I developed a tool designed to create distinctive division effects, aiming to generate patterns that display self-similarity across various scales, thus creating a sense of organized randomness.

This tool offers a unique feature: the degree of division and the behavior of the patterns can be influenced by external data. This allows for an additional level of customization and variance, creating an ever-changing landscape of division patterns, and bridging the gap between data-driven design and artistic expression.