Fashion Shopping with Nearest Neighbors
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…
In plain words
Fashion Shopping with Nearest Neighbors lets users browse similar clothing items across multiple retailers simultaneously. The tool uses GPU-accelerated nearest neighbor search across approximately 3 million product images to find visually similar fashion pieces. Built with Swift and running on a Mac Mini, it searches a latent vector space and allows users to adjust result variety by changing the stride through sorted matches. The backend uses pure matrix multiplications without a vector database, and the underlying model was trained entirely in Swift.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 which produces the vectors is also something I trained in pure Swift. The underlying data is about 2TB scraped from https://www.shopltk.com/. All the code is at https://github.com/unixpickle/LTKlassifier
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