alt.hn

6/4/2026 at 1:54:28 PM

Inside FAISS: Billion-Scale Similarity Search

https://fremaconsulting.ch/blog/faiss

by tohms

6/6/2026 at 2:38:19 AM

Great viz; the original paper wasn't peer reviewed; it might be great but I've learned its a waste of time to read those in current times (sorry and take this as one data point that suggests they should have done that).

That said, I've found FAISS great for certain use cases; wanted to say thx for surfacing - its not updated to work with most packages these days outside of faiss-cpu - curious why Meta dropped its maintenance; was it due to its slower speed or otherwise priorities?

by rooftopzen

6/6/2026 at 9:37:59 AM

Thanks ! the article is trying to explain several other papers like the Product Quantization paper which was peer‑reviewed in IEEE but i totally agree.

yeah, my read is most production users stay on CPU and if they need heavier/faster workdloads in they might often move toward managed vector services like pinecone, mongodb atlas, or pgvector, and attention follows usage. Also FAISS came out of FAIR's team in Paris, Meta’s recent massive reorganization and layoffs which splitted FAIR's team likely have an impact in its maintenance.

by tohms

6/5/2026 at 11:27:05 PM

Phenomenal interactive website. Thank you.

by runamuck

6/6/2026 at 12:53:58 AM

Seconded. Quality content.

by yeshman

6/5/2026 at 11:32:04 PM

Thanks to your comment I checked it out and loved it. Thanks.

by testycool

6/6/2026 at 2:21:00 AM

[flagged]

by hanzeweiasa