Intermediate
Watching a Neural Network Become Universal
Slide neurons in and out of a tiny network and watch it approximate a target curve live. Train an actual MLP in the browser to feel why one hidden layer can match any shape.
Interactive Lab
Scroll-driven articles that make the mechanism visible: concrete examples first, live figures second, and formal math only when it earns its place.
Intermediate
The Optimizer Race
Drop a starting point on any of four loss landscapes (ravine, saddle, plateau, Rosenbrock) and watch SGD, Momentum, and Adam race to the minimum with live step counters, loss scoreboards, and log-scale loss curves.
Intermediate
Attention, Calculated
Pick an ambiguous sentence, drag the query and key arrows, and watch dot products, softmax, and value blending update live to produce one contextual meaning.
Ages 6+
Why the Moon Changes Shape
A kid-friendly interactive explainer where you drag the Moon around Earth, watch the sunlight shift, learn the eight phase names, and test yourself with a simple moon game.
Introductory
Feeling p-values through re-splitting
Start with two tiny samples, re-split the combined values in all possible ways, and see a p-value as an exact fraction instead of a mysterious ritual.
Intermediate
RAG, From Scratch
A step-by-step walkthrough that builds RAG from two things you already know: next-token prediction and k-nearest neighbours. Interactive 2D embeddings, live retrieval, prompt assembly, and examples across air quality, cooking, and history.
Intermediate
Text Diffusion, Tiny
A hands-on explainer that trains a discrete denoising diffusion model on Indian names in your browser, showing how iterative unmasking generates text without predicting one token at a time.
Intermediate
Seeing the Multivariate Normal
A narrative explainer that starts with the ordinary bell curve, then builds a 2D Gaussian through examples, sampling, parameter learning, marginals, and conditional slices.
Intermediate
Autograd, Seen
A visual explainer for automatic differentiation that first distinguishes autodiff from finite differences and symbolic algebra, then walks a single computation graph forward and backward before showing modules and mini-batch training.