AiCodeCalc - LLM Development Efficiency Calculator

AiCodeCalc is a sophisticated calculator designed to analyze and compare the costs and efficiency of LLM-powered development versus traditional human development. This tool helps organizations make data-driven decisions about implementing AI-assisted development workflows.

Overview

AiCodeCalc provides detailed cost and efficiency analysis by considering multiple factors:

• Project complexity and scope
• LLM model configurations and costs
• Development overhead factors
• Human resource metrics
• Agent system configurations
• Operational expenses

Key Benefits

• Cost Analysis: Compare direct costs between LLM-powered and human development
• Efficiency Metrics: Analyze time savings and productivity improvements
• Resource Optimization: Identify optimal configurations for LLM usage
• Risk Assessment: Evaluate overhead factors and potential bottlenecks
• Team Planning: Make informed decisions about resource allocation

Features

• Project Setup

  • Lines of code estimation
  • Complexity assessment
  • Timeline planning

• LLM Configuration

  • Multiple model support
  • Usage share optimization
  • Cost tracking per 1K tokens

• Human Metrics

  • Team size and composition
  • Experience level consideration
  • Productivity metrics
  • Overhead time allocation

• Agent Configuration

  • Multiple operation modes (single, swarm, parallel)
  • Advanced memory management
  • Resource allocation strategies
  • Performance monitoring

• Results Analysis

  • Detailed cost breakdowns
  • Time comparisons
  • Token usage analytics
  • Efficiency metrics
  • OPEX calculations

Installation

1. Clone the repository:

   git clone https://github.com/ruvnet/AiCodeCalc.git
   cd AiCodeCalc

2. Install dependencies:

   npm install
   # or
   yarn install
   # or
   bun install

3. Start the development server:

   npm run dev
   # or
   yarn dev
   # or
   bun dev

Usage

1. Project Setup

   • Enter your project details including total lines of code and complexity
   • Specify project timeline and requirements

2. Configure LLM Models

   • Select and configure LLM models (default: GPT-4o 60%, GPT-4o-mini 40%)
   • Adjust usage shares and cost parameters

3. Human Development Metrics

   • Input team size and composition
   • Set productivity metrics and overhead factors

4. Review Results

   • Analyze cost comparisons
   • Review efficiency metrics
   • Export or share analysis results

Technologies Used

• Frontend Framework: React with TypeScript
• Build Tool: Vite
• UI Components: shadcn/ui
• Styling: Tailwind CSS
• State Management: React Context
• Deployment: Fly.io

Deployment

The application can be deployed using the included Dockerfile and deployment scripts:

# Build the Docker image
docker build -t aicalc .

# Deploy to fly.io
fly deploy

Development

To contribute to the project:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

License

MIT License

Support

For support, please open an issue in the GitHub repository or contact the development team.