Alternatives
Products that do what Exa (YC S21) – embeddings search agent with >20x recall than Google does
Hey HN! I'm Will, founder of Exa (YC21 - https://exa.ai). Today we're opening up Websets, a search engine that finds massive lists of correct results given complex queries. For example, you can search for: - “software engineers in the Bay Area, with experience in startups and big tech, who know Rust and have published technical content”: https://websets.exa.ai/cm7ax5bl8003276q689v0dde5 - “US based healthcare companies, with over 100 employees and a technical founder": https://websets.exa.ai/cm6lc0dlk004ilecmzej76qx2 - "research paper about ways to…
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2025 · blog.wilsonl.in
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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…
2024 · papermatch.mitanshu.tech
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Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality. Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused…
May 2026 · github.com
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I built a search engine for engineering blogs because I was tired of manually checking individual company blogs to find real-world production examples. The problem: When learning a new technology, the best insights often come from how companies like Google, Meta, or Stripe actually implement it in production. But these gems are scattered across dozens of separate engineering blogs with no way to search across them. What I built: Engineering.fyi indexes engineering blogs from ~15 companies (Google, Meta, OpenAI, Anthropic, Stripe, Uber, etc.) and makes them searchable in one place. You can…
2025 · engineering.fyi
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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Hi HN! I'm Jonathan and I built Hacker Search (https://hackersearch.net), a semantic search engine for Hacker News. Type a keyword or a description of what you're interested in, and you'll get top links from HN surfaced to you along with brief summaries. Unlike HN's otherwise very valuable search feature, Hacker Search doesn't require you to get your keywords exactly right. That's achieved by leveraging OpenAI's latest embedding models alongside more traditional indexes extracted from the scraped and cleaned up contents of the links. I think there are many more interesting things…
2024 · hackersearch.net
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Hello HN, I'm Ghita, co-founder of ZeroEntropy (YC W25). We build high accuracy search infrastructure for RAG and AI Agents. We just released two new state-of-the-art rerankers zerank-1, and zerank-1-small. One of them is fully open-source under Apache 2.0. We trained those models using a novel Elo score inspired pipeline which we describe in detail in the blog attached. In a nutshell, here is an outline of the training steps: * Collect soft preferences between pairs of documents using an ensemble of LLMs. * Fit an ELO-style rating system (Bradley-Terry) to turn pairwise comparisons into…
2025 · zeroentropy.dev
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We (Nick, Dens, Denzell, Fede, Drew, Aaryan, and Daniel) have been building HN Discovery, a discovery-focused search engine for Hacker News, in our spare time for the past 6 months and are excited to show it! It adds the following features relative to the existing keyword search interface and preserves the existing ones: - no-JS version (hnnojs.trieve.ai) - site:{required_site} and site:{negated-site} filters - public analytics - LLM generated query suggestions based on random stories - recommendations - dense vector semantic search - SPLADE fulltext search - RAG AI chat - order by…
2024 · hn.trieve.ai
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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…
2024 · devv.ai
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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,…
2023 · avse.vercel.app
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Hi HN, I built an open-source AI agent that has already indexed and can search the entire Epstein files, roughly 100M words of publicly released documents. The goal was simple: make a large, messy corpus of PDFs and text files immediately searchable in a precise way, without relying on keyword search or bloated prompts. What it does: - The full dataset is already indexed - You can ask natural language questions - Answers are grounded and include direct references to source documents - Supports both exact text lookup and semantic search Discussion around these files is often fragmented. This…
Jan 2026 · epstein.trynia.ai
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Hey HN, we're Lennert and Rauf. We’re building Airweave (https://github.com/airweave-ai/airweave), an open-source tool that lets agents search and retrieve data from any app or database. Here’s a general intro: https://www.youtube.com/watch?v=EFI-7SYGQ48, and here’s a longer one that shows more real-world use cases, examples of how Airweave is used by Cursor (0:33) and Claude desktop (2:04), etc.: https://youtu.be/p2dl-39HwQo A couple of months ago we were building agents that interacted with different apps and were frustrated when they…
2025 · github.com
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Hi Hacker News! We’re Vadim and Chris from Highlight.io [1]. We do web app monitoring and are working on using LLMs/embeddings to add new functionality to our error monitoring product. Given that there’s a lot of founders/engineers using LLMs in their products, we figured we’d share how we built the new functionality, their impact on our workflows, and how you can try it out. Our goal was to build two features: (1) tagging errors (e.g. deeming an error as “authentication error” or a “database error”); and (2) grouping similar errors together (e.g. two errors that have a different…
2023 · github.com
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Watup Hackernews, We have created a new type of search engine that searches on the full source code of webpages, not just the on-page text. It's called NerdyData (nerdydata.com) and we're launching today! Our search engine is different from search engines you've used before. Traditional search engines are geared towards providing answers, whereas our goal is to give you the best list of results for a query. Our crawler has visited over 140 million homepages and collected terabytes of HTML, Javascript, and CSS code. We've also designed several search interfaces that allow anybody to query…
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Hey HN - I’m Jeff, co-founder of Chroma. In December of 2022, I was scrolling Twitter in the wee-hours of the morning holding my then-newborn daughter. ChatGPT had launched, and we were all figuring out what this technology was and how to make it useful. Developers were using retrieval to bring their data to the models - and so I DM’d every person who had tweeted about “embeddings” in the entire month of December. (it was only 120 people!) I saw then how AI was going to need to search to all the world’s information to build useful and reliable applications. Anton Troynikov and I started…
2025 · trychroma.com
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Coding agents don't have long-term memory. But you do have months of full-fidelity agent transcripts stored on your machine. A simple solution that goes a long way: ingest those transcripts and logs into a structured SQLite database, then search them with ranked text match. Everything is fully local and doesn't require anything fancy like a graph database or hosted memory service. This is the idea behind ctx, a Rust CLI that handles the ingestion and searching. We give our agents a skill that tells them to reference past sessions before working in an area. Usually we do this through an…
Jul 2026 · github.com
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Hi HN! We’re Abhilash Chowdhary, Chris Pisarski and Manmohit Grewal. We built Crustdata (YC F24). Today we’re launching our web search API for AI agents, which not only returns the most relevant documents from the web but also maps them to the correct entity (person, company or event). Demo video here https://youtu.be/IouWW97hBN8 If you run agents at scale, tokens become a line item. The web data is the worst input: long pages, repeated content, mixed entities, stale claims. The usual web search -> scrape -> summarize + structure forces the agent to spend tokens doing…
Feb 2026 · crustdata.com
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Hey HN, we're excited to show you client-vector-search, a client-side library that helps you embed, store, search, and cache vectors in your browser or node env. We needed it at https://searchbase.app and that's why we've built it. with it you get: 1. easy setup: you only need to add 5 lines of code to build a semantic search 2. no embedding api needed: you don't need an api and have to pay for it unless ure scaling up millions 3. faster search: modern hardware is better than cheap cloud computers (0.5vCPUs) 4. zero latency: no back-and-forth with server-side 5. easy integration…
2023 · clientvectorsearch.com
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