New ask Hacker News story: I built a 151k-node GraphRAG swarm that autonomously invents SDG solutions
I built a 151k-node GraphRAG swarm that autonomously invents SDG solutions
2 by wisdomagi | 0 comments on Hacker News.
Hi HN, I wanted to share a passion project I've been building: PROMETHEUS AGI. I got frustrated that most LLM/RAG applications just summarize text. I wanted to see if an agentic swarm could actually perform cross-domain reasoning to invent new physical solutions (focusing on UN SDGs). The Stack: Neo4j Aura (Free tier maxed out at 151k nodes / 400k edges) Ingestion: Google BigQuery (Patents) + OpenAlex API LLMs: Ollama (Llama 3) for zero-cost local entity extraction, Claude 3.5 via MCP for deep reasoning. UI: Streamlit (Digital Twin dashboard) + React/Vite (Landing). How it works: The swarm maps problems (e.g., biofouling in water filters) to isolated technologies across different domains (e.g., materials science + nanobiology) and looks for "Missing Links"—combinations that don't exist in the patent database yet. So far, the pipeline has autonomously drafted 261+ concept blueprints (like Project HYDRA, a zero-power water purifier). We are currently looking for domain experts (engineers, materials scientists) to validate these AI-generated blueprints and build physical prototypes, as well as grants to scale the graph to 1M+ nodes. Dashboard: https://ift.tt/Tu9ZBJO Landing/Deck: https://ift.tt/iky1udx I would love to hear your brutally honest feedback on the architecture, the Neo4j schema design, or the multi-agent approach!
2 by wisdomagi | 0 comments on Hacker News.
Hi HN, I wanted to share a passion project I've been building: PROMETHEUS AGI. I got frustrated that most LLM/RAG applications just summarize text. I wanted to see if an agentic swarm could actually perform cross-domain reasoning to invent new physical solutions (focusing on UN SDGs). The Stack: Neo4j Aura (Free tier maxed out at 151k nodes / 400k edges) Ingestion: Google BigQuery (Patents) + OpenAlex API LLMs: Ollama (Llama 3) for zero-cost local entity extraction, Claude 3.5 via MCP for deep reasoning. UI: Streamlit (Digital Twin dashboard) + React/Vite (Landing). How it works: The swarm maps problems (e.g., biofouling in water filters) to isolated technologies across different domains (e.g., materials science + nanobiology) and looks for "Missing Links"—combinations that don't exist in the patent database yet. So far, the pipeline has autonomously drafted 261+ concept blueprints (like Project HYDRA, a zero-power water purifier). We are currently looking for domain experts (engineers, materials scientists) to validate these AI-generated blueprints and build physical prototypes, as well as grants to scale the graph to 1M+ nodes. Dashboard: https://ift.tt/Tu9ZBJO Landing/Deck: https://ift.tt/iky1udx I would love to hear your brutally honest feedback on the architecture, the Neo4j schema design, or the multi-agent approach!
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