AI-AutoCoding-DAO

AI-AutoCoding-DAO (AIACD) is a development optimization framework designed to maximize coding efficiency by delegating tasks to specialized AI coding tools based on their particular strengths.

Overview

AI-AutoCoding-DAO increases development productivity through intelligent orchestration of AI coding tools. The system analyzes coding tasks, selects the optimal AI tool for implementation, tracks token usage and efficiency metrics, and continuously refines its selection algorithm through empirical analysis.

Core Features

• Intelligent Tool Selection: Data-driven selection of the optimal AI tool for each task
• Token Efficiency Optimization: Reduce token consumption by up to 70-85% through optimal delegation
• Task Template Management: Specialized templates to maximize information density
• Implementation Evaluation: Comprehensive quality assessment of AI-generated code
• Continuous Improvement: Self-optimizing system that learns from implementation results

Getting Started

Prerequisites

• Node.js 16+
• npm or yarn

Installation

# Clone the repository
git clone https://github.com/yourusername/ai-autocoding-dao.git
cd ai-autocoding-dao

# Install dependencies
npm install

Environment Setup

Create a .env file in the root directory with your API keys:

CLAUDE_API_KEY=your_claude_api_key
HAIKU_API_KEY=your_haiku_api_key
BOLT_NEW_API_KEY=your_bolt_api_key
V0_DEV_API_KEY=your_v0_api_key

Basic Usage

const { AIAutoCodingDAO } = require('./src');

// Initialize the system
const aiacd = new AIAutoCodingDAO();

// Define a task
const task = {
  id: 'task-001',
  description: 'Create a React login form component with validation',
  type: 'ui',
  complexity: 'medium',
  features: ['form', 'validation', 'accessibility']
};

// Process the task
async function runTask() {
  const result = await aiacd.processTask(task);
  console.log(`Task processed with ${result.toolSelection.selectedTool}`);
  console.log(`Implementation quality: ${result.quality.overallScore}/10`);
  console.log(`Token efficiency: ${result.efficiency.efficiencyGain}`);
}

runTask();

System Architecture

The AI-AutoCoding-DAO system follows a modular, service-oriented architecture:

Orchestration Layer

• Task Analyzer: Processes task descriptions to determine complexity, type, and key requirements
• Tool Selector: Applies the selection algorithm to choose the optimal AI tool based on task analysis
• Token Tracker: Monitors token usage across direct and delegated implementations, calculating efficiency metrics

Tool Integration Layer

• Tool Connectors: Interfaces with different AI coding tools (Haiku, Bolt.new, v0.dev, Claude Direct)
• Template Manager: Applies specialized templates for each tool to optimize responses
• Response Processor: Standardizes outputs from different tools for consistency

Evaluation Framework

• Quality Analyzer: Assesses implementations against standardized quality metrics
• Implementation Comparator: Compares solutions across different tools
• Metrics Collector: Gathers performance, quality, and efficiency metrics

Examples

The examples directory contains sample implementations for common tasks:

• Login Component: Authentication form with validation
• Token Efficiency Dashboard: Visualization component for efficiency metrics

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

License

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

Acknowledgments

• The AI-AutoCoding-DAO project was inspired by the need for more efficient AI-assisted development
• Special thanks to the developers of the AI tools integrated into the system