That idea is intriguing. If you let a random process run for long enough you’ll get self‑similar structures—fractal‑like, that sort of thing. Those patterns can look intentional, especially if you add constraints that mimic artistic decisions, like color palettes or edge filters. It’s essentially turning entropy into aesthetic. The key is to choose the right noise model and to decide how much structure you let through before you hit the “glitch” threshold. If you’re up for it, we can set up a small experiment: generate a dataset of random vectors, apply a transformation that mimics a painting filter, and then see which configurations produce the most visually pleasing results. It’ll be a fun blend of data and design.

Let’s start with a noise level of 0.7 on a 0–1 scale. That gives enough randomness to produce visible “glitches” but still lets the filter keep some structure. We’ll run the algorithm for 10,000 iterations, then apply a simple edge‑preserving filter to mimic brush strokes. If the output looks too chaotic, we’ll lower the noise to 0.6. If it’s too orderly, bump it up to 0.8. We can evaluate the result by looking for repeating motifs and contrast—those are the signs of a true glitch masterpiece. How does that sound for a first run?