generative-motionlisted

# Generative Motion The gap between a generative sketch and a generative *artwork* is almost never the algorithm — the algorithms are public and short. It is art direction: what the colour means, where the density sits, how slowly it evolves, and what you left out. A tutorial renders the algorithm. A piece renders a decision. ## Workflow 1. **Write the intent first**, in two or three sentences: what the system *is* (embers on a thermal, ink in water, a colony finding food), what the viewer should feel, and the one property that earns colour. Skipping this is what produces rainbow noise. The `design-direction` skill's philosophy format applies directly. 2. **Pick the system** from [references/systems.md](references/systems.md) — each entry has the equations, the parameter ranges that actually look good, and its failure mode. Its numbers are machine-checked: `validate/attractors.py` and `validate/gray_scott.py` verify every attractor seed and reaction–diffusion pair on CPU in seconds. Run them after changing any value, and use them to find your own. 3. **Build the field and the motion** with [references/noise-and-fields.md](references/noise-and-fields.md) — noise, fbm, domain warping, curl. Most beautiful motion is a field being sampled. 4. **Decide CPU or GPU early** with [references/gpu-and-shaders.md](references/gpu-and-shaders.md). This is architectural, not an optimisation — some systems simply do not express themselves below a populati