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A novel approach to generating ultra-efficient code using AI language models, demonstrated through a browser-based game

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🎮 Ultra-Efficient AI Game Development

MIT License Medium Article

A revolutionary approach to generating ultra-efficient code using AI language models, demonstrated through a complex browser-based idle game.

Note: This project is intentionally hosted via Claude's artifact system to demonstrate the complete, self-contained nature of the AI-generated code. While traditional deployment methods were considered, maintaining the code in its original form better showcases the optimization techniques achieved through AI prompting.

Try the GameRead the ArticleFollow Me

Game Demo

🚀 Project Overview

This project showcases a novel technique for instructing AI language models (specifically Claude) to generate highly optimized, efficient code. The approach is demonstrated through a fully-functional idle game featuring:

  • Real-time particle physics
  • Complex game mechanics
  • Resource management
  • Visual effects
  • All in a single, optimized codebase

📖 Key Features

  • Ultra-Efficient Code Generation: Novel prompting techniques for AI that result in extremely optimized code
  • Advanced Game Mechanics: Complete game system with upgrades, particle effects, and resource management
  • Innovative AI Interaction: New paradigm for teaching AI models to generate efficient code

💻 Technical Stack

  • React
  • TypeScript
  • Tailwind CSS
  • Canvas API
  • Claude AI

📁 Repository Structure

.
├── src/
│   ├── game/              # Core game components
│   │   ├── engine.tsx     # Game engine and physics
│   │   └── ui.tsx         # UI components
│   └── prompts/           # AI prompting system
│       ├── base.md        # Base prompting template
│       └── optimized.md   # Optimization instructions
├── docs/                  # Documentation
│   ├── ARTICLE.md         # Medium article
│   └── OPTIMIZATION.md    # Optimization guide
└── examples/              # Example implementations

🎯 Key Optimization Techniques

// Traditional approach
const gameConfig = {
    units: [{
        identifier: 'cart',
        name: 'Mining Cart',
        cost: 15
    }]
}

// Optimized approach
const G={U:[{i:'c',n:'Cart',c:15}]}

🚀 Getting Started

  1. Clone the repository:
git clone https://github.com/yourusername/project-name.git
  1. Install dependencies:
npm install
  1. Run the development server:
npm run dev

📖 Documentation

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

📝 License

MIT

🔗 Links

  • Medium Article
  • X-Twitter

👋 About Me

I'm a developer interested in pushing the boundaries of AI-assisted development. This project represents my exploration into optimizing AI-generated code and teaching AI models new ways of approaching problems.

⭐️ Show your support

Give a ⭐️ if this project helped you!


Note: This project is part of my ongoing research into AI-assisted development. Feel free to reach out if you'd like to contribute or have questions!

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A novel approach to generating ultra-efficient code using AI language models, demonstrated through a browser-based game

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