Alternatives
Products that do what Embedify - Embeddings and audits for AI does
Embeddings made super easy
- 1
- 2
- 3
- 4IM
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
- 5EA
2016 · embedbox.io
- 6
- 7IR
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com
- 8EA
2021 · github.com
- 9
- 10
- 11
- 12UL
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
- 13AB
Hey HN! We're building an open-source CMS designed to help creators with every part of the content production pipeline. We're showing our tiny first step: A tool designed to take in a Twitter username and produce an "identity card" based on it. We expect to use an approach similar to [Constitutional AI] with an explicit focus on repeatability, testability, and verification of an "identity card." We think this approach could be used to create finetuning examples for training changes, or serve as inference time insight for LLMs, or most likely a combination of the two. The tooling we're…
2025 · contentfoundry.com
- 14AO
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
- 15NN
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
- 16UE
User Embeddings lets you build user-intent AI agents, hyper-personalized semantic search, and bring up-to-date information to GenAI applications in a personalized way. Docs: https://firstbatch.gitbook.io/firstbatch-sdk/ If you are a YC company , you can get User Embeddings free for a year by signing up here: https://www.firstbatch.xyz/subscribe
2023 · userembeddings.firstbatch.xyz
- 17IB
Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
Oct 2025 · github.com
- 18PF
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
- 19CA
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
- 20OA
Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai
- 21AU
2014 · embedkit.com
- 22BC
We are a small group of undergrads interested in building human in the loop coding agents. We dream of a world where building complex agent workflows feels as simple and creative as playing with legos. When we were building stuff we needed a tool that made it easy to try out different code embedding models so that we could see which ones worked best in different scenarios and understand their strengths and weaknesses. So to speed that process up we made PurpleSearch an 'instant' search engine for your local codebases. This tool lets you quickly deploy any open source embedding model on…
2025
- 23VD
Discover, evaluate, and access relevant embeddings in your go-to framework. Skip all the infra for scraping, cleaning, indexing, and updating high-quality embeddings.
2023 · embedding.store
- 24
An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.
22d ago · pinglin.tw
Ranked by how close each launch is in meaning, then by votes. Refine with a description →