AI Code That Fixes Itself (An MCP You Can Try Now)
AI Summary
The video discusses the challenges and solutions of AI coding assistants, focusing on improving their reliability by addressing hallucinations and errors. The creator introduces the use of knowledge graphs combined with AI coding assistance to detect and correct hallucinated functions and parameters in code. A knowledge graph built from the Pyantic AI GitHub repository is demonstrated, highlighting its structure and how it enables precise validation against hallucinations. The video also showcases a hallucination detection script that uses deterministic code and the knowledge graph to spot incorrect code usage without relying on large language models. Additionally, it presents the integration of this approach into the Crawl for AI RAG MCP server and how this server works with Claude code to build a reliable Pyantic AI agent with minimal hallucinations. The video touches on other tools like Bolt, Lovable, and Browserbase for frontend development and browser automation, emphasizing their self-correcting capabilities. Finally, a preview of the upcoming Archon V2 overhaul is given, which aims to unite knowledge graphs, task management, and AI coding assistance for robust development support. The video concludes with an encouragement to explore the open-source tools and the creator’s plans to continue refining these technologies for AI coding assistance.