🚀 Introducing Tetrad: Quadruple Consensus Code Validation for AI-Assisted Development
I'm excited to share a project I've been working on: Tetrad - a high-performance MCP (Model Context Protocol) server that brings multi-AI consensus to code review.
🎯 The Problem:
When using AI coding assistants, how do you know the generated code is truly reliable? A single AI can have blind spots, biases, or simply miss edge cases.
💡 The Solution:
Tetrad orchestrates THREE independent AI code evaluators (Codex, Gemini CLI, and Qwen) to validate code through a quadruple consensus protocol. No code gets approved without agreement from multiple AI intelligences + the developer.
🔧 Key Features:
• Multi-AI Consensus: Golden (unanimity), Strong (3/3), and Weak (2/3) voting rules
• ReasoningBank: SQLite-backed pattern learning that improves over time
• Smart Caching: LRU cache to avoid re-evaluating identical code
• MCP Integration: Seamlessly works with Claude Code and other MCP-compatible tools
• Written in Rust: Fast, safe, and reliable
📦 Available Now:
• crates.io: cargo install tetrad
• npm: npx tetrad
🔗 How It Works:
1. tetrad_review_plan - Review implementation plans before coding
2. tetrad_review_code - Validate code before saving
3. tetrad_review_tests - Ensure test quality
4. tetrad_final_check - Get certification before commit
The future of AI-assisted development isn't about trusting a single AI - it's about building consensus systems that leverage multiple perspectives.
Check it out: https://github.com/SamoraDC/tetrad
#OpenSource #Rust #AI #CodeQuality #MCP #DeveloperTools #ArtificialIntelligence #SoftwareEngineering #ClaudeCode #Innovation
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What strategies do you use to validate AI-generated code? I'd love to hear your thoughts! 👇