Randomness Visualizer

Displays random black and white pixels. True randomness should show no patterns - any visible structure suggests non-random behavior.

What to look for: Uniform "static" with no lines, clusters, or repeating patterns

Creates smooth, natural-looking noise by interpolating between random values. Unlike pure randomness, this creates coherent patterns.

Use case: Terrain generation, textures, procedural content

Each pixel's red, green, and blue channels are randomly determined, creating colorful static.

What to look for: Even distribution of colors with no banding or patterns

Plots random values along a spiral path, making it easier to spot patterns or periodicities in the sequence.

What to look for: Smooth color transitions without sudden changes or repeating segments

Plots triplets of random numbers in 3D space. Good RNGs should fill the cube uniformly.

What to look for: Even distribution with no planes, lines, or clusters

Shannon entropy measures the randomness/information content. Higher entropy (closer to maximum) indicates better randomness.

• High entropy: Data is unpredictable and random
• Low entropy: Data has patterns and is predictable
• Maximum entropy: All values equally likely (perfect randomness)

Continuously regenerates the visualization to help spot patterns that might emerge over time. Poor RNGs may show repeating cycles or drift.