Dan Am

🚨 BREAKING: Andrej Karpathy said someone should build this. 48 hours later it appeared on GitHub. For free. It's called Graphify. Bookmark it for later. Karpathy keeps a /raw folder where he drops papers, tweets, screenshots, and notes. He called for a real product to turn that chaos into structured knowledge. Someone answered. One command. Any folder. Full knowledge graph. /graphify ./raw What comes out: → An interactive knowledge graph you can click, search, and filter by community → A plain-language report with god nodes, surprising connections, and suggested questions → An Obsidian vault with backlinked articles (opt-in) → A wiki starting at index.md that any agent can navigate The number that matters: 71.5x fewer tokens per query vs reading raw files. On a mixed corpus of repos, papers, and images, average query cost dropped from ~123K tokens to ~1.7K. That is not optimization. That is a different paradigm for how AI reasons over large codebases. Fully multimodal. Code in 19 languages via tree-sitter AST. PDFs. Images via Claude Vision. Screenshots, diagrams, whiteboard photos. Everything goes into one graph. No vector database. No embeddings. Leiden community detection finds clusters by edge density. The graph topology is the similarity signal. Auto-syncs as your codebase changes. Git hooks rebuild the graph after every commit and branch switch. SHA256 cache means re-runs only process changed files. Works with Claude Code, Codex, OpenCode, OpenClaw, and Factory Droid. pip install graphify && graphify install MIT License. 100% Opensource. (Link in the comments)