Dr. Owl: High-Speed Medical RAG AI
Trustworthy AI medical insights with zero-hallucination RAG.
What it does
Dr. Owl is a Medical RAG Assistant that eliminates AI hallucinations by grounding every response in 45,000+ verified clinical records. Powered by Llama 3.3 70B on Groq's LPU for near-instant answers. Every response includes transparent citations — so users see exactly where the information came from. Built with FAISS vector search, FastAPI, and React. "Not AI guessing. AI citing."
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Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
- EAEvidex – AI Clinical Search (RAG over PubMed/OpenAlex and SOAP Notes)Dec 2025 · getevidex.com · ▲36
Hi HN, I’m a solo dev building a clinical search engine to help my wife (a resident physician) and her colleagues. The Problem: Current tools (UpToDate/OpenEvidence) are expensive, slow, or increasingly heavy with pharma ads. The Solution: I built Evidex to be a clean, privacy-first alternative. Search Demo (GIF): https://imgur.com/a/zoUvINt Technical Architecture (Search-Based RAG): Instead of using a traditional pre-indexed vector database (like Pinecone) which can serve stale data, I implemented a Real-time RAG pattern: Orchestrator: A Node.js backend performs…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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