Alessio Fanelli

speaker

4,204 appearances 33 recordings 1 series first heard Sep 2025 last heard 20 May

Alessio Fanelli’s voice in public audio — every appearance, attributed to the second.

Trend

recordings per month · last 12 months

Recordings per month over the last 12 months — 28 in all, peaking in Oct 2025 with 7.

Appearances

newest first · ▶ plays the moment

Latent Space: The AI Engineer Podcast · Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Hørup Eskildsen of Turbopuffer · 12 Mar 2026 podcast

Welcome to the Latent Space Podcast.

This is Alessio, founder of KernelLabs, and I'm joined by Swix, editor of Latent Space.

I think I would love to get from you the definition of TurboPuffer, because I think you could be a VectorDB, which is maybe a bad word now in some circles.

You could be a search engine.

It's like, let's just start there, and then we'll maybe run through the history of how you got to this point.

And let's break it down.

So people might say, well, didn't Elasticsearch already do this?

And then some other people might say, is this search on my data?

Is this closer to RAG than to an XR, like a public search thing?

How do you segment the different types of search?

In that moment, from a workload perspective, you're thinking this is going to be like a read heavy thing because they're doing recommended, like it's the fact that like rights are so expensive now.

Oh, with AI, you're actually not writing that much.

Can you imagine talking to the GCP rep and it's like, no, we're going to buy because we know we're going to churn.

We're going to churn from you guys and go to AWS in like six months.

But in the meantime, we'll do this.

I mean,

and what were the actual workloads because i think when you think about ai it's like 14 milliseconds it's like really doesn't really matter in the scheme of like a model generation

the evolution of the workload, because even like search, like maybe two years ago, it was like one search at the start of like an LLM query to build the context.

Now you have a Genentech search, however you want to call it, where like the model is both writing and changing the code and it's searching it again later.

Showing 641–660 of 4,204 · page 33 of 211