Show HN: 2M+ free stock photos in novel T-SNE based search engine

It works by taking all the images, using T-SNE to lay them out in a gigantic grid, then putting a quad-tree on top of that grid and letting you smoothly navigate that tree. It's a simple but surprisingly powerful concept: you can actually "walk" an entire catalog!

The search is really cool. Instead of just getting a paginated / infiniscroll listing, you actually get two full quad trees: one with search hits only, and one with the results shown in the context of all the other images. And you can switch seamlessly between them. The latter can be really powerful for finding poorly keyworded images.

Once you've gotten the hang of it (pan & zoom, very similar to Google Maps), you can explore the images at a far faster pace than you can realistically do with traditional search engines.

And T-SNE organizing the images works wonders for creating a sensible layout (be sure to zoom in - at the outer layers the sub-sampling makes it look jumbled even when it isn't).

For building a more precise 2D embedding or image similarity search engine you may want to rely on a neural embedding + FAISS or Annoy instead of compressing into 2D with TSNE. When doing the later, distances don't mean much anymore.