Visualizes how internal random bits r turn an algorithm A(x; r) into distributions over outputs and runtimes (shown as shifting histograms). Contrasts Las Vegas (always correct, random runtime with an evolving runtime histogram and estimated E[T]) versus Monte Carlo (fixed time, probabilistic correctness). Demonstrates amplification by independent repetitions and majority vote: more runs increase cost linearly while the displayed effective error probability drops exponentially.
Pure Canvas2D, green-on-black blocky rendering via grid snapping. Uses time-cycled story steps (distribution → Las Vegas → Monte Carlo amplification). Monte Carlo repetitions are re-sampled periodically with a seeded LCG for stable visuals; Las Vegas runtime histogram accumulates samples over time and shows an online mean. Easing-based pulsing highlights the active concept.