Apr 30
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If you’re using AI coding tools like Claude Code or Cursor on anything bigger than a small project, you’ve probably run into the same problem: massive token usage, slow responses, and answers that don’t fully understand your codebase. In this video, we break down how Graphify is changing that by turning your entire repo into a structured, queryable knowledge graph. Instead of sending raw folders and reprocessing everything on every prompt, Graphify gives your AI persistent context, dramatically reducing token usage (up to 70x in real examples) while improving accuracy across cross-file questions. 🔗 Relevant Links Graphify Site - https://graphify.net/ Graphify Repo - https://github.com/safishamsi/graphify ❤️ More about us Radically better observability stack: https://betterstack.com/ Written tutorials: https://betterstack.com/community/ Example projects: https://github.com/BetterStackHQ 📱 Socials Twitter: https://twitter.com/betterstackhq Instagram: https://www.instagram.com/betterstackhq/ TikTok: https://www.tiktok.com/@betterstack LinkedIn: https://www.linkedin.com/company/betterstack 📌 Chapters: 0:00 Why AI Coding Tools Break on Real Projects (Token Problem) 0:40 The Hidden Cost of AI Coding: Tokens, Context, and Hallucinations 1:11 Live Demo: Turn Any Repo Into a Knowledge Graph (Graphify) 2:17 What Is Graphify? (AI Knowledge Graph for Codebases Explained) 2:43 How Graphify Works (Tree-sitter, LLMs, Graph Clustering) 3:25 Graphify vs RAG: Why Similarity Search Fails for Code 3:40 Graphify Pros: Token Savings, Multi-Modal Support, Better Reasoning 4:00 Graphify Cons: First Run Cost, Accuracy Limits, Early Stage 4:30 Is Graphify Worth It for Developers? 4:55 Final Thoughts