I was super-excited about vector search and embeddings in 2024 but my enthusiasm has faded somewhat in 2025 for a few reasons:

- LLMs with a grep or full-text search tool turn out to be great at fuzzy search already - they throw a bunch of OR conditions together and run further searches if they don't find what they want

- ChatGPT web search and Claude Code code search are my favorite AI-assisted search tools and neither bother with vectors

- Building and maintaining a large vector speech index is a pain. The vector are usually pretty big and you need to keep them in memory to get truly great performance. FTS and grep are way less hassle.

- Vector matches are weird. So you get back the top twenty results... those might be super relevant or they might be total garbage, it's on you to do a second pass to figure out if they're actually useful results or not.

I expected to spend much of 2025 building vector search engines, but ended up not finding them as valuable as I had thought.

What about re-ranking? In my limited experience, adding fast+cheap re-ranking with something like Cohere to the query results took an okay vector based search and made top 1-5 results much stronger

There’s a lot of previously intractable problems that are getting solved with these new embeddings models. I’ve been building a geocoder for the past few months and it’s been remarkable how close to google places I can get with just slightly enriched open street maps plus embedding vectors

Site has a neat feature where you can see the pointers of other people, marked by regional? notations, scrolling through the content.