In a gym, raw weights are the fundamental tool — no machines, no shortcuts. Neural network weights work the same way. We go from those raw numbers all the way to how they power the modern AI field.
The Analogy
The name is intentional. Gym weights and neural network weights share a deeper connection than you'd think.
In a gym, "raw" means no machines — just iron, gravity, and your body. There's nowhere to hide. You feel every pound.
In AI, weights are the actual numbers your model adjusts to learn. They encode everything the model knows — in raw numbers.
We start with that raw mathematical foundation and trace how it becomes the intelligence powering modern LLMs, agentic systems, and production AI. Analogy first, always.
See the Tiny Transformer We Built →Interactive
No setup. No Python. Learn by doing — directly in your browser.
Gradient descent, activation functions, neural networks — all interactive with real-time visualization.
Explore →From vanilla RNNs through LSTM to multi-head self-attention. 13 interactive visualizers.
Start the journey →Visualize convolution, pooling, and feature maps. See what the layers actually detect.
Explore →Interactive VAE, GAN, and CycleGAN explorer. Understand how AI creates new data.
Explore →Deep RL with policy gradients. Watch an agent learn to balance, navigate, and optimize.
Play →Gradient descent made visual. Adjust learning rate and watch the loss surface converge in real time.
Experiment →Where AI models break down, hallucinate, or refuse. Edge cases and failure modes explored interactively.
Probe →AI eval in practice. Score your model outputs, compare prompts, and prove performance before deploying.
Start evaluating →Latest
Field notes, guides, and build logs from the practice of building AI.
Deep Dives
Structured, interactive learning for the hard parts of AI.
Not sure where to start? Follow a curated sequence from first principles to production.