标签：#hack

This weekend is the ABC Classic FM countdown, which prompted me to dust off an old un-published data visualisation of rankings from previous years. I've considered adding a search function, but I also kind of like that it requires a bit of exploration in the current form. Some of the code is a bit clunky and I wouldn't mind refactoring it. I'm also not sure about browser compatibility - I've only got access to a couple of devices to test it on.

We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMed study on NAD+ precursors and actually knowing what dose to take is a chasm of manual research. What if you could build an LLM Agent that monitors research papers, processes them through a RAG (Retrieval-Augmented Generation) pipeline, and maps findings to your specific health profile? In this tutorial, we are building Paper-to-Action , a state-of-the-art agentic workflow using LangGraph , ChromaDB , and Mistral-7B . This isn't just a simple bot; it's a multi-stage reasoning engine designed to turn raw academic data into actionable health interventions. If you've been looking to master AI agents and personalized medicine automation, you’re in the right place. 🚀 The Architecture: From Raw Paper to Personalized Habit Traditional RAG pipelines are linear. To handle the nuance of medical research, we need a "looping" logic. We use LangGraph to manage the state of our agent, allowing it to decide if a paper is relevant before attempting to extract a protocol. System Flow graph TD A[Start: Keyword Trigger] --> B[Search PubMed/Arxiv API] B --> C{Relevance Filter} C -- No --> B C -- Yes --> D[Store in ChromaDB] D --> E[RAG: Extract Intervention Protocol] E --> F[Cross-Reference with User Profile] F --> G[Generate Personalized Action Plan] G --> H[End: Push to Health Checklist] Prerequisites To follow this advanced guide, you'll need: LangGraph : For the agentic state machine. ChromaDB : As our high-performance vector store. Mistral-7B : Running via Ollama or vLLM for local, private inference. Python 3.10+ Step 1: Defining the Agent State In LangGraph, everything revolves around the State . We need to track the fetched papers, the extracted data, and the final recommendation. from typing import Annotated , List , TypedDict from langgraph.graph import StateGraph , END class AgentState ( TypedDict ): keywords : List [ str ] user

Hi HN, I made this after seeing a few similar projects on the front page. NBA API endpoints are public and there’s a pretty robust python package ( https://github.com/swar/nba_api ) that I referenced for the endpoint structure to build an sdk in go. used BubbleTea and LipGloss for styling. It was a bit tricky to test the live endpoints but I watched Friday’s Final game with this and it worked pretty well playball - https://news.ycombinator.com/item?id=45451577 faceoff - https://news.ycombinator.