The agent wasteland, federated workflows, and a computer for computers

**Ben Lloyd Pearson** (0:05)
AI agents everywhere. I'm curious, Andrew, you got your hands on some of my agents in the last week. How many hours do you think they saved you?

**Andrew Ziegler** (0:12)
Oh, well, your agent saved me, actually, a lot of hours this last week, because they were super useful for the tasks that we had at hand. But maybe also that same question back at you, how many hours of my agents saved you in the last week?

**Ben Lloyd Pearson** (0:26)
Well, in the last week, nothing, because I've been using my agents this whole time. It is really fun how we're just ping-ponging information back and forth between our agents at this point. They just grab whatever they can, go over to our little laboratory we have for them, and then do what we want, and then eventually the signals go back to the other agents. It's pretty fun.

**Andrew Ziegler** (0:47)
It's pretty funny how quickly I was able to change anything that you handed off to me as well, to the point where I had to bring in a whole dedicated agent just to figure out how your project and my project to go back together. And I got to say, this is still one of the biggest unsolved problems. And I think collaborative agent tech engineering. I experienced this last year when I was on Codetv's episode for Goose. And we did a, it was a two person challenge, vibe coding with sub agents. So you have two people using teams of sub agents. And then at the end, I had to combine my front end with someone's back end. And you can watch the video to see how that turned out. It's a pretty hilarious saga.

**Ben Lloyd Pearson** (1:29)
Yeah, we need to go from like one to two. It's like one person with agents to two people with agents working together. Like how do we actually do that? Which I think we're going to cover today. So let's just get into the Friday Deploy. I'm your host, Ben Lloyd Pearson.

**Andrew Ziegler** (1:42)
And I'm your host, Andrew Ziegler.

**Ben Lloyd Pearson** (1:44)
Yeah, and here's what we're covering today. We have OpenClaw rocketing into a risky orbit. Steve Yegge back unveiling the agent wasteland, which is what I want to talk about related to what we were just bantering over. We have perplexity launching personal agents and then dev work falling below minimum wage. Andrew, let's just start right at the top and talk about OpenClaw, because I feel like not a week goes by at this point where we something doesn't hit the news about it. So what do we have here?

**Andrew Ziegler** (2:11)
Yeah, so OpenClaw rocketed to the top of most starred status on star charts, which is the famous way of seeing how quickly and virally projects get adopted. It rocketed up to 250,000 plus stars within really just a few months. It's one of the fastest growing projects ever on the platform. I think it speaks to the viral takeoff of this project, the fact that it has definitely left the original audience that it was built for. It's kind of staggering to see that huge number. Ben, what do you think of this?

**Ben Lloyd Pearson** (2:45)
Yeah, so to be clear, this is GitHub stars that we're talking about and OpenClaw, just this phenomenal growth to 250,000 stars in like four months, which I looked at the leaderboard this morning, and I guess that puts it just outside of the top 10, which is pretty crazy considering that how long many of those top 10 repos have been around. A lot of people are comparing it to Linux because it just surpassed Linux, like the number of stars that Linux has. But I honestly don't think that comparison is really fair because if you know anything about Linux, you know that Linux development still is happening over on git.kernel.org and the GitHub repo is just like the, it's a simple mirror. There's no community activity that happens there. So I've wondered just with this news, if Linux was birthed in the GitHub era, like would it rival the current success of OpenClaw? But you know, I think really what I take away from this is that it's just ongoing proof of this prediction that I've had that the biggest AI benefits that we're going to achieve in the near term future will come from improving how LLMs are applied to practical everyday situations, rather than better and better foundation models. Like I just, like foundation models are great, but like, you know, people would just need agents that work with the tools that we work with every day.