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Reaction-diffusion · ling-2.6-1t

Run a Gray-Scott reaction-diffusion simulation on a canvas that grows organic Turing patterns in real time, with the mouse seeding new growth.

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user

Run a Gray-Scott reaction-diffusion simulation on a canvas that grows organic Turing patterns in real time, with the mouse seeding new growth.

assistant

I'll create a single-page web app that simulates the Gray-Scott reaction-diffusion system, generating organic Turing patterns with mouse interaction for seeding growth.

write /tmp/ws/index.html (21341 bytes)
toolResult

Successfully wrote 21341 bytes to /tmp/ws/index.html

assistant

I've created a complete Gray-Scott reaction-diffusion simulation as a single HTML file. Here's what it includes: ## Features: **Simulation Core:** - Full Gray-Scott reaction-diffusion equations implemented in JavaScript - Two chemical concentrations (u and v) that react and diffuse across a 2D grid - Laplacian diffusion using 5-point stencil for smooth spreading **Visual Design:** - Rich organic color gradient mapping from deep purple/blue → cyan → teal → green-gold → bright highlights - Subtle glow effects on high-concentration areas - Crosshair cursor with visual feedback showing the seeding radius **Interactivity:** - **Mouse move** - Seeds new growth patterns as you drag across the canvas - **Mouse click** - Strong injection of chemical B to create bursts of activity - **Keyboard shortcuts**: Space = randomize, 1-4 = pattern presets, R = reset - **Touch support** for mobile devices **Pattern Presets:** 1. **Spiral Waves** - Classic rotating spiral patterns 2. **Mazes** - Labyrinthine branching structures 3. **Solitons** - Isolated stable spots 4. **Pulsing** - Rhythmic oscillating patterns **Controls:** - Reset button - Clears and reinitializes the grid - Randomize button - Generates new F/K parameters with random seeds - Pattern buttons - Switch between curated reaction parameters The simulation runs efficiently using Float32Arrays for the concentration grids, with double-buffering for smooth updates. Multiple update steps per frame ensure fast pattern evolution while maintaining performance.

TAKE IT TO CODE

A single self-contained HTML file — copy it and open in any browser, or reuse the prompt with your own pi-agent run.

RUN

modelling-2.6-1t
vendorinclusionai
harnesspi-agent
generated2026-08-01

EVALUATION

4/5 working

Gray-Scott simulation grows organic Turing patterns, animates on its own, and reacts to click, drag, wheel, and keys with visible seeding.

Vision grade · anthropic/claude-sonnet-5

MEASUREMENTS

Raw readings from the capture, not a verdict — the grade above is the only judgement. Percentages are how much of the screen changed; the measure under-reports sparse motion on a dark background.

idle motion3.0%
on click8.5%
on drag11.8%
on wheel15.4%
on enter + space20.1%
on arrow keys32.6%
on w/a/s/d9.3%
frame spread19.9 / 255
console errors0
js errors none

METRICS

runtime43.4s
tokens in9.4k
tokens out6.7k
cached1.5k
cost / run$0.0048