Evidence receipt / belief
Published · transcript-backedShawn Wang: belief
12 Mar 2026 Latent Space Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Hørup Eskildsen of Turbopuffer
“I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid.”
Source trail
Everything needed to verify it.
- Speaker
- Shawn Wang
- Attribution
- Verified speaker
- Claim type
- belief
- Recorded
- 12 Mar 2026
- Publisher
- Latent Space
Transcript context
…Uh, we, we, we see demand. And so, I mean, I’m. I like case studies. I don’t like, like just doing like thought pieces on this is where it’s going. And like trying to be all macroeconomic about ai, that’s has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they’re doing and I hope some of the other coding labs that use Turbo Puffer will do the same. Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths. They like you. It’s very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer’s bucket. Um, so it’s, it’s, it’s really, really well designed. And so this is like extra stuff they did to work with you because you are not part of Cursor. Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql. I dunno. Um, but like, it’s silly to like be all in on like one particularly query pattern. I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I’m gonna butcher it here. Um, and you know, I’m a, I’m a database scalability person. I’m not a, I, I dunno anything about training models other than, um, what the internet tells me and what. The way he describes is that this is just like cash compute, right? It’s like you have a point in time where you’re looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that. And, um, how the value of that will change over time. I’m, I’m not sure, but there seems to be a lot of value in that.…
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