Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q roadmap. Everyone in your category has, has similar things. But I think probably the two of you are leading the two most unique and differentiated initiatives, uh, on, uh, in the landscape. Maybe we'll start with, uh, with, uh, Omnigent, and then we'll, we'll, we'll, we'll go into it. I do think that a lot of People are exploring this sort of meta harness concept. What led you to it?
A Yeah, there were actually a couple of like converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have a really great, uh, dev info team. Uh, they built something called Isaac that's basically like a wrapper on cloud code and, and codex and, uh, let's you use them either on the web and like, Sandboxes or, uh, just on your dev machine or on your laptop or whatever. And then, you know, they were adding all kinds of stuff there. And, and we saw all the, the sort of more advanced engineers, like, uh, were building their own workflows with tons of agents and they were building their own UIs and stuff on top or even on top of that. And then the other one was like us building agents. We ship this like data science agent called Genie on the research team, which I, I co-lead basically. We, Also build a lot of internal ones for various things. And then we have all the customer ones and all of them running into this thing of like, oh, I need to switch model and harness and so on, uh, you know, every few months. Plus the agent is like completely useless if you can't share sessions with someone and have history and have search and all this like layer on top of it for collaboration. I thought a bit about it from both contexts and, uh, at first people thought it was weird. They're like, why are you doing…
AI assessment note: “there were actually a couple of like converging lines, which I think is a good”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q Yeah, you need to, basically people want to take something that works in Forkid, and you might as well have something open source. Yeah, which, which also was another question, which is, Interesting for a Databricks, like what do you choose to open source? What do you choose to make it proprietary? And I mean, this goes back to Spark, right?
A Yeah. One, so, I mean, one of the reasons to open source something is if you think it's a layer that will actually, there'll be some network effect. It'll benefit from many people collaborating, um, on it. So, uh, for example, with Spark, I don't know if you, if you know what, when, when Spark came out, we, we also focused a lot on letting you have libraries on So like they used to be different distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, you know, we, we don't have the time to write like connectors to like, you know, a thousand like different databases and, and file formats, but we can just use the ones people make. And of course they benefit from joining, uh, You know, kind of this, uh, this thing. So that's like one of the reasons. Another way to think about it is like, imagine, you know, uh, we, our thing wasn't open. We had some kind of agent hosting thing, but it's not open. And then there is an open one. If you're, which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like, you just can't, uh, even deliver as open source that are things the company does. Like …
AI assessment note: “one of the reasons to open source something is if you think it's a layer”
