telekinesis VS vectordb

https://payperrun.com/%3E/search?displayParams={%22q%22:%22S...

(There are quite a few, you might want to filter by date!)

Hey everyone, I just made this thread easier to search through here:

https://payperrun.com/%3E/search?displayParams={%22q%22:%22D...

It uses LLM embeddings to sort postsby semantic proximity, but you can also filter out posts with comma separated values like this:

https://news.ycombinator.com/item?id=33118584

[Shameless plug: I found all these on my llm-embedding based search engine I launched today: https://payperrun.com/%3E/search?displayParams={%22q%22:%22A...

It's much better than HN's default search: https://hn.algolia.com/?q=Ask+HN%3A+What+do+you+regret+doing... ]

https://payperrun.com/%3E/search?displayParams={%22q%22:%22A...

There have been a few attempts at a crowdsourced-rank search engine (which is similar to what you're suggesting - people indexing the content), but it seems to be a hard cookie, most of the examples of similar ideas I could find on ProductHunt or ShowHN seem dead:

https://payperrun.com/%3E/search?displayParams={%22q%22:%22c...

(btw, I just launched this llm-embedding based search service that lets you check if a startup idea has already been tried/failed).

I don't know if this idea has a higher death rate than the baseline, but my guess is Google/PageRank is good enough for most use-cases, and then if you want quality sources, you can just follow them on YouTube, Twitter, Instagram, etc. Wait, maybe I shouldn't try to compete with Google?

I like the section on how it works: https://payperrun.com/%3E/search?display=How%20this%20servic...

The vector search is using https://lancedb.com/ and OpenAI embeddings.

Behaves as I expected now!

I went here looking for more info about payperrun https://payperrun.com/%3E/welcome and clicked on the "Spotlight" section and saw 4 popups blocked - I never see popups anywhere these days and have to admit that sends me away pretty quickly.

We needed a low latency, on premise solution that we can run on edge nodes (so lightweight) with sane defaults that anyone in the team can whim in a sec.

Result is this and we constantly benchmark performance of different embeddings to ensure best defaults.

[1] https://github.com/kagisearch/vectordb#embeddings-performanc...

If you are looking for lightweight, low- latency, fully local, end-to-end solution (chunking, embedding, storage and vector search), try vectordb [1]

Just spent a day updating it with latest benchmarks for text embedding models.

[1] https://github.com/kagisearch/vectordb