Semantic Search: A Deep Dive Into Vector Databases (with Zain Hasan)

[@tylercloutier537]

This was fantastic. The questions you ask really get to the heart of it so quickly.

[@stefjoosten5440]

This is really helpful. Thank you ever so much 🎉😊

[@JariVasell]

Wow! What an amazing interview! Really skilled questions. You will definitely have over 100k subs in 6 months if this is the normal level of quality that you deliver 💪🏻

[@DeveloperVoices]

Thanks so much! I hope your prediction comes true. 😁

[@Undecidable_]

This is such a great explanation of vector embeddings and vector databases for a nonmathematician audience!

[@JunaidAzizChannel]

What an outclass podcast!

[@YuriKhrustalev]

Thanks for the talk, would be great to hear more on the vector databases from someone involved.

[@robfielding8566]

It's my understanding that it's cosine similarity, aka dot product: (a1 + a2 + a3) . (b1 + b2 + b3) = (a1 b1 + a2 b2 + a3 b3) .... if you divide this vector by the length, it is literally cos( angle(a,b) )

[@mondomicio]

Well done thanks, very interesting

[@mattanimation]

awesome interview, great information

[@DeveloperVoices]

Thanks for listening!

[@FernandoAlmeida-b2v]

Very well done.

[@connor-shorten]

Awesome!

[@DeveloperVoices]

Thanks! 😊

[@TJInTech10]

clear explanation, thx

[@aryanmn1569]

I need a whole podcast series about these topics

[@Stella-se1lg]

I am a new sub😂😂❤