Alternatives
Products that do what Stable IDs for noisy embeddings: Idemapi does
From similarity to certainty
- 1
- 2
- 3AF
2018 · github.com
- 4EA
2021 · github.com
- 5
- 6IM
Click an image to get similar images. I crawled Tumblr and used SigLIP to get vector embeddings for many images. When you click an image, it finds the most similar vector embeddings in the database, and returns the corresponding images.
2024 · mood-amber.vercel.app
- 7
- 8I4
It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com
- 9PP
2022 · github.com
- 10FD
I made an app to fuzzy-deduplicate my Google Sheets and CRM records - No manual configuration required - Works out-of-the-box on most data types (ex. people, companies, product catalog) Implementation details: - Embeds records using an E5-family model - Performs similarity search using DuckDB w/ vector similarity extension - Does last-mile comparison and merges duplicates using Claude Demo video: https://youtu.be/7mZ0kdwXBwM Github repo (Apache 2.0 licensed): https://github.com/SnowPilotOrg/dedupe_it Background story: My company has a table for…
2024 · app.dedupe.it
- 11
AI-native coding assistant that helps developers in any IDE
Jun 2026 · polygram.dev
- 12ZA
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is. It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one…
2023 · github.com
- 13FE
Hey everyone, I have updated my fuzzy search library for the frontend. It now supports substring and prefix search, on top of fuzzy matching. It's fast, accurate, multilingual and has zero dependencies. GitHub: https://github.com/m31coding/fuzzy-search Live demo: https://www.m31coding.com/fuzzy-search-demo.html I would love to hear your feedback and any suggestions you may have for improving the library. Happy coding!
Oct 2025 · github.com
- 14AF
Hi HN, We’ve been building [memU](https://github.com/NevaMind-AI/memU), an open-source memory framework for AI agents that supports both classic RAG and LLM-based direct file reading. RAG has become the default in LLM systems, but many of its failures don’t come from the model — they come from the retrieval assumptions. Embedding-based retrieval is fundamentally an approximation over semantic similarity. It works well for fuzzy recall, but it often breaks when relevance ≠ correctness, which is common in real systems. From a retrieval perspective, RAG struggles with: -…
Jan 2026 · github.com
- 15UL
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
- 16IA
May 2026 · github.com
- 17SA
2020 · siev.io
- 18NN
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
- 19FS
2019 · gitlab.com
- 20IW
2024 · gitlab.com
- 21FS
2015 · github.com
- 22ML
We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com
- 23CA
We open-sourced catsu, a Python client for embedding APIs. The problem: every embedding provider has a different SDK with different bugs. OpenAI has undocumented token limits. VoyageAI's retry logic was broken until September. Cohere breaks downstream libraries every release. LiteLLM's embedding support is minimal. catsu provides: - One API for 11 providers (OpenAI, Voyage, Cohere, Jina, Mistral, Gemini, etc.) - Bundled database of 50+ models with pricing, dimensions, and benchmark scores - Built-in retry with exponential backoff - Automatic cost tracking per request - Full async support…
Dec 2025 · catsu.dev
- 24SM
2016 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →