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What is AI vs. Human, a GitHub Visualization?

AI vs. Human is a GitHub visualization tool that analyse code repositories to quantify the proportion of commits and code written by AI tools versus human developers. By connecting to any public or private GitHub repository, the tool provides detailed breakdowns showing contributions from popular AI coding assistants like Claude, GitHub Copilot, Dependabot, and other automation tools. This visualization helps development teams understand their reliance on AI-assisted coding, track adoption of AI tools within their workflows, and gain insights into the human versus machine composition of their codebase. The tool is designed for developers, engineering managers, and tech leaders interested in analysing their team's coding patterns and AI tool usage in real-time.

Key Features

GitHub repository analysis

Connect and analyse any public or private GitHub repository

Multi-tool attribution

Identifies commits from Claude, Copilot, Dependabot, and other AI/automation tools

Commit breakdown visualization

Visual representation of AI vs. human code contributions

Historical tracking

Analyze coding patterns over time to see AI adoption trends

Team insights

Understand how different team members or bots contribute to projects

Private repository support

Works with both public and private repositories for thorough analysis

Pros & Cons

Advantages

  • Provides clear visibility into AI tool adoption and usage patterns within development teams
  • Works with both public and private repositories, offering thorough coverage
  • User-friendly visualization makes complex commit data easy to understand and share
  • Freemium model allows individuals and small teams to access basic insights at no cost
  • Helps teams make data-driven decisions about AI tooling and development practices

Limitations

  • Accuracy depends on proper commit attribution, which may vary if AI tools aren't consistently identified in commit messages
  • Limited to GitHub repositories; doesn't support other version control systems like GitLab or Bitbucket
  • May not capture all forms of AI-assisted coding if tools don't leave distinct traces in commits

Use Cases

Engineering managers tracking team adoption of AI coding assistants

Open source projects analysing community contributions from bots and AI tools

Development teams measuring productivity impact of Copilot or Claude adoption

Technical leaders understanding code quality implications of AI-assisted development

HR and talent teams discussing AI tool usage during performance reviews