaled dot-product attention is computed, why it is divided by the square root of the dimension, and how causal masking works, with every matrix computed and checked.