An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More
Hey folks, Elias here. Excited to unveil my latest project. Why I Built This: Traditional keyword search isn't cutting it. I've used LLM-embeddings to provide more nuanced, relevant results. How It Works: LLM-embedding similarity on curated datasets for semantically similar results. No need to iterate over keywords any more. Current Datasets: - YC Companies - Show HN Posts, - Ask HN Posts - ProductHunt Startups - Github Top 200k Repos Use Cases: - Validate a product idea's existence - Check if someone already Asked HN something - Have fun - search random terms and see what pops up Want to…
An Embedding-Based Search Service over ShowHN, AskHN, GitHub, More launched on October 25, 2023 with 7 votes, #1,559 of 4,393 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
In the maker’s words, at launch
Hey folks, Elias here. Excited to unveil my latest project. Why I Built This: Traditional keyword search isn't cutting it. I've used LLM-embeddings to provide more nuanced, relevant results. How It Works: LLM-embedding similarity on curated datasets for semantically similar results. No need to iterate over keywords…
Does a similar job
all alternatives →
Building a web search engine from scratch with 3B neural embeddings2025 · blog.wilsonl.in · ▲699End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
Turn any website into a knowledge base for LLMs2024 · embedding.io · ▲305I 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...
- IMI made a website to semantically search ArXiv papers2024 · papermatch.mitanshu.tech · ▲324
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…
Subreddit Finder - Trained on 4M Reddit Posts from 4K Subreddits2020 · valohai.com · ▲152We built Valohai LLM for tracking and comparing LLM evaluation results. Whether your evals live in notebooks and spreadsheets, or you're using an observability tool that wasn't built for comparison, this gives you a purpose-built eval comparison dashboard. Run evals with a Python library (pip install valohai-llm), results stream in, and you can compare up to 6 configurations side by side. Group by any dimension (model, category, difficulty) to see where each model excels. It doesn't do tracing or production observability, for now just eval tracking and comparison. What's cool is that you can…
- ATAnalyzing top HN posts with language models2022 · ▲117
Hi HN, I spent a few weeks looking at the top HN posts of all time. This included exploration, clustering, creating visualizations, and zooming in on what (to me personally) seems like some of the best discussions on here. Three things in this post: 1- The interesting groups of HN posts 2- The interactive visualizations that you can explore in your browser 3- The data from this exploration -- this includes CSV of the titles as well as the text embeddings of 3,000 Ask HN articles. Blog post about this whole process here: [1] ============ 1- The interesting groups of HN posts From the…
Demystifying Advanced RAG Pipelines2023 · github.com · ▲131I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
More ai this month
the category →



Paintings by AI models: programs run through a simulation of oil paint.
AI · 21d ago · stillwet.art
PixVerse R2▲381A real-time world model you can explore and change
AI · 30d ago · world.pixverse.video

Source: Show HN post ↗ · launch text quoted in part
Launched alongside, October 2023
the whole month →
Nudge 2.0▲1,051In-app experiences to activate, retain, & understand users
Growth · 2023 · nudgenow.com


Unlock AI magic for elevated customer engagement, fast
AI · 2023 · tiledesk.com


Effortlessly discover API behaviour with a Chrome extension that automatically generates OpenAPI specifications in real time for any app or website.
Dev tools · 2023 · github.com