I'm deciding between WebGL and CPU-based approaches for the Gray-Scott simulation, weighing performance against complexity. A CPU simulation at 200x200 resolution with multiple iterations per frame rendered via ImageData seems like a good balance—fast enough without the overhead of WebGL setup.

Actually, WebGL2 with a minimal CPU fallback makes more sense for a smoother, more impressive result. I'll use ping-pong textures with float formats (RGBA32F or RG16F) for the simulation state, write a shader that computes the 9-point Laplacian for the reaction-diffusion equations, and map the B channel to a color palette for display. Mouse interaction will seed the simulation by passing brush position and radius to the shader. I'll add preset controls for feed/kill rates, reset, palette selection, and speed adjustment, keeping the UI minimal.

Writing the simulation shader... Writing the full implementation... Writing shader code...

I'm planning to build a Gray-Scott reaction-diffusion simulation using WebGL2 with float textures for performance, implementing a ping-pong buffer approach for the computation. I'll add a UI with preset controls for the f and k parameters, brush size adjustment, reset functionality, and a color palette selector. Writing careful GLSL shaders for the simulation, and I'll include a fallback message if WebGL isn't available.

For the shaders, I'm implementing an update pass that reads the current u and v concentrations, computes a 5 or 9-point Laplacian, applies the reaction-diffusion equations with the feed and kill rates, and handles mouse input for seeding the v concentration. The display shader will map the v values to colors. I'll use RGBA16F or RGBA32F textures and run multiple simulation steps per frame for stability. I'm also considering interactive presets for different pattern types like spots and worms, with an optional gradient mode to vary the parameters spatially for more organic results.