Ankur Goyal

speaker

292 appearances 1 recordings 1 series first heard Jun 2026 last heard 15 Jun

Ankur Goyal’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.

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How I AI · How Braintrust uses AI agents, evals, and CI to ship better software | Ankur Goyal · 15 Jun 2026 podcast

Someone is trying to find a needle in a haystack of some specific kind of interaction someone had in their product.

And they're looking at billions and billions of traces.

And they want to find the 5,000 or something that match.

And this is over a 90-day period or something, a lot of data.

Yeah.

And that's one example of a query in like, okay, there are all these things that you can do in database literature, like different indexes you can build and different ways you can prefetch data and blah, blah, blah, all this stuff.

But how do you try all those things?

And how do you run all the experiments required to actually do something like this?

So what we do and what I've personally spent a lot of time working on is trying to figure out, you know, manually is fine, but automatically is even better.

Like, what are the patterns of queries that people are running that are slow?

And then we will reproduce those things and use a coding agent.

to try out a bunch of ideas from database literature.

So like download a bunch of data locally and then maybe try different, in this case right now, I'm trying out different column store formats.

So we use an index underneath the scenes called Tantive, which has a built-in column store, but it's not that great.

Like the

thing overall is great, but their column store is not like that great.

And so what we're doing right now is like exhaustively trying every open source column store format out there and then exhaustively trying every column store execution engine out there and sort of computing the matrix of this.

And, you know, it's like it's amazing.

You can actually use production data, too.

But for some subset of things and with the right engineering in place, you can just run on production data.

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