The Exchanges

Q So then that somehow in 2024 ish turned to E to B?

A 2023, I think March 23. And we were pretty burned out. Thomas and I, we were working from Prague, from the Czech Republic, from my apartment. I like nothing was really moving, no growth. And GPT 3.5 came out like really first model kind of good ish with Cogen. So we took a, like, let's, let's take a 10 days break, two weeks break from, from DevBook. And because, like, everyone was trying things with AI, it was very clear, like, this is something, ah, where the future might go. So we wanted to just, like, from out of curiosity, try things out. We wanted to build, like, a AI, like, DevIn, kind of, like, thing. The first idea we had, had was, like, let's automate our work, ah, because with every project we were starting, There was a set of tools you always want to, want to integrate in your backend, like Stripe, like for size business, like Stripe, analytics, Slack notifications, emails, uh, sending out emails. And so we gave the agent tools to run code and we needed some kind of sandbox. We were like, yeah, that's good coincidence. We have sandbox from, from DevBook. And, uh, we posted about it on Twitter. Basically the agent actually Pulled GitHub repository, wrote code, started the server, tested everything, and at that point, I think we deployed it to Railway. Railway had, like, the best DX, and it was easiest to just plug it, plug it in, into the agent. I tweeted about it, an…

AI assessment note: “2023, I think March 23. And we were pretty burned out.”

Q Like when were the models ready for people to start using it? Just, you know, you demoed at one of our first events as well, and I think there's always this lag between the infrastructure that you build and like the capabilities of the model. When did you go from just code interpreter to start saying, okay, now it's time to do computer use, now it's time to do RFT?

A That was probably end of 24, uh, start of 25, so when you look even at our data, like use, 24 we are growing, we are growing good, but 25 is like up to the right, and so it feels like at 24 people are like figuring out these agents, um, and, and building them and trying them, and 25 is everyone moving them into production and finding more and more use cases. Around end of 24, start of 25, we started seeing things like using Sandbox for Uh, reinforcement learning type of use cases or using sandbox as a for computer use, which was very interesting when Anthropic launched their computer use. We had like a desktop version of a sandbox that was sitting like in our GitHub repository for six months. We were like, okay, this is probably interesting, but no model can actually use it. So, uh, when Anthropic announced it, it was like, oh, like we have something here. Uh, we can show you using, uh, with, with like Lavables and Blitz's type of products also, like, started using the sandbox for more than just, like, run code snippet, like data analysis, and then deep research agents. That, that has been something really big in the last few months.

AI assessment note: “That was probably end of 24, uh, start of 25”

Q So why move to SF? Do you think that everybody in your similar, kind of similar situation should? Any pros and cons that you're experiencing?

A I think it's, ah, you can definitely build a DevTool company from Europe. I think it's a lot about question of how easy you want it to be in the earlier days, especially if you're building like a, you know, kind of like red ocean versus blue ocean waters. If you are building in a field that already exists and you have like large competitors, you're building something that's 10 times, hundred times better. It's probably, you probably don't need to be NSF. Uh, you eventually probably will need some kind of US base because for sales and customers, but You can very well build this, uh, from Europe. I know great companies doing that, uh, because all it's, the knowledge is already among all the developers. But I would maybe argue that it might be harder to find people that are comfortable with a fast iteration loop and changing things early on, you know, almost we are pivoting every week, every month. But the main motivation for us was we just wanted to be very close to our users. And it was clear after a few weeks that SF is becoming this AI hub. And what we, uh, used to do, and we, we still, we still do it sometimes, but slightly less because of, like, not, uh, having that much time and resources, but we just met with the customers that had problems, aren't users, and we just, like, implemented it to be for them, like, next to them, like, we made a PR.

AI assessment note: “the main motivation for us was we just wanted to be very close to our users.”

Q and then the re-merging? Because I think people understand the forking, but then it's like, okay, how do I monitor which of the leaves is successful, and then how do I merge them back in the thing? Do you think that's something that you want to help people do, like, kind of spread out, parallelize, and then find the winner? Or is that something people should do on their own?

A I think this is a sort of like a framework discussion on top of E-to-B. So this is some, like, we are looking at it. I think eventually, like, we should go more higher level, and I don't know if, like, framework is the right type of a thing. I, I, we like to, in the team, think about it as a toolkit. Like, instead of, like, building opinionated framework, how to build agents, we give you, um, sort of, like, wrapper around E-to-B that makes it really easy for the LMs to For example, merge these states or navigate these three. So I think it will eventually move there. I think, um, it's a good question to ask how it's going to look like. I still think like building a framework is very hard in the AI as things are moving very, uh, very fast.

AI assessment note: “we give you, um, sort of, like, wrapper around E-to-B that makes it really easy”

Q Is there anything that you're really looking for that would say, like, okay, that's really what we want, that maybe Orb or Metronome haven't really adjusted for AI yet?

A I don't think this is AI-specific problem. Uh, this is, this is infrastructure as, as, as you know it. For us, some things that didn't work, uh, when we look at some of the providers where like the cut they took from the revenue, for example, they take from you. Uh, so some of the pricings, uh, I don't know what's the latest, but like, I, I don't remember which one was it honestly, but I knew that pricing was basically they take a small cut from the revenue each month, which is basically like what Stripe does when you are pressing payments. It's just like The value was really high. And then it's, it's a lot about how hard it's good. It would be to, uh, integrate it. Like how much time we are spending on it, uh, because we know we don't want to build this in-house. It's more like, is the switch worth it? Basically.

AI assessment note: “I don't think this is AI-specific problem. Uh, this is, this is infrastructure”

Q under that, that term. Exa. Exa, yeah, yeah. Uh, so, like, basically, like, you don't specifically yourself use LLMs internally in your products, but you enable others to work, to augment their LLMs with your infrastructure. Yeah. Um, I don't, any, any reflections on just the general LLMOS landscape? You have other competitors, like, you know, how, how is this evolving? How do you, how do you position in it?

A A lot of people are saying, like, if you are a GPT wrapper in 23, you were in a really bad position, uh, because, like, all the value will be captured by the AI labs. It's a good to be a GPT wrapper, because you get all the advantages from a new model. You just switch it. I mean, the just is, like, not so simple. Probably have evals. I need to change the prompt a little bit, but I would say it's increasingly easier to switch models. So, uh, we need to Think about it the same way. Like, you, our users are switching models a lot. We need to be agnostic, uh, to the, to the LLMs. Oftentimes, like, people want to deploy us in their cloud or on prem, on premise. So that's also something very important. I think a good analogy here is sort of, like, technologically, it's kind of, you want to be the Kubernetes of the world for the agent, but with much better DX, uh, and, and easier, easier to use.

AI assessment note: “you want to be the Kubernetes of the world for the agent”