Neil DeGrasse Tyson teaches you about vector search

As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…

Hi HN, I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (https://devv.ai). In simple terms, it is an AI-powered search engine specifically designed for developers. Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one? We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines…

Hi there! When Supabase announced their recent hackathon, I thought it was a good time to build something to learn more about so many of the new AI models and tech out there. From the different techniques of embedding documents to the future RAG. With the rise of short form content with TikTok and Youtube. A lot more knowledge is in videos than ever before. Finding specific answers within millions of videos can be difficult for any one person to go through. So the question is if there is Google that indexes text on website making it easier to find based on the context of on your question,…

Hey HN! I've always found it hard to keep up with the latest AI research, so I built Arxiv Feed! https://arxiv-feed.vercel.app/ It's basically a feed of AI research papers + a one-liner explaining what problem its solving, etc. You can also click on any paper to get a TL;DR. Right now, I've only indexed a few hundred large language model papers, but will expand to indexing AI papers in other topics. Thinking of also adding a way for people to up-vote/down-vote papers. Would love to hear any thoughts/feedback! :D Thanks!

This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.

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