AI broke CI. Now what?

Daniel Worku (0:00)

The amount of code being generated has grown exponentially. GitHub themself, I think, shared that they've seen a 4x growth in GitHub Action Minutes. Anthropic recently published an article about how CI became their biggest bottleneck as a company. They had 25x CI job volume growth in six months.

Shane Thomas (0:23)

Welcome to the show, Daniel. Before you jump in, I do want to just set the stage for people of why. I kind of pulled this together last minute. We weren't planning on having a guest today, but a bunch of things came across my feed, and I'm sure your feed as well, since you're way in the space. The first one, I think, was this that I saw. My GitHub Action spend is going out of control. And then from there, Flo, friend from Lindy, said, CI is becoming the top bottleneck of every AI engineering team or every engineering team I talk to, including Lindy. Our CI spend has become stratospheric. And then I saw another post, which was kind of tagged onto that, which I responded to, which is saying the cost of coding is plummeted, but long builds have become a bottleneck for many companies. There's a bunch of startups trying to solve it. I, of course, chimed in that we use StarSling to help solve it over here, and we've done a case study. But I thought, what better way to talk about this, because it is very relevant and very timely, of CI in the age of AI, who better to bring on than you? So introducing, this is Daniel from StarSling. Tell us a little bit about StarSling, about yourself, and yeah, I'd love to talk a bit more about CI.

Daniel Worku (1:25)

Well, first, thanks for having me on, Shane and Abhi. We're in the code verification bottleneck moment, where all the factories are churning out code, and now everyone's like, oh no, either I'm waiting on CI or I'm spending a bunch on CI. So I really appreciate that you two brought me on. I'm a co-founder here at StarSling. Previously, I worked in Big Tech and a bunch of startups, most recently at Netflix.

I led the team that built the most used internal tool for developers called Netflix Console. But at StarSling, we do two things. We give you faster, self-improving CI, and we just launched last week our new product, which is cheaper code review. So we have a review runner that makes it much cheaper to run code reviews.

Abhi Aiyer (2:10)

We use StarSling at Mastra. We've been talking about the CI problem, and we had this problem even before we had our factory running. We were just churning out so much code that our CI times just went through the roof.

Thankfully, as an open source project, you do get the subsidy of GitHub runners. But many people are not in that world. They're paying for action minutes, et cetera. And so when Jonas and you pitched us for using StarSling, I was wondering, what was the biggest edge that you guys could provide? And I was just wondering, could you explain to the viewers, what's the problem with CI today? And how are you guys tackling the problem?

Daniel Worku (2:51)

Yeah, that's a great question, right? Because there's a lot of solutions in the CI space out there. It is an old problem. We've been running CI as long as people have been writing code, and now all of our agents write the code. So most solutions to give you faster or cheaper CI at their core are really just about faster hardware. I shouldn't assume what's going on at GitHub, for the reason why.

But they use very, very old machines when they run your CI jobs. Whether you're private or open source, you're getting like three or four generation old CPUs, slower disk, slower network. And so most solutions in the CI space said, okay, we'll just use top of the line machines. We can charge you less per minute than you would pay GitHub for your actions. You'll save time and you'll save money in two ways.

You'll execute your CI faster, so you pay for less minutes, and then the pricing per minute is lower. So that's a traditional solution. It's just hardware. And so StarSling does have that, right? We run fast CI on ephemeral sandboxes, but really the differentiator in our Edge is we make your CI a self-improving system. So after each run, we track the telemetry on the load that's running for our customer's CI jobs. And then we have a background agent that runs in a loop. It's a performance optimization agent. It's actually built on the Mastra framework. And it creatively comes up with different proposals for how it can make your CI run faster. And then it A-B tests those using the exact same machines, the same environment that would appear in GitHub Actions. And so what this looks like to you as a customer is, every few weeks you'll get a PR from StarSling that's like, hey, you can make this test 5x faster if you merge this in. And here's the results. I've run it 10 times on the A and the B leg. And that's what really changes it for people, is you get this CI system that adapts and improves the code, not just hardware.