Technical essays and explanations that make a difficult concept feel obvious in hindsight.
When my from-scratch Transformer started training with an unexpectedly high loss, I traced the problem back to parameter initialization. This is what I learned about initialization, residual connections, and why GPT-style models use depth-aware scaling.
Geometry, normalization, and the practical tradeoffs behind L1 and L2 distances.