It's a "Vector Search Playground" really, but the bigger value so far has come from not running maximum stress tests, but demonstrating people how you can join vector search results to the rest of your (existing) application schema. Plenty of people have thought that you need a completely separate, isolated vectorstore behind some API for this...
Edit: Also the setup part includes running a "generate_embeddings.py" script that uses PyTorch under the hood (on CPUs or CUDA/GPUs) to generate embeddings from the 25k photos (or 9M when using the rotated variants). That process can also be sped up and optimized for sure - my whole point is that once everything runs OK enough from end to end, then it's time to start measuring and optimizing the whole process - for learning and fun.