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Alternatives

Products that do what Embeddinghub does

An open-source database for machine learning

  1. 1EA
  2. 2
    MindsDB235

    In-database machine learning

    2022

  3. 3

    Compute & interactively visualize large embeddings

    2025

  4. 4

    A curated collection of machine learning projects

    2017

  5. 5

    Design and build AI architectures

    2024

  6. 6

    Build remarkable analytics experiences, 10x faster

    2023

  7. 7NN

    Hi HN. Peter here. As a machine learning engineer, I mostly think in terms of feature vectors, embeddings, and matrices. One of the most useful byproducts of deep neural networks is embeddings because they allow us to represent high-dimensional data in terms of lower-dimensional latent vectors. These feature vectors can be used for downstream applications like similarly search, recommendation systems and near duplicate detection. As an ML engineer, I was frustrated by the lack of a datastore in which vectors are first-class citizens. As a result, most ML engineers, including myself, end up…

    2021

  8. 8DO

    Hi HN! I am an undergrad student trying to build interesting things with AI. Recently, I was looking for a dataset I could use for a new project. I realized that it is really frustrating to go through all the government websites (with terrible UX) just to find some usable dataset. I set out to build a GitHub for datasets, named DataHub. Right now, we have more than 1000 datasets from Montréal and New York City, with more cities coming soon (and possible government agencies). All of this is wrapped into a powerful search. It's a breeze to find a dataset to work on. I'd be interested to know…

    2017

  9. 9

    Massive internet datasets, embedded, open-sourced and free

    2023

  10. 10EA
  11. 11

    Lightning-Fast Embedded Analytics

    2024

  12. 12PF

    Introducing embeds.ai: an embedding playground to compare how embedding models work on a real world use case (retrieval augmented generation for Wikipedia articles + Elad Gil's High growth handbook) A few weeks ago, Shreyan and I were looking for an embedding model to use for RAG. We eventually came across the MTEB leaderboard, but we struggled to understand the benchmark scores. We wanted a tool to test various embedding models with example queries on real-world datasets. After unsuccessfully looking for such a “playground”, we decided to just build one ourselves! We embedded HuggingFace’s…

    2023 · embeds.ai

  13. 13
    Embedful 150

    Easy data visualizations. Embed and share anywhere.

    Mar 2026

  14. 14
    HelixDB105

    An open-source OLTP graph-vector database built in Rust.

    Feb 2026

  15. 15

    Help teams apply machine learning to real-world applications

    2017

  16. 16AO

    Hi, I'm Ben, the co-creator of Embedbase. Embedbase lets you use OpenAI Embeddings and Pinecone seamlessly. For example, you can add Embedbase to your app and pair it with GPT3 to allow people to search using natural language (e.g. How many workouts did I complete last week?), or simply expanding your current search experience beyond full-text search (e.g. looking for "similar" documents in Notion to find other related information) Managing embeddings is uncharted territory, we needed to discover the best practices ourselves. Now we're happy to share our learnings with Embedbase. Shoot if…

    2023 · embedbase.xyz

  17. 17GF

    2018 · dataturks.com

  18. 18WA
  19. 19AL

    Hi HN! I am Maria, solo founder of DataQA (https://dataqa.ai/), a tool to search and label documents for various NLP tasks (e.g. entity extraction, entity linking, etc). I have worked as a data scientist and ML engineer for the better part of a decade, and over that time have specialised mainly in applications involving natural language processing (NLP). One of the key questions I have always had at the back of my mind is whether my time was well spent. Whenever I spent more time on feature engineering or trying different models, I always wondered whether I would get better…

    2021

  20. 20SV

    Hi HN, I'm Daniel from Superlinked! We have built an open-source framework that improves vector search relevance and usefulness by combining structured metadata with unstructured data in your embeddings. We included self-hostable API server that sits between your data sources and vector database. Docs: https://docs.superlinked.com/ We're launching our cloud offering soon where you can use Superlinked to orchestrate high-performance retrieval for RAG, Search & Recommendation apps in your own cloud. Looking for feedback and happy to answer questions!

    2024 · github.com

  21. 21DC
  22. 22SF

    2023 · github.com

  23. 23PF

    Hi there Hacker News, I've started a side project http://datasourcehub.com which aims to be a platform for data scientists. The project is still in the idea phase so the UI/UX and functionality are all subject to change. Feel free to play around, below is a guest login, and make sure files are content type of 'text/csv'. All data is subject to deletion, it's just a sandbox right now! By reaching out to the Hacker News community I hope to reach expert data scientists and get their feedback. Below are some questions I'd like to answer and some proposed directions that this…

    2013

  24. 24

    Embeddings, Semantic Search & RAG Explained

    23d ago

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