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Products that do what Fashion Shopping with Nearest Neighbors does
I made this website with my wife in mind; it makes it possible to browse for similar fashion products over many different retailers at once. The backend is written in Swift, and is hosted on a single Mac Mini. It performs nearest neighbors on the GPU over ~3M product images. No vector DB, just pure matrix multiplications. Since we aren't just doing approximate nearest neighbors but rather sorting all results by distance, it's possible to show different "variety" levels by changing the stride over the sorted search results. Nearest neighbors are computed in a latent vector space. The model…
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We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for…
2024 · github.com
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Hi HN! I built Agora as a side-project leading up to the holiday season. I wanted to find an easier way to find Christmas gifts, without needing to go store-by-store. My wife asked me for a a pair of red shoes for Christmas. I quickly typed it into Google and found a combination of ads from large retailers and links to a 1948 movie called 'Red Shoes'. I decided to build Agora to solve my own problem (and stay happily married). The product is a search engine that automatically crawls thousands of Shopify stores and makes them easily accessible with a search interface. There's a few additional…
2023 · searchagora.com
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I recently found myself computing the similarity between lots of very high dimensional vectors (i.e., sentence embedding vectors from LLMs), and I wanted to try some more powerful measures of similarity/dependency than just Cosine similarity, which seems to be the default for everything nowadays because of its computational efficiency. There are many other more involved measures that can detect more subtle relationships, but the problem is that some of them are quite slow to compute, especially if you're trying to do it in Python. For my favorite measure of statistical dependency,…
2023 · github.com
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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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This is a vector index I built that supports insertion and k-nearest neighbors (k-NN) querying, optimized for GPUs. It operates entirely in CUDA and can process queries on half a billion vectors in under 200 milliseconds. The codebase is structured as a standalone library with an HTTP API for remote access. It’s intended for high-performance search tasks—think similarity search, AI model retrieval, or reinforcement learning replay buffers. The codebase is located at https://github.com/rodlaf/BinaryGPUIndex.
2025 · rlafuente.com
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2024 · github.com
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We're a small team of 3 engineers, and wanted to make a better way to look for for properties online. Traditional portals (esp. outside of US) are just a bit rubbish for doing anything other than basic searching. So we've: - Created a crawler that reads through estate agents websites to find homes for sale - Parses that through a series of LLMs and other models to understand each home in depth (e.g. floor type, location, total sqft) - Parses every photo through an embeddings vector space so that people can search for whatever they want. Check it out: https://jitty.com Currently…
2025
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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
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Hey HN, A pattern I realized when I shopped for anything online is that by the end of my shopping session, I would accumulated over 15+ tabs. It's so easy to click on "Open in New Tab" that I figured other people have this issue as well.
2024 · curate.fit
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In an effort to understand it, I put together a simple, pure python implementation of HNSW, an approximate nearest neighbor library. Learned a lot, and I think for anyone interested in vector search it's an exercise that's absolutely worth doing. The code is optimized (imo) for readability, and working (albeit, quite slowly) on putting together a tutorial that walks through the motivation and implementation of HNSW. There's also working code examples for using the library for text and image search with sentence transformers and CLIP!
2025 · github.com
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Just a fun hack I did while bored over the weekend. My wife was busy shopping, it got me thinking that can VLMs solve the indoor location problem in a mall? Can I just show a VLM a map and an image and have it doa good enough job locating me? I hacked this P.O.C and it seems to work.
2025 · arjo129.github.io
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