interactive demo
Beyond Market Intelligence keeps interactive demo in one place: 3 stories so far. The section currently leads with “Explore how your phone visualizes AI attention with each word it chooses”, “Watch an AI learn Clash Royale defense in a live browser demo”, and “Visualize Neural Network Learning: An Interactive Exploration”. Watching your phone visualize attention as it generates each word feels like peeking inside a working brain. Watching a policy learn is different from reading about it. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every interactive demo story on Beyond Market Intelligence, newest first.
Explore how your phone visualizes AI attention with each word it chooses
Watching your phone visualize attention as it generates each word feels like peeking inside a working brain. This browser-based demo runs SmolLM2-135M entirely on-device, no server, just int8 precision and ONNX runtime. You see token probabilities in real time, plus attention links drawn back to earlier words, weighted and clickable. The developer initially thought his code was broken; everything kept attending to the first token. That turned out to be the attention sink phenomenon, a fascinating bug that became the lesson.
Watch an AI learn Clash Royale defense in a live browser demo
Watching a policy learn is different from reading about it. This live browser demo makes that visible: a 5,629-parameter agent trains with REINFORCE in plain JavaScript, while every rollout runs through the C++ engine compiled to WebAssembly. The chart shows the brute-forced optimum, so the gap between learned behavior and best answer is always in view. One honest detail stands out: Giant vs Cannon traps most runs in a lane placement worth 75% of optimal.
Visualize Neural Network Learning: An Interactive Exploration
A single hidden layer in a neural network doesn't just memorize points; it draws straight lines. With ReLU activations, one layer of width three can carve the input space into up to four linear segments. Stack another layer, and those segments multiply, so "3 3" can theoretically hit 16. The catch is that training rarely pushes the network to that theoretical max. It's a tidy, visual way to see why depth matters.