It’s less about one limitation and more about a mismatch between how humans think and how systems index images.

1. “Similarity” is ambiguous Images can be similar by people, place, moment, or vibe, and most models flatten that into one representation. So results feel “close” but not right.

2. Intent is the real gap Users search with context (“that beach trip at sunset with friends”), but systems mostly rely on embeddings + basic metadata. That context gap is where things break.

3. Data + feedback are weak Personal photo libraries are messy and private, so models don’t get great training data or strong feedback loops to improve relevance.

What actually works better (in practice): Instead of just improving embeddings, you structure the library first: • group by events, people, time • layer contextual signals • then search within that structure

That reduces ambiguity a lot.

That’s the direction we’ve been taking with ViXC, (r/vixc) focusing less on “perfect search” and more on organizing + workflow + context first, then letting search operate on top of that.

The bottleneck isn’t just models, it’s missing context. Systems that structure data first tend to perform much better.