Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/crates/sona/wasm-example/README.md

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+# SONA WASM Example
+
+Interactive browser demo of the Self-Optimizing Neural Architecture (SONA).
+
+## Quick Start
+
+1. Build the WASM module (if not already built):
+```bash
+cd ..
+wasm-pack build --target web --features wasm
+cp -r pkg wasm-example/
+```
+
+2. Serve the example:
+```bash
+cd wasm-example
+python3 -m http.server 8080
+```
+
+3. Open in browser:
+```
+http://localhost:8080
+```
+
+## Features
+
+- **Real-time Learning**: Record trajectories and see instant updates
+- **LoRA Visualization**: Watch transformation in real-time
+- **Statistics Dashboard**: Monitor patterns, quality, and performance
+- **Interactive Controls**: Adjust configuration and run experiments
+
+## Files
+
+- `index.html` - Demo page with UI
+- `index.js` - JavaScript logic using WASM bindings
+- `package.json` - NPM configuration
+- `pkg/` - Generated WASM package
+  - `sona.js` - JavaScript bindings
+  - `sona_bg.wasm` - WebAssembly binary
+  - `sona.d.ts` - TypeScript definitions
+
+## Usage Example
+
+```javascript
+import init, { WasmSonaEngine } from './pkg/sona.js';
+
+async function main() {
+  await init();
+  
+  const engine = new WasmSonaEngine(256);
+  const trajectoryId = engine.start_trajectory(new Float32Array(256).fill(0.1));
+  engine.record_step(trajectoryId, 42, 0.8, 1000);
+  engine.end_trajectory(trajectoryId, 0.85);
+  
+  const output = engine.apply_lora(new Float32Array(256).fill(1.0));
+  console.log('Transformed output:', output);
+}
+
+main();
+```
+
+## Performance
+
+- WASM file size: ~1.5MB (release build)
+- Initialization: < 100ms
+- Per-trajectory overhead: < 1ms
+- LoRA application: < 0.1ms (256-dim)
+
+## Browser Support
+
+- Chrome/Edge 91+
+- Firefox 89+
+- Safari 14.1+
+
+## License
+
+MIT OR Apache-2.0