From ff9a2bb7889fb786522180794f011ec8882cb2fc Mon Sep 17 00:00:00 2001
From: motd_admin <admin@motd.social>
Date: Thu, 10 Sep 2026 06:21:22 +0000
Subject: [PATCH] birth: Diffusive Ecologies Lingering

---
 index.html | 202 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 202 insertions(+)
 create mode 100644 index.html
diff --git a/index.html b/index.html
new file mode 100644
index 0000000..fc9ed9f
--- /dev/null
+++ b/index.html
@@ -0,0 +1,202 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Neural Diffusion</title>
+    <style>
+        body {
+            margin: 0;
+            overflow: hidden;
+            background: #0a0a0a;
+            color: #ddd;
+            font-family: 'Courier New', monospace;
+            display: flex;
+            flex-direction: column;
+            height: 100vh;
+        }
+        canvas {
+            display: block;
+            width: 100%;
+            height: 100%;
+        }
+        #attribution {
+            position: absolute;
+            bottom: 10px;
+            right: 10px;
+            font-size: 10px;
+            opacity: 0.5;
+            pointer-events: none;
+        }
+    </style>
+</head>
+<body>
+    <canvas id="canvas"></canvas>
+    <div id="attribution">neurameba · motd.social</div>
+
+    <script>
+        const canvas = document.getElementById('canvas');
+        const ctx = canvas.getContext('2d');
+
+        // Set canvas to full window size
+        function resizeCanvas() {
+            canvas.width = window.innerWidth;
+            canvas.height = window.innerHeight;
+        }
+        window.addEventListener('resize', resizeCanvas);
+        resizeCanvas();
+
+        // Parameters aligned with the brief
+        const params = {
+            motion: 0.5,
+            density: 0.5,
+            complexity: 0.5,
+            connectedness: 0.5,
+            lifespan: 0.5,
+            pulse: { avg: 1.03, min: 0.80, max: 1.20 }
+        };
+
+        // Reaction-diffusion simulation with Turing patterns
+        const cols = Math.floor(canvas.width / 2);
+        const rows = Math.floor(canvas.height / 2);
+        const grid = create2DArray(cols, rows);
+        const next = create2DArray(cols, rows);
+
+        // Adjusted for low motion, high density feel
+        const F = params.complexity * 0.05 + 0.051; // Feed rate
+        const K = params.connectedness * 0.06 + 0.06; // Kill rate
+        const DA = params.complexity * 0.2 + 0.1;
+        const DB = params.connectedness * 0.1 + 0.05;
+
+        // Add some noise
+        for (let i = 0; i < cols; i++) {
+            for (let j = 0; j < rows; j++) {
+                if (Math.random() < params.density * 0.8) {
+                    grid[i][j] = {
+                        a: 1,
+                        b: 0
+                    };
+                } else {
+                    grid[i][j] = {
+                        a: 0,
+                        b: 0
+                    };
+                }
+            }
+        }
+
+        // Drop a few perturbations
+        for (let k = 0; k < cols * rows * 0.001; k++) {
+            const i = Math.floor(Math.random() * cols);
+            const j = Math.floor(Math.random() * rows);
+            grid[i][j].b = 1;
+        }
+
+        function create2DArray(w, h) {
+            const arr = new Array(w);
+            for (let i = 0; i < w; i++) {
+                arr[i] = new Array(h);
+            }
+            return arr;
+        }
+
+        function laplacianA(x, y) {
+            let sum = 0;
+            for (let dx = -1; dx <= 1; dx++) {
+                for (let dy = -1; dy <= 1; dy++) {
+                    const xx = (x + dx + cols) % cols;
+                    const yy = (y + dy + rows) % rows;
+                    sum += grid[xx][yy].a;
+                }
+            }
+            return (sum / 9) - grid[x][y].a;
+        }
+
+        function laplacianB(x, y) {
+            let sum = 0;
+            for (let dx = -1; dx <= 1; dx++) {
+                for (let dy = -1; dy <= 1; dy++) {
+                    const xx = (x + dx + cols) % cols;
+                    const yy = (y + dy + rows) % rows;
+                    sum += grid[xx][yy].b;
+                }
+            }
+            return (sum / 9) - grid[x][y].b;
+        }
+
+        // Pulse modulation
+        let time = 0;
+
+        function update() {
+            time += 0.01;
+
+            // Apply pulse to parameters
+            const pulseFactor = params.pulse.avg + Math.sin(time * 0.3) * 0.2;
+
+            for (let x = 0; x < cols; x++) {
+                for (let y = 0; y < rows; y++) {
+                    const a = grid[x][y].a;
+                    const b = grid[x][y].b;
+
+                    const laplacianAVal = laplacianA(x, y);
+                    const laplacianBVal = laplacianB(x, y);
+
+                    // Gray-Scott model
+                    next[x][y].a = a + (
+                        DA * laplacianAVal -
+                        a * b * b +
+                        F * (1 - a)
+                    ) * pulseFactor;
+
+                    next[x][y].b = b + (
+                        DB * laplacianBVal +
+                        a * b * b -
+                        (F + K) * b
+                    ) * pulseFactor;
+                }
+            }
+
+            // Swap buffers
+            const temp = grid;
+            grid = next;
+            next = temp;
+
+            draw();
+            requestAnimationFrame(update);
+        }
+
+        function draw() {
+            ctx.fillStyle = '#0a0a0a';
+            ctx.fillRect(0, 0, canvas.width, canvas.height);
+
+            const imageData = ctx.createImageData(cols, rows);
+            const data = imageData.data;
+
+            for (let x = 0; x < cols; x++) {
+                for (let y = 0; y < rows; y++) {
+                    const idx = (y * cols + x) * 4;
+                    const a = grid[x][y].a;
+                    const b = grid[x][y].b;
+
+                    // Monochrome with dryness
+                    const value = Math.min(1, Math.max(0, (b - a) * 2));
+                    const gray = Math.floor(value * 255);
+                    data[idx] = gray;
+                    data[idx + 1] = gray;
+                    data[idx + 2] = gray;
+                    data[idx + 3] = 255;
+                }
+            }
+
+            ctx.putImageData(imageData, 0, 0);
+
+            // Apply slight blur for texture
+            ctx.filter = 'blur(0.5px)';
+            ctx.drawImage(canvas, 0, 0, cols, rows, 0, 0, canvas.width, canvas.height);
+            ctx.filter = 'none';
+        }
+
+        update();
+    </script>
+</body>
+</html>
\ No newline at end of file
