A new writeup simplifies the theoretical machine learning paper On the Complexity of Neural Computation in Superposition. The author strips away dense computer science mathematics to explain how neural networks store more features than they have dimensions. This distillation helps practitioners grasp the core mechanics of superposition without requiring a theoretical CS background.