A new writeup simplifies the theoretical machine learning paper On the Complexity of Neural Computation in Superposition. The author strips away dense theoretical computer science math to explain how models store more features than they have dimensions. This distillation helps alignment researchers grasp the mechanics of superposition without a PhD in mathematics.