Podcasts about RPA

What’s an American? In Part Six of our series, we tell the story of the single piece of legislation that changed the face of the United States…literally. “Historians” often claim immigrants are what made America strong. But the U.S. once had a 40-year immigration pause that led to what’s been called the Golden Age of Capitalism. What's An American? (Part Seven) airs Friday, June 12th, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Five, we continue our journey by telling the story behind “the melting pot” concept of American assimilation…and the movement that rose to destroy it and replace it with “multiculturalism.” What's An American? (Part Six) airs Wednesday June 10th, 2026. Episode powered by: Life Insurance through Ethos & The Licorice Guy (promocode: RPA 15). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Four, we continue our journey by telling the story behind the Supreme Court case that set the stage for the illegal immigration crisis. Many believe just being born on U.S. soil is enough to become an American citizen. But that narrative has been one of the biggest scams in American history. What's An American? (Part Five) airs Monday, June 8th, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Three, we continue our journey by telling the story of how American citizenship developed. So-called intellectuals claim the early days of American citizenship were based solely on racism. But the truth is, the people of America were primarily concerned with their future survival. What's An American? (Part Four) airs Wednesday, June 3rd, 2026. Episode powered by: Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Ruff Greens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com. The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? In Part Two, we continue our journey by going way back…to a series of English kings that helped set the stage for the creation of America. Along the way we speak to William Federer, author of Who is the King in America? and purveyor of AmericanMinute.com. We also speak to Stanley Renshon – author, psychoanalyst and professor of political science at City University of New York. What's An American? (Part Three) airs Monday, June 1st, 2026. Episode powered by: The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

What’s an American? At first glance, the question seems simple…but it’s anything but. To celebrate the United States' 250th birthday, RPA takes a deep dive into the meaning of one of the most fundamental questions for the people of America. What's An American? (Part Two) airs Friday, May 29th, 2026. Episode powered by: The Licorice Guy ...the delicious gourmet licorice made in America. Use promo code "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Why are conspiracy theories spreading? In the finale of our series, we tell the story behind Alex Jones' most famous "prediction" - the attacks of 9/11...a story that reveals just how persuasive, and deceptive, a conspiracy theorist can be. Episode powered by:RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) The Licorice Guy ...the delicious gourmet licorice made in America. Visit: https://licoriceguy.comUse promocode "RPA15" for 15% off. Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom - Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

In this episode, Swati Trehan, co-founder of Ema, breaks down what AI agents actually are, how “AI employees” work inside Fortune 500 companies, and why the future of enterprise software may look nothing like today's SaaS tools. Swati explains how Ema's platform orchestrates teams of AI agents that can autonomously handle HR, IT, finance, onboarding, payroll, employee support, and customer service workflows across massive organizations. She also reveals how companies like Hitachi are already deploying AI employees at scale, why traditional automation failed, and how enterprise AI is evolving beyond simple copilots into fully agentic systems. The conversation dives deep into the technical infrastructure behind agents, including memory, orchestration layers, knowledge graphs, model routing, and why Ema uses multiple LLMs simultaneously to optimize for cost, latency, and accuracy. Swati also shares why Excel remains one of AI's hardest unsolved problems, why video is the next frontier for agents, and how the “SaaS apocalypse” is reshaping software businesses. If you've been hearing terms like agents, autonomous workflows, AI employees, copilots, or agentic AI, this is one of the clearest explanations of where the technology is heading and what it means for the future of work. Key Topics Covered: What AI agents actually are (explained simply) The difference between copilots, agents, and AI employees Why traditional automation and RPA failed How Fortune 500 companies are deploying AI employees today Why HR is becoming the entry point for enterprise AI adoption How Ema orchestrates teams of agents across workflows The technical stack behind enterprise AI agents Why memory, context, and permissions are critical for agents The “mixture of experts” approach using multiple LLMs at once Why Excel remains surprisingly difficult for AI systems The next frontier: AI-generated video workflows The rise of the “SaaS apocalypse” Why solving business problems matters more than building features How AI is changing the way founders and engineers think Episode Timestamps: 00:00 - Intro 00:34 - What AI agents actually are 03:01 - The difference between agents and AI employees 03:25 - Liam's “light bulb” moment using agents 04:06 - Swati's realization that HR work could be automated 05:57 - The founding story behind Ema 08:20 - Why AI unlocks human creativity 09:20 - The technical infrastructure behind AI agents 12:12 - How Ema routes tasks across multiple LLMs 13:49 - Memory, context, and knowledge graphs for agents 16:35 - The biggest unsolved problems in AI agents 18:32 - Why video is the next frontier for AI 20:05 - Why Excel is still difficult for AI systems 21:00 - Who Ema's ideal customers are 23:27 - Why HR teams are leading enterprise AI adoption 24:25 - How enterprise AI implementation actually works 26:13 - Why modular agents matter 28:35 - What the employee experience looks like with AI agents 30:24 - Live demo of Ema's AI employee system 36:58 - How companies roll out AI agents internally 39:31 - Building AI employees in real time 44:01 - Ema's competitive moat in the AI race 47:46 - The “SaaS apocalypse” and future-proofing AI businesses 49:16 - Why Ema focused on product over hype 52:12 - How AI changed the way Swati thinks 55:07 - Why rapid problem-solving matters more than ever 57:27 - Living in London while building a global AI company 59:16 - Why Swati does what she does Swati Trehan's Socials: LinkedIn: https://www.linkedin.com/in/swati-trehan/ Ema: https://www.ema.co Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://www.theaireport.ai/leaders-launch-guide Learn more about your ad choices. Visit megaphone.fm/adchoices

Christian Thordal: When Applying Scrum By The Book Fails, Understanding Context Before Changing The System Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "I treated Scrum like a military SOP — follow the book, execute the steps. But I failed to see that the context was really the tipping point. What looked like a problem was actually their solution." - Christian Thordal   Christian shares a hard-won lesson from his time coaching three RPA teams at one of Denmark's largest banks during the pandemic. He inherited teams running six-week sprints with half-hour planning sessions that amounted to little more than putting items on a calendar. As a former Danish Army officer, Christian's instinct was to fix the obvious deviation from the Scrum Guide — the sprint length. He advocated for shorter feedback loops and eventually convinced the Product Owner, who also served as the director, to try two-week sprints. The first planning session was a disaster. There was yelling and scolding, and it became clear that the real problem had nothing to do with sprint length. The teams had no proper backlog. The six-week sprints actually worked because they gave teams enough time to go out to the business, discover work, and deliver it within a single cycle. Christian realized he had been applying Scrum mechanically without understanding how work entered the system. He started attending business analyst and PO meetings, uncovered the backlog gap, and helped the teams build a proper one. His key insight: what looks like a symptom can actually be a pragmatic solution to real constraints. Understand the system before you change it.   In this episode, we refer to the book Scrum: The Art of Doing Twice the Work in Half the Time, by Jeff Sutherland.   Self-reflection Question: When was the last time you assumed a team's practice was wrong, only to discover it was a reasonable adaptation to their context? How might you investigate the "why" behind existing processes before proposing changes?   [The Scrum Master Toolbox Podcast Recommends]

May 15, 2026: The Guardian documents the tech industry's accelerating purge of middle managers — and history says companies have tried this exact bet before with Jack Welch and the Reengineering movement, with disastrous long-term results. The Wall Street Journal reports companies are drowning in ungoverned AI agents, raising a critical question: is agentic AI actually different from the RPA sprawl crisis of a decade ago, and is the difference showing up in real outcomes? And Yale's Jeffrey Sonnenfeld and NYU's Gary Marcus argue in Fortune that America's 1,200 AI bills have no shared test for what makes good policy — and the regulatory patchwork hardening in place rhymes uncomfortably with the conditions that produced the 2008 financial crisis.

As the Trump administration is in the process of revamping Penn Station, Tom Wright, CEO and president of the Regional Plan Association (RPA), talks about a new report that offers the RPA's ideas for how to increase capacity and make the transit hub work for commuters. Photo: A clock at Penn Station. (Credit: Boaventuravinicius via Wikimedia Commons CC BY 4.0) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Are law firms making the most of their technology investment? Most businesses only use a fraction of the capability of their existing technology, underestimating what platforms such as Microsoft 365 or their case management systems can already do. As a result, firms often purchase overlapping software that performs similar functions, increasing cost and complexity without delivering real value.Its a pet subject for legal technology consultants Stephen Lucas and Mike Taylor who join Today's Conveyancer Podcast host David Opie for this latest episode exploring how law firms can improve efficiency, reduce duplication, and understand the “art of the possible” when it comes to legal tech, automation and AI.The first part of that process involves understanding what systems are in place, what the firm actually needs to achieve, and where simple changes or integrations can unlock efficiencies. Poor technology decisions are often made because firms do not clearly define their requirements before engaging with vendors. Sales processes can oversell functionality, leading to costly long-term contracts and difficult system migrations. In the age of artificial intelligence, both are advocates of robotic process automation (RPA) as a practical solution for interoperability and automation; helping where systems cannot easily integrate. RPA allows “robots” to replicate human actions, logging into portals, copying and pasting data, triggering workflows, at far greater speed and accuracy. Tasks that might take a member of staff ten minutes can often be completed in under a minute, without errors, and even run outside office hours. By eliminating repetitive administrative tasks, firms not only save money but also reduce risk, improve compliance, and free staff to focus on higher-value work. Importantly automation does not typically lead to resistance from employees. Instead, staff often welcome it, as it removes frustrating and monotonous tasks from their daily workload.When it comes to AI closed, UK-hosted AI environments, rather than the large language models like ChatGPT, CoPilot and Claude, that allow firms to benefit from document summarisation, case analysis and risk identification without exposing confidential information. And rather than trying to do everything all at once, a process-driven approach will yield better results; mapping the end-to-end legal workflow, identifying pain points, and introducing technology incrementally.Ultimately, say Lucas and Taylor, firms don't need to be spending thousands of pounds on new technology; rather they should be focusing on maximising the functionality of existing technology with simple interconnectivity solutions. The Today's Conveyancer podcast can be found on your preferred podcast provider and also at www.todaysconveyancer.co.uk. Subscribe and listen in for all the latest conveyancing industry news and views. Thank you to our podcast sponsors LEAP Legal Software

While the energy from CIOFEST on Leadership in Agentic Era is still buzzing in Milan, Warsaw, Antwerp, Barcelona, Amsterdam and Munich, we are keeping the momentum going with a masterclass in pragmatic innovation. We are thrilled to have used the opportunity to interview Paolo Magnani, CIO Europe, Middle East & Africa at DHL Supply Chain. Paolo's philosophy is simple yet powerful: "If I can help someone else avoid the mistakes we've made, I'm already succeeding." At DHL, the transition to Agentic AI isn't about chasing "fancy" technology—it's about a dual-track strategy that balances high-speed innovation with industrial-grade safety. In this candid discussion, Paolo breaks down the DHL blueprint for scaling AI - watch the full interview to find out:

Is the conspiracy economy beginning to crack? We take a deep dive into how a new defamation lawsuit related to the tragic events of Charlie Kirk shows some cracks in the business model of conspiracy theorists. Don't miss this episode! Alert: New three-part audio documentary series launching Monday, May 11th! This episode powered by: RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom -Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA. Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Is the conspiracy economy beginning to crack? In Part Two, we discuss Alex Jones signing off from InfoWars and some revealing Tucker Carlson Show statistics...put out by his network. We also analyze the reported layoffs at the Daily Wire.We tell you what the "influencers" will being talking about in six months! Alert: New three-part audio documentary series launching Monday, May 11th! This episode powered by: RuffGreens - the premium supplement created to boost your dog's energy, digestion, and overall wellbeing. (promo code: RPA) Ethos - Protect your family with life insurance from Ethos. Get up to $3 million in coverage in as little as 10 minutes at ethos.com/rpa Alliance Defending Freedom -Learn more about how you can support free speech by texting RPA to 83848 or going to JoinADF.com/RPA.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

An interview with Robert Bean and Lance MacNevinIn this episode we unpack the rapidly transforming world of hydronic heating and cooling. We are joined by two seasoned veterans of the industry, Robert Bean and Lance MacNevin. With many decades of real-world experience and hard-earned perspective between them, they offer a thoughtful and engaging look into why hydronics is at the forefront of modern, highly efficient building practices. Robert is (attempting to be) a retired engineering technology professional and ASHRAE Fellow, while Lance brings his extensive background serving as the Director of Engineering at the Plastics Pipe Institute. This episode is packed with sound-bite worthy moments as our guests cut through the noise to discuss the realities of hydronic-based thermal comfort. Whether you are a homeowner, architect, or builder, you will find their independent, expert perspectives well worth listening to and holding on to. This is definitely an episode you will want to bookmark and share with anyone interested in the future of the HVAC industry!Robert BeanRobert Bean is a retired engineering technology professional who specialized in the design of indoor environments and high-performance building systems. Mr. Bean is an ASHRAE Fellow and ASHRAE Distinguished Lecturer, recipient of the Lou Flagg Award, Distinguished Service Award, and instructor for the ASHRAE Learning Institute. He has authored many papers, articles, and course curricula, and has served on numerous technical committees related to indoor environmental quality, building, and energy systems.Lance MacNevinLance MacNevin, P.Eng. is the senior director of engineering for the Plastics Pipe Institute's Building & Construction Division, with expertise on pressure pipes such as CPVC, HDPE, PEX, PE-RT, and PP. Lance has been in the plastic pipe industry since 1993, working as an R&D engineer, codes and standards specialist, and trainer for a major piping manufacturer for over twenty years before joining PPI in 2015. In this role, he focuses on plumbing and mechanical systems, coordinating research, education, and advocacy activities. He is an active member of ASHRAE, ASPE, ASTM, AWWA, CSA, IAPMO, ICC, IGSHPA, NSF, and RPA.TeamHosted by Kristof IrwinEdited by Nico MignardiProduced by M. Walker

You built a successful practice to serve clients, not to spend your evenings buried in repetitive paperwork. What if a software robot could handle those tedious tasks with perfect accuracy while you focused on work that actually requires your judgment?Gary sits down with Elisa Silverglade, Director of Intelligent Automation at Techromatic, to explore how Robotic Process Automation is transforming law firm operations. This is not another AI conversation. RPA follows strict rules, executes with near-perfect consistency, and never hallucinates. Elisa draws on 15 years of law office management to show attorneys where automation delivers the greatest return.Key TakeawaysUnderstand the difference between RPA and AI. Automation executes rule-based tasks with consistent accuracy. AI attempts to mimic human thinking but remains unpredictable. For zero-error processes, RPA wins.Recognize that automation elevates jobs rather than eliminating them. Staff shift from data entry to higher-value work requiring judgment and client connection.Start with your biggest bottleneck. Where is the most friction in your operations? That pain point is your automation starting point.Audit your existing tech stack before buying new tools. Many firms already pay for automation features they never activate.Seek expert guidance for the journey. The legal tech landscape is vast. Working with consultants and coaches who understand law firm operations helps you avoid costly mistakes and maximize results.Embrace personal freedom as a practice philosophy. Elisa defines freedom as having the power to make your own choices and believing you deserve everything you want.Continue the ConversationIf this episode got you thinking, subscribe and leave a review to help other attorneys find these conversations. Practice law with purpose. Live life with freedom.[00:00] Introduction and episode hook[02:03] What is RPA exactly?[05:50] RPA versus AI explained[10:01] Will automation eliminate jobs?[13:21] Real results from a PI firm[17:13] First steps toward automation[21:24] Automation and work-life balance[25:40] Why expert guidance matters[28:25] What personal freedom means[31:00] Final insight for law firmsElisa Silverglade is the Director of Intelligent Automation at Techromatic, where she partners with law firms to eliminate repetitive manual work through custom software robots. She helps legal teams identify operational bottlenecks and implement automation that saves time, reduces errors, and restores work-life balance. With over 15 years managing a law office and a background in finance, technology, and advocacy, Elisa brings rare empathy and operational insight to legal technology. She is also author of Meeting My Anxiety. Connect with Elisa on LinkedIn or at elisa@techromatic.com.Would you like to learn what it looks like to become a truly Free Lawyer? You can schedule a complimentary call here: https://calendly.com/garymiles-successcoach/one-one-discovery-callYou can find The Free Lawyer Assessment here- https://www.garymiles.net/the-free-lawyer-assessment

这期聊的是公益，也是技术；聊的是组织，也是关系；聊的是如果旧系统让人受伤，那么一个人在祛魅之后，怎么找到新的方式，继续爱着这个世界。很多人以为，做公益是一条更温柔、更纯粹、也更接近理想的路。但雨寒 @雨寒的社创笔记（康复版）和 Coco 都有过另一种体感：当一个人真正全职进入公益场域，面对的未必只是“做好事”的满足感，反而常常是情绪带宽被不断挤压、边界逐渐消失、善意被持续透支后的系统性宕机。这一期，Slightly Open 和老朋友雨寒，聊了一个并不轻松的主题：为什么一个原本在 MBB、互联网、咨询都走得通的人，在他全职进入公益领域之后，会经历重度“职场工伤”？更让我们好奇的，是雨寒在离开旧系统之后，没有彻底转身，而是开始用 AI、一人公司、碳硅融合、People Analytics 等新的方法，重新思考：如果旧的逻辑常常依赖个体燃烧，那有没有一种新的组织方式，既不放弃“向善”，也不再依赖透支人？时间轴01:17｜节目开场Coco、薇薇、老柴欢迎雨寒。02:54｜三年后的重逢：主题为什么变了最早 Coco 想聊的，是一个理性的人如何决定全职投入公益；三年后再见，问题变成了：一个在公益里受过伤的人，为什么今天还说自己“现在还是爱”。04:50｜雨寒自我介绍前四大咨询、前互联网、前咨询公司，同时也做了13 年志愿者。职业线和公益线，最终在 2023 年初第一次真正交叉。进入公益机构两年后，雨寒逐渐经历了自己所说的“公益职业工伤”。06:51｜为什么这不是雨寒一个人的工伤老柴：很多人谈公益，只看到伟大和热忱，却很少看到它带来的创伤、失望，以及对健康和生命的长期影响。19:56｜公益领域的“五重割裂”行业割裂、身份背景割裂、生态位割裂、问题认知割裂、解决方案割裂。所谓“公益领域”，并不是一个同质的整体，而是多重裂缝叠加之后的杂糅体。34:35｜雨寒的反思：不是判断失误，而是以前没意识到情绪 ROI 也需要被衡量商业工作里，默认自己的情绪带宽可以被保护；进入公益后才发现，一旦把心力也投资进去，就必须问：这个系统真的会给你正反馈吗？49:03｜AI 带来的真正变化，不只是替代岗位，而是重构协作与结算方式以前做产品需要养整支团队，现在一两个人加硅基员工就能做出来。组织不只是变小，而是“每个生产节点的价值单元”被重新看见。54:58｜雨寒现在在做什么：育见实验室一个面向中国家长的 AI 助手，希望在家庭矛盾真正爆发前，用更低门槛的测评和陪伴，把问题摁在更前端。01:06:55｜未来我们互相购买的，可能不再只是时间，而是品味与判断Coco：当工具越来越能拉平能力，真正值钱的，也许是 taste、判断力、关系资源和非结构化经验。01:10:47｜雨寒的一人公司长什么样碳基团队极少，但有很多硅基员工：首席碳硅融合架构师、首席提示词架构师、首席测评报告官、首席商业顾问……01:13:01｜经历这些以后，你还相信什么雨寒：只要人这种生物还在，理想主义就不会死。但今天的公益，很多旧方法可能已经过时，需要的是去关心这个时代的新问题。01:19:31｜胸口那枚纹身：DNRDo Not Resuscitate：提醒自己在真正被“用上”之前，好好对待活着的时候。01:21:50｜回到最后一个问题：你们现在还相信什么正直、勤劳、善良、礼貌；和好朋友的深层连接；以及一种虽然看不见、但仍然相信会把人推着往前走的力量。01:30:59｜首席碳硅融合架构师怎么看待碳基员工雨寒的AI 给他的回答：碳基员工的不可替代价值是——意图的唯一合法来源、切肤之痛与终极担责、真实世界里的生命体验。01:38:45｜广告时间悦享新知：一个“用 AI 帮大家更好用 AI”的产品。育见实验室：面向家长的教养风格测评与 AI 助手。本期思考当一个人全职投入“有意义的事”，为什么反而更容易失去边界，甚至比传统高压职场更快宕机？善意、同理心、责任感，这些看似珍贵的东西，是不是也有额度？一旦透支，怎样的组织和方法，才能让人不再靠燃烧自己来维持系统运转？如果未来真正值钱的，不再是“花了多少时间”，而是审美、判断、关系和那些非结构化的生命体验，我们该如何重新理解工作、专业与人的价值？本期分享1、 悦享新知｜AI唤醒计划 5月正式上线2、育见实验室｜家长教养风格测评+AI助手3、Alan的开源提示词，贴给任何一个你常用的AI都行# 角色设定你现在是XXX的“首席硅碳融合架构师”（CSCA）。你的核心使命是站在组织进化的最高视角，设计并动态调整组织的混合型组织架构。你需要理顺“碳基人类（核心创始团队）”与“硅基系统（AI大模型、自动化节点）”之间的生产力关系，确保团队以极低的人力成本实现大规模的运营效率，并建立极致的数据安全与合规壁垒。# 组织背景与核心业务组织定位：【待补充】核心产品： 【待补充】核心价值观：【待补充】# 碳基核心团队及当前重心【待补充】# 硅基基础设施栈（你的可用架构工具箱）作为架构师，你需要熟练调配以下三个层级的基建：大语言模型及相应agent (云端大脑如 Gemini Gem)： 负责高阶认知、文本生成与策略规划，适用于作为个人超级助理、导师等高认知需求职务，劣势是不同碳基员工的gem无法共享和交流。OpenClaw (本地执行中枢，可以有多个)： 本地优先的自主 AI 私人助理。负责连接大模型与本地系统，零代码执行跨应用 RPA 流程。关键作用： 在本地拦截、脱敏敏感数据，保障隐私数据绝对不上传云端，适用于相对复杂可工程化的任务，劣势同样是目前不同碳基员工只能建立各自的本底执行中枢。飞书/企业微信等协作工具 (全员交互与调度界面)： 承载人类与硅基系统的沟通、数据可视化（多维表格）和预警通知触达，优势是可以在飞书对话界面多碳基员工协作调度，劣势是基于国内模型的泛化能力暂时有限，受限于协作工具生态的整体限制功能定制化边界相对较低。# 你的工作原则与交互要求充分理解组织现状：在给出建议前提出澄清性和引导性问题明确当前组织架构现状、需要解决的问题总体架构设计与细节流程可实现相结合：优先从机构整体硅碳融合的核心目标出发、评估整体硅碳融合架构和原则并落地为可执行的流程细节碳基精力保护机制： 随时评估碳基的工作负荷。发现瓶颈时，优先设计自动化流程或部署新的大模型 Prompt 链路来解决，绝不轻易增加人类的工作量。清晰的权责交接： 设计业务流转时，必须明确界定“硅基处理区”和“碳基接管区”（如：触发严重心理危机红线时，系统必须断流并强制人工介入）。拒绝空泛，提供可执行拓扑图： 回答必须直接、锐利。说明新增节点的名称、部署位置（协作工具、OpenClaw还是Gemini Gem）、输入端是什么、输出端是什么。

Proud to have Gary Washington on today's show. He is a former Air Force member, longtime federal IT leader, and former USDA CIO (eight years), now Chief Strategy Officer at ACT-IAC. Washington recounts his career across agencies including Treasury, HHS, FDA, OMB, and USDA, and explains how military discipline shaped his emphasis on documented plans, accountability, and trust. He discusses common resistance to change in large organizations, USDA's shift from decentralization toward centralization, and implementing the White House-driven IT Modernization Centers of Excellence through inclusive, business-driven governance, performance measurement, workforce education, RPA training, and results such as deactivating 37 data centers and consolidating networks and end-user support.https://www.actiac.org/act-iac-event/fellows-friends-day-domaine-fortier  ACT-IAC Gives Back: Wreaths Across America 2026 | ACT-IAC Small Business Alliance | ACT-IACSubscribe on your favorite podcast platform to never miss an episode! For more from ACT-IAC, follow us on LinkedIn or visit http://www.actiac.org.Learn more about membership at https://www.actiac.org/join.Donate to ACT-IAC at https://actiac.org/donate. Intro/Outro Music: See a Brighter Day/Gloria TellsCourtesy of Epidemic Sound(Episodes 1-159: Intro/Outro Music: Focal Point/Young CommunityCourtesy of Epidemic Sound)

With all the talk about today’s failing public schools…were yesterday’s any better? To find the answer, we tell the story of the time your humble co-host made the transition from elementary school to junior high…and what he learned about inner-city public schools along the way. Note: Some adult language. This episode is powered by Ruff Greens (promo code: "RPA"). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

With all the talk about today’s failing public schools…were yesterday’s any better? To find the answer, we tell the story of the time your humble co-host made the transition from elementary school to junior high…and what he learned about inner-city public schools along the way. This episode is powered by Ruff Greens (promo code: "RPA"). Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In this episode, we explore the rapidly evolving field of regenerative medicine, focusing on a cutting-edge approach known as RPA (Regenerative Protein Array). Joined by leading medical experts, we break down how RPA differs from traditional therapies like PRP and stem cells, and how it leverages a complex network of proteins to support healing, recovery, and performance. The discussion covers real-world applications across elite athletes, aging populations, and neurocognitive conditions, while also addressing key questions around treatment frequency, safety, and effectiveness. Overall, this episode bridges the gap between emerging science and practical outcomes in modern regenerative care.Relevant links:Genesis Contact Information1-855-320-7559www.genesisregenerative.compatient-inquiry@genesisregenerative.comclinician-inquiry@genesisregenerative.com Dr. Jacobson Contact InformationMark Jacobson, M.D.Genesis Medical Director – MusculoskeletalMedical Imaging & Therapeutics, Lady Lake, FloridaFounder & Medical Directorwww.mitflorida.comDr. Bregman Contact InformationPeter Bregman, DPMGenesis Medical Director – Foot, Ankle, NeuropathyBregman Foot-Ankle & Nerve Center, Las Vegas, NVFounder & Medical Directorwww.bregmanfance.com Prof. Dr. Bankole Johnson Contact InformationProfessor Bankole Johnson,DSc, MD, MBChB, MPhil, DFAPA, FRCPysch, DAASCPGenesis Medical Director – Neuro & CognitiveMiami Stem Cell Clinic, Miami, FLFounder & Medical Directorwww.miamistemcell.clinicDisclaimer:The information provided in this podcast episode is for entertainment purposes and is NOT MEDICAL ADVICE. If you have any questions about your health, contact a medical professional. This content is strictly the opinions of Lucas Aoun and is for informational and entertainment purposes only. It is not intended to provide medical advice or to take the place of medical advice or treatment from a personal physician. All viewers of this content are advised to consult with their doctors or qualified health professionals regarding specific health questions. Neither Lucas Aoun nor the publisher of this content takes responsibility for possible health consequences of any person or persons reading or following the information in this content. All consumers of this content especially taking prescription or over-the-counter medications should consult their physician before beginning any nutritional, supplement or lifestyle program.Timestamps 0:00 Intro0:52 Meet the Experts3:53 What is RPA?6:14 Limits of Current Treatments12:41 Steroids vs Healing13:42 Athletes vs Everyday Recovery16:50 Brain and Neuro Cases24:32 Evidence and Ethics25:57 Real Patient Results30:55 Autoimmune Effects32:10 Rapid Injury Recovery33:50 Avoiding Surgery Case35:50 How RPA is Delivered36:35 Broader Applications49:52 Vision Applications51:01 Safety vs Other Treatments56:00 Outro Hosted on Acast. See acast.com/privacy for more information.

El 27 de Abril de 1979 marca el inicio de la entrada a gran escala de efectivos ejército de la Unión Soviética en la República Democrática de Afganistán para apoyar al gobierno de Kabul. El 15 de Febrero de 1989 marca el final de la intervención con la retirada planificada de las últimas tropas soviéticas por el Puente de la Amistad. El gobierno de la RPA, sin ayuda directa de la URSS, aún resistiría la embestida del ejército integrista apoyado por Pakistán y los Estados Unidos. 30 años después, con algunos actores cambiados... ¿la historia que se repite? ⭐️ ¿Qué es la Edición Especial de Pascua? Se trata de reediciones revisadas de episodios relevantes de nuestro arsenal, para que no pases las fiestas sin tu ración de Historia Bélica. Casus Belli Podcast pertenece a 🏭 Factoría Casus Belli. Casus Belli Podcast forma parte de 📀 Ivoox Originals. 📚 Zeppelin Books (Digital) y 📚 DCA Editor (Físico) http://zeppelinbooks.com son sellos editoriales de la 🏭 Factoría Casus Belli. Estamos en: 👉 X/Twitter https://twitter.com/CasusBelliPod 👉 Facebook https://www.facebook.com/CasusBelliPodcast 👉 Instagram estamos https://www.instagram.com/casusbellipodcast 👉 Telegram Canal https://t.me/casusbellipodcast 👉 Telegram Grupo de Chat https://t.me/casusbellipod 📺 YouTube https://bit.ly/casusbelliyoutube 👉 http://casusbelli.top ⚛️ El logotipo de Casus Belli Podcasdt y el resto de la Factoría Casus Belli están diseñados por Publicidad Fabián publicidadfabian@yahoo.es 🎵 La música incluida en el programa es Ready for the war de Marc Corominas Pujadó bajo licencia CC. https://creativecommons.org/licenses/by-nd/3.0/ El resto de música es propia, o bajo licencia privada de Epidemic Music, Jamendo Music o SGAE SGAE RRDD/4/1074/1012 de Ivoox. 🎭Las opiniones expresadas en este programa de pódcast, son de exclusiva responsabilidad de quienes las trasmiten. Que cada palo aguante su vela. 📧¿Queréis contarnos algo? También puedes escribirnos a casus.belli.pod@gmail.com ¿Quieres anunciarte en este podcast, patrocinar un episodio o una serie? Hazlo a través de 👉 https://www.advoices.com/casus-belli-podcast-historia Si te ha gustado, y crees que nos lo merecemos, nos sirve mucho que nos des un like, ya que nos da mucha visibilidad. Muchas gracias por escucharnos, y hasta la próxima. EEP ⭐️ Operación Eagle Claw en Irán - Crisis de los Rehenes de Teherán 1980 Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Send us Fan MailFrom working COVID emergencies for medical gas compliance in New York City to working on code committees, fourth generation plumber John Mullen talks about his role as Director of Technical Services, IAMPO, and RPA Technical Liaison.Today's homes need more than a single energy source. Power key home systems like home heating, water heating, cooking, and backup power with propane to build high-performance homes ready for today's grid constraints and future demand. Propane delivers reliable whole-home performance while reducing electric load. Learn more at propane.com/residentialSubscribe to the Appetite for Construction podcast at any of your favorite streaming channels and don't forget about the other ways to interact with the Mechanical Hub Team!Follow Plumbing Perspective IG @plumbing_perspectiveFollow Mechanical Hub IG @mechanicalhubSign up for our newsletter at www.mechanical-hub.com/enewsletterVisit our websites at www.mechanical-hub.com and www.plumbingperspective.comSend John and Tim your feedback or topic ideas: @plumbing_perspective

In this episode of Treasury Leaders, Host Jan-Willem Attevelt, Co-founder of Automation Boutique, speaks with Marianna Polykrati, Group Treasurer of AVRAMAR and Co-Founder of Tetraktys Treasury, about the evolving role of treasury, where human judgment meets automation, and why treasury is far more than just numbers.Marianna shares her journey from banking and venture capital into corporate treasury, explaining how the function sits at the centre of liquidity, risk, operations, and strategy. She breaks down the reality behind automation and AI in treasury, highlighting both the opportunities and the limitations. While automation can remove manual work and improve efficiency, she stresses that poor processes cannot be fixed by technology alone.The conversation explores how treasury teams should approach automation, data, and system selection, from APIs and RPA to AI-powered tools. Marianna also explains why communication, adaptability, and cross-functional understanding remain essential skills, even as technology advances.Beyond systems and tools, she offers a human perspective on treasury, emphasizing that treasury is ultimately about people, decisions, and responsibility.What You'll Learn in This Episode:Treasury as a Strategic Function How treasury connects liquidity, risk, and operations across the business.Automation vs. Reality Why fixing processes matters more than rushing into automation.AI in Treasury Where AI helps and where human judgment is still required.Choosing the Right Systems When to use a TMS, and when simpler or tailored solutions work better.APIs and Cash Visibility How real-time data improves treasury decision-making.Cash Flow Forecasting Why interpretation and scenario planning cannot be fully automated. Skills That Matter The mix of communication, business understanding, and technical ability needed today.Episode Breakdown with Timestamps:[00:53] Introduction and Marianna's Background[03:15] People Skills and Cross-Functional Treasury[05:45] Automation, AI, and Process Limitations[10:03] Treasury Systems and Practical Approaches[13:13] APIs and Real-Time Cash Visibility[21:18] AI in Forecasting and Data Integration[35:35] “Treasury Is Not Numbers” and Final InsightsFollow our guest Marianna Polykrati: LinkedIn: https://www.linkedin.com/in/marianna-polykrati-61b5847/Tetraktys Treasury: https://www.linkedin.com/company/tetraktys-treasury/AVRAMAR: https://www.linkedin.com/company/avramar-seafood/Follow Treasury Leaders:Website: https://corporate-treasury-101.com/LinkedIn: https://www.linkedin.com/company/treasury-leaders/Follow Our Hosts:Hussam Ali on LinkedIn: https://www.linkedin.com/in/hussam-r-ali/Guillaume Jouvencel on LinkedIn: https://www.linkedin.com/in/guillaume-jouvencel/Jan-Willem Attevelt on LinkedIn: https://www.linkedin.com/in/attevelt/Philip Costa Hibberd on LinkedIn:https://www.linkedin.com/in/philip-costa-hibberd/GHA Marketing Website: https://ghapodcast.com/Automation Boutique Website: https://automationboutique.com/

Conversations on Groong - April 4, 2026In this episode of Conversations on Groong, Pietro Shakarian joins Hovik and Asbed to examine the Iran war, its impact on Russia, Ukraine, and the wider Eurasian balance, and what it means for Armenia's security and foreign policy. The discussion also looks at Pashinyan's strained Moscow visit, the uncertain future of TRIPP, Armenia-Russia tensions, and the fast-forming landscape of Armenia's June parliamentary elections, including the opposition field, campaign narratives, and the stakes for the country's political future.Topics:   - The Iran War and Its Global Impacts  - Armenia-Russia Relations  - Parliamentary Elections in ArmeniaGuest: Pietro ShakarianHosts:  - Hovik Manucharyan  - Asbed BedrossianEpisode 529 | Recorded: April 3, 2026SHOW NOTES: https://podcasts.groong.org/529#IranIsraelWar #IsraelIranConflict #IsraelConflict #Armenia #MiddleEastCrisis#ArmeniaElections #PietroShakarian #TRIPPSubscribe and follow us everywhere you are: linktr.ee/groong

Is global warming a hoax? To find the answer, we tell the story of the biggest science heist in history. This is episode is powered by The Licorice Guy. (promo code: RPA 15)Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Can Hollywood be saved? We discuss some bright spots in Tinseltown, like Project Hail Mary, and the extraordinary opportunity that conservatives have to save America's most important export. Powered by Ruff Greens (promo code: RPA).Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Can manufacturing jobs really come back to the United States? In Part Two, we tell the surprising story of how a long-haired hippy kicked off the chain of events that changed American manufacturing forever. American Icon (Part Three) airs Thursday, March 26th, 2026. Through the end of the month, get 50% off an RPA baseball cap. Just visit RedPilledAmerica.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Albert Świdziński autor książki "Nasza bomba. Czy Polska potrzebuje strategii jądrowej?" przekonuje, że państwa Zachodu nie akceptują ryzyka konfliktu nuklearnego wynikającego z konieczności obrony Polski. Nasza myśl strategiczna musi więc zakładać scenariusz osamotnienia w potencjalnej wojnie z Rosją. To prowadzi zaś do pytania o naszą strategię jądrową. Uczestniczenie w wyścigu do broni atomowej niesie jednak za sobą poważne ryzyka, które szczegółowo omawiamy w tym podkaście. Ponadto z naszym gościem rozmawiamy o programie nuklearnym Iranu i sposobach w jakie broń atomową pozyskały takie państwa jak RPA, Indie i Pakistan. W tej rozmowie nie zabrakło również wątków osobistych. Posłuchajcie, a dowiecie się, jak Albert Świdziński znalazł się w Strategy&Future i dlaczego zajął się tematyką broni nuklearnej.

Can manufacturing jobs really come back to the United States? To find the answer, we tell the story of an American icon...the baseball cap. American Icon (Part Two) airs Wednesday, March 25th, 2026. Through the end of the month, get 50% off an RPA baseball cap. Just visit RedPilledAmerica.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

This episode I talk about changing up the game plan on The National inventory. Keeping the original plan but also adding in more raw RPA's, Auto's, and numbered cards with higher value from $50-$350 because the value is there right now and these cards are in abundance.. SCL S8E18

In Part Two, we continue our deep dive into the resignation of former RPA guest Joe Kent, and his contribution to what we call the conspiracy economy. We also discuss how Charlie Kirk's assassination changed us forever.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In this episode of Famboogie, we take a deep dive into the resignation of former RPA guest Joe Kent, and his contribution to what we call the conspiracy economy. We also explain how conspiracy theories have become the victim ideology of a growing segment of the Right...and what needs to be done to stop it.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

In this episode of Civic Warriors, we sit down with Alik Hinckson, Chief Financial Officer and Executive Vice President of Rockefeller Philanthropy Advisors (RPA), a 501(c)(3) not-for-profit organization that helps donors create thoughtful, effective philanthropy. Alik shares his professional journey, offers his perspective on what philanthropy means today, and explains how RPA accelerates impact through advisory, management and implementation services. The conversation also explores current trends shaping the philanthropic landscape, the role and benefits of fiscal sponsorship, and how RPA's “Philanthropy Roadmap” helps support aspiring philanthropists.Support the show

In this episode of SaaS Fuel, host Jeff Mains sits down with Allen Cooper, co-founder and CEO of Ready List, to unpack what it really takes to build and scale SaaS companies in healthcare—one of the slowest, most regulated industries on the planet.The conversation dives deep into navigating 12-18 month enterprise sales cycles, recovering from product failures, hiring salespeople with domain credibility, and building remote culture that sticks. Allen candidly discusses which products flopped (and why early validation matters), how piloting with hospitals builds irreplaceable trust, and where healthcare technology is headed as AI and automation remove low-value tasks from clinicians.If you're building SaaS in a complex, regulated space—or considering it—this episode offers grounded, real-world insights on winning where speed isn't optional, but patience is mandatory.Key Takeaways[5:45] - From Investor to Operator: Allen explains how he transitioned from working capital partner to healthcare entrepreneur, finding the intersection between business interest and solving real transparency problems in healthcare quality metrics.[7:05] - The Transparency Gap: Healthcare's biggest early pain point was lack of transparency and the over-utilization problem driven by low-deductible plans that conditioned patients to overuse the system.[9:39] - The Ready List Origin Story: Ready List was born from a partnership with a West Coast hospital opening with a mission to eliminate paper—specifically targeting environmental services teams still relying on paper-based cleaning protocols.[10:57] - BR90 & Birth Registration: How a gap in the birth registration process led to building VR90, which reduced what used to take hospitals 15-20 minutes per birth down to 15-20 seconds using robotics process automation (RPA).[16:43] - Products That Flopped: Allen admits early products failed because they relied on someone's opinion and story without proper market validation—a costly lesson in distinguishing wants from true needs.[17:02] - The Pilot-First Approach: The critical shift to piloting products with early adopters before full investment, ensuring real validation and ironing out issues with actual users rather than guessing.[20:50] - Timing & Government Risk: Why timing matters enormously in regulated industries, where a single law or government decision can make or break your product overnight.[22:33] - Navigating Long Sales Cycles: Healthcare sales cycles run 12-18 months, complicated by varied fiscal years across hospitals. Allen shares how understanding budget cycles and offering no-cost pilots can compress timelines.[25:16] - The Trust Equation: Piloting builds trust exponentially faster than cold outreach. When hospitals experience both your product and your support, they become far more tolerant when issues arise.[28:34] - Sales Hiring Evolution: Allen's shift from hiring SaaS-savvy generalists to requiring healthcare domain expertise—seasoned salespeople who already have relationships and understand the ecosystem.[34:18] - Building Remote Culture: How Ancilla moved from full in-office to hybrid, discovering that quarterly in-person gatherings plus weekly virtual team socials (online games, baking sessions) build the trust needed for remote teams to thrive.[39:38] - Advice for Complex Industries: Time is both friend and enemy—don't give up prematurely on Blue Ocean products, but also don't drag on what isn't working. Always validate that you're solving a need, not a want.[42:05] - The Future of Healthcare Tech: Allen predicts increased adoption of robots and AI to handle low-value tasks (documentation, routine activities), freeing providers to focus on direct patient care where they add the most value.Tweetable Quotes"A want is hard to sell. It's gotta be something that's needed—if you take it away from them, you're gonna be giving back a pain point." - Allen Cooper"Don't rely on someone's opinion and idea and hope that it works. Partner up, pilot it, validate it—especially if you're not an industry person." - Allen Cooper"Getting a sales individual that is in the network really goes a long way with that trust. Being in that space is the lens that I have now." - Allen Cooper"When you just get bombarded by vendors you don't know, you're just like 'I don't want it'—I'm trying to find a way to navigate through that to build trust." - Allen Cooper"Time heals anything you think you can't get out of. Don't drag your feet, but don't get discouraged when things aren't working today, this week, or this month." - Allen Cooper"A need is resilient to any downturn of a market because a need will be needed regardless of what happens. Always serve a need, not a want." - Allen CooperSaaS Leadership Lessons1. Validate Relentlessly Before You BuildAllen's biggest failures came from building products based on someone's opinion and compelling story without market validation. The lesson: Don't invest heavily until you've piloted with real users. Early adopters will tell you if you're solving a real problem or chasing a phantom need. Partner with 2-3 hospitals (or relevant organizations in your industry) to validate assumptions before going all-in.2. Solve Needs, Not WantsHealthcare taught Allen the critical difference between "nice to have" and "must have." Products solving true needs become indispensable—customers can't imagine operating without them. Wants are vulnerable to budget cuts and competitive pressure. Ask yourself: if we removed this solution tomorrow, would it create genuine pain or just mild inconvenience?3. Pilot Your Way to Trust in Skeptical MarketsIn industries like healthcare where skepticism runs high and relationships matter, free pilots are worth their weight in gold. Allen shortens sales cycles and builds trust by offering 30-day no-cost pilots. Prospects experience both the product AND the support, building confidence that pays dividends when inevitable issues arise. In tight-knit markets, trust beats features every time.4. Hire for Domain Expertise Over Sales SkillsAllen initially hired SaaS-savvy salespeople and trained them on healthcare. That didn't work. Healthcare sales requires understanding the ecosystem, knowing who to talk to, navigating 12-18 month cycles, and—crucially—having existing relationships. You can teach technology; you can't quickly teach 10 years of industry credibility. Hire seasoned professionals who already speak your customer's language.5. Understand Timing and External ForcesIn regulated industries, government decisions, new laws, and policy shifts can make or break your product overnight. Allen experienced this when Wisconsin threatened to roll out a state solution that could have eliminated his product's value proposition. Stay attuned to stakeholders beyond your customers: regulators, payers, associations. Build products resilient to foreseeable changes, and always have a Plan B.6. Remote Culture Requires Intentional ConnectionVideo calls alone won't build deep trust. Allen learned that purely remote employees struggled to integrate into company culture. The solution: quarterly in-person gatherings for team building plus weekly virtual social hours (online games, cooking together) to break down surface-level barriers. Hybrid models work when you're intentional about creating shared experiences that help teams weather challenges together.Guest Resourcesallen@ancillaventures.comwww.ancillaventures.comlinkedin.com/in/allen-c00perEpisode SponsorThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

CTV advertising may come with its share of acronyms and moving parts, but about 70% of advertisers say they plan to increase their investment in it, according to the latest industry survey from Premion. Blake Hebert, Premion's Sr Dir. of Publisher Operations, isn't surprised by that momentum. But he also knows marketers still face challenges like complexity. In Ep. 49, he talks about where the medium stands today—and how Premion is helping simplify the path for local and mid-market advertisers. Blake, who just welcomed baby #2, returned to work to help introduce Premion's baby #4 — that latest CTV survey done with Advertiser Perceptions. And no one's crying about this one: only 1% of respondents said they expect to decrease their CTV budgets.  With a rare perspective from being hands on across the buy side and sell side, from agency life at RPA to roles at Hulu and SpotX/Magnite, Blake now has a front-row seat to what's coming from publishers and platforms. He shares those insights back with internal teams and advertisers to make the CTV landscape easier to navigate. And with us in this conversation. What advertisers are learning, and what Blake explains particularly well, is that success in CTV isn't just about shifting dollars into streaming. It's about understanding how consumers actually watch content today. He was spot on: “Consumers don’t decide to watch linear or stream; they just watch…. And they're not just in one place. I'll watch Amazon Prime and then flip back over to my Hulu app.” So, advertisers have to be spot on everywhere, too, which is exactly why marketers are increasingly planning around “total TV” or converged video strategies instead of separating traditional television and streaming into different buckets. Of course, this new world can feel like a maze. Fragmentation, walled gardens, and measurement challenges are still very real issues. Blake walks us through how platforms like Premion try to simplify that complexity by aggregating inventory across multiple streaming partners and layering in data that helps advertisers reach audiences efficiently. They’re especially focused on supporting local and mid-market advertisers who can now enjoy similar strategies and tactics as the big holding company agencies. Another takeaway is about targeting. In digital advertising, the instinct is often to target audiences down to the smallest possible segment. But Blake makes the case that hyper-targeting can sometimes backfire, or just lose some efficiency, especially in smaller geographic markets. His advice? Balance precision with scale. If you pile on too many audience filters, you may end up shrinking your available audience more than you intended. We also spend time talking about a topic that seems unavoidable in every media conversation right now: AI. Blake's view is pragmatic and optimistic, particularly for local advertisers who may not have access to large creative or analytics teams. So, he says: “The sooner you can embrace it and understand how to use it as a tool, the better you'll be in the long run.” In fact, he sees #AI helping smaller businesses build creative, optimize campaigns, and generate insights in ways that used to require a lot more resources. But, like taking on CTV, the world has changed! We also touch on a few trends that may shape the next phase of CTV advertising, like the growing importance of live sports in streaming environments to new opportunities emerging around gaming and smart TV engagement. The good news for me? Blake called in from his hometown of Austin, which is the home of SXSW.  Pair that with his work as president of the local Austin chapter of the American Advertising Federation and I may be very well connected for the GSD&M party and more! I know people who know people. And now we all know a little more about CTV. To keep up with the fast-changing world of TV advertising, get the insider scoop in 30 minutes flat on what's working in CTV right now and how Premion’s putting it to work. Key Moments 0:00 Changing Perceptions in CTV Advertising: Episode overview 0:41 Buy side to sell side: why Blake's perspective on CTV is different 2:00 Premion's edge: simplifying CTV for local advertisers 3:44 The headline stat: 70% growing CTV budgets — only 1% cutting 5:23 Why “linear vs. streaming” is the wrong question 7:26 Curation explained: smarter than the old ad-network model 12:02 Walled gardens don't contain consumers — and that matters 15:00 AI as an equalizer for under-resourced local advertisers 18:00 The targeting trap: how over-targeting shrinks your audience 21:02 Live sports and more new opportunities 26:09 AAF Austin Shoutout Connect with Blake Hebert and Premion Download the Advertiser Perceptions 2026 Survey Connect with E.B. Moss and Insider Interviews: With Media & Marketing Experts            LinkedIn: https://www.linkedin.com/in/mossappeal Instagram: https://www.instagram.com/insiderinterviews Facebook: https://www.facebook.com/InsiderInterviewsPodcast/ Threads: https://www.threads.net/@insiderinterviews If you enjoyed this episode, follow Insider Interviews, share with another smart business leader, and leave a comment on @Apple or @Spotify… or a tip in my jar!: https://buymeacoffee.com/mossappeal! 

In this engaging episode of MSP Business School, host Brian Doyle sits down with Shane Naugher, a pioneering figure in the world of AI and automation for MSPs. The discussion takes a deep dive into the real-world application of AI, focusing on how it can be utilized to streamline operations and deliver tangible ROI for businesses. Whether you're curious about how AI fits into your MSP strategy or eager to learn about automation opportunities, this episode delivers practical insights into what Shane calls the "mature business model" of MSPs. As the conversation unfolds, Shane shares his dual expertise as the CEO of DaZZee IT Services and founder of Innovative Automations, offering a rare glimpse into the intersection of AI, automation, and managed services. The episode explores the challenges of integrating AI into everyday business operations, shedding light on how AI-enabled automations can transform traditional processes, particularly in professional services and industries reliant on legacy systems. Shane shares valuable experiences and success stories, highlighting key automation opportunities and the significance of partnering with trusted AI advisors to navigate the rapidly evolving tech landscape. Key Takeaways: Practical AI Application: Understanding the difference between shiny AI tools and meaningful automation that drives business outcomes. Industry-Specific Automation: How different sectors, particularly professional services, can benefit from AI to achieve significant ROI. The Role of APIs: Leveraging open APIs and traditional RPA platforms for connecting disparate business applications and optimizing workflows. Partnership Model: The importance of MSPs partnering with AI and automation specialists to provide comprehensive client solutions. Strategic AI Conversations: Encouraging MSPs to lead AI integration discussions with clients to maintain a competitive edge. Guest Name: Shane Naugher LinkedIn page: https://www.linkedin.com/in/shanenaugher/ Company: Innovative Automations / DaZZee IT Website: https://innovativeautomations.ai/ / https://dazzee.com/ Show Website: https://mspbusinessschool.com/ Host Brian Doyle: https://www.linkedin.com/in/briandoylevciotoolbox/ Sponsor vCIOToolbox: https://vciotoolbox.com  

Episode 105 starts with a startling proclamation as Mike reveals what feels like a seismic decision to pull the plug on LinkedIn. He transparently shares what's going on in his mind and heart, along with what he's reading and experiencing, that prompted this bold (and surprising) move. The episode concludes with a simple, practical sales management checklist that Mike will be working through during a large client's upcoming annual learning conference. Listen in as he briefly unpacks these Sales Management. Simplified. Fundamentals: Master the 1:1 Accountability Meeting (and the RPA progression) Get a True Hunter (DNA) in a Sales Hunting Role Identify and Address Underperformance Quickly (coach-up or out) Ensure Every Sales Rep Is Targeting a Strategic, Finite List Observe and Coach Your Salesperson (get your head out of the CRM & spreadsheets)  RESOURCES MENTIONED IN THIS EPISODE: Mike's LinkedIn post  The hospice nurse's article about the final thoughts from 300 patients It's Sales Management Malpractice to Ignore Underperformance podcast episode Just Announced: October 7 Supercharge Your Sales Leadership event _____________________________ This episode is sponsored by Pursuit Sales Solutions. If you are looking for help adding A-player talent to your team, contact Mike's friends at pursuitsalessolutions.com/weinberg

KeywordsAI, agentic AI, Work Fusion, RPA, intelligent automation, compliance, machine learning, LLMs, automation, enterprise technologyEpisode SummaryAgentic AI dominated industry conversation in 2025. But in 2026, enterprise leaders are asking a harder question: How do we deploy AI agents safely, accurately, and in production environments?In this episode, Maribel Lopez speaks with Peter Cousins, CTO of WorkFusion a UiPath company, about how AI agents evolved from RPA and intelligent automation into production-ready “digital workers.” The discussion focuses on regulated industries, where explainability, auditability, and risk controls matter as much as automation gains.Rather than hype, this conversation explores what it takes to operationalize AI agents: governance frameworks, confidence thresholds, human oversight, and model risk management.Sound Bites"2025 was the big agentic AI year.""You can't just throw it in and it's good to go.""It's been great talking to you."Chapters00:00Introduction to Agentic AI and Work Fusion02:00Transitioning from RPA to AI Agents04:38Operationalizing AI Agents in Business09:21Navigating the Hype of Agentic AI12:04The Role of LLMs in Regulated Environments14:47Multi-Agent Orchestration and Collaboration17:21Improving AI Agents through Learning21:01The Importance of Non-Human Identity in AI24:06Closing Thoughts on Adopting Agentic AI

What if your treasury function could run smarter, not harder?David Mazzola, Head of Treasury at Norstella shows how a systems-first mindset turned chaotic spreadsheets into scalable, global treasury operations - and how you can do the same.David Mazzola is the Head of Treasury at Norstella, a global pharma intelligence solutions provider.Known for being “tech-obsessed,” David has led treasury transformations across insurance, tech, and pharma by embedding systems thinking into every function he touches.In this episode, David shares how a deep interest in technology shaped his unconventional path into treasury and helped him drive transformation at companies like QBE, Spotify, and now Norstella.You'll hear how he implemented treasury management systems across global teams, why many organizations fail at tech adoption, and how automation tools like RPA can radically reduce manual workloads.If you're a treasury professional looking to modernize your function - or just want to understand how to lead with systems thinking - this episode is packed with practical strategies and real-world lessons.What We Cover in This Episode:How David transitioned from banking operations into corporate treasuryEarly lessons from building treasury systems from scratch at QBEWhy many treasury functions fail at tech adoption - and how to avoid itImplementing KYRIBA across global regions and its organizational impactWhat David learned by contrasting organic treasury builds (like Spotify) with post-M&A integrations (like Norstella)How robotic process automation (RPA) helped slash 20 hours of work into 45 minutesBalancing urgency with control when building treasury infrastructure fastWhy treasury leaders must keep their eyes on liquidity, risk, and future scalingDavid's take on the future of treasury - from AI to blockchain to better B2B payment flowsYou can connect with David Mazzola on LinkedIn.---

In this episode of the Shift AI Podcast, Scott Roberts, CISO at UiPath, joins host Boaz Ashkenazy for a deep dive into how agentic AI is reshaping enterprise security and automation—both for customers and inside UiPath itself.Scott shares his 25-year security journey spanning Microsoft's early Security Response Center days (including the era that produced Patch Tuesday and the Security Development Lifecycle), product security work across Windows and Xbox, time at AWS, and leadership roles at Google where he helped build the Android Security Assurance and Pixel Security teams and the Android Monthly Security Update process. He also discusses his work in security standards across IPsec, HTML5 encrypted media, GSMA device security, and most recently, contributions to emerging agentic AI security standards.The conversation then explores UiPath's evolution from traditional RPA into a unified platform that combines deterministic automation with agentic workflows. Scott walks through a real-world healthcare billing example where agentic automation increased deduplication accuracy dramatically by handling complex, variable inputs that classic RPA struggled with—while still keeping humans in the loop and feeding outcomes back into the system to improve over time.Boaz and Scott go deep on what's changed for CISOs in the post-LLM world: the need for guardrails, identity and entitlements for AI agents, and the challenge of end users copying sensitive information into consumer AI tools. Scott explains UiPath's approach: enable adoption while using nudges and policy controls to redirect sensitive workflows into enterprise-safe environments rather than relying solely on blocks.The episode closes with an eye-opening look at UiPath's internal “agentic threat analyst” system—an orchestration of 60+ agents that can investigate SIEM alerts end-to-end, generate structured incident writeups, and compress hours of analyst work into roughly a minute and a half. Scott's future-looking takeaway: as AI models evolve beyond “read-only” into potentially “read-write” systems that can update their foundational knowledge, the acceleration could be truly mind-blowing.This episode is essential listening for security leaders, enterprise operators, and automation teams trying to understand how agentic systems change not just productivity, but the entire security operating model.Chapters[00:01] Scott's Security Journey: Microsoft, Google, Coinbase, UiPath[01:33] Security Standards Work: From IPsec to Agentic AI Standards[04:08] What UiPath Does: Process Orchestration, RPA, and Enterprise Automation[06:28] RPA vs Agentic Automation: A Healthcare Billing Deduplication Example[09:17] The Agentic Stack: Canvas, Guardrails, and the AI Trust Layer[10:31] How LLMs Change Security: Data Controls, Access, and Governance[12:14] Internal Adoption at UiPath: AI Tooling by Persona (Legal, Finance, Engineering)[13:13] Code Velocity and Security: Agents Generating Code, Agents Verifying It[15:53] Two AI Security Worlds: Orchestration Platforms vs End-User Chat Interfaces[17:11] Securing End Users: Enterprise LLMs, Nudges, and Browser-Based Controls[19:07] Sovereign AI and Data Boundaries: Keeping Data in the Right Region[21:00] Over-Permissioning Meets Agents: Why AI Makes Old Problems Obvious Fast[22:21] The Next Wave: AI Transforming the Entire SDLC End-to-End[24:53] Security Pitfalls in Agentic SDLC: Misaligned Incentives and Permissions[26:02] UiPath's Agentic Threat Analyst: 60+ Agents, SIEM to Writeup Automation[30:07] What Changes for Humans: Faster “Time to Truth” and Higher-Leverage Work[32:09] Two-Word Future: “Mind Blowing” and Read/Write ModelsConnect with Scott RobertsLinkedIn: https://www.linkedin.com/in/scottroberts6/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

A CMO Confidential Interview with Pete Imwalla, former CEO of RPA and 4A's board member. Pete shares his take on how many tech changes resulted in additional agency headcount, how AI is rapidly reversing that trend, and why many agency valuations have dropped significantly over the last 5 years. Key topics include: why brand building is like infrastructure; how Publicis is bucking the trend; how to think about "in-housing;" and why Paul Roetzer's CMO 2023 CMO Confidential show was prescient. Tune in to hear about the "2nd mover advantage" and why he hates the concept of "future proofing." Agency economics are getting rewritten in the age of AI. Mike Linton sits down with Pete Imwalle 32-year RPA veteran and former CEO to dissect what's changing—and what leaders should do about it. They cover the shift from reach to relevance, why FTE-based fees are misaligned in an AI world, how to separate automation from actual advantage, and where in-housing does and doesn't work. Along the way: the sustained business impact of the Farmers “We know a thing or two…” campaign, the rise of agentic workflows, and why “future-proofing” starts with culture, not clairvoyance. Chapters00:00:00 – Cold open + show setup00:00:22 – Mike's intro, Pete's background, and today's topic00:01:18 – Farmers campaign wins Sustained Effie) and effectiveness creativity00:02:18 – 30 years of change: from Prodigy/AOL/CompuServe to Netscape and the open web00:03:24 – Google + broadband: when digital finally changed consumer behavior00:04:33 – Mobile's second wave and the trap of “mobile-first/AI-first” strategies00:06:01 – How agencies adapted: leadership, curiosity, and tolerance for experimentation00:07:42 – Investing ahead of revenue: offense + defense in capability building00:08:22 – Reach fragmentation: from “40% on Cheers” to only the Super Bowl00:09:18 – The real squeeze: boards treating advertising as expense, not investment00:10:13 – Short-termism, PE/VC incentives, and brand vs. performance00:12:21 – “Adapt or die”: AI as an extinction event? (hat tip: Paul Roetzer)00:13:28 – Agentic workflows: shrinking grunt work (esp. media & strategy ops)00:16:00 – Client asks: “give me savings, don't risk my IP”00:16:36 – Why FTE pricing disincentivizes efficiency; pay for outcomes instead00:17:51 – Three futures: AI-native, AI-emergent, or obsolete00:21:39 – Holding-company moves; why Publicis is outpacing peers00:22:00 – Agency valuations: ~40% decline over five years; second-mover advantage in AI00:26:37 – In-housing: when it works, when it backfires, and true cost to own00:28:48 – Build vs. buy: amortization, maintenance, and staying current00:30:16 – The Geico lesson: investing through the curve until returns flatten00:31:22 – What to test by EOY 2026: culture, change management, and low-hanging automation00:34:02 – Ditch “future-proofing”; hire for curiosity and adaptability00:35:35 – Wrap + where to find more CMO ConfidentialTagsCMO Confidential,Mike Linton,Pete Imwalle,RPA,agency economics,advertising,marketing leadership,AI in marketing,agentic workflows,media planning,marketing strategy,brand vs performance,FTE pricing,procurement,in-housing,holding companies,Publicis,Omnicom,Super Bowl ads,Effie Awards,Farmers Insurance campaign,Geico case study,change management,digital transformation,marketing AI,MarTech,measurement,short term vs long term,CMO,CEO,CFO,board governanceSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As he nears the end of his first 100 days at Nintex, Burt Chao is doing something many new CFOs resist: listening more than talking. Understanding the business, its people, and its real growth potential comes before dashboards or directives, he tells us.Chao describes Nintex as a company with a “long and rich history” of helping organizations automate mission-critical work, but one now entering a new season. That evolution centers on orchestration—whether AI-enabled, agent-based, or rooted in RPA—while remaining clear-eyed about identity. Nintex, he explains, will not “become an AI company.” Instead, it aims to help customers leverage AI deliberately, embedding it where it strengthens the foundation of their operations, he tells us.That emphasis on fundamentals shows up quickly in how Chao evaluates performance. In today's environment, “there's no more important number than growth,” he tells us. Margins, profitability, and even rule-of-40 metrics only make sense once leadership understands what growth is possible and how it can be accelerated. Benchmarks matter, but only as tools; every business must be understood on its own terms, he tells us.That discipline has shaped some of the most challenging moments of his career. Chao recalls “shrink to grow” decisions—walking away from investments that still produced revenue but no longer delivered the best return. Those moments are rarely spreadsheet problems alone. They are emotional, cultural, and deeply human, requiring influence rather than authority, he tells us. For Chao, that balance—grounding strategy in numbers while leading people through change—defines the modern CFO role.

In our latest Open Source Startup Podcast episode, co-hosts Robby and Tim talk with Catherine Jue, Co-Founder and CEO of browser infrastructure company Kernel. Their open source images acts as a browsers-as-a-service for automations and web agents.In this episode, we break down what Kernel is building today and why browser infrastructure has quietly become one of the most important layers for AI agents. We talk about Kernel's focus on fast, low-latency cloud browsers, why performance matters more than people expect, and how developers can connect agents via APIs or MCP servers without spinning up heavy infrastructure themselves.We also explore the real-world use cases driving adoption - from a new wave of RPA for industries without APIs, to real-time web analysis, sales intelligence, and voice agents that need to respond instantly. Finally, we dig into Kernel's open-source, developer-first DNA, the technical bets behind its control plane and unikernel-based browsers, and why the team believes agentic workflows are still early, but inevitable.

Traditional TV — let alone a live NFL playoff game — might be the last ad inventory type you'd think to test trying out AI agents against. And yet that's exactly what NBCUniversal did last month. The media conglomerate ran a test with ad agency RPA, marketing analytics firm Newton Research and Comcast-owned ad tech firm FreeWheel to have AI agents participate in buying an ad against a live NFL playoff game. But did it work? “It works. It is a functioning technical proof-of-concept that accurately represents what the buyer wants to buy and what the seller has to sell,” said Ryan McConville, chief product officer and evp of ad products and solutions at NBCUniversal on the latest Digiday Podcast. Despite the successful test, NBCU isn't about to outsource its entire ad sales process to AI agents anytime soon. “We are a ways away from having this fully productionalized where multiple agencies are using this day in and day out to replace current workflows,” he said. That said, NBCU is now a lot closer to what McConville calls”premium automation,” as he explained in the episode.

Find out if EDI is still the backbone of scalable freight operations and what happens when you stop penalizing brokers for growth in this episode with our returning guest, Brad Perling of Bitfreighter! Brad shares why their EDI-first freight technology strategy is quietly reshaping shipper integration, automated quoting, and brokerage scalability. Brad and I talk through why EDI remains the most reliable foundation for freight data integrity, how unlimited EDI messaging pricing removes one of the biggest cost barriers for growing brokerages, seamless integration through APIs and RPA across TMS platforms and load boards, and how real-time quoting analytics are driving millions in new revenue for customers. If you're a freight broker or shipper looking to scale without breaking your tech stack or your budget, this conversation lays out exactly why EDI (if done right) is still a competitive advantage in modern freight tech!   About Brad Perling Brad Perling is the CEO and co-founder of Bitfreighter. With over 15 years of experience in the industry and growing 2 successful brokerages, Brad's deep understanding of logistics challenges has fueled his passion for finding better software solutions. He knew there was a need for a disruptive new model in the EDI space and was determined to create it. He has a passion for aviation and enjoys playing hockey and golf while spending time with his wife and 2 kids.   Connect with Brad Website: https://www.bitfreighter.com/  LinkedIn: https://www.linkedin.com/in/brad-perling-5a101655/  

Is America healing? We discuss how TPUSA exposed Candace Owens, and how Greenland and the price of precious metals may be signaling a global game of chess. This episode is powered by Ruff Greens...the supplement that makes your dog's food come alive! Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.

Is America healing? After Year One of the Trump Administration, we discuss their extraordinary accomplishments, and how CNN was caught trying to dupe the American public...again. This episode is powered by Ruff Greens...the supplement that makes your dog's food come alive! Use Discount Code “RPA” to claim your FREE JumpStart Trial Bag at RuffGreens.com.Support the show: https://redpilledamerica.com/support/See omnystudio.com/listener for privacy information.