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Nexus Visual - Adaptive Pixels

Behavior-driven adaptive visual system with machine learning

Nexus Visual implements an advanced adaptive pixel system that learns from user interactions and reflects them in real-time visuals. The system combines realistic behavior tracking, simple heuristic adaptation, and optional machine learning to create personalized visual experiences.

✨ Features

🧠 Adaptive Learning System

Real-time Behavior Tracking: Click, hover, scroll, and keyboard interaction monitoring
Grid-based Heatmap: Spatial analysis of user interaction patterns
Pixel Adaptation: 10,000 pixels that adapt based on user behavior
Confidence Scoring: Machine learning confidence metrics

🎨 Visual Adaptation

Color Shifting: Dynamic color adaptation based on context and preferences
Speed Modulation: Movement speed adaptation based on interaction patterns
Glow Intensity: Visual feedback intensity based on user engagement
Size Scaling: Pixel size adaptation for different interaction styles

🤖 Machine Learning Integration

TensorFlow.js: Optional ML model training and prediction
Behavior Prediction: Predicts user preferences from interaction patterns
Model Persistence: Saves trained models to localStorage
Feature Flag Control: ML features only load when enabled

📊 Analytics Dashboard

Real-time Stats: Live adaptation metrics and confidence scores
Heatmap Visualization: Interactive heatmap of user activity
Preference Controls: Manual adaptation controls and theme switching
ML Model Management: Train, predict, and manage ML models

🛠️ Technical Stack

React 18 - Component framework
TypeScript - Type safety
Vite - Build tool and dev server
Tailwind CSS - Styling framework
Framer Motion - Animations
Zustand - State management
Canvas API - High-performance rendering
TensorFlow.js - Machine learning (optional)

🚀 Quick Start

Prerequisites

Node.js 18+
npm or yarn

Installation

cd apps/nexus-visual
npm install

Environment Setup

cp .env.local.example .env.local

Configure environment variables:

# Feature Flags
VITE_ENABLE_PIXEL_LEARNING=true
VITE_ENABLE_PIXEL_ML=false

# Optional Supabase Integration
VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=your-anon-key

Development

npm run dev

Visit http://localhost:3004 to see the adaptive pixels in action.

🎮 Usage

Basic Interaction

Click: Interact with pixels to trigger adaptation
Hover: Influence nearby pixels with mouse movement
Scroll: Affect global pixel behavior patterns
Keyboard: Track keyboard interaction patterns

Keyboard Shortcuts

P: Toggle pause/play animation
R: Reset pixel learning
D: Toggle dashboard visibility

Dashboard Controls

Stats Tab: View adaptation metrics and confidence scores
Heatmap Tab: See interaction patterns and activity zones
Preferences Tab: Apply manual adaptations and themes
ML Tab: Train and manage machine learning models

🔧 Configuration

Feature Flags

# Enable behavior learning system
VITE_ENABLE_PIXEL_LEARNING=true

# Enable machine learning features (requires TensorFlow.js)
VITE_ENABLE_PIXEL_ML=false

Pixel Configuration

// Adjust pixel count and density
const PIXEL_COUNT = 10000
const ADAPTATION_RADIUS = 100
const GRID_SIZE = 50

Context Modes

ghost-studio: Purple theme (#B84DFF)
nova-pilot: Cyan theme (#00FFE7)
nexus: Accent theme (#9AF7EE)

📊 Data Model

User Behavior

interface UserBehavior {
  clickHeatmap: Map<string, number>
  hoverAreas: Map<string, number>
  scrollSpeed: number
  timeOnPage: Record<string, number>
  interactions: {
    buttons: string[]
    features: string[]
    timeOfDay: string
  }
}

Pixel Preferences

interface PixelPreferences {
  colorShift: number        // -1..1
  speedMultiplier: number   // 0.5..2
  size: number              // 0.5..1.5
  opacity: number           // 0.3..1
  glowIntensity: number     // 0.5..2
}

🧪 Testing

# Run tests
npm run test

# Run tests with UI
npm run test:ui

# Test specific modules
npm run test behaviorTracker
npm run test adaptivePixels
npm run test storage

Test Coverage

Behavior Tracker: Heatmap bucketing, notification radius
Adaptive Pixels: Preference changes on interactions
Storage: Persistence get/set roundtrip
ML Integration: Model training and prediction

🔌 Integration

Embedding in Other Apps

import { AdaptivePixels } from '@/components/AdaptivePixels'

function MyApp() {
  return (
    <div>
      <MyAppContent />
      <AdaptivePixels 
        mode="real"
        context="nova-pilot"
        density={0.5}
      />
    </div>
  )
}

Initialization Helper

import { initAdaptivePixels } from '@/lib/adaptivePixels'

// Initialize with specific configuration
initAdaptivePixels({
  mode: 'real',
  context: 'ghost-studio',
  enableLearning: true,
  enableML: false
})

📈 Performance

Optimization Features

RequestAnimationFrame: Smooth 60fps animation
Canvas Rendering: Hardware-accelerated graphics
Throttled Events: Optimized interaction tracking
Lazy Loading: ML features load only when needed
Memory Management: Efficient pixel data structures

Performance Metrics

Target: 60fps with 10,000 pixels
Memory: < 50MB for full system
Bundle Size: < 2MB (without ML features)
ML Bundle: +3MB when enabled

🔒 Privacy & Security

Data Handling

Local Storage: Primary data persistence
Optional Supabase: Cloud backup for pro users
No Tracking: No external analytics or tracking
User Control: Full data export and deletion

Security Features

Input Validation: All user inputs validated
XSS Protection: Content sanitization
CSRF Protection: Request token validation
Environment Variables: Secure configuration

🚀 Deployment

Build

npm run build

Environment Variables

# Production
VITE_ENABLE_PIXEL_LEARNING=true
VITE_ENABLE_PIXEL_ML=false
VITE_SUPABASE_URL=https://your-project.supabase.co
VITE_SUPABASE_ANON_KEY=your-anon-key

Deployment Platforms

Vercel: Automatic deployment with environment variables
Netlify: Static site deployment
Docker: Containerized deployment
CDN: Static asset optimization

📚 API Reference

Hooks

useBehaviorTracker(): Track user interactions
useAdaptivePixels(): Manage pixel adaptation
useSimpleAdaptation(): Apply heuristic adaptations
usePixelML(): Machine learning integration

Components

AdaptivePixelsCanvas: Main rendering canvas
PixelLearningDashboard: Analytics and controls
HeatmapCanvas: Interaction visualization

Utilities

pixelStorage: Local storage management
pixelSupabase: Cloud persistence
utils: Color, math, and helper functions

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Submit a pull request

Development Guidelines

Use TypeScript strict mode
Follow existing code patterns
Add tests for new features
Update documentation
Ensure performance targets

📄 License

MIT License - see LICENSE file for details.

🆘 Support

Issues: GitHub Issues
Documentation: This README
Examples: See /examples directory
Community: Discord server

Built with ❤️ for the Son1kVers3 ecosystem

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