Side-by-side comparison of two Model Context Protocol servers — install paths, tools, quality signals, and directory engagement so you can pick the right one for Claude, Cursor, and other MCP clients.
Multi-LLM deliberation with anonymized peer review. Runs a 3-stage council: parallel responses → anonymous ranking → synthesis. Based on Andrej Karpathy's LLM Council concept.
Intelligent learning sidecar for AI coding assistants. Helps developers learn from AI-generated code changes through interactive blocking quizzes and provides agents with persistent project-specific debugging memory using silent RAG tools. Features 56% token optimization and multi-language support.