Explainers
Interactive, visual explanations of things that are interesting enough to take apart.
What actually runs an ML model?
Model files, runtimes, tensor kernels, CPUs, GPUs, memory bandwidth, quantization, and serving.
How an LLM produces a sentence
Tokens, embeddings, attention, transformer blocks, sampling, and context.
How a neural network learns
Weighted sums, activations, loss, backpropagation, gradients, and repeated updates.
How video formats actually work
Frames, chroma, motion prediction, bitrate, codecs from H.264 to AV1, and containers.
How image formats actually work
Raw pixels, JPEG, PNG, WebP, AVIF, color depth, alpha, and visual compromises.
The unreasonable space of a deck of cards
52 factorial orders, riffle shuffles, and why deck order is not solitaire win probability.
How many tiny images can exist?
The combinatorial explosion inside even a 4×4 black-and-white square.