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77
crates/sona/wasm-example/README.md
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crates/sona/wasm-example/README.md
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# SONA WASM Example
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Interactive browser demo of the Self-Optimizing Neural Architecture (SONA).
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## Quick Start
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1. Build the WASM module (if not already built):
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```bash
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cd ..
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wasm-pack build --target web --features wasm
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cp -r pkg wasm-example/
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```
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2. Serve the example:
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```bash
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cd wasm-example
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python3 -m http.server 8080
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```
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3. Open in browser:
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```
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http://localhost:8080
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```
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## Features
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- **Real-time Learning**: Record trajectories and see instant updates
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- **LoRA Visualization**: Watch transformation in real-time
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- **Statistics Dashboard**: Monitor patterns, quality, and performance
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- **Interactive Controls**: Adjust configuration and run experiments
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## Files
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- `index.html` - Demo page with UI
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- `index.js` - JavaScript logic using WASM bindings
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- `package.json` - NPM configuration
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- `pkg/` - Generated WASM package
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- `sona.js` - JavaScript bindings
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- `sona_bg.wasm` - WebAssembly binary
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- `sona.d.ts` - TypeScript definitions
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## Usage Example
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```javascript
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import init, { WasmSonaEngine } from './pkg/sona.js';
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async function main() {
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await init();
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const engine = new WasmSonaEngine(256);
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const trajectoryId = engine.start_trajectory(new Float32Array(256).fill(0.1));
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engine.record_step(trajectoryId, 42, 0.8, 1000);
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engine.end_trajectory(trajectoryId, 0.85);
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const output = engine.apply_lora(new Float32Array(256).fill(1.0));
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console.log('Transformed output:', output);
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}
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main();
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```
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## Performance
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- WASM file size: ~1.5MB (release build)
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- Initialization: < 100ms
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- Per-trajectory overhead: < 1ms
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- LoRA application: < 0.1ms (256-dim)
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## Browser Support
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- Chrome/Edge 91+
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- Firefox 89+
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- Safari 14.1+
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## License
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MIT OR Apache-2.0
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281
crates/sona/wasm-example/index.html
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crates/sona/wasm-example/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>SONA WASM Demo - Self-Optimizing Neural Architecture</title>
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<style>
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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min-height: 100vh;
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padding: 20px;
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}
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.container {
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max-width: 1200px;
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margin: 0 auto;
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background: white;
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border-radius: 12px;
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box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
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overflow: hidden;
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}
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header {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 30px;
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text-align: center;
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}
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header h1 {
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font-size: 2.5em;
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margin-bottom: 10px;
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}
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header p {
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font-size: 1.1em;
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opacity: 0.9;
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}
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.content {
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padding: 30px;
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}
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.section {
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margin-bottom: 30px;
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padding: 20px;
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background: #f8f9fa;
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border-radius: 8px;
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border-left: 4px solid #667eea;
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}
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.section h2 {
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color: #667eea;
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margin-bottom: 15px;
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}
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.controls {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 15px;
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margin-bottom: 20px;
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}
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button {
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background: #667eea;
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color: white;
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border: none;
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padding: 12px 24px;
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border-radius: 6px;
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font-size: 1em;
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cursor: pointer;
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transition: all 0.3s ease;
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}
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button:hover {
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background: #764ba2;
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transform: translateY(-2px);
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box-shadow: 0 4px 12px rgba(0, 0, 0, 0.2);
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}
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button:active {
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transform: translateY(0);
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}
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button:disabled {
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background: #ccc;
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cursor: not-allowed;
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transform: none;
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}
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input[type="number"], input[type="range"] {
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width: 100%;
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padding: 8px;
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border: 2px solid #e0e0e0;
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border-radius: 4px;
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font-size: 1em;
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}
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.stats-grid {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
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gap: 15px;
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margin-top: 15px;
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}
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.stat-card {
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background: white;
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padding: 15px;
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border-radius: 6px;
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text-align: center;
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box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
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}
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.stat-card .value {
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font-size: 2em;
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font-weight: bold;
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color: #667eea;
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}
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.stat-card .label {
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font-size: 0.9em;
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color: #666;
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margin-top: 5px;
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}
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#console {
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background: #1e1e1e;
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color: #00ff00;
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padding: 15px;
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border-radius: 6px;
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font-family: 'Courier New', monospace;
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font-size: 0.9em;
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max-height: 300px;
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overflow-y: auto;
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white-space: pre-wrap;
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}
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.visualization {
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background: white;
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padding: 20px;
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border-radius: 6px;
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margin-top: 15px;
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}
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canvas {
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width: 100%;
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height: 200px;
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border: 1px solid #e0e0e0;
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border-radius: 4px;
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}
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.loading {
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text-align: center;
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padding: 40px;
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font-size: 1.2em;
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color: #667eea;
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}
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.spinner {
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border: 4px solid #f3f3f3;
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border-top: 4px solid #667eea;
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border-radius: 50%;
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width: 40px;
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height: 40px;
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animation: spin 1s linear infinite;
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margin: 20px auto;
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}
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@keyframes spin {
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0% { transform: rotate(0deg); }
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100% { transform: rotate(360deg); }
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}
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.success {
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color: #28a745;
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}
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.warning {
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color: #ffc107;
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}
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.error {
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color: #dc3545;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<header>
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<h1>🧠 SONA WASM Demo</h1>
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<p>Self-Optimizing Neural Architecture in Your Browser</p>
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</header>
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<div class="content">
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<div id="loading" class="loading">
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<div class="spinner"></div>
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<p>Loading WASM module...</p>
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</div>
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<div id="app" style="display: none;">
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<!-- Configuration Section -->
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<div class="section">
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<h2>⚙️ Configuration</h2>
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<div class="controls">
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<div>
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<label>Hidden Dimension:</label>
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<input type="number" id="hiddenDim" value="256" min="64" max="2048" step="64">
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</div>
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<div>
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<label>Micro-LoRA Rank:</label>
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<input type="number" id="microRank" value="2" min="1" max="8">
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</div>
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<div>
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<label>Base-LoRA Rank:</label>
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<input type="number" id="baseRank" value="16" min="4" max="64" step="4">
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</div>
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</div>
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<button onclick="initializeEngine()">Initialize Engine</button>
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</div>
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<!-- Learning Section -->
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<div class="section">
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<h2>📚 Learning Controls</h2>
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<div class="controls">
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<button onclick="runSingleTrajectory()">Run Single Trajectory</button>
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<button onclick="runBatch()">Run Batch (10 trajectories)</button>
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<button onclick="forceLearn()">Force Background Learning</button>
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<button onclick="clearEngine()">Reset Engine</button>
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</div>
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</div>
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<!-- Statistics Section -->
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<div class="section">
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<h2>📊 Engine Statistics</h2>
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<div class="stats-grid">
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<div class="stat-card">
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<div class="value" id="trajCount">0</div>
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<div class="label">Trajectories</div>
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</div>
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<div class="stat-card">
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<div class="value" id="patternCount">0</div>
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<div class="label">Patterns</div>
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</div>
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<div class="stat-card">
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<div class="value" id="loraUpdates">0</div>
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<div class="label">LoRA Updates</div>
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</div>
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<div class="stat-card">
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<div class="value" id="avgQuality">0.00</div>
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<div class="label">Avg Quality</div>
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</div>
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</div>
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</div>
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<!-- Visualization Section -->
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<div class="section">
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<h2>📈 LoRA Transformation Visualization</h2>
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<div class="visualization">
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<canvas id="loraCanvas"></canvas>
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</div>
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</div>
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<!-- Console Section -->
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<div class="section">
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<h2>💻 Console Output</h2>
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<div id="console"></div>
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</div>
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</div>
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</div>
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</div>
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<script type="module" src="./index.js"></script>
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</body>
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</html>
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20
crates/sona/wasm-example/package.json
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20
crates/sona/wasm-example/package.json
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{
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"name": "sona-wasm-example",
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"version": "0.1.0",
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"description": "SONA WASM Example - Self-Optimizing Neural Architecture in the browser",
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"type": "module",
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"scripts": {
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"build": "cd .. && wasm-pack build --target web --features wasm --out-dir wasm-example/pkg",
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"serve": "python3 -m http.server 8080",
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"dev": "npm run build && npm run serve"
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},
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"keywords": [
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"wasm",
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"neural",
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"learning",
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"lora",
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"adaptive"
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],
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"author": "RuVector Team",
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"license": "MIT OR Apache-2.0"
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}
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