Satoshi's Arcade MCP

An arcade that learns from you — every move makes it smarter.

A browser-based AI arcade showcasing the fusion of retro gameplay, blockchain-inspired data structures, and machine learning. Experience games like Ping-Pong and Tetris with AI opponents that adapt and learn from your gameplay patterns.

Features

AI-Powered Gameplay

• Adaptive AI: Opponents learn from your moves and adapt difficulty
• Reinforcement Learning: AI improves through gameplay data
• Model Context Protocol: AI memory layer for contextual performance
• Real-time Learning: AI adjusts strategy during gameplay

Retro Games

• Ping-Pong: Classic paddle game with AI opponent
• Tetris: Block-stacking puzzle with AI analysis
• Hard Bass Boom: Epic sound effects with Web Audio API
• PWA Ready: Play on mobile and desktop

Immersive Design

• Neon-Bauhaus: Glowing UI with IBM Plex Sans typography
• Responsive Layout: Works on iPhone, desktop, and tablets
• Motion Effects: Smooth animations and visual feedback
• Dark Theme: Easy on the eyes for extended play

AI Architecture

• Modular Intelligence: Easy to extend with new games
• SQLite Database: Lightweight storage for player data
• Session Management: Track gameplay across sessions
• Performance Analytics: Detailed AI learning metrics

Quick Start

Prerequisites

• Python 3.8+
• Modern web browser
• Git

Installation

1. Clone the repository

git clone https://github.com/polydeuces32/satoshis-arcade-mcp.git
cd satoshis-arcade-mcp

2. Install dependencies

pip install -r requirements.txt

3. Start the server

python3 -m uvicorn api.main:app --host 0.0.0.0 --port 8000 --reload

4. Open your browser

http://localhost:8000

How to Play

Ping-Pong

• Move mouse to control your paddle
• Hit the ball to score points
• AI learns from your paddle movements
• First to 5 points wins!

Tetris

• Arrow keys to move and rotate pieces
• Space for hard drop
• P to pause
• AI analyzes your block placement efficiency

Architecture

satoshis-arcade-mcp/
├── ai/                    # AI intelligence engine
│   └── difficulty_agent.py   # Reinforcement learning agent
├── api/                   # FastAPI backend
│   ├── main.py              # Application entry point
│   └── routes/              # Game API endpoints
│       ├── pingpong.py      # Ping-Pong game logic
│       ├── tetris.py        # Tetris game logic
│       └── leaderboard.py   # Score tracking
├── frontend/              # Web interface
│   ├── arcade/              # Main menu
│   ├── pingpong/            # Ping-Pong game
│   ├── tetris/              # Tetris game
│   ├── manifest.json        # PWA configuration
│   └── service-worker.js    # Offline capabilities
├── database.py            # SQLite database management
├── render.yaml            # Deployment configuration
└── requirements.txt       # Python dependencies

AI Learning System

Reinforcement Learning

The AI uses a custom reinforcement learning algorithm that:

• Observes player behavior patterns
• Adapts difficulty based on performance
• Learns optimal strategies over time
• Remembers context across sessions

Model Context Protocol (MCP)

• Memory Layer: Stores gameplay context and performance data
• Replay Analysis: Analyzes successful strategies
• Cross-Session Learning: AI remembers you between games
• Performance Metrics: Tracks learning progress

Sound System

Hard Bass Boom Effects

• Web Audio API: Advanced audio synthesis
• Multiple Oscillators: Rich, layered bass sounds
• Deep Frequencies: 40Hz-200Hz range for maximum impact
• Distortion & Reverb: Epic, spacious sound design

Sound Mapping

• Hit Sounds: Punchy paddle impacts
• Score Sounds: Deep celebration booms
• Line Clear: Epic Tetris completion sounds
• Level Up: Victory fanfare with multiple frequencies

Deployment

Render.com (Recommended)

1. Connect your GitHub repository to Render
2. The render.yaml file will auto-configure deployment
3. Your arcade will be live at https://your-app.onrender.com

Manual Deployment

# Install dependencies
pip install -r requirements.txt

# Start production server
uvicorn api.main:app --host 0.0.0.0 --port 8000

Development

Adding New Games

1. Create game logic in api/routes/
2. Add frontend in frontend/
3. Integrate with AI system in ai/difficulty_agent.py
4. Update main menu in frontend/arcade/

AI Customization

• Modify ai/difficulty_agent.py for different learning algorithms
• Adjust difficulty curves in game routes
• Add new performance metrics to database schema

Performance Metrics

The AI tracks various metrics:

• Win/Loss Ratios: Overall performance
• Reaction Times: Player response speed
• Strategy Patterns: Common move sequences
• Difficulty Progression: Learning curve analysis

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Roadmap

Phase 1 (Completed)

• Deploy Ping-Pong and Tetris
• Implement AI learning system
• Add sound effects
• Create PWA functionality

Phase 2 (In Progress)

• Integrate Bitcoin/Stacks tokenization
• Add more games (Crypto Brick Breaker, Hash Puzzle Arena)
• Implement global leaderboards
• Add multiplayer functionality

Phase 3 (Planned)

• Launch Satoshi's Arcade Network (SAN)
• Decentralized AI gaming protocol
• Cross-platform mobile apps
• NFT integration for achievements

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• IBM Plex Sans for the beautiful typography
• FastAPI for the robust backend framework
• Web Audio API for the epic sound system
• SQLite for lightweight data storage

Links

• Live Demo: Play Now
• GitHub: polydeuces32/satoshis-arcade-mcp
• Documentation: API Docs

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Built with passion by Giancarlo Vizhnay

"The code you write makes you a programmer. The code you delete makes you a good one. The code you don't write makes you a great one."