Podcasts about Concurrency

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubA N M Bazlur Rahman - Java Champion & Author of "Modern Concurrency in Java"Michael Redlich - Java Champion & Lead Java Queue News Editor at InfoQCheck out more here:https://gotopia.tech/episodes/443RESOURCESBazlurhttps://bsky.app/profile/bazlur.cahttps://x.com/bazlur_rahmanhttps://github.com/rokon12https://www.linkedin.com/in/bazlurhttps://bio.site/bazlurhttps://bazlur.caMichaelhttps://twitter.com/mpredlihttps://github.com/mpredli01https://www.linkedin.com/in/michael-redlich-13a966https://about.me/mpredliDESCRIPTIONIn this GOTO Book Club episode, Java Champion A N M Bazlur Rahman joins host and fellow Java Champion Michael Redlich to discuss Modern Concurrency in Java — the first comprehensive update to Java concurrency literature in 20 years. Bazlur traces his motivation to the arrival of virtual threads in JDK 21, which he describes as a fundamental shift in Java's concurrency cost model: platform threads were expensive and scarce, demanding careful pooling; virtual threads are cheap, plentiful, and behave like ordinary threads from the developer's perspective, without requiring a new programming model. The book covers this evolution end-to-end, from the history of threads through to structured concurrency, scope values, and the modern frameworks that have already adopted virtual threads — most with a single config change.The conversation also takes a nuanced look at reactive programming's future. Bazlur's conclusion is that reactive remains compelling in specific contexts — event-driven streaming systems, architectures needing end-to-end back-pressure — but it's no longer the default answer to scalability. For most microservices doing blocking I/O, virtual threads are now the stronger default, and reactive becomes a deliberate architectural choice rather than an automatic one. The book's goal is to give developers both the conceptual grounding and the practical guidance to make that choice confidently — understanding the tool one level deep, so they can design better systems, not just configure their way through a framework.RECOMMENDED BOOKSA N M Bazlur Rahman • Modern Concurrency in Java • https://amzn.to/42w8cOkBen Evans & Jim Gough • Optimizing Cloud Native Java • https://amzn.to/41nivD9Ben Evans, Jason Clark & David Flanagan • Java in a Nutshell • https://amzn.to/43FDoMAIan F. Darwin • Java Cookbook 5th ed. • https://amzn.to/3QH0NZyVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

This episode is part of our 2026 Summerfest TechAI series, sponsored by Cream City Cyber. Steve's guests on this one include:Celeste Cuffie, Chief People Officer and Co-founder, Cream City CyberSheldon Cuffie, CEO & Co-founder, Cream City CyberLena DeLaet, Director of Sales & Summerfest TechAIWhat you'll get:Sheldon and Celeste share how they bootstrapped Cream City Cyber from scratch into a nearly 40-person, multimillion-dollar cybersecurity firm in the last 18 months. They also announced they're now launching a second Milwaukee venture, They make the case that AI is moving at “light speed,” but the real risk isn't the technologyOn the cybersecurity front, Sheldon warns that AI is becoming “a firecracker on top of a rocket ship” The conversation then turns to Milwaukee's biggest challengeThanks to Concurrency and Secure Compliance Solutions, sponsors of Experience Milwaukee!

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 Season 15 episode 3, Charles Suggs sits down with Greg Medland, aka “The Elixir Fixer,” to talk about the current state of hiring and the software jobs market in 2026.   Greg shares what he's seeing from both sides of the hiring process as an Elixir-focused recruiter, from shifting company expectations to the growing importance of specialization, communication skills, and real-world product thinking. We discuss how the market has changed since the 2021–2022 hiring boom, why things feel more uncertain today, and how developers are adapting to a slower, more competitive landscape.   The conversation also explores how AI is affecting hiring workflows, résumé quality, technical interviews, and even the rise of fraudulent candidates. Greg explains why human relationships and reputation still matter more than ever, especially in smaller ecosystems like Elixir where community connections carry real weight.   Along the way, we talk about what junior developers are up against, why senior engineers with domain expertise continue to stand out, and what developers can do to position themselves more effectively in today's market. Greg shares practical advice for building a sustainable career, developing a clear professional identity, and navigating a rapidly changing industry.   Topics discussed in this episode: The current state of the Elixir job market Hiring trends and market shifts since 2021–2022 How AI is changing hiring and recruiting workflows Fraudulent candidates and AI-generated résumés Domain expertise vs. generalist engineering skills Product thinking and customer-focused development What companies are looking for in 2026 Junior developer challenges in the current market Why senior specialists remain in demand Networking and relationship-building in tech Open source contributions and visibility in the Elixir community Standing out in a crowded hiring environment Résumé quality and application strategies The role of personal branding for developers Remote work trends and geographic hiring patterns Technical interview expectations and evaluation changes Startup vs. enterprise hiring differences Human connection in an increasingly automated industry Career resilience and long-term positioning Building a sustainable software engineering career   Links mentioned: Socially Responsible Recruitment https://sr2rec.com/en/ Greg's LinkedIn https://www.linkedin.com/in/elixirfixer/ Greg's email address: greg@sr2rec.com

https://clearmeasure.com/developers/forums/ Stephen Cleary is a software developer, author, and independent consultant with deep expertise in asynchronous and concurrent programming in .NET. He is the author of "Concurrency in C# Cookbook" (O'Reilly, 2nd edition), the definitive practical reference on async, parallel, reactive, and multithreaded programming in C#. Stephen is one of the top-ranked users on Stack Overflow, widely recognized for his authoritative answers on async/await, and he has published extensively on the subject through MSDN Magazine, conference talks, and his long-running blog. His most recent blog post, "Debug Dumps in Visual Studio," was published in December 2025 and continues his tradition of sharing hard-won, practical knowledge with the .NET community. Website: https://stephencleary.com  Blog: https://blog.stephencleary.com Book: https://stephencleary.com/book/ GitHub: https://github.com/StephenCleary Twitter/X: https://x.com/astevecleary Github - Comparers Nuget - Nito Comparers  Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

In this episode of the HR Mixtape, Dr. Shari Simpson speaks with Meghan Focht, Human Capital Director at Concurrency, about the intentional design of workplace culture. They discuss how to identify cultural strengths and weaknesses through values and behaviors, and how to implement systems that support a positive environment. Listeners will learn practical strategies for assessing their organization's culture and making meaningful changes. • Understand the importance of aligning values with behaviors in the workplace. • Identify key systems to evaluate when assessing organizational culture. • Learn how to create a culture of recognition that reinforces desired behaviors. • Discover ways to engage leaders in cultural conversations and accountability. • Explore practical steps to implement cultural changes in your organization. 00:00 -- Introduction to the episode 00:54 -- Meghan's experience with culture change 02:43 -- Key systems to assess culture 04:42 -- Linking values to behaviors and competencies 05:15 -- Hiring for cultural fit 06:19 -- Celebrating values through recognition programs 08:11 -- The role of technology in culture 10:39 -- Engaging leaders in cultural alignment 12:50 -- Identifying cultural misalignments 16:00 -- Signs of a healthy culture 17:55 -- Building values from scratch 19:39 -- Actionable steps for cultural improvement Guest(s): Meghan Focht is the Human Capital Director at Concurrency, where she focuses on developing strong people systems that foster growth and a positive culture. She has extensive experience in identifying and addressing cultural challenges within organizations. Meghan is passionate about creating environments that empower employees and enhance collaboration. Keywords: workplace culture, assessing culture, values and behaviors, recognition programs, leadership accountability, cultural change, hiring for fit, technology in HR, employee engagement, culture systems

Jay's bio:Jay Kolbe, a Milwaukee area resident, is a 20-year communications pro and co-founder / managing partner of Impact Partners, a New York-based strategic communications and growth advisory firm.He works closely with high-net-worth individuals, family offices, investors, and emerging technology companies.Jay also advises a wide range of technology and media-related brands and is known for helping founders, firms, and leaders create sharper, more ownable narratives around who they are and why they matter.In this episode, Jay offers a sharp outside-insider perspective on Milwaukee and Wisconsin: a place with real substance, pride, and values, but one that too often under-owns its wins, hesitates to tell its story, and leaves too much room for bigger coastal markets to define the conversation instead.Steve and Jay also get into one of the most uncomfortable but important tensions in the region's tech ecosystem.Sponsored by Concurrency and Secure Compliance Solutions.

A new president brings a new future to the B-1 Lancer program as the Reagan rearmament program sees development of the B-1 accelerated. The concurrent development and production brings its own headaches as the Lancer matures in time for a twenty year period of constant operations over Afghanistan and Iraq.Buy The Supersonic BONE: A Development and Operational History of the B-1 Bomber by Kenneth P. Katz at The Aviation Show Bookshop. 10% of each sale supports the show.UK: https://uk.bookshop.org/a/16621/9781399020299US: https://bookshop.org/a/111804/9781399020299This is an edited version of our podcast that was released in 2022.-----------------------------------------------------

In this episode, Fab Brasca (SVP at Kinaxis) discusses the evolution of supply chain technology from 1990s silos to modern, integrated systems. The three key takeaways: Concurrency over Silos: Moving beyond sequential processes, concurrency allows for instantaneous, cross-functional visibility. A change in one area—such as a forecast adjustment—immediately ripples across the entire network. Actionable Control Towers: While many towers only monitor, the sources emphasize that they must become actionable. Through scenario planning, teams can evaluate disruptions (like labor shortages) and agree on responses before committing changes to a master plan. AI Democratization: Agentic AI and LLMs are lowering skill barriers, allowing non-planners to use natural language to identify supply chain trouble spots. However, human-in-the-loop governance is essential to ensure reliability. Ultimately, as volatility becomes structural, firms must build adaptable environments to thrive. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Bienvenue dans le trois-centième épisode de CacaoCast! Dans cet épisode, Philippe Casgrain et Philippe Guitard discutent des sujets suivants: electricite-quebec.info - La demande provinciale au bout des doigts Electron Liquid Glass - On n'arrête pas le progrès! Swift concurrency - Enfin un guide simplifié Icônes dans les menus de Tahoe - Vous pouvez les enlever dans votre application Subtext - Un éditeur de texte pour iOS simple et gratuit Unixv4.dev - Unix original dans votre navigateur Sloppy - La nouvelle mascotte IA de Microsoft Drone et ski - Un petit film de Philippe Ecoutez cet épisode

In this special edition of the Liquid Weekly Podcast, hosts Karl Meisterheim and Taylor Page are joined by Eytan Seidman, VP of Product at Shopify, to discuss the major announcements from Winter Editions '26. Eytan reveals how Shopify is rewiring the platform to be fully AI-native, democratizing access to the Catalog MCP for building agentic shopping experiences, and introducing App Intents for Sidekick, allowing apps to register capabilities that Sidekick can invoke directly from merchant queries.The group also dives deep into the "boring but powerful" platform updates, including massive improvements to Bulk Operations concurrency, advanced filtering and increased limits for Metaobjects, and the shift to server-side rendering for Shopify.dev.Find Eytan Seidman OnlineLinkedIn: / eytanseidman Twitter (X): / eytan Timestamps00:00 Teaser: App Intents & Sidekick Capabilities00:41 Intro & Welcome01:24 Introducing Eytan Seidman, VP of Product at Shopify02:51 High-Level Overview of Winter Editions '26 Updates05:31 AI-Native Platform: Dev Assistant & MCP Server Updates11:57 Technical Deep Dive: Shopify.dev moving to Server-Side Rendering (Remix)16:38 Catalog MCP: Democratizing Agentic Shopping Experiences23:39 App Intents for Sidekick: Registering Tools & Context30:30 Merchant Control & Privacy in Sidekick Extensions39:16 Boring Updates: Bulk Operations Improvements (Concurrency & Mutation Support)42:40 Boring Updates: Metaobjects (Advanced Filtering & Increased Limits)51:13 Eytan's Top 3 Takeaways for Developers54:20 OutroResourcesShopify Editions: https://www.shopify.com/editionsMetaobjects Documentation: https://help.shopify.com/en/manual/cu...Bulk Operations API: https://shopify.dev/docs/api/usage/bu...Model Context Protocol (MCP): https://modelcontextprotocol.io/Dev ChangelogThe hosts and Eytan discuss the following updates from the Winter Editions '26 release:AI-Native Dev Platform: The Dev Assistant and Dev MCP Server now cover the entire platform, including generating Checkout UI Extensions, modifying TOML files, and utilizing the latest Polaris components.Shopify.dev Server-Side Rendering: The documentation site has moved to server-side rendering (using Remix) to improve crawler access and performance for AI agents.Catalog MCP: Developers now have access to the full catalog of products across the merchant base (normalized and searchable) to build agentic shopping experiences.App Intents for Sidekick: Apps can now register "tools" and capabilities that Sidekick can invoke based on natural language merchant queries (e.g., "Show me my recent campaign stats").Bulk Operations Improvements:Now supports all mutations. Concurrency increased to allow up to 5 operations per API client per shop simultaneously.New query to check the status of all running bulk operations.Metaobject Enhancements: Advanced Filtering: Query metaobjects by value ranges on metafields (e.g., filter testimonials by date range). Increased Limits: Separate pools for apps—each app can create up to 128 definitions with up to 1 million entries each.Sign Up for Liquid WeeklyDon't miss out on expert insights and tips—subscribe to Liquid Weekly for more content like this: https://liquidweekly.com/

What are the steps to get started building a FastAPI application? What are the different types of concurrency available in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder's Weekly articles and projects.

Hi, Spring fans! In this installment, I sit down with the legendary Bruce Eckel, who has probably forgotten more about programming languages than I will ever know, and whose book _Thinking in Java_ helped launch me into a career.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview here:https://gotopia.tech/episodes/393Victor Grazi - Oracle Java Champion & Co-Author of "Real-World Java"Jeanne Boyarsky - Oracle Java Champion, Co-Author of "Real-World Java" & "OCP 21 Java Cert Book"Barry Burd - Professor at Drew University, Owner at Burd Brain Consulting & Author of "Java for Dummies"RESOURCESVictorhttps://x.com/vgrazihttps://www.linkedin.com/in/victorgraziJeannehttps://bsky.app/profile/jeanneboyarsky.bsky.socialhttps://www.linkedin.com/in/jeanne-boyarskyBarryhttps://x.com/allmycodehttps://www.linkedin.com/in/barry-burdLinkshttps://projectlombok.orghttps://www.selikoff.net/2014/12/07/why-i-like-regular-expressions-who-says-they-arent-readableDESCRIPTIONBarry interviews Victor and Jeanne  about their book "Real-World Java: Helping You Navigate the Java Ecosystem".Victor emphasizes that knowing how to use your development tools, particularly IDE refactoring features, is a better indicator of developer experience than algorithm tests.Rather than just teaching "hello world" examples, the authors focus on the essential ecosystem components needed to succeed in enterprise Java environments, making it accessible for anyone who knows the Java language but needs to understand the broader technological landscape they'll encounter in professional development roles. RECOMMENDED BOOKSVictor Grazi & Jeanne Boyarsky • Real-World Java • https://amzn.to/4oCEeBRJeanne Boyarsky &Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts Spotify Canada NowBold ideas with the people shaping Canada's next chapter.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Alvin is a technical writer and software developer specializing in Microsoft technologies, specifically .NET, C#, and XAML. He has over 30 years of experience developing applications for Windows, mobile, and the web. He has authored two books for Packt Publishing and is a full-time content developer on the Windows developer docs on Microsoft Learn. His published books are 'Learn WinUI 3' and 'Parallel Programming and Concurrency with C# 10 and .NET 6'.You can find Alvin on the following sites:WebsiteLinkedInGitHubBlueskyMastodonPLEASE SUBSCRIBE TO THE PODCASTSpotifyApple PodcastsYouTube MusicAmazon MusicRSS FeedYou can check out more episodes of Coffee and Open Source on https://www.coffeeandopensource.comCoffee and Open Source is hosted by Isaac Levin

Bienvenue dans le deux-cent-quatre-vingt-quinzième épisode de CacaoCast! Dans cet épisode, Philippe Casgrain et Philippe Guitard discutent des sujets suivants: Apple Event “Awe Dropping” - Les nouveautés de l'automne AFP - Maintenant obsolète avec macOS Sequoia 15.5 Swift concurrency - La documentation mise-à-jour GitDesktop - Un client Git simplifié VoltStar - Le chargement de votre Polestar dans la barre de menus SillyBalls - Un classique Mac QuickDraw réinventé Commodore - Le retour du C64 AltWeatherCan - Si vous vous ennuyez de l'application météo d'Environnement Canada Ecoutez cet épisode

Today, the Elixir Wizards wrap up Season 14 “Enter the Elixirverse.” Dan, Charles, and Sundi look back at some common themes: Elixir plays well with others, bridges easily to access languages and tools, and remains a powerful technology for data flow, concurrency, and developer experience. We revisit the popular topics of the year, from types and tooling to AI orchestration and reproducible dev environments, and share what we're excited to explore next.   We also invite your questions and takeaways to help shape future seasons and conference conversations. Season 14 doubles as a handy primer for anyone curious about how Elixir integrates across the stack.   Key topics discussed in this episode:   * Lessons from a season of interoperability * Set-theoretic types and what new compiler warnings unlock * AI in practice: LLM orchestration, fallbacks, and real-world use * SDUI and GraphQL patterns for shipping UI across web/iOS/Android * Dataframes in Elixir with Explorer for analytics workflows * Python interoperability (ErlPort, PythonX) and when to reach for it * Reproducible dev environments with Nix and friends * Performance paths: Rustler and Zig for native extensions * Bluetooth & Nerves: Blue Heron and hardware integrations * DevEx upgrades: LiveView, build pipelines, and standard project setup * Observability and ops: Prometheus/Grafana and sensible deployments * Community feedback, conferences, and what's on deck for next season   Links mentioned in this episode: Cars.com S14E06 SDUI at Scale with Elixir https://youtu.be/nloRcgngTk?si=g4Zd4N1s56Ronrtw https://hexdocs.pm/phoenixliveview/Phoenix.LiveView.html https://wordpress.com/ https://elixir-lang.org/ S14E01 Zigler: Zig NIFs for Elixir https://youtu.be/hSAvWxh26TU?si=d55tVuZbNw0KCfT https://ziglang.org/ https://hexdocs.pm/zigler/Zig.html https://github.com/blue-heron/blueheron https://github.com/elixir-explorer/explorer S14E08 Nix for Elixir Apps https://youtu.be/yymUcgy4OAk?si=BRgTlc2VK5bsIhIf https://nixos.org/ https://nix.dev/ S14E07 Set Theoretic Types in Elixir https://youtu.be/qMmEnXcHxL4?si=Ux2lebiwEp3mc0e S14E10 Python in Elixir Apps https://youtu.be/SpVLrrWkRqE?si=ld3oQVXVlWHpo7eV https://www.python.org/ https://hexdocs.pm/pythonx/ https://github.com/Pyrlang/Pyrlang https://github.com/erlport/erlport S14E03 LangChain: LLM Integration for Elixir https://youtu.be/OwFaljL3Ptc?si=A0sDs2dzJ0UoE2PY https://github.com/brainlid/langchain S14E04 Nx & Machine Learning in Elixir https://youtu.be/Ju64kAMLlkw?si=zdVnkBTTLHvIZNBm S14E05 Rustler: Bridging Elixir and Rust https://youtu.be/2RBw7B9OfwE?si=aRVYOyxxW8fTmoRA https://github.com/rusterlium/rustler Season 3: Working with Elixir https://youtube.com/playlist?list=PLTDLmInI9YaDbhMRpGuYpboVNbp1Fl9PD&si=hbe7qt4gRUfrMtpj S14E11 Vibe Coding the LoopedIn Crochet App https://youtu.be/DX0SjmPE92g?si=zCBPjS1huRDIeVeP Season 5: Adopting Elixir  YouTubeLaunchisode and Outlaws Takeover with Chris Keathley, Amos King, and Anna Neyzberg S13E01 Igniter: Elixir Code Generation https://youtu.be/WM9iQlQSF_g?si=e0CAiML2qC2SxmdL Season 8: Elixir in a Polyglot Environment https://youtube.com/playlist?list=PLTDLmInI9YaAPlvMd-RDp6LWFjI67wOGN&si=YCI7WLA8qozD57iw !! We Want to Hear Your Thoughts *!!* Have questions, comments, or topics you'd like us to discuss on the podcast? Share your thoughts with us here: https://forms.gle/Vm7mcYRFDgsqqpDC9

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereBen Evans - Senior Principal Software Engineer at Red Hat & Co-Author of "Optimizing Cloud Native Java" and many more BooksHolly Cummins - Senior Principal Software Engineer on the Red Hat Quarkus TeamRESOURCESBenhttps://mastodon.social/@kittylysthttps://www.linkedin.com/in/kittylysthttps://www.kittylyst.comHollyhttps://hollycummins.comhttps://bsky.app/profile/hollycummins.comhttps://hachyderm.io/@holly_cumminshttps://linkedin.com/in/holly-k-cumminsDESCRIPTIONHolly Cummins talks with Ben Evans about his latest book "Optimizing Cloud Native Java", which updates his previous work "Optimizing Java" to reflect the realities of cloud native environments.Ben explains that performance engineering is not just technical but also psychological, emphasizing the importance of user expectations and defining clear performance goals. They discuss how modern Java performance must account for cloud native architectures, with applications running across distributed microservices and containerized, single-core environments.The book focuses on the importance of measuring relevant data, warns against relying on misleading micro-benchmarks, and highlights how system-level benchmarks offer a clearer picture. Ben also delves into the JVM's hidden complexities, such as changes in Java 17 and the impact of virtual threads. Practical, real-world examples in the book, like the "fighting animals" microservices application, help developers learn how to optimize Java performance in real network environments.Finally, Ben touches on the future of Java concurrency, with virtual threads and structured concurrency offering new ways to handle performance challenges in cloud native systems.RECOMMENDED BOOKSBen Evans & Jim Gough • Optimizing Cloud Native JavaBen Evans, Jason Clark & David Flanagan • Java in a NutshellBen Evans, Martijn Verburg & Jason Clark • The Well-Grounded Java DeveloperBen Evans, Jim Gough & Chris Newland • Optimizing JavaBen Evans & Martijn Verburg • The Well-Grounded Java DeveloperBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

הנסיעה הראשונה שלנו ברכבלית החדשה בחיפה הביאה אותנו כל הדרך ל… משרד הדיקנית של הפקולטה להנדסת חשמל ומחשבים בטכניון. הנוף משגע, הכיבוד טעים, והמרואיינת - וואו! לכם רק נשאר להישען לאחור, להניח רגל על רגל, ללמוד ולהנות. המרואיינת שלנו הפעם היא פרופ׳ עדית קידר - דיקנית הפקולטה להנדסת חשמל בטכניון. עדית היא מומחית בחישוב מקבילי ומבוזר, והיא שיתפה אותנו בחוויות שלה מהמסע האקדמי שלה בצורה מעשירה ומעניינת. אז על מה דיברנו? - ההבדלים בין חישוב מקבילי ומבוזר. - מתי כדאי לממש חישוב מקבילי? - איך מדבגים מערכת מבוזרת? - מה זו אי-היתכנות? - שילוב נשים בפקולטה להנדסת חשמל ומחשבים בטכניון. - עתיד הפקולטה להנדסת חשמל ומחשבים בטכניון. - מה הקשר בין הכנת עוגה וחישוב מקבילי? אחרי שהאזנתם לפרק מוזמנים להצטרף לקבוצת המאזינים שלנו - שם אנחנו מבזרים הודעות בצורה מרוכזת >>> https://chat.whatsapp.com/KwUu8pQsxx220qS7AXv04T מוזמנים ליצור איתנו קשר במייל podcasthardreset@gmail.com

In this special episode of Book Overflow, Carter and Nathan are joined by Kirill Bobrov, author of Grokking Concurrency! Join them as Kirill reflects on what it was like writing a book for the first time, why concurrency interests him so much, and more!-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Grokking Concurrency by Kirill Bobrovhttps://amzn.to/3GRbnby (paid link)https://mng.bz/Z99m (45% off!)----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Чтобы научиться программировать и разбираться в тонкостях Python 3.12 записывайтесь на базовый курс Learn Python — https://clck.ru/3MuSmw Новости выпуска:  State of Free Threading Python (FTP) — https://pyfound.blogspot.com/2025/06/python-language-summit-2025-state-of-free-threaded-python.html  Проблемы честной Concurrency — https://pyfound.blogspot.com/2025/06/python-language-summit-2025-fearless-concurrency.html  Как дела у Python на мобилке — https://pyfound.blogspot.com/2025/06/python-language-summit-2025-python-on-mobile.html  Python can run Mojo now — https://koaning.io/posts/giving-mojo-a-spin/ Заменит ли AI джунов? — https://blog.adarshd.dev/posts/pycon-us-ai-and-future-of-programming/  Как дизайнить DSL-и в эпоху LLM — https://kirancodes.me/posts/log-lang-design-llms.html  Ссылки выпуска: Курс Learn Python — https://learn.python.ru/advanced Канал Миши в Telegram — https://t.me/tricky_python Канал Moscow Python в Telegram — https://t.me/moscow_python Все выпуски — https://podcast.python.ru Митапы Moscow Python — https://moscowpython.ru Канал Moscow Python на Rutube — https://rutube.ru/channel/45885590/ Канал Moscow Python в VK — https://vk.com/moscowpythonconf Курс «Основы Python» от Learn Python — это отличный старт для новичков в программировании. За несколько уроков вы освоите базовый синтаксис, научитесь работать с данными и получите первый опыт для успешного старта карьеры в ИТ. Подробности: https://clck.ru/3MuSjG

Zack Kayser, Staff Software Engineer at cars.com, joins Elixir Wizards Sundi Myint and Charles Suggs to discuss how Cars.com adopted a server-driven UI (SDUI) architecture powered by Elixir and GraphQL to deliver consistent, updatable interfaces across web, iOS, and Android. We explore why SDUI matters for feature velocity, how a mature design system and schema planning make it feasible, and what it takes, culturally and technically, to move UI logic from client code into a unified backend. Key topics discussed in this episode: SDUI fundamentals and how it differs from traditional server-side rendering GraphQL as the single source of truth for UI components and layouts Defining abstract UI components on the server to eliminate duplicate logic Leveraging a robust design system as the foundation for SDUI success API-first development and cross-team coordination for schema changes Mock data strategies for early UI feedback without breaking clients Handling breaking changes and hot-fix deployments via server-side updates Enabling flexible layouts and A/B testing through server-controlled ordering Balancing server-driven vs. client-managed UI Iterative SDUI rollout versus “big-bang” migrations in large codebases Using type specs and Dialyxir for clear cross-team communication Integration testing at the GraphQL layer to catch UI regressions early Quality engineering's role in validating server-driven interfaces Production rollback strategies across web and native platforms Considerations for greenfield projects adopting SDUI from day one Zack and Ethan's upcoming Instrumenting Elixir Apps book Links mentioned: https://cars.com https://github.com/absinthe-graphql/absinthe Telemetry & Observability for Elixir Apps Ep: https://youtu.be/1V2xEPqqCso https://www.phoenixframework.org/blog/phoenix-liveview-1.0-released https://hexdocs.pm/phoenixliveview/assigns-eex.html https://graphql.org/ https://tailwindcss.com/ https://github.com/jeremyjh/dialyxir https://github.com/rrrene/credo GraphQL Schema https://graphql.org/learn/schema/ SwiftUI https://developer.apple.com/documentation/swiftui/  Kotlin https://kotlinlang.org/ https://medium.com/airbnb-engineering/a-deep-dive-into-airbnbs-server-driven-ui-system-842244c5f5 Zack's Twitter: https://x.com/kayserzl/ Zack's LinkedIn: https://www.linkedin.com/in/zack-kayser-93b96b88  Special Guest: Zack Kayser.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

In this episode of Book Overflow, Carter and Nathan discuss the second half of Grokking Concurrency by Kirill Bobrov! Join them as they discuss the mutexes, semaphores, the reactor pattern, and more!-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Grokking Concurrency by Kirill Bobrovhttps://amzn.to/3GRbnby (paid link)----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

In this episode of Book Overflow, Carter and Nathan discuss the first half of Grokking Concurrency by Kirill Bobrov! Join them as they discuss the basic building blocks of concurrency, how concurrency has evolved over time, and how building concurrent applications can increase performance!Go Proverbs: https://go-proverbs.github.io/-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------Grokking Concurrency by Kirill Bobrovhttps://amzn.to/3GRbnby (paid link)Web Scalability for Startup Engineers by Artur Ejsmonthttps://amzn.to/3F1VWwF (paid link)----------------00:00 Intro02:07 About the Book and Author03:35 Initial Thoughts on the Book09:12 What is Concurrency vs Parallelism12:35 CPUs and Moore's Law22:19 IO Performance, Embarrassingly Parallel and Conway's Law28:25 Building Blocks of Concurrency: Processes and Threads33:05 Memory Sharing vs Communicating39:13 Multitasking and Context Switching45:24 Task Decomposition and Data Pipelines52:35 Final Thoughts----------------Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Tim Sweeney is a legendary video game programmer, founder and CEO of Epic Games that created the Unreal Engine, Fortnite, Gears of War, Unreal Tournament, and many other groundbreaking and influential video games. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep467-sc See below for timestamps, and to give feedback, submit questions, contact Lex, etc. CONTACT LEX: Feedback - give feedback to Lex: https://lexfridman.com/survey AMA - submit questions, videos or call-in: https://lexfridman.com/ama Hiring - join our team: https://lexfridman.com/hiring Other - other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Tim's X: https://x.com/timsweeneyepic Epic Games: https://epicgames.com/ SPONSORS: To support this podcast, check out our sponsors & get discounts: Notion: Note-taking and team collaboration. Go to https://notion.com/lex MasterClass: Online classes from world-class experts. Go to https://masterclass.com/lexpod Shopify: Sell stuff online. Go to https://shopify.com/lex AG1: All-in-one daily nutrition drink. Go to https://drinkag1.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex OUTLINE: (00:00) - Introduction (08:25) - 10,000 hours programming (11:42) - Advice for young programmers (19:54) - Video games in the 80s and 90s (22:02) - Epic Games origin story (34:40) - Indie game development (40:34) - Unreal Engine (1:06:30) - Technical details of Unreal Engine (1:11:23) - Constructive solid geometry (1:17:21) - Dynamic lighting (1:21:51) - Volumetric fog (1:25:19) - John Carmack (1:27:05) - Evolution of Unreal Engine (1:33:21) - Unreal Engine 5 (1:44:32) - Creating realistic humans (1:53:41) - Lumen global illumination (1:58:11) - Movies (2:12:53) - Simulating reality (2:25:08) - Metaverse (2:27:44) - Fortnite (2:31:40) - Scaling (2:47:04) - Game economies (2:48:33) - Standardizing the Metaverse (2:56:46) - Verse programming language (3:18:19) - Concurrency (3:25:56) - Unreal Engine 6 (3:30:34) - Indie game developers (3:33:32) - Apple (3:48:12) - Epic Games Store (4:11:03) - Future of gaming (4:17:03) - Greatest games ever made (4:22:39) - GTA 6 and Rockstar Games (4:25:58) - Hope for the future PODCAST LINKS: - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips SOCIAL LINKS: - X: https://x.com/lexfridman - Instagram: https://instagram.com/lexfridman - TikTok: https://tiktok.com/@lexfridman - LinkedIn: https://linkedin.com/in/lexfridman - Facebook: https://facebook.com/lexfridman - Patreon: https://patreon.com/lexfridman - Telegram: https://t.me/lexfridman - Reddit: https://reddit.com/r/lexfridman

This week we talk to Charles Lowell, a developer and consultant who has created a library called Effection. Effection is a library that allows you to write structured concurrency code in JavaScript. What is structured concurrency and how could it be useful for you? Find out in this episode!https://frontside.com/effectionhttps://github.com/thefrontside/effectionhttps://frontside.com/effection/contrib/https://frontside.com/Become a paid subscriber our patreon, spotify, or apple podcasts for the ad-free episode.https://www.patreon.com/devtoolsfmhttps://podcasters.spotify.com/pod/show/devtoolsfm/subscribehttps://podcasts.apple.com/us/podcast/devtools-fm/id1566647758https://www.youtube.com/@devtoolsfm/membership

In this episode of PodRocket, Dev Agrawal, dev advocate and developer, talks about building efficient asynchronous UIs, the challenges and solutions for handling complex state management, utilizing React and Solid frameworks, and the potential of suspense boundaries and transitions in modern web development. Links https://devagr.me https://github.com/devagrawal09 https://www.linkedin.com/in/dev-agrawal-88449b157 https://medium.com/@devagrawal09 https://www.youtube.com/channel/UCDXzM8ijdxkVA6NbQiQCKag https://x.com/devagrawal09 https://events.codemash.org/2025CodeMashConference#/agendaday=4&lang=en&sessionId=76186000004278631&viewMode=2 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Dev Agrawal.

In this episode of the Modern Web Podcast, hosts Rob Ocel and Danny Thompson sit down with Mariano Cocirio, Staff Product Manager at Vercel, to discuss Fluid Compute, a new cloud computing model that blends the best of serverless scalability with traditional server efficiency. They explore the challenges of AI workloads in serverless environments, the high costs of idle time, and how Fluid Compute optimizes execution to reduce costs while maintaining performance. Mariano explains how this approach allows instances to handle multiple requests efficiently while still scaling to zero when not in use. The conversation also covers what developers need to consider when adopting this model, the impact on application architecture, and how to track efficiency gains using Vercel's observability tools.Is Fluid Compute the next step in the evolution of serverless? Is it redefining cloud infrastructure altogether?Keypoints Fluid Compute merges the best of servers and serverless – It combines the scalability of serverless with the efficiency and reusability of traditional servers, allowing instances to handle multiple requests while still scaling down to zero. AI workloads struggle with traditional serverless models – Serverless is optimized for quick, stateless functions, but AI models often require long processing times, leading to high costs for idle time. Fluid Compute solves this by dynamically managing resources. No major changes required for developers – Fluid Compute works like a standard Node or Python server, meaning developers don't need to change their code significantly. The only consideration is handling shared global state, similar to a traditional server environment. Significant cost savings and efficiency improvements – Vercel's observability tools show real-time reductions in compute costs, with some early adopters seeing up to 85% savings simply by enabling Fluid Compute.Chapters0:00 – Introduction and Guest Welcome1:08 – What is Fluid Compute? Overview and Key Features2:08 – Why Serverless Compute Struggles with AI Workloads4:00 – Fluid Compute: Combining Scalability and Efficiency6:04 – Cost Savings and Real-world Impact of Fluid Compute8:12 – Developer Experience and Implementation Considerations10:26 – Managing Global State and Concurrency in Fluid Compute13:09 – Observability Tools for Performance and Cost Monitoring20:01 – Long-running Instances and Post-operation Execution24:02 – Evolution of Compute Models: From Servers to Fluid Compute29:08 – The Future of Fluid Compute and Web Development30:15 – How to Enable Fluid Compute on Vercel32:04 – Closing Remarks and Guest Social Media InfoFollow Mariano Cocirio on Social Media:Twitter:https://x.com/mcocirioLinkedin:https://www.linkedin.com/in/mcocirio/Sponsored by This Dot:thisdot.co

Bienvenue dans le deux-cent-quatre-vingt-huitième épisode de CacaoCast! Dans cet épisode, Philippe Casgrain et Philippe Guitard discutent des sujets suivants: Bambu Lab - La controverse CotEditor - Éditeur en code source AppIconKit - Pour que vos utilisateurs changent l'icône de votre application AutoDock - Cachez votre dock en fonction de la taille de vos écrans Swift concurrency - Un glossaire Ecoutez cet épisode

Matt Masicotte comes on the show to clear up all our misconceptions about concurrency in Swift 6.

Part 2 of our chat with Donny - we discuss the job market, AI, Vision Pro, and of course Swift UI.GuestDonny WalsDonny Wals

Donny comes on yet again to give his thoughts on 2024 - Swift Data, Swift Testing and Swift Macros while we mourn the death of Combine.GuestDonny WalsDonny Wals

Zack Kayser and Ethan Gunderson, Software Engineers at Cars Commerce, join the Elixir Wizards to share their expertise on telemetry and observability in large-scale systems. Drawing from their experience at Cars.com—a platform handling high traffic and concurrent users—they discuss the technical and organizational challenges of scaling applications, managing microservices, and implementing effective observability practices. The conversation highlights the pivotal role observability plays in diagnosing incidents, anticipating system behavior, and asking unplanned questions of a system. Zack and Ethan explore tracing, spans, and the unique challenges introduced by LiveView deployments and WebSocket connections. They also discuss the benefits of OpenTelemetry as a vendor-agnostic instrumentation tool, the significance of Elixir's telemetry library, and practical steps for developers starting their observability journey. Additionally, Zack and Ethan introduce their upcoming book, Instrumenting Elixir Applications, which will offer guidance on integrating telemetry and tracing into Elixir projects. Topics Discussed: Cars.com's transition to Elixir and scaling solutions The role of observability in large-scale systems Uncovering insights by asking unplanned system questions Managing high-traffic and concurrent users with Elixir Diagnosing incidents and preventing recurrence using telemetry Balancing data collection with storage constraints Sampling strategies for large data volumes Tracing and spans in observability LiveView's influence on deployments and WebSocket behavior Mitigating downstream effects of socket reconnections Contextual debugging for system behavior insights Observability strategies for small vs. large-scale apps OpenTelemetry for vendor-agnostic instrumentation Leveraging OpenTelemetry contrib libraries for easy setup Elixir's telemetry library as an ecosystem cornerstone Tracing as the first step in observability Differentiating observability from business analytics Profiling with OpenTelemetry Erlang project tools The value of profiling for performance insights Making observability tools accessible and impactful for developers Links Mentioned https://www.carscommerce.inc/ https://www.cars.com/ https://hexdocs.pm/telemetry/readme.html https://kubernetes.io/ https://github.com/ninenines/cowboy https://hexdocs.pm/bandit/Bandit.html https://hexdocs.pm/broadway/Broadway.html https://hexdocs.pm/oban/Oban.html https://www.dynatrace.com/ https://www.jaegertracing.io/ https://newrelic.com/ https://www.datadoghq.com/ https://www.honeycomb.io/ https://fly.io/phoenix-files/how-phoenix-liveview-form-auto-recovery-works/ https://www.elastic.co/ https://opentelemetry.io/ https://opentelemetry.io/docs/languages/erlang/ https://opentelemetry.io/docs/concepts/signals/traces/ https://opentelemetry.io/docs/specs/otel/logs/ https://github.com/runfinch/finch https://hexdocs.pm/telemetry_metrics/Telemetry.Metrics.html https://opentelemetry.io/blog/2024/state-profiling https://www.instrumentingelixir.com/ https://prometheus.io/ https://www.datadoghq.com/dg/monitor/ts/statsd/ https://x.com/kayserzl https://github.com/zkayser https://bsky.app/profile/ethangunderson.com  https://github.com/open-telemetry/opentelemetry-collector-contrib Special Guests: Ethan Gunderson and Zack Kayser.

AJ (Alykhan Jetha), CEO and CTO of Marketcircle, joins the Elixir Wizards to share his experience building and evolving Daylite, their award-winning CRM and business productivity app for Apple users. He details his experiences as a self-taught programmer and how Marketcircle has navigated pivots, challenges, and opportunities since its founding in 1999. AJ explains why they migrated Daylite's backend to Elixir, focusing on their sync engine, which demands high concurrency and fault tolerance. He highlights how Elixir has improved performance, reduced cloud costs, and simplified development with its approachable syntax and productive workflows. The conversation also touches on the technical hurdles of deploying native apps for Apple devices and the potential for integrating new technologies like LiveView Native to streamline cross-platform development. For technical founders, AJ emphasizes the importance of leveraging your strengths (“superpowers”), staying deeply connected to the development process, and finding stability in tools like Elixir amidst a rapidly evolving tech ecosystem. He also shares Marketcircle's roadmap for migrating more customers to Elixir-powered systems and explores the potential for new features in their native apps. Tune in for insights on building resilient systems, navigating technical and business challenges, and how Elixir is shaping Marketcircle's future. Topics discussed in this episode: AJ's journey as a self-taught programmer and entrepreneur Marketcircle's evolution since 1999 and lessons from their pivots Daylite's growth as a flagship product for Apple users Migrating to Elixir for high concurrency and fault tolerance How Elixir improved performance and reduced cloud costs The simplicity of Elixir and its impact on developer onboarding Challenges in managing a growing microservices architecture Insights into deploying native apps for the Apple ecosystem Exploring LiveView Native for future cross-platform development Advice for technical founders: leveraging your superpowers Staying connected to development to maintain system understanding The role of Elixir in improving development efficiency and stability Planning gradual customer migrations to an Elixir-powered backend Potential new features for Daylite's native apps Benefits of collaboration with the Elixir community #ElixirMullet -- native app in the front, Elixir in the back Navigating a rapidly evolving tech ecosystem as a founder Leveraging Elixir to future-proof Marketcircle's systems Balancing technical and business priorities in a startup environment AJ's thoughts on the future of Elixir in powering business tools Links mentioned: https://www.marketcircle.com/ Daylite.app https://www.nextcomputers.org/ https://www.digitalocean.com/ Python Async https://docs.python.org/3/library/asyncio.html https://github.com/sinatra/sinatra https://github.com/dependabot https://kafka.apache.org/ https://www.djangoproject.com/ https://github.com/socketry/falcon https://github.com/puma/puma https://www.swift.org/blog/announcing-swift-6/ https://en.wikipedia.org/wiki/Async/await https://www.ffmpeg.org/ https://www.sqlite.org/ https://github.com/commanded/commanded https://pragprog.com/titles/khpes/real-world-event-sourcing/ https://en.wikipedia.org/wiki/ShipofTheseus https://reactnative.dev/ https://www.electronjs.org/ https://en.wikipedia.org/wiki/WebOS https://www.linkedin.com/in/alykhanjetha/ https://bsky.app/profile/ajetha.bsky.social Special Guest: Alykhan Jetha.

In this episode, Scott talks to Florian Engelhardt about concurrency in PHP. Links Florian on Mastodon – https://phpc.social/@flowcontrol Florian's Website – https://dotbox.org Article – Concurrency in PHP: What are my options? The post Community Corner: Concurrency With Florian Engelhardt appeared first on php[architect].

Nick Lockwood of SwiftFormat (not dash) joins the show to talk about the server side Swift conference, quirks of Swift you didn't know about, language design, and the future of Swift.GuestNick Lockwood@nicklockwood (Github)Nick Lockwood (@nicklockwood@mastodon.social)Nick Lockwood | LinkedInAnnouncementsNeed help with your projects this year? BrightDigit has openings.Join Bushel BetaJoin our Brand New Patreon Page!BrightDigit #100 - Top 10 Emails Ever!

Jaim Zuber returns after over 5 years to give us an overview of looking for a job in iOS development in 2024, how has it changed, what are some things you can do now, and when becoming a manager is the right call.GuestJaim Zuber@sharpfive (GitHub)Jaim Zuber (LinkedIn)@jaimzuber@mspsocial.net (Mastodon)AnnouncementsNeed help with your projects this year? BrightDigit has openings.Join Bushel BetaJoin our Brand New Patreon Page!BrightDigit #100 - Top 10 Emails Ever!

Today on Elixir Wizards, Bryan Green shares how he transformed a vintage 1930s rotary phone into a fully functional cell phone using Elixir, Nerves, and a mix of hardware components. Bryan shares the highs and lows of his project, from decoding rotary dial clicks to troubleshooting hardware issues with LED outputs. He explains why Nerves was the perfect fit for this project, offering SSH access, over-the-air updates, and remote debugging. You'll also hear how Elixir's concurrency model helped him manage hardware inputs and outputs efficiently using GenStateMachine and Genservers. Elixir and Nerves really shine when modeling real-world systems. Bryan dives into how he used a finite state machine to track the phone's states and handled inputs from the rotary dial and hook switch via GPIO. For hardware enthusiasts, Bryan's advice is to embrace this “golden age” of DIY electronics. Whether you're experienced with embedded systems or just curious on where to start, Bryan's VintageCell can inspire you to tinker with a hardware engineering project. Key topics discussed in this episode: Advantages of functional programming and immutability in Elixir Building hardware projects using Adafruit components Why Nerves was the best choice for the VintageCell project Interpreting rotary dial clicks using GPIO and circuits.gpio Troubleshooting hardware issues with LED diagnostics Challenges in optimizing wiring and PCB design Benefits of Nerves: SSH access, OTA updates, and remote debugging Modeling real-world systems with Elixir and Nerves Implementing a finite state machine with GenStateMachine Managing input with Genservers for rotary dial and hook switch Leveraging community resources like Discord, Elixir Slack, and forums Practical advice for keeping hardware projects on track Potential applications from SMS servers to home automation Links mentioned: Vintage Cellphone: Bridging the Past and Future with Elixir (https://www.youtube.com/watch?v=U4hetzVpjmo) Seven Languages in Seven Weeks https://pragprog.com/titles/btlang/seven-languages-in-seven-weeks/ Seven More Languages https://pragprog.com/titles/7lang/seven-more-languages-in-seven-weeks/ Node.js https://github.com/nodejs https://nerves-project.org/ https://www.arduino.cc/ Adafruit Circuit Playground https://www.adafruit.com/category/965 Adafruit 3D Printed Star Trek Communicator https://learn.adafruit.com/3d-printed-star-trek-communicator Adafruit FONA 3G Cellular + GPS Breakout https://learn.adafruit.com/adafruit-fona-3g-cellular-gps-breakout/overview https://github.com/elixir-circuits/circuitsgpio Nerves SSH https://hex.pm/packages/nervesssh OTA (over-the-air) Updates with NervesHub https://www.nerves-hub.org/ https://github.com/kicad Waveshare 4G Hat for Raspberry Pi https://www.waveshare.com/sim7600e-h-4g-hat.htm https://hexdocs.pm/genstatemachine/GenStateMachine.html https://hexdocs.pm/elixir/GenServer.html https://www.sparkfun.com/ https://www.digikey.com/ USB-C Gadget Mode with Nerves https://github.com/nerves-project/nervessystemrpi4/issues/18 https://livebook.dev/ https://codestorm.me/ https://github.com/codestorm1/vintage_cell/ Special Guest: Bryan Green.

To kick off Elixir Wizards Season 13, The Creator's Lab, we're joined by Zach Daniel, the creator of Igniter and the Ash framework. Zach joins hosts Owen Bickford and Charles Suggs to discuss the mechanics and aspirations of his latest brainchild, Igniter—a code generation and project patching framework designed to revolutionize the Elixir development experience. Igniter isn't just about generating code; it's about generating smarter code. By leveraging tools like Sourcerer and Rewrite, Igniter allows developers to modify source code and batch updates by directly interacting with Elixir's AST instead of regex patching. This approach streamlines new project setup and package installations and enhances overall workflow. They also discuss the strategic implications of Igniter for the broader Elixir community. Zach hopes Igniter will foster a more interconnected and efficient ecosystem that attracts new developers to Elixir and caters to the evolving needs of seasoned Elixir engineers. Topics discussed in this episode: Advanced package installation and code generation improve the developer experience Scripting and staging techniques streamline project updates Innovative methods for smoother installation processes in Elixir packages High-level tools apply direct patches to source code Progressive feature additions simplify the mix phx.new experience Chaining installers and composing tasks for more efficient project setup Continuous improvement in developer experiences to boost Elixir adoption Encourage listeners to collaborate by sharing code generation patterns Introduction of a new mix task aimed at removing the "unless" keyword in preparation for Elixir 1.18 You can learn more in the upcoming book "Building Web Applications with Ash Framework" by Zach and Rebecca Links mentioned: https://smartlogic.io/ https://alembic.com.au/blog/igniter-rethinking-code-generation-with-project-patching https://hexdocs.pm/igniter/readme.html https://github.com/ash-project/igniter https://www.zachdaniel.dev/p/serialization-is-the-secret https://www.zachdaniel.dev/p/welcome-to-my-substack https://ash-hq.org/ https://hexdocs.pm/sourceror/readme.html https://smartlogic.io/podcast/elixir-wizards/s10-e09-hugo-lucas-future-of-elixir-community/ https://github.com/hrzndhrn/rewrite https://github.com/zachdaniel https://github.com/liveshowy/webauthn_components https://hexdocs.pm/elixir/Regex.html https://github.com/msaraiva/vscode-surface https://github.com/swoosh/swoosh https://github.com/erlef/oidcc https://alembic.com.au/ https://www.zachdaniel.dev/ Special Guest: Zach Daniel.

Tonight on GeekNights, we talk about cores and concurrency and threads and such. Also Rym and Emily fought a bear. In the news, Intel's chip instability saga has concluded, and Python 3.13.0 is pretty good.Related LinksForum ThreadMulticore Multiprocess Concurrency and SuchDiscord ChatMulticore Multiprocess Concurrency and SuchThings of the DayRym - How Mei Blows Up the GameScott - Popular Science (May 1872)

Daniel Steinberg comes in the podcast to talk about his latest book on Swift Testing as well as the state of Swift development in 2024.GuestDim Sum Thinkingdimsumthinking (@dimsumthinking@mastodon.social) - MastodonDaniel Steinberg | LinkedInAnnouncementsCome see me and Daniel at Server-Side Swift Conference. 26th-27th September 2024. London, UKuse EMPOWERAPPS to get 15% offNeed help with your projects this year? BrightDigit has openings.Join Bushel BetaJoin our Brand New Patreon Page!LinksThe Case of the Crimson Test SuiteThe Case of the Vanishing BodiesThe Curious Case of the Async CafeKeynote: A Mathematician Muses about Macros, @Models, and ML - Daniel H Steinberg - SwiftCraft 2024 - YouTubeRelated EpisodesFunctional Programming with Daniel SteinbergWWDC Notes with Cihat GündüzSOTU 2024 with Peter WithamHacking with Ignite with Paul HudsonWe Have All The Heroes with Stefano MondinoSwiftly Tooling with Pol Piella AbadiaEdge of Concurrency with Matt MassicotteSupercharged with Pedro PiñeraMicroapps Architecture with Majid JabrayilovTest-Driven Development in Swift with Gio LodiSocial MediaEmailleo@brightdigit.comGitHub - @brightdigitTwitter BrightDigit - @brightdigitLeo - @leogdionLinkedInBrightDigitLeoPatreon - brightdigitCreditsMusic from https://filmmusic.io"Blippy Trance" by Kevin MacLeod (https://incompetech.com)License: CC BY (http://creativecommons.org/licenses/by/4.0/) (00:00) - What is Swift Testing (10:04) - Benefits of Swift Testing (17:22) - Parameterized Tests (20:30) - Swift 6 (24:47) - WWDC 2024 (31:20) - Swift Data (35:10) - Swift Data

Ivan Akulov, Senior Performance Engineer at Framer, discusses optimizing React performance and major advancements in React, including hooks, concurrency, and the crucial invisible performance enhancements that make modern web applications smoother and faster. Links https://iamakulov.com https://x.com/iamakulov https://github.com/iamakulov https://www.linkedin.com/in/iamakulov https://3perf.com We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Ivan Akulov.

Guest: Oleg Shanyuk, Platform Security, Delivery Hero [@deliveryherocom]On LinkedIn | https://www.linkedin.com/in/oleg-shanyuk/____________________________Hosts: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]On ITSPmagazine | https://www.itspmagazine.com/sean-martinMarco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli____________________________Episode NotesIn this On Location episode, Sean Martin discusses the complexities of application security (AppSec) and the challenges surrounding the integration of artificial intelligence (AI) with Oleg Shanyuk at the OWASP Global AppSec Global conference in Lisbon. The conversation delves into various aspects of AppSec, DevSecOps, and the broader scope of securing both web and mobile applications, as well as the cloud and container environments that underpin them.One of the core topics Martin and Shanyuk explore is the pervasive influence of AI across different sectors. AI's application in coding, for instance, can significantly expedite the development process. However, as Sean Martin highlights, AI-generated code may lack the human intuition and contextual understanding crucial for error mitigation. This necessitates deeper and more intricate code reviews by human developers, reinforcing the symbiotic relationship between human expertise and AI efficiency.Shanyuk shares insightful anecdotes about the history and evolution of programming languages and how AI's rise is reminiscent of past technological shifts. He references the advancement from physical punch cards to assembly languages and human-readable code, drawing parallels to the current AI boom. Shanyuk stresses the importance of learning from past technological evolutions to better understand and leverage AI's full potential in modern development environments.The conversation also explores the practical applications of AI in fields beyond straightforward coding. Shanyuk discusses the evolution of automotive batteries from 12 volts to 48 volts, paralleling this shift with how AI can optimize various processes in different industries. This evolution demonstrates the potential of technology to drive efficiencies and reduce costs, emphasizing the need for ongoing innovation and adaptation.Martin further navigates the discussion towards platform engineering, contrasting its benefits of consistency and control with the precision and customization needed for specific tasks. The ongoing debate encapsulates the broader dialogue within the tech community about finding the right balance between standardization and flexibility. Shanyuk's perspective offers valuable insights into how industries can leverage AI and platform engineering principles to achieve both operational efficiency and specialized functionality.The episode concludes with forward-looking reflections on the future of AI-driven models and their potential to transcend the limitations of human language and traditional coding paradigms. The thoughtful dialogue between Martin and Shanyuk leaves listeners with a deeper appreciation of the challenges and opportunities within the realm of AI and AppSec, encouraging continued exploration and discourse in these rapidly evolving fields.Be sure to follow our Coverage Journey and subscribe to our podcasts!____________________________Follow our OWASP AppSec Global Lisbon 2024 coverage: https://www.itspmagazine.com/owasp-global-2024-lisbon-application-security-event-coverage-in-portugalOn YouTube:

The idea of smart robots automating away boring household chores sounds enticing, yet these devices rarely work as advertised: they get stuck, they break down, or are security nightmares. And so it's refreshing to see a company like Matic taking a different approach by attempting to build truly smart, reliable, and privacy-respecting robots. They use Rust for 95% of their codebase, and use camera vision to navigate, vacuum, and mop floors.I sit down with Eric Seppanen, Software Engineer at Matic, to learn about vertical integration in robotics, on-device sensor processing, large Rust codebases, and why Rust is a great language for the problem space.

In today's episode, Scott and Wes dive into the final part of our JavaScript Promises series, covering deferred promises, fetch, flow control, concurrency with libraries like p-map and p-limit, and integrating TypeScript. Show Notes 00:00 Welcome to Syntax! 00:31 Brought to you by Sentry.io. 01:11 Deferred promises. Promise.withResolvers(). Wes' TikTok. 06:10 Fetch. 09:04 Flow Control. 12:19 Concurrency, Throttling, Queuing. p-map. p-limit. 16:13 TypeScript and Promises. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott:X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

In Elixir Wizards Office Hours Episode 2, "Discovery Discoveries," SmartLogic's Project Manager Alicia Brindisi and VP of Delivery Bri LaVorgna join Elixir Wizards Sundi Myint and Owen Bickford on an exploratory journey through the discovery phase of the software development lifecycle. This episode highlights how collaboration and communication transform the client-project team dynamic into a customized expedition. The goal of discovery is to reveal clear business goals, understand the end user, pinpoint key project objectives, and meticulously document the path forward in a Product Requirements Document (PRD). The discussion emphasizes the importance of fostering transparency, trust, and open communication. Through a mutual exchange of ideas, we are able to create the most tailored, efficient solutions that meet the client's current goals and their vision for the future. Key topics discussed in this episode: Mastering the art of tailored, collaborative discovery Navigating business landscapes and user experiences with empathy Sculpting project objectives and architectural blueprints Continuously capturing discoveries and refining documentation Striking the perfect balance between flexibility and structured processes Steering clear of scope creep while managing expectations Tapping into collective wisdom for ongoing discovery Building and sustaining a foundation of trust and transparency Links mentioned in this episode: https://smartlogic.io/ Follow SmartLogic on social media: https://twitter.com/smartlogic Contact Bri: bri@smartlogic.io What is a PRD? https://en.wikipedia.org/wiki/Productrequirementsdocument Special Guests: Alicia Brindisi and Bri LaVorgna.

Craig Clayton from the DesignToSwiftUI Youtube channel comes on to talk about his favorite tips as as designer when it comes to SwiftUI as well as previews his upcoming talk on testing at Deep Dish Swift.GuestCraig Clayton (YouTube @DesigntoSwiftUI)Craig Clayton (@thedevme@mastodon.cloud) - mastodon.cloudmastodon.cloud/@thedevme (@thedevme) / XCraig Clayton | LinkedInAnnouncementsNeed help with your projects this year? BrightDigit has openings.Join Bushel BetaLiveStreaming on YouTube Join me at SwiftCraftJoin our Brand New Patreon Page!Related LinksUsing ModelActor in SwiftDataRelated EpisodesTriple Glazed Apple Development with Malin Sundberg and Kai DombrowskiPixelBlitz in Public with Martin LasekLearning Judo with Sean RuckerThe Making of Callsheet with Casey LissArm Sling for Apple Watch Developers with Hidde van der PloegMy Taylor Deep Dish Swift Heroes World TourGoing Pro with Sean AllenA Swiftly Year in Review with Antoine van der LeePosture Island with Jordi BruinEmpower Station with Matt BraunIndie Dev #4 - Making an App Best-in-Class with Jordan MorganiPad Development with Adam ShawUI Design for Developers with Heidi Helen PilypasSocial MediaEmailleo@brightdigit.comGitHub - @brightdigitTwitter BrightDigit - @brightdigitLeo - @leogdionLinkedInBrightDigitLeoPatreon - brightdigitCreditsMusic from https://filmmusic.io"Blippy Trance" by Kevin MacLeod (https://incompetech.com)License: CC BY (http://creativecommons.org/licenses/by/4.0/) (00:00) - Designing with SwiftUI (11:16) - SwiftUI Tips (18:53) - What's Countdown to Binge? (25:31) - Concurrency, SwiftData, and Storyboards? (35:05) - Shapes and Fonts in SwiftUI (39:37) - Deep Slice of Design and Testing (42:45) - Vision Pro Stuff Thanks to our monthly supporters Steven Lipton ★ Support this podcast on Patreon ★

Topics covered in this episode: 6 ways to improve the architecture of your Python project (using import-linter) Mountaineer Why Python's Integer Division Floors Hatchet Extras Joke Watch on YouTube About the show Sponsored by ScoutAPM: pythonbytes.fm/scout Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Brian #1: 6 ways to improve the architecture of your Python project (using import-linter) Piglei Using import-linter to define architectural layers check to make sure imports don't violate (import from upper layers) can also check for more contracts, such as forbidden - disallow a specific from/to import independence - list of modules that shouldn't import from each other Fixing violations a process introduced to set exceptions for each violation in a config file then fix violations 1 at a time (nice approach) use the whole team if you can Common methods for fixing dependency issues Merging and splitting modules Dependency Injection, including using protocols to keep type hints without the need to import just for types Use simpler dependency types Delaying function implementations module global methods set by caller, or adding a simple plugin/callback system Configuration driven Setting import statements in a config file and using import_string() at runtime Replace function calls with event-driven approaches Michael #2: Mountaineer Mountaineer is a batteries-included web framework for Python and React. Mountaineer focuses on developer productivity above all else, with production speed a close second.

When leveraged correctly, AI can enhance human creativity and allow businesses to connect disparate systems and make informed decisions. So, what choices can manufacturing businesses take to ensure they're using AI the right way? We all know some aspects of our jobs can feel robotic, so Concurrency Chief Technology Officer, Nathan Lasnoski, suggests leaving the manual tasks to AI so you can focus on creativity. On this episode of Manufacturing Happy Hour, he shares how AI can be used to boost your team's creative abilities and explains why ROI should be your lodestar as you look to incorporate AI. Plus, he provides expert tips on how leaders can start making the right AI choices and tells us the similarities between its explosion and the Industrial Revolution. Nathan's been involved with AI for EIGHT years – so he knows what he's talking about! In this episode, find out: Nathan explains how companies can focus on “real productive AI” and transform the way they think of AI in the context of their business Why ROI should be the “guiding light” of a business' use of AI and why it is solving old problems in a brand-new way Nathan shares the story of how he and Concurrency began using AI, hiring their first data scientist around eight years ago before the AI explosion of 2022 How AI is automating intuition and connecting disparate systems to allow business leaders to make data-driven decisions with a holistic view Nathan outlines his comparisons between the AI revolution and the Industrial Revolution, exploring the positives of automating workplace processes with AI How the AI revolution has the potential to increase, not decrease, human creativity by taking manual tasks off our hands Nathan tells us the right questions executives should be asking themselves about AI, and why they need to focus on their present and future goals How the frontline workforce play a vital part in manufacturing leaders knowing which incremental and disruptive changes to make with the help of AI Enjoying the show? Please leave us a review here. Even one sentence helps. It's feedback from Manufacturing All-Stars like you that keeps us going! Tweetable Quotes: “The bar has been lowered for businesses to get into the game.” “Even medium- and small-sized organizations can translate repeatable processes into automated processes and free up their teams to be more.” “AI gives us the opportunity to unlock what is really special about each person and bring it to the forefront of our organizations.” Links & mentions: Concurrency, Devs aim for client-friendly interfaces, full support, and smooth cross-device experiences, all while ensuring scalable global accessibility Brian Evergreen on Manufacturing Happy Hour, Episode 118 Defining “Autonomous Transformation” and Avoiding “Pilot Purgatory” Jeff Winter on Manufacturing Happy Hour, Episode 149 Thriving with AI: Artificial Intelligence Strategies for Manufacturers Central...