Podcasts about Git

Database branching has, for a long time, been a troublesome piece in the modern developer workflow puzzle: a good idea in principle but in practice a slow and often expensive challenge. Get it right and you can accelerate productivity and remove bottlenecks; get it wrong and you're potentially creating all sorts of trouble for yourself, from privacy risks to additional complexity. However, things are changing. Thanks to the emergence of new platforms such as Neon, Supabase and Databricks Lakebase, branching a database can become as familiar to developers as managing code branches and multiple environments with, say, Git and Terraform.  On this episode of the Technology Podcast, host Ken Mugrage is joined by his Thoughtworks colleague Cam Casher and Databricks' Kevin Hartman to discuss the work Thoughtworks and Databricks have been doing together on Lakebase. They discuss the platform, their experience using it with Spotify's Backstage and the opportunities database branching can offer software engineering teams in an increasingly AI-assisted and agentic world. Read Cam and Kevin's recent series on using Databricks Lakebase with Backstage: https://www.thoughtworks.com/insights/blog/data-engineering/backstage-lakebase-databricks

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Marek Šuppa, creator of the Missing GitHub Status page, a project that reconstructs GitHub's historical uptime data and reveals discrepancies between official status reporting and the platform's actual reliability. Marek tells us about his dev journey from open source contributor at DuckDuckGo to machine learning engineer at Cisco-acquired Slido. Then, we discuss GitHub's evolution from a hosted Git service into a critical developer tool. We cover reliability, transparency, AI-driven platform growth, developer workflows, and the challenges of balancing convenience with resilience. Along the way, we cover alternative platforms, self-hosted solutions, and whether recent outages are changing how developers think about ownership, dependency, and the future of software collaboration. Topics Discussed in this Episode: Why did Mr. Shu create the Missing GitHub Status Page? GitHub's reported uptime versus developer experiences How open source contributions shaped Marek's career The evolution of GitHub from tool to critical infrastructure Centralization risks in modern software development Git's distributed roots and today's platform-centric workflows Developer reactions to GitHub outages Transparency and accountability in status reporting AI's impact on developer platforms and infrastructure demands Microsoft's stewardship of GitHub Forgejo, Codeberg, and alternative Git hosting platforms Self-hosted Git solutions and tradeoffs Network effects and platform lock-in The social side of software collaboration Building resilience into developer workflows What GitHub outages teach us about infrastructure dependency Links Mentioned: The Missing GitHub Status Page https://mrshu.github.io/github-statuses/ Slido https://www.slido.com/ https://duckduckgo.com/ The official GitHub Status Page https://www.githubstatus.com/ Statuspage.iohttps://www.atlassian.com/software/statuspage Zig Leaves GitHub https://ziglang.org/news/migrating-from-github-to-codeberg/ Ghostty Leaves GitHub https://mitchellh.com/writing/ghostty-leaving-github GitLab https://about.gitlab.com/ Codeberg https://codeberg.org/ https://git.kernel.org/ Forgejo Lightweight Self-Hosting https://forgejo.org/ Former GitHub CEO Thomas Dohmke launches Entire https://entire.io/news/former-github-ceo-thomas-dohmke-raises-60-million-seed-round Update on Spain and LALIGA blocks of the internet https://vercel.com/blog/update-on-spain-and-laliga-blocks-of-the-internet

Sherwood Callaway is the founder of Sazabi (YC P26), the AI-native observability platform built for engineering teams who ship fast. He previously founded and exited a YC company — now he's back, betting that logs are all you need to replace Datadog.Logs Are All You Need: Rethinking Observability with AI Agents // MLOps Podcast #381 with Sherwood Callaway, the Founder of Sazabi

rsync's founder came back, patched real security bugs with AI help, and triggered an open source meltdown. Plus, two more projects reject AI-generated code as the community's newest fault line cracks wide open.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:ConnecTen Internet — Get $35 off your order total with Jupiter35

This week we have a technical segment focused on Linux! Paul released a script that helps you get a handle on Linux supply chain security, and new features allow you to assess the state of Secure Boot on your Linux systems (that also use MS certificates, ironically). The script is in his Git repo: https://github.com/pasadoorian/Linux_Hacks. In the security news: The CVE chase The new security basics Enterprises are lacking more than AI Detections are falling behind Why DOOM!?! Chromium vulnerability The ambitious Flipper One I'm still curious who was behind these leaks Mitre moves Caldera to Apache foundation Wind cybersecurity PQC updates YellowKey Bitlocker Bypass updates The software supply chain is in deep trouble Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-928

This week we have a technical segment focused on Linux! Paul released a script that helps you get a handle on Linux supply chain security, and new features allow you to assess the state of Secure Boot on your Linux systems (that also use MS certificates, ironically). The script is in his Git repo: https://github.com/pasadoorian/Linux_Hacks. In the security news: The CVE chase The new security basics Enterprises are lacking more than AI Detections are falling behind Why DOOM!?! Chromium vulnerability The ambitious Flipper One I'm still curious who was behind these leaks Mitre moves Caldera to Apache foundation Wind cybersecurity PQC updates YellowKey Bitlocker Bypass updates The software supply chain is in deep trouble Show Notes: https://securityweekly.com/psw-928

This week we have a technical segment focused on Linux! Paul released a script that helps you get a handle on Linux supply chain security, and new features allow you to assess the state of Secure Boot on your Linux systems (that also use MS certificates, ironically). The script is in his Git repo: https://github.com/pasadoorian/Linux_Hacks. In the security news: The CVE chase The new security basics Enterprises are lacking more than AI Detections are falling behind Why DOOM!?! Chromium vulnerability The ambitious Flipper One I'm still curious who was behind these leaks Mitre moves Caldera to Apache foundation Wind cybersecurity PQC updates YellowKey Bitlocker Bypass updates The software supply chain is in deep trouble Show Notes: https://securityweekly.com/psw-928

Episode 180: rightFolds in an AI world? rightFolds as a pun on Mark's recent right vocal fold surgery, healing means we're good to record again, plus IA celebrates 17 years of existence, even if episodes have seriously lacked of late. Last episode Aug 27, 2025 - it's been a while. Does language theory and evolution have a place/need in an AI world? New JVM language features vs Syntactic sugar ala Clojure/Scala features Bun's recent zig->rust total AI rewrite Vercel engineer built Zero, a programming Language for AI Agents | Yeamt Why Did They Build This? jank now has its own custom IR Do any of these funky languages matter in an AI world? Is 'Good Enough' Good Enough: Mindsets and Behaviors for Sales Excellence Is "good enough" good enough?!. A common misunderstanding of the… | by Ted Rau Is Good Enough, Good Enough? (Part 1) AI and the increased threat of Supply Chain attacks How We Got a CISA GitHub Leak Taken Down in Under a Day NPM and its recent attacks Package Managers are Evil - gingerBill The Aesthetic Problem of Namespacing - gingerBill Tooling Highlights from Git 2.54 "Git history" FTW, unless you're using Jujitsu

Käsehoch und Lamenbert gucken den Jimmy Norze Livestream und merken: es ist was faul im Staate Dänemark. Vielleicht ist's auch nur der "Queru" aus der neuen Rubrik: "Git's das Formaggio?"

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

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

This is a recap of the top 10 posts on Hacker News on May 18, 2026. This podcast was generated by wondercraft.ai (00:30): Elon Musk has lost his lawsuit against Sam Altman and OpenAIOriginal post: https://news.ycombinator.com/item?id=48182754&utm_source=wondercraft_ai(01:56): Show HN: Files.md – Open-source alternative to ObsidianOriginal post: https://news.ycombinator.com/item?id=48179677&utm_source=wondercraft_ai(03:23): Garry Tan, the CEO of YC, accused me of unethical reportingOriginal post: https://news.ycombinator.com/item?id=48181041&utm_source=wondercraft_ai(04:50): We stopped AI bot spam in our GitHub repo using Git's –author flagOriginal post: https://news.ycombinator.com/item?id=48181125&utm_source=wondercraft_ai(06:16): Anthropic acquires StainlessOriginal post: https://news.ycombinator.com/item?id=48182281&utm_source=wondercraft_ai(07:43): Eric Schmidt speech about AI booed during graduationOriginal post: https://news.ycombinator.com/item?id=48177785&utm_source=wondercraft_ai(09:10): Show HN: Auto-identity-remove – Automated data broker opt-out runner for macOSOriginal post: https://news.ycombinator.com/item?id=48178184&utm_source=wondercraft_ai(10:36): Project Glasswing: what Mythos showed usOriginal post: https://news.ycombinator.com/item?id=48179732&utm_source=wondercraft_ai(12:03): Actually, democracy dies in H.R.Original post: https://news.ycombinator.com/item?id=48180091&utm_source=wondercraft_ai(13:30): Iran starts Bitcoin-backed ship insurance for Hormuz straitOriginal post: https://news.ycombinator.com/item?id=48182592&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

In this episode, hosts Lois Houston and Nikita Abraham break down the differences between Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. The conversation explores how each framework influences control, cost efficiency, expansion, reliability, and contingency planning.   Cloud Tech Jumpstart: https://mylearn.oracle.com/ou/course/cloud-tech-jumpstart/152992 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Radhika Banka, and the OU Studio Team for helping us create this episode. --------------------------------------- Episode Transcript: 00:00 Hi there! We're hitting rewind for the next few weeks and bringing back some of our most popular episodes. So, sit back and enjoy these highlights from our archive. 00:12 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:38 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hey there! Last week, we spoke about how hypervisors, virtual machines, and containers have transformed data centers. Today, we're moving on to something just as important—the main cloud models that drive modern cloud computing. Nikita: Orlando Gentil, Principal OCI Instructor at Oracle University, joins us once again for part four of our discussion on cloud data centers.  01:14 Lois: Hi Orlando! Glad to have you with us today. Can you walk us through the different types of cloud models?  Orlando: These are commonly categorized into three main service models: Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service. Let's use the idea of getting around town to understand cloud service models. IaaS is like renting a car. You don't own the car, but you control where it goes, how fast, and when to stop. In cloud terms, the provider gives you the infrastructure—virtual machines, storage, and networking—but you manage everything on top—the OS, middleware, runtime, and application. Thus, it's like using a shuttle service. You bring your bags—your code, pick your destination—your app requirements, but someone else drives and maintains the vehicle. You don't worry about the engine, fuel, or routing planning. That's the platform's job. Your focus stays on development and deployment, not on servers or patching. SaaS is like ordering a taxi. You say where you want to go and everything else is handled for you. It's the full-service experience. In the cloud, SaaS is software UXs over the web—Email, CRM, project management. No infrastructure, no updates, just productivity.  02:45 Nikita: Ok. How do the trade-offs between control and convenience differ across SaaS, PaaS, and IaaS? Orlando: With IaaS, much like renting a car, you gain high control. You are managing components like the operating system, runtime, your applications, and your data. In return, the provider expertly handles the underlying virtual machines, storage, and networking. This model gives you immense flexibility. Moving to PaaS, our shuttle service, you shift to a medium level of control but gain significantly higher convenience. Your primary focus remains on your application code and data. The provider now takes on the heavy lifting of managing the runtime environment, the operating system, the servers themselves, and even the scaling. Finally, SaaS, our taxi service, offers the highest convenience with the lowest control level. Here, your responsibility is essentially just using the application and managing your specific configurations or data within it. The cloud provider manages absolutely everything else—the entire infrastructure, the platform, and the application itself. 04:05 Nikita: One of the top concerns for cloud users is cost optimization. How can we manage this? Orlando: Each cloud service model offers distinct strategies to help you manage and reduce your spending effectively, as well as different factors that drives those costs. For Infrastructure-as-a-Service, where you have more control, optimization largely revolves around smart resource management. This means rightsizing your VMs, ensuring they are not overprovisioned, and actively turning off idle resources when not in use. Leveraging preemptible or spot instances for flexible workloads can also significantly cut costs. Your charges here are directly tied to your compute, storage, and network usage, so efficiency is key. Moving to Platform-as-a-Service, where the platform is managed for you, optimization shifts slightly. Strategies include choosing scalable platforms that can efficiently handle fluctuating demand, opting for consumption-based pricing where available, and diligently optimizing your runtime usage to minimize processing time. Costs in PaaS are typically based on your application usage, runtime hours, and storage consumed. Finally, for Software-as-a-Service where you can consume a ready-to-use application, cost optimization centers on licensing and usage. This involves consolidating tools to avoid redundant subscriptions, selecting usage-based plans if they align better with your needs, and crucially, eliminating any unused license. SaaS costs are generally based on subscription or per user fees. Understanding these nuances is essential for effective cloud financial management.  06:05 Lois: Ok. And what about scalability? How does each model handle the ability to grow and shrink with demand, without needing manual hardware changes? Orlando: How you achieve and manage that scalability varies significantly across our three service models. For Infrastructure-as-a-Service, you have the most direct control over scaling. You can implement manual or auto scaling by adding or removing virtual machines as needed, often leveraging load balancers to distribute traffic. In this model, you configure the scaling policies and parameters based on your specific workload. Moving to Platform-as-a-Service, the scaling becomes more automated and elastic. The platform automatically adjusts resources based on your application's demand, allowing it to seamlessly handle traffic spikes or dips. Here, the provider manages the underlying scaling behavior, freeing you from that operational burden. Finally, with Software-as-a-Service, scalability is largely abstracted and invisible to the user. The application scales automatically in the background, with the entire process fully managed by the provider. As a user, you simply benefit from the application's ability to handle millions of users without ever needing to worry about the infrastructure. Understanding these scaling differences is crucial for selecting the right model for your application's need.  07:45 Join the Oracle University Learning Community and tap into a vibrant network of over 1 million members, including Oracle experts and fellow learners. This dynamic community is the perfect place to grow your skills, connect with likeminded learners, and celebrate your successes. As a MyLearn subscriber, you have access to engage with your fellow learners and participate in activities in the community. Visit community.oracle.com/ou to check things out today!  08:18 Nikita: Welcome back! We've talked about cost optimization and scalability in cloud environments. But what about ensuring availability? How does that work?  Orlando: Availability refers to the ability of a system or service to remain accessible in operational, even in the face of failures or extremely high demand. The approach of achieving and managing availability, and crucially, your role versus the provider's, differs greatly across each model. With Infrastructure-as-a-Service, you have the most direct control over your availability strategy. You will be responsible for designing an architecture that includes redundant VMs, deploying load balancers, and potentially even multi-region setups for disaster recovery. Your specific roles involves designing this architecture and managing your failover process and data backups. The provider's role, in turn, is to deliver the underlying infrastructure with defined service level agreements, SLAs, and health monitoring. For Platform-as-a-Service, the platform itself offers a higher degree of built-in, high availability, and automated failover. While the provider maintains the runtime platform's availability, your role shifts. You need to ensure your application's logic is designed to gracefully handle retries and potential transient failures that might occur. Finally, with Software-as-a-Service, availability is almost entirely handled for you. The provider ensures fully abstracted redundancy and failover behind the scenes. Your role becomes largely minimal, often just involving a specific application's configurations. The provider is entirely responsible for the full application uptime and the underlying high availability infrastructure. Understanding these distinct roles in ensuring availability is essential for setting expectations and designing your cloud strategy efficiently. 10:32 Lois: Building on availability, let's talk Disaster Recovery. Orlando: DR is about ensuring your systems and data can be recovered and brought back online in the event of a significant failure, whether it's a hardware crash, a natural disaster, or even human error. Just like the other aspects, the strategy and responsibilities for DR vary significantly across the cloud service models. For Infrastructure-as-a Service, you have the most direct involvement in your DR strategy. You need to design and execute custom DR plans. This involves leveraging capabilities like multi-region backups, taking VM snapshots, and setting up failover clusters. A real-world example might be using Oracle Cloud compute to replicate your VMs to a secondary region with block volume backups to ensure business continuity. Essentially, you manage your entire DR process here. Moving to Platform-as-a-Service, disaster recovery becomes a shared responsibility. The platform itself offers built-in redundancy and provide APIs for backup and restore. Your role will be to configure the application-level recovery and ensure your data is backed up appropriately, while the provider handles the underlying infrastructure's DR capability. An example could be Azure app service, Oracle APEX applications, where your apps are redeployed from source control like Git after an incident. Finally, with Software-as-a-Service, disaster recovery is almost entirely vendor managed. The provider takes full responsibility, offering features like auto replication and continuous backup, often backed by specific Recovery Point Objective (RPO) and Recovery Time Objective (RTO) SLAs. A common example is how Microsoft 365 or Salesforce manage user data backups in restoration. It's all handled seamlessly by the provider without your direct intervention. Understanding these different approaches to DR is crucial for defining your own business continuity plans in the cloud. 12:59 Lois: Thank you, Orlando, for this insightful discussion. To recap, we spoke about the three main cloud models: IaaS, PaaS, and SaaS, and how each one offers a different mix of control and convenience, impacting cost, scalability, availability, and recovery.  Nikita: Yeah, hopefully this helps you pick the right cloud solution for your needs. If you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and search for the Cloud Tech Jumpstart course. In our next episode, we'll take a close look at the essentials of networking. Until then, this is Nikita Abraham… Lois: And Lois Houston, signing off! 13:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

✏️ Suscribirse https://www.youtube.com/watch?v=zNEtVzsR_JM Delegar más trabajo técnico ya no va solo de automatizar tareas sueltas. En este episodio 252 de Negocios y WordPress la conversación junta dos planos que cada vez están más conectados: por un lado, el mantenimiento real de webs con Modular 3.0; por otro, una forma más madura de trabajar con IA, agentes, WordPress, MCP y sistemas propios sin perder control ni criterio. Modular 3.0 aprieta justo donde más duele en mantenimiento WordPress La primera mitad del episodio tiene un bloque muy práctico con Héctor de Prada para repasar qué cambia en Modular 3.0 y por qué eso importa de verdad en operación diaria. No se habla de una mejora cosmética, sino de funciones que atacan problemas muy concretos: escaneo de malware, detección de enlaces rotos, backups, safe updates y restauración cuando algo se rompe tras una actualización. Uno de los puntos más útiles es que el mantenimiento se plantea desde la realidad de quien gestiona muchas webs. No se trata solo de mirar una instalación cada vez, sino de poder aplicar configuraciones globales, presets por plan de mantenimiento y altas masivas de sitios para no repetir el mismo trabajo una y otra vez. También se comenta algo importante: las herramientas de este tipo no valen solo para el técnico. Sirven para trasladar mejor el valor al cliente, explicar incidencias, documentar vigilancias y demostrar que detrás del mantenimiento hay criterio operativo, no solo “tener plugins instalados”. En esa misma línea aparecen otras piezas interesantes del roadmap: regiones de datos, staging en el propio servidor, una API pública y la posibilidad de abrir más el sistema hacia agentes y automatizaciones futuras. Si quieres seguir esa parte, en el episodio recuerdan el acceso a Modular desde Negocios y WordPress. WordPress 7 mete la IA dentro del admin y no en un chat aparte Otra parte potente del episodio es la revisión práctica de WordPress 7 y de sus conectores oficiales de IA. Lo interesante no es tanto que “WordPress tenga IA”, sino cómo la integra: botones contextuales para sugerir títulos, extractos, etiquetas alt, términos o incluso imágenes destacadas dentro del sitio donde ya estás trabajando. Ese enfoque cambia bastante la experiencia, porque la IA deja de estar en una pestaña externa y pasa a estar justo en el punto donde editas contenido o tomas decisiones. La conversación también menciona algunos límites y pequeños fallos, pero la sensación general es que el camino tiene sentido. Además de eso, se comentan otros cambios de WordPress 7: `view transitions` para evitar el salto brusco entre pantallas una paleta de comandos más visible gestor de fuentes visibilidad condicional por dispositivo CSS personalizado por bloque El debate de fondo no es si todo eso es espectacular, sino si WordPress está empezando a colocar mejor las capacidades que realmente ahorran tiempo dentro del flujo normal de trabajo. Codex remoto y objetivos largos: menos chat suelto y más continuidad Cuando el episodio entra en Codex, la idea clave ya no es “preguntarle algo a la IA”, sino convertirla en una capa operativa continua. Ahí se habla de control remoto, trabajo desde móvil, conexión entre dispositivos y tareas más largas que no se limitan a una única respuesta. La parte más interesante es el concepto de trabajar con objetivos en Codex. En vez de lanzar una acción aislada, se define una meta concreta y el sistema sigue iterando hasta completarla o hasta alcanzar un criterio verificable. Eso acerca mucho más la IA a una forma real de delegación técnica que a un simple chat de apoyo. También se comenta el uso de herramientas intermedias para control remoto, la aparición de la función oficial para trabajar con Codex desde cualquier sitio y pequeños detalles como el seguimiento de uso con herramientas como CodexBar o la continuidad entre máquinas. El fondo, sin embargo, es más importante que la herramienta exacta: si puedes mantener contexto, estado y objetivo, empiezas a trabajar de otra forma. Kilo Code, Gastown y la idea de montar una “empresa” de agentes El episodio amplía esa visión con Kilo Code, Gastown y Wasteland, que aparecen casi como un experimento de hacia dónde puede ir este modelo de trabajo. La propuesta suena incluso un poco exagerada: una especie de empresa de agentes especializados con infraestructura en la nube, roles concretos y una lógica más autónoma para ejecutar tareas de desarrollo. Más allá del nombre o de la capa más friki del concepto, la parte relevante es esta: la conversación ya no gira solo alrededor del mejor modelo, sino de qué arquitectura de trabajo construyes encima. Qué roles hay, cómo se reparte el contexto, cómo se versiona, cómo se valida y qué piezas siguen siendo humanas. Ese matiz es importante porque aterriza una idea bastante útil para cualquiera que esté mezclando IA con desarrollo real: la ventaja no está solo en que el sistema escriba código, sino en que pueda encajar dentro de un flujo con prioridades, checkpoints y especialización. MCP, artefactos y maquetación con IA sin volver al builder Uno de los bloques más valiosos del episodio es la defensa de un flujo más limpio para diseñar y maquetar con IA. En lugar de meter capas y plugins intermedios porque sí, la propuesta es trabajar con una fuente de verdad clara: arquitectura del proyecto, CPTs, campos personalizados, wireframe y framework CSS propio. Ahí entra MCP con JetEngine como pieza de contexto. La gracia no es “hablar con WordPress” de forma genérica, sino poder extraer la estructura real del proyecto y usarla para que la IA maquete con sentido desde la primera pasada. Si el sistema conoce los tipos de contenido, los campos y la estructura que debe pintar, se equivoca menos y necesita menos correcciones. La conversación lo contrapone bastante bien con el uso indiscriminado de builders. No porque Elementor o Bricks sean inútiles, sino porque si ya has resuelto el diseño, el contexto y la implementación con artefactos bien definidos, volver a traducirlo todo a otra capa puede meter más fricción que valor. Además, se insiste en algo práctico: cuando la base está bien montada, ya no solo se acelera la maquetación. También se vuelven más accesibles pequeñas mejoras que antes daban pereza, como sliders ligeros, ajustes visuales o comportamientos más avanzados sin cargar el proyecto de complejidad innecesaria. Skills, workshop y criterio: la IA funciona mejor cuando el sistema está bien pensado El cierre del episodio refuerza una idea que atraviesa toda la conversación: lo importante no es acumular herramientas, sino convertir procesos repetidos en piezas reutilizables. Por eso las skills aparecen como núcleo del sistema: ahorran contexto, reducen ruido y permiten que la IA repita mejor lo que ya has validado. También se habla del workshop, de sistemas propios, de Git como base para versionar, de staging, de validaciones y de todo lo que todavía no conviene automatizar del todo. Ese matiz es clave porque baja el discurso a tierra: delegar no significa desaparecer del proceso, sino diseñar mejor los puntos donde la IA puede ayudar sin romper nada. Incluso cuando aparecen herramientas más pequeñas o laterales, como TidyCal para reservas de pago o NovaMira para trabajar con WordPress y builders, el criterio sigue siendo el mismo: si una pieza simplifica un problema concreto, bien; si añade otra capa innecesaria, probablemente sobra. Cierre Este episodio 252 deja una lectura bastante clara: delegar el código a los agentes no va de entregarles el volante sin más, sino de construir un sistema mejor. Modular 3.0, WordPress 7, Codex remoto, Kilo, MCP, JetEngine o las skills apuntan todos en la misma dirección: más contexto útil, más automatización con sentido y menos dependencia de flujos torpes o repetitivos. Si estás mezclando WordPress, IA, mantenimiento, diseño y desarrollo real, aquí hay una idea que merece quedarse: antes de añadir otra herramienta, revisa si ya tienes una fuente de verdad clara, un proceso versionable y un criterio de delegación sólido. Ahí es donde la IA empieza a aportar de verdad.

pnpm lead maintainer Zoltan Kochan joins PodRocket to unpack pnpm 11's biggest shifts: a new minimum release age default that blocks npm registry packages under 24 hours old, a cleaner allow builds config replacing scattered post-install script settings, and the experimental global virtual store that slashes install times with Git worktrees. Zoltan also shares why a Rust rewrite of pnpm's engine is now underway, and how AI-assisted development made it possible far sooner than expected. Links Website: https://www.kochan.io/ Github: https://github.com/zkochan LinkedIn: https://www.linkedin.com/in/zkochan X: https://x.com/zoltankochan Mastodon: https://fosstodon.org/@zkochan Bluesky: https://bsky.app/profile/kochan.io Resources pnpm release blog post: https://pnpm.io/blog/releases/11.0 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? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, 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 understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction 01:00 The 24-Hour Minimum Release Age Default 03:30 Community Pushback and the Polling Shift 05:00 Trusted Policy and OIDC Provenance Checking 07:00 Performance Trade-offs of Full Metadata Fetching 08:00 The New Allow Builds Configuration 10:30 Global Installs and the Virtual Store Explained 13:00 Which Packages Break with the New Layout 14:30 Global Virtual Store for Local Dev and Worktrees 16:30 TypeScript Go and the Golden Age of Development 17:30 AI Agents Influencing pnpm's Design Decisions 19:00 The Rust Rewrite — and Why Now 20:00 Dropping Node 18 and the Standalone Executable 22:00 Installing Node.js via pnpm and the New GitHub Action 24:00 Moving Config from npmrc to pnpm-workspace.yaml 26:00 Upgrade Smoothness and Common Migration Pain Points 28:30 pnpm v12 Roadmap — Frozen Installs in Rust 31:00 Contributing to pnpm and the Open Source PR Tsunami 33:00 Wrap-up

Incorruptible with Eric RiesWhat if the companies that last the longest are the ones building enough trust that people want to keep participating in them? That's the idea behind this conversation with Eric Ries — entrepreneur, author of The Lean Startup, and now Incorruptible.Through stories such as Volvo giving away the seatbelt patent, Tony's Chocolonely opening its ethical supply chain to competitors, and Mary Parker Follett's idea of the “invisible leader,” we explore how organizations create lasting advantage through trust, shared purpose, and systems that hold together as companies scale.We also unpack why so many businesses drift toward short-term extraction, what leaders misunderstand about organizational health, and why AI is exposing deeper weaknesses in how companies operate.If you're building a company and questioning whether business-as-usual is still the right operating system, this conversation is for you.Key TakeawaysEthical business can outperform extractive business models: Eric argues that mission-driven companies are not sacrificing performance. In many cases, trust, alignment, and long-term thinking create stronger economic outcomes.Volvo used open ecosystems as strategy: Giving away the three-point seat belt patent helped establish safety as an industry standard while positioning Volvo as the global leader in automotive safety.Tony's Chocolonely treats its mission as infrastructure: The company's goal is not simply selling chocolate. Its mission is to eliminate child slavery from the cacao supply chain through systems that competitors can also adopt.Positive externalities can strengthen competitive advantage: Eric explains how companies can create value by improving the broader ecosystem around them instead of maximizing short-term value extraction.Organizations are shaped by invisible leadership: Mary Parker Follett's idea of the “invisible leader” shows how shared purpose influences decisions when executives are not in the room.Organizational health cannot be commanded: Leaders can issue instructions, but trust, accountability, and commitment have to be cultivated through systems and behavior over time.Additional InsightsThe current business narrative rewards extraction over durability: Barry and Eric discuss how modern startup culture often glorifies hyper-efficient solo founders, aggressive cost-cutting, and short-term returns while ignoring long-term organizational health.AI is amplifying leadership weaknesses, not solving them: As companies use AI to accelerate decision-making and productivity, leaders are being forced to confront whether their systems actually create clarity, trust, and aligned behavior.Mission statements are easy. Mission transmission is harder: Eric argues that values only matter when they shape real decisions, incentives, hiring, product tradeoffs, and customer experience.Open systems can expand both impact and market position: From Linux and Git to Netflix influencing AWS through open source tooling, the episode explores how sharing infrastructure can strengthen an ecosystem while also benefiting the originating company.Profit becomes dangerous when it ignores externalities: Eric explains how traditional profit models often fail to account for long-term brand damage, human cost, environmental impact, and deferred liabilities.Episode Highlights00:00 – Episode RecapEric Ries explains why organizations are living systems, not machines to be controlled. Leaders can command action, but organizational health has to be cultivated through purpose, trust, and the systems people use when no one is watching.00:57 – Barry's Opening ReflectionBarry connects AI, leadership, and decision-making systems before introducing Eric's new book, Incorruptible.02:14 – Guest Introduction: Eric RiesBarry introduces Eric Ries, entrepreneur, author of The Lean Startup, and author of Incorruptible, framing the conversation around ethical business as a path to long-term prosperity.04:34 – Researching the Stories Behind IncorruptibleEric shares how much research went into the book, including the challenge of finding stories that were not just interesting, but genuinely useful for leaders.08:07 – Volvo and the “Seatbelt Heist”Eric breaks down how Volvo's decision to give away the three-point seat belt patent created a prosperity cascade that reshaped the industry while strengthening Volvo's long-term brand position around safety.16:45 – Open Source as StrategyBarry connects Volvo's story to Netflix and cloud computing, where open sourcing internal tools helped shape the direction of the broader ecosystem.17:57 – Positive Externalities as Business StrategyEric explains why companies often overlook opportunities to create value by improving the wider system around them.20:18 – Tony's Chocolonely and Slave-Free ChocolateEric tells the story of how a Dutch journalist turned frustration over child labor in cacao production into a fast-growing chocolate company with a much larger mission.24:03 – Mission Beyond the ProductTony's mission is not simply making chocolate. The business exists to eliminate child slavery from the cacao supply chain and align economics with ethical sourcing.26:00 – Tony's Open ChainEric explains how Tony's opened its ethical supply chain to competitors while requiring them to commit to the same standards across all their chocolate products.30:32 – The False Tradeoff Between Ethics and PerformanceEric challenges the business-school assumption that companies must choose between mission and profit, arguing that the data often shows the opposite.33:23 – Redefining ProfitBarry and Eric discuss why traditional definitions of profit often ignore externalities, deferred liabilities, human cost, and long-term brand damage.39:19 – The Myth of the Solo FounderBarry pushes back on modern founder mythology and explains why anything built to last depends on systems, teams, and shared ownership.40:36 – Mary Parker Follett and the Invisible LeaderEric introduces management thinker Mary Parker Follett and explains why her ideas about shared purpose and distributed authority were decades ahead of their time.45:00 – What Guides Decisions When Leaders Aren't PresentEric explores Follett's idea of the invisible leader: the shared sense of purpose that influences behavior when no executive is in the room.49:35 – Organizations as Living SystemsEric compares organizations to emergent intelligence systems like ant colonies or the human body, arguing that leaders can cultivate organizational health but cannot directly command it.52:30 – Closing ReflectionsBarry and Eric reflect on the need for new business models that prioritize trust, mission alignment, and long-term value creation over extraction.Useful ResourcesEric Ries — IncorruptibleEric Ries — The Lean StartupEric Ries on LinkedIn - https://www.linkedin.com/in/eries/ The Eric Ries Show YouTube - https://www.youtube.com/@theericriesshow Barry O'Reilly — Artificial Organizations - https://geni.us/artificialorgsFAQsQ1: What is Eric Ries' book Incorruptible about?Incorruptible explores how leaders can build companies that stay aligned with their mission as they grow. Eric looks at stories from business history to show how purpose, governance, incentives, and ownership shape whether companies create long-term value or lose their way.Q2: Why does Eric Ries use Volvo as an example?Volvo's three-point seat belt story shows how a company can create value by spreading a mission beyond its own products. By making the patent available to others, Volvo helped establish safety as an industry standard while strengthening its own reputation for safety.Q3: What is Tony's Chocolonely trying to change?Tony's Chocolonely is trying to eliminate child slavery from the cacao supply chain. The company sells chocolate, but the deeper mechanism is building an ethical supply chain that other companies can use through Tony's Open Chain.Q4: What does Mary Parker Follett mean by the invisible leader?The invisible leader is the shared purpose that guides people's decisions when no formal leader is present. It is what shapes behavior in everyday moments, such as how teams handle quality issues, customer problems, or ethical tradeoffs.Q5: Can leaders...

Java 26 est là, GraalVM cartonne chez Trivago (43 à 12 réplicas !), OpenJDK interdit le code généré par LLM, Spring et Quarkus enchaînent les releases. Côté IA : ADK 1.0, A2A, Lyria 3 chante (mal ?), Yann LeCun lance Ami Labs et ses World Models. Mythos d'Anthropic fait trembler la sécu, Claude Code a leaké son source, et les git worktrees envahissent vos terminaux. Bonus : la mort annoncée de l'IDE, vagues de licenciement chez Oracle et Block, et nos voix toutes clonées. Bon week-ends de mai ! Enregistré le 7 mai 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-340.mp3 ou en vidéo sur YouTube. News Langages Retour d'expérience d'une migration vers graalVM chez Trivago https://medium.com/graalvm/inside-trivagos-graalvm-migration-native-image-for-graphql-at-scale-912bca9df841 La passerelle GraphQL de Trivago (point d'entrée de tout le trafic vers 48 microservices) souffrait de pics de timeout au démarrage JVM Résultats spectaculaires après migration vers GraalVM Native Image : réduction des réplicas de 43 à 12, CPU de 15 à 5 cœurs, images Docker plus légères Obstacles techniques : incompatibilité Log4j → migration vers Logback, remplacement de Mockk par Testcontainers, compilation CI/CD très gourmande Netflix DGS et d'autres librairies manquaient de support GraalVM → l'équipe a contribué des correctifs upstream en open source Approche recommandée : commencer par les services les moins complexes, investir massivement dans les tests automatisés À la 14e migration, le processus était si rodé qu'il allait plus vite que la toute première tentative OpenJDK Interim Policy on Generative AI - https://openjdk.org/legal/ai OpenJDK adopte une politique intérimaire interdisant toute contribution incluant du contenu généré par des LLMs, modèles de diffusion ou systèmes deep-learning Le périmètre est large : code source, texte, images dans les dépôts Git, pull requests GitHub, emails, pages wiki et issues JBS Les contributeurs peuvent utiliser les outils d'IA de manière privée pour comprendre, déboguer et relire le code OpenJDK, mais ne peuvent pas contribuer le contenu généré Trois risques justifient cette politique : surcharge des relecteurs face au code plausible mais incorrect, risques de sûreté/sécurité pour une plateforme critique, et risques de propriété intellectuelle (l'OCA exige que les contributeurs possèdent les droits IP de leurs contributions) Même éditer partiellement du code AI-généré ne le rend pas acceptable à la contribution Oracle, sponsor corporatif d'OpenJDK, travaille sur une politique complète à soumettre au Governing Board GraalVM Native Image et la Closed-World Assumption en Java https://pvs-studio.com/en/blog/posts/java/1357/ Un bon article de rappel du contexte de closed world en Java GraalVM Native Image compile les applications Java en exécutables natifs statiques, sans JVM au runtime. La JVM fonctionne en monde ouvert : les classes sont chargées à la demande, les appels sont des références symboliques résolues dynamiquement. Native Image impose la "closed-world assumption" : tous les chemins d'exécution doivent être connus à la compilation. Les fonctionnalités dynamiques Java (réflexion, proxies, chargement de classes) créent des chemins cachés invisibles à l'analyse statique. C'est pourquoi Native Image exige des fichiers de configuration explicites pour la réflexion, les proxies, les ressources et la FFM API. L'article illustre le problème avec la Foreign Function & Memory API pour appeler printf natif : fonctionne sur JVM, échoue en Native Image sans config. Inclure tout le bytecode accessible serait inutilisable : binaire géant, compilation très lente, et la réflexion nécessite des métadonnées précises. La configuration n'est pas un défaut de conception mais une conséquence logique du passage du dynamique au statique. Java 26 : les nouveautés https://foojay.io/today/java-26-whats-new/ Java est le langage de la JVM, publié tous les 6 mois depuis Java 9 ; Java 26 est une version non-LTS avec 10 JEPs. JEP 500 : protection des champs final modifiés par réflexion profonde, avec des avertissements configurables. JEP 504 : suppression définitive de l'API Applet, plus supportée par les navigateurs. JEP 516 : le cache AOT (Project Leyden) fonctionne désormais avec n'importe quel garbage collector. JEP 517 : support HTTP/3 dans le client HTTP, HTTP/2 reste le défaut mais HTTP/3 est accessible à la demande. JEP 522 : amélioration du débit du GC G1 en réduisant la synchronisation entre threads applicatifs et threads GC. Nouveau support des UUIDv7 via UUID.ofEpochMillis(), naturellement triables et adaptés aux identifiants de bases de données. Process devient AutoCloseable, utilisable dans un try-with-resources. Aucune fonctionnalité en preview n'est graduée en standard ; Structured Concurrency en est à sa 6e preview. Librairies Guillaume a créé une petite librairie Java sans dépendance pour extraire le JSON d'une réponse d'un LLM un peu verbeux https://glaforge.dev/posts/2026/03/22/extracting-json-from-llm-chatter-with-jsonspotter/ Les LLM génèrent souvent du JSON, mais il est parfois entouré de bla-bla et/ou contient des erreurs (ex: commentaires, virgules finales) qui bloquent les parseurs JSON standards. Guillaume a créé une petite librairie légère sans dépendance pour localiser et extraire la structure la plus longue ressemblant à du JSON (même malformé) On peut ensuite passé cette chaîne à un parseur "lénient" (plus tolérant) comme Jackson pour ensuite avoir de bons vieux objets Java fortement typés Librairie dispo sur Maven Central ADK Java sort sa version 1.0 (Agent Development Kit par Google) https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/ ADK est un framework open source de Google pour créer des agents IA, initialement en Python, maintenant multi-langages (Python, Java, Go, Typescript). Nouvelles fonctionnalités majeures : Outils puissants : GoogleMapsTool, UrlContextTool, ContainerCodeExecutor, VertexAiCodeExecutor, abstraction ComputerUseTool. Architecture de plugins centralisée : Nouveau conteneur App pour gérer les Plugins à l'échelle de l'application (ex: LoggingPlugin, GlobalInstructionPlugin). Context engineering amélioré : Compaction d'événements pour gérer la taille des fenêtres de contexte (résumé et rétention). Human-in-the-Loop (HITL) : Supporte les workflows ToolConfirmation pour approbation humaine des actions d'agent. Services de session et de mémoire : Contrats clairs pour la gestion de l'état (InMemory, VertexAI, Firestore) et la mémoire à long terme. Support Agent2Agent (A2A) : Collaboration native entre agents distants de différents frameworks via le protocole A2A. Dans cet autre article, Guillaume partage comment il a développé l'application Comic Trip montrée dans la vidéo YouTube et qui utilise ADK 1.0 https://glaforge.dev/posts/2026/03/30/building-my-comic-trip-agent-with-adk-java-1-0/ Nouvelle version du SDK Java pour Agent2Agent Protocol, avec le support de la version 1.0 de la spécification https://medium.com/google-cloud/a2a-java-sdk-1-0-0-beta1-released-e83c414b34cc Alignement avec la version 1.0 de la spécification Nouveau groupId org.a2aproject.sdk et package org.a2aproject.sdk Protocoles de transport : support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Gestion des erreurs : introduction de codes d'erreur et détails structurés pour une meilleure observabilité. Optimisation HTTP : ajout d'en-têtes de cache pour les métadonnées des agents (Agent Card). Flexibilité du client HTTP : support par défaut du JDK HttpClient, avec option Vert.x pour les environnements Quarkus. Nouvelles fonctionnalités techniques : méthode DataPart.fromJson() pour la création simplifiée d'objets depuis du JSON brut. Prochaines étapes (v1.0.0.GA) : support simultané des versions 1.0.0 et 0.3.0 du protocole pour assurer l'interopérabilité. JPA 4.0 Milestone 2 : nouvelles fonctionnalités pour Jakarta Persistence https://in.relation.to/2026/04/23/JPA-4-M2/ Jakarta Persistence (JPA) est la spécification standard Java pour le mapping objet-relationnel (ORM), implémentée notamment par Hibernate. JPA 4.0 M2 est la deuxième milestone de la prochaine version majeure de la spécification, annoncée par Gavin King. Construction de requêtes Criteria à partir de chaînes JPQL, offrant plus de flexibilité dans la composition dynamique des requêtes. Nouveaux types d'expressions spécialisés (TextExpression, NumericExpression) pour simplifier l'écriture des requêtes Criteria. Nouvelle interface FetchOption pour contrôler explicitement la stratégie de chargement des associations, dont un BatchSize intégré. Nouvelle annotation @EntityListener qui découple les classes entités de leurs listeners, supprimant les dépendances à la compilation. Les listeners peuvent cibler plusieurs types de callbacks et s'appliquer globalement à toute l'unité de persistance. Introduction de FlushModeType.EXPLICIT et QueryFlushMode pour un contrôle plus fin de la synchronisation avec la base de données. La méta-annotation @Discoverable permet de placer des annotations comme @NamedQuery sur n'importe quelle classe ou interface. Améliorations du DDL via @Index amélioré et clarifications de la spécification via la javadoc. Quarkus 3.35 : tree-shaking, PGO et AOT Semeru https://quarkus.io/blog/quarkus-3-35-released/ Quarkus est un framework Java cloud-natif optimisé pour GraalVM et HotSpot, conçu pour les microservices et les environnements conteneurisés. Nouveau JAR tree-shaking expérimental : analyse des dépendances à la compilation pour supprimer les classes inutilisées. Sur le CLI Quarkus, cela supprime plus de 6 000 classes et économise environ 18 Mo (39,5 %). Support du Profile-Guided Optimization (PGO) pour les builds natifs via quarkus.native.pgo.enabled=true. Le PGO est une fonctionnalité Oracle GraalVM, non disponible dans la Community Edition. Support de l'AOT IBM Semeru : le démarrage passe de ~380 ms à ~190 ms dans les premiers tests. Nouvelle extension quarkus-reactive-transactions : support de @Transactional pour les méthodes Hibernate Reactive retournant Uni. Configuration CORS dédiée pour l'interface de management, indépendante de l'interface HTTP principale. Les tests n'utilisent plus les System Properties pour la propagation de configuration, facilitant la parallélisation future. Le serializer jackson sans reflection n'est pas le default du aux retours de cas limites, encore du travail This Week in Spring - 21 avril 2026 https://spring.io/blog/2026/04/21/this-week-in-spring-april-21-2026 Spring Framework 6.2.18 et 7.0.7 corrigent trois failles de sécurité : DoS via fichiers multipart WebFlux, empoisonnement de cache de ressources statiques, et DoS sur Windows. Le support open source de Spring Framework 5.3.x et 6.1.x est terminé, la migration est recommandée. Spring Data 2026.0.0-RC1 introduit l'upsert (MERGE/INSERT ON CONFLICT) dans l'API Template de Spring Data Relational. Spring Data ajoute un RedisMessageSendingTemplate pour la cohérence avec les listeners Redis, et une optimisation de réinitialisation de caches en un seul appel. Spring AI introduit une Session API (série Agentic Patterns, partie 7) : architecture event-sourcée pour la mémoire des agents IA. La Session API supporte la compaction turn-safe, l'isolation de sous-agents en parallèle, et la persistence JDBC (PostgreSQL, MySQL, MariaDB, H2). Elle vise Spring AI 2.1 (novembre 2026) et remplacera à terme l'API ChatMemory. Spring Vault 4.1.0-RC1 et 4.0.2 sont disponibles. Netflix a présenté son usage de Java, Spring Boot et Spring AI dans une vidéo. This Week in Spring - 28 avril 2026 https://spring.io/blog/2026/04/28/this-week-in-spring-april-28-2026 Cette série hebdomadaire de Josh Long compile les nouveautés de l'écosystème Spring : articles, outils, podcasts et annonces de la communauté. Spring Boot 4 introduit un package natif de résilience org.springframework.resilience avec une nouvelle API de retry qui remplace les approches fragiles via Spring Retry ou Resilience4j. L'API retry native de Spring Boot 4 a des noms d'attributs et sémantiques différents des anciennes bibliothèques, rendant les tutoriels pré-2025 obsolètes et sources de bugs silencieux. Le SDK Spring AI pour Amazon Bedrock AgentCore est disponible en GA : il intègre les capacités AgentCore dans Spring AI via annotations et auto-configuration. Le SDK AgentCore gère automatiquement le contrat runtime AgentCore : endpoint /invocations, health check /ping, SSE avec backpressure. Il offre mémoire court terme (sliding window) et long terme (sémantique, préférences, résumé, épisodique), ainsi que des outils pour navigateur et exécution de code en sandbox. Un plugin Maven (Nullability Maven Plugin) simplifie l'intégration de JSpecify et NullAway pour enforcer la null-safety à la compilation dans les projets Java. Le plugin génère automatiquement les fichiers package-info.java par package et configure le compilateur pour traiter les violations de nullabilité comme des erreurs. Josh Long et Dr. Venkat Subramaniam ont co-présenté à Voxxed Days Amsterdam sur "Intelligent Kotlin", avec un épisode de podcast associé. Cloud Amazon S3 Files https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-s3-files/ Amazon S3 Files est un nouveau service donnant un accès système de fichiers direct aux données stockées dans les buckets S3 Basé sur la technologie Amazon EFS, il supprime la barrière entre stockage objet et interface système de fichiers sans dupliquer les données Débit en lecture pouvant atteindre plusieurs téraoctets par seconde ; des milliers de ressources de calcul peuvent y accéder simultanément Les données restent accessibles via les deux interfaces : S3 API classique et système de fichiers standard, sans migration nécessaire Cas d'usage : agents IA pour la persistance de mémoire entre pipelines, équipes ML sans staging, simplification des data lakes Disponible dans 34 régions AWS Data et Intelligence Artificielle Comment générer de la musique et des clips audio en Java avec le modèle Lyria 3 https://glaforge.dev/posts/2026/03/25/generating-music-with-lyria-3-and-the-gemini-interactions-java-sdk/ Génération musicale avec Lyria 3 (DeepMind) et le SDK Java Gemini Interactions. Lyria 3 : modèle d'IA générative pour créer musique avec paroles ou pistes instrumentales. Utilisation via le SDK Java de l'API Gemini, nécessite une clé API Gemini. Deux versions de modèle Lyria 3 : lyria-3-clip-preview : Clips courts (30s), extraits. lyria-3-pro-preview : Chansons complètes (jusqu'à 3 min), structurées. Personnalisation via les prompts : Fournir ses propres paroles ou les faire générer. Contrôler la structure de la chanson ([Intro], [Verse], [Chorus], [Outro]). Générer des morceaux instrumentaux uniquement. Utiliser des images comme source d'inspiration (modèle multimodal). Sortie : Audio (MP3) et texte (paroles/structure) directement, sans décodage complexe. Facilite l'intégration de la génération musicale dans les applications Java. Les world model, la prochaine étape pour les IA https://www.lepoint.fr/sciences-nature/comment-le-commando-de-yann-le-cun-se-prepare-a-ringardiser-les-geants-mondiaux-de-lia-depuis-paris-OZVUWTDYBNE25C6WF44265ZQKE/ Yann LeCun a quitté Meta FAIR pour créer AMI Labs (Advanced Machine Intelligence) basée à Paris Sa thèse : les LLMs ne mèneront pas à l'intelligence générale, la vraie IA doit partir de la compréhension du monde physique AMI Labs a levé 1,03 milliard de dollars en seed (le plus grand seed round de l'histoire européenne) à 3,5 milliards de valorisation Les world models apprennent à prédire et comprendre la réalité physique plutôt qu'à prédire le prochain token d'une séquence Slogan d'AMI : "Real intelligence does not start in language. It starts in the world." Paris comme base stratégique pour challenger la Silicon Valley dans la prochaine rupture de l'IA Debezium 2026 : résultats du sondage communautaire https://debezium.io/blog/2026/04/27/debezium-2026-survey-results/ Debezium est un outil de Change Data Capture (CDC) open source qui capture les modifications de bases de données en temps réel pour les diffuser vers des systèmes comme Kafka. 98,6% des répondants utilisent Debezium activement ou prévoient de le faire dans l'année, avec 91,3% déjà en production. 63,8% des déploiements tournent sur Kubernetes, 60,9% utilisent Kafka Connect auto-géré, et 17,4% restent sur des VMs ou bare metal. Helm charts est l'approche dominante pour la gestion de configuration, souvent combiné avec GitOps, CI/CD, Ansible ou Terraform. PostgreSQL domine les connecteurs utilisés à 69,6%, suivi de MySQL (33,3%), SQL Server (29%) et Oracle (27,5%). Les volumes de changements capturés vont de 1-25 modifications par minute jusqu'à 1-2 millions par minute selon les environnements. Infinispan rejoint l'écosystème OGX comme fournisseur de stockage vectoriel https://infinispan.org/blog/2026/04/17/infinispan-joins-ogx-ecosystem OGX (anciennement Llama Stack) est un serveur API agentique open source pour construire des applications d'IA complètes. OGX compose des fournisseurs d'inférence, des stores vectoriels, des backends de sécurité, des runtimes d'outils et du stockage de fichiers en un seul serveur déployable. OGX se positionne comme une alternative à l'API OpenAI, déployable sur diverses infrastructures et modèles. OGX cible les workflows RAG (Retrieval-Augmented Generation) et les applications agentiques. Infinispan s'y intègre comme fournisseur de vector IO, apportant recherche vectorielle, par mots-clés et hybride. Je n'ai pas entendu parlé de ce renommage, vous le voyez dans vos deploiements ? Outillage cmux un nouveau terminal basé sur Ghostty spécialisé pour les coding agents https://cmux.com/ Application macOS native construite sur le moteur de rendu Ghostty (libghostty), offrant une accélération GPU pour une fluidité maximale Conçu spécifiquement pour le multitâche et les workflows assistés par IA, avec des onglets verticaux affichant la branche Git, le répertoire et les ports actifs Intègre des notifications qui illuminent les panneaux lorsqu'un agent IA (Claude Code, Codex, etc.) nécessite l'attention de l'utilisateur Propose un navigateur web intégré et scriptable qui peut être affiché en écran scindé à côté du terminal via une API Alternative moderne à tmux, ne nécessitant pas de fichiers de configuration complexes ou de préfixes de touches pour la gestion des vitres et des sessions Supporte nativement tous les agents de codage en ligne de commande et permet l'automatisation via une API socket et une interface CLI dédiée Git Worktree comme un chef https://www.metal3d.org/blog/2026/git-worktree-comme-un-chef/ Article par Patrice Ferlet Git Worktree: Travailler sur plusieurs branches simultanément via des répertoires distincts. Évite git stash ou clones multiples pour le changement de contexte rapide. Méthode "bare" (recommandée): Cloner le dépôt en mode bare (ex: .bare). Lier le dossier racine au dépôt bare via un fichier .git. Configurer le remote tracking pour voir toutes les branches distantes. Ajouter des worktrees pour chaque branche (git worktree add ). Avantages: Économie d'espace, source de vérité unique (un git fetch met tout à jour), hooks/configs partagés, sécurité. Conseils: Ne jamais faire de git checkout à l'intérieur d'un worktree. git fetch --all depuis n'importe quel worktree pour tout mettre à jour. git worktree add --detach pour tester des merges temporaires sans créer de branche. Supprimer: git worktree remove puis git worktree prune. Un script wtree est fourni pour automatiser l'initialisation du setup "bare". Améliore considérablement le workflow. L'IDE meurt et vite https://x.com/jdegoes/status/2036931874057314390?s=46&t=C18cckWlfukmsB_Fx0FfxQ Des leaders techniques prédisent la fin rapide de l'IDE traditionnel, remplacé par des interfaces conversationnelles agentiques Le changement de paradigme : le développeur n'écrit plus des lignes de code mais exprime son intention et supervise des agents autonomes Des outils comme Claude Code, Copilot et Cursor transforment déjà radicalement les workflows de développement quotidiens L'IDE centré sur l'éditeur de code perd sa raison d'être quand l'agent lit, modifie et structure le code de manière autonome La transition est comparable au passage du desktop au mobile : les pratiques établies depuis 30 ans remises en question en quelques mois Le source de Claude Code a leaké via probablement le codemap et un site decrit sont fonctionnement https://ccunpacked.dev/ Le 31 mars 2026, Anthropic a accidentellement inclus les sourcemaps dans un package npm de Claude Code, exposant ~512 000 lignes de TypeScript La fuite n'était pas un piratage mais une erreur humaine : un "*.map" oublié dans .npmignore Le site ccunpacked.dev a été lancé pour analyser et visualiser le code source décompressé Le code révèle un agent background permanent nommé "KAIROS", un mode furtif pour cacher les contributions des employés Anthropic à l'open source, et 44 feature flags cachés Une fonctionnalité inédite "Buddy" (animal de compagnie électronique dans le terminal) et un mode "dream" pour l'idéation continue ont été découverts Anthropic a confirmé : "Aucune donnée client sensible n'était impliquée. Erreur humaine dans le packaging de la release." Gemini CLI passe aux agents https://x.com/srithreepo/status/2039794081925382307?s=46&t=GLj1NFxZoCFCjw2oYpiJpw Gemini CLI, l'agent IA open source de Google pour le terminal, introduit des hooks dans sa boucle agentique Les hooks permettent d'exécuter des scripts automatiquement (scanners de sécurité, vérifications de conformité, logging) à chaque étape de l'agent Lancement de Gemini CLI GitHub Actions : un agent autonome pour les repositories qui peut exécuter des tâches de codage de routine Support des MCP servers pour étendre les capacités et des "Agent Skills" pour des workflows spécialisés Mode agent disponible dans VS Code et IntelliJ avec accès aux outils du système de fichiers et terminal Wispr, le speech to text en local sur macOS http://wispr.stormacq.com/ Wispr est une application macOS de dictée vocale entièrement locale, propulsée par Whisper (OpenAI) sur appareil, sans cloud ni tracking Sébastien Stormacq a développé Wispr en un jour et demi sans écrire une seule ligne de code, grâce à Kiro CLI (agent IA Amazon) Disponible en open source sur GitHub et via Homebrew Détection automatique de la langue, insertion du texte au curseur dans n'importe quelle application via un raccourci global En un mois : 19 releases incluant mode mains-libres, suppression des mots de remplissage, auto-envoi pour les chats, et un outil CLI Exemple concret de développement vibe coding produisant un outil de qualité production sans expertise Swift préalable Comment, Gordon, l'assistant spécialisé en Docker est né https://n9o.xyz/posts/202603-building-gordon/ Nuno Coração (n9o.xyz) détaille comment Gordon, l'assistant spécialisé Docker, a été construit sur docker-agent, le runtime d'agents IA open source de Docker écrit en Go Les agents sont définis en YAML déclaratif et distribués comme des artefacts OCI, sans mise à jour binaire nécessaire L'architecture initiale en essaim de 9 agents spécialisés a été abandonnée au profit d'un agent racine unique avec un prompt soigneusement conçu Le modèle utilisé est Claude Haiku 4.5, suffisant après optimisation des prompts Principe clé "show, then do" : toute action de l'agent nécessite une approbation explicite de l'utilisateur La description des outils impacte fortement la précision du LLM : ajouter des outils peut paradoxalement dégrader les performances existantes Le prompt est une spécification détaillée (identité, patterns d'accès fichiers, règles de sécurité) plutôt qu'une simple instruction IBM Bob https://bob.ibm.com/blog/announcing-ibm-bob-launch IBM Bob assistant IA d'IBM pour coder sur de vraies codebases (lancé avril 2026) 5 modes : Ask, Plan, Code, Advanced (MCP), Orchestrator Détecte la complexité du code en temps réel et propose des refactos Fait des revues de code automatiques sur tes branches/issues GitHub Permet d'écrire en langage naturel directement dans l'éditeur Fonctionne aussi en terminal/CLI et dans les pipelines CI/CD Sécurité : approbation manuelle, .bobignore, checkpoints, pas de training sur tes prompts How I use Claude - 50 tips pratiques https://www.youtube.com/watch?v=mZzhfPle9QU Staff Engineer Meta partage 50 tips après 6 mois d'utilisation intensive de Claude Code Basé sur ~12h/jour d'usage perso et professionnel Couvre tout : bases, workflows avancés, parallélisation Objectif : partager ce qu'il aurait voulu savoir dès le départ Méthodologies Quelqu'un rale sur la non soutenabilité des bases de code écritent avec des agents https://mariozechner.at/posts/2026-03-25-thoughts-on-slowing-the-fuck-down/ Mario Zechner estime que les agents IA font les mêmes erreurs répétitivement sans apprendre, accumulant la complexité à grande vitesse faute de bottlenecks humains Sans vision globale, les agents créent du cargo-cult : les "best practices" de l'industrie appliquées localement sans cohérence architecturale La croissance de la base de code dégrade la capacité des agents à retrouver le code existant → duplication et incohérences croissantes Il cite des pannes AWS et des initiatives qualité Microsoft comme signes préoccupants liés au code généré par IA Solution : réserver les agents aux tâches délimitées et évaluables, garder l'architecture, les APIs et les systèmes critiques écrits à la main Maintenir une revue de code rigoureuse et traiter les humains comme les gardiens finaux de la qualité On m'oblige à utiliser l'IA https://n.survol.fr/n/on-moblige-a-utiliser-lia Éric D. défend l'adoption obligatoire de l'IA comme décision stratégique légitime, comparable au choix du full remote ou de la stack technique Il distingue la décision stratégique (adoption IA) de la méthode d'accompagnement (qui reste collaborative et bienveillante) La compétence IA devient un critère de recrutement : chercher des candidats déjà curieux et explorateurs de ces outils L'alignement culturel sur les pratiques et outils est un prérequis à la cohésion d'équipe Le refus d'adopter certains outils stratégiques peut justifier de ne pas recruter un candidat autrement compétent Encore une metodo SPDD https://martinfowler.com/articles/structured-prompt-driven/ Problème : l'IA accélère le dev individuel mais amplifie ambiguïtés et incohérences à l'échelle d'une équipe. martinfowler SPDD : traiter les prompts comme des artefacts versionnés, révisables et réutilisables plutôt que des échanges jetables. martinfowler Canvas REASONS : 7 dimensions (Requirements, Entities, Approach, Structure, Operations, Norms, Safeguards) pour guider le LLM de l'intention à l'exécution. martinfowler Workflow en 6 étapes : exigences → analyse → contexte → prompt structuré → code → tests unitaires, chaque étape s'appuyant sur la précédente. martinfowler 3 compétences clés : abstraction d'abord, alignement de l'intention, revue itérative. martinfowler Limites : fort ROI sur du code métier complexe, peu adapté aux hotfixes urgents, scripts jetables ou travail créatif/visuel. m Sécurité Le projet Glasswing pour sécuriser les logiciels https://www.anthropic.com/glasswing Anthropic lance Glasswing, une initiative de cybersécurité utilisant Claude Mythos Preview pour identifier des vulnérabilités zero-day 12 partenaires fondateurs dont AWS, Apple, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft et NVIDIA Anthropic investit 100 millions de dollars en crédits de modèle et 4 millions en dons aux organisations de sécurité open source Le modèle opère avec une autonomie substantielle, identifiant des milliers de vulnérabilités dans les OS, navigateurs et infrastructures critiques Plus de 40 organisations supplémentaires ont accès pour scanner et sécuriser leurs systèmes Objectif : donner l'avantage aux défenseurs avant que les techniques de hacking assistées par IA ne se généralisent chez les attaquants LinkedIn vous espionne https://frenchbreaches.com/blog/linkedin-est-accuse-de-fouiller-dans-votre-ordinateur-illegalement Scandale "BrowserGate" : LinkedIn injecte du JavaScript qui tente de détecter les extensions Chrome installées sur votre navigateur Le script analysé contient une liste codée en dur de 6 222 extensions Chrome avec identifiants et chemins de fichiers internes Croissance alarmante de la liste ciblée : 38 extensions en 2017 → 461 en 2024 → ~1 000 en mai 2025 → 6 222 début 2026 Les données collectées incluent aussi CPU, RAM, résolution d'écran, timezone et état batterie pour du fingerprinting Certaines extensions ciblées sont liées à la neurodivergence, aux pratiques religieuses ou aux opinions politiques → violation grave du RGPD LinkedIn défend que le scan vise uniquement à détecter les extensions qui pratiquent le scraping de données Post mortem de la supply chain attack sur la librairie NPM axios https://github.com/axios/axios/issues/10636 Le 31 mars 2026, deux versions malveillantes d'axios (1.14.1 et 0.30.4) ont été publiées via un compte mainteneur compromis Vecteur d'attaque : RAT installé via ingénierie sociale ciblée sur la machine personnelle du mainteneur principal La 2FA ne protège pas si la machine de l'utilisateur est compromise : l'attaquant contrôle tout et peut agir comme l'utilisateur Les packages malveillants injectaient plain-crypto-js@4.2.1, un cheval de Troie multi-plateforme (macOS, Windows, Linux) Détection communautaire en ~3 heures, suppression par npm, mesures correctives : rotation complète des credentials Changements préventifs : publication via OIDC, releases immuables, amélioration des pratiques GitHub Actions Passbolt un gestionnaire de mots de passe open source https://lesjoiesducode.fr/passbolt-gestionnaire-de-mots-de-passe-gratuit-open-source-que-votre-equipe-merite-vraiment Gestionnaire de mots de passe open source conçu pour le partage d'identifiants en équipe, utilisé par plus de 50 000 organisations Chiffrement individuel par utilisateur et par version de credential, pas de coffre-fort partagé — architecture zero-knowledge "Forward secrecy" : quand un membre quitte l'équipe, ses copies chiffrées sont automatiquement révoquées sans reset manuel Supporte TOTP, clés SSH, tokens API et champs personnalisés avec piste d'audit complète de tous les accès Édition communautaire entièrement gratuite avec utilisateurs illimités, auto-hébergeable ou cloud Chiffrement OpenPGP nécessitant passphrase + clé privée, avec tokens visuels anti-phishing Loi, société et organisation Anthropic fait un don d'1,5 millions de dollars à la fondation Apache https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-1-5m-donation-from-anthropic Anthropic donne 1,5 million de dollars à l'ASF pour soutenir l'infrastructure, la sécurité et la communauté open source Vitaly Gudanets (CISO d'Anthropic) : "Soutenir l'ASF est un investissement direct dans la résilience et l'intégrité des systèmes dont dépend l'IA moderne" Les fonds financeront les systèmes de build, les processus de sécurité et les services aux projets Apache Ce don est le déclencheur de l'initiative IA responsable à 10 millions de dollars de l'ASF L'infrastructure Apache est invisible mais critique : des systèmes financiers aux plateformes de santé, elle sous-tend l'écosystème logiciel mondial L'ASF lance l'initiative IA responsable https://news.apache.org/foundation/entry/the-apache-software-foundation-launches-10m-responsible-ai-initiative-with-initial-1-75m-donation L'ASF lance une initiative pour une IA responsable dotée d'un budget de 10 millions de dollars sur 3 ans minimum Anthropic est le premier donateur avec 1,5 million de dollars ; Alpha-Omega contribue 250 000 dollars L'initiative fournit aux projets Apache un accès à des modèles IA pour l'expérimentation et la sécurité Elle soutient l'ensemble de la chaîne IA/ML : pipelines de données, infrastructure, frameworks de deep learning Des tracks de conférences, hackathons et bourses de voyage sont prévus pour élargir la communauté Les principes directeurs incluent la supervision humaine, l'intégrité des licences et la sécurité open source Oracle vire 30000 personnes https://rollingout.com/2026/03/31/oracle-slashes-30000-jobs-with-a-cold-6/ Oracle licencie 20 000 à 30 000 employés, 18% de ses effectifs mondiaux. Les salariés ont appris leur licenciement par un simple email à 6h du matin, sans aucun préavis. L'accès à tous les systèmes (Slack, Zoom, badges) a été coupé immédiatement après. But : libérer 8 à 10 milliards de dollars pour construire des centres de données IA. Oracle a déjà contracté 50 milliards de dettes en 2026 pour financer ses projets IA. Paradoxe : l'entreprise affiche un bénéfice record de 6,13 milliards, mais ses liquidités sont dans le rouge. L'action Oracle a perdu plus de la moitié de sa valeur depuis septembre 2025. Et si l'IA n'était qu'un prétexte pour licencier https://eventuallycoding.com/p/ia-licenciements-et-si-l-intelligence-artificielle-n-etait-qu-une-excuse Hugo Lassiège (eventuallycoding) estime que les entreprises utilisent l'IA comme narratif commode pour masquer des erreurs de gestion passées (Block a triplé ses effectifs post-COVID sans croissance des revenus correspondante) Moins de 1% des licenciements technologiques seraient réellement dus à des gains de productivité IA selon les analyses citées Mesurer la productivité des développeurs reste un problème non résolu, mais les entreprises affirment des gains d'efficacité sans preuves Des pressions économiques réelles (inflation, guerres commerciales, coûts énergétiques) sont masquées derrière le discours IA Les restructurations nécessaires sont présentées comme des transformations AI-driven positives pour rassurer les investisseurs Il y voit une fenêtre d'opportunité pour l'Europe pendant que les géants américains se restructurent GitHub Copilot va utiliser les interacitons pour entrainer ses modèles sauf si vous vous délistez https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/ À partir du 24 avril 2026, GitHub utilise par défaut les interactions des utilisateurs Copilot Free, Pro et Pro+ pour entraîner ses modèles Les données collectées incluent le code accepté ou modifié, les snippets envoyés, les noms de fichiers et structures de dépôts, et les retours utilisateurs Les utilisateurs Copilot Business, Enterprise et les dépôts d'entreprise sont exclus de cette collecte de données d'entraînement Opt-out disponible dans les paramètres GitHub > "Privacy" ; les préférences de désactivation préalables sont conservées automatiquement Objectif déclaré : améliorer la précision des modèles sur les langages et cas d'usage du monde réel Grosse percée de Claude Code dans les commits sur GitHub https://aifoc.us/damn-claude-thats-a-lot-of-commits/ Explosion de Claude Code : En six mois, Claude Code est passé de 0,7 % à 4,5 % de tous les commits publics sur GitHub, surpassant tous les autres outils d'IA combinés. Adoption massive des agents IA : Environ 5 % des commits publics sur GitHub sont désormais générés par des agents IA, un chiffre en croissance rapide depuis fin 2025. Domination des bots sur GitHub : Au-delà des commits, les outils d'IA sont omniprésents dans la gestion des pull requests et des problèmes (Copilot et CodeRabbit notamment). Limites méthodologiques : Les données ne concernent que les dépôts publics (les entreprises utilisent massivement des dépôts privés, invisibles ici). Le comptage dépend fortement de la visibilité des signatures (certains outils comme Claude marquent systématiquement leurs commits, d'autres non) L'API de recherche GitHub présente une fiabilité variable à cette échelle. Changement de paradigme : Le développement logiciel vit une transition majeure, comparable au passage du desktop au mobile. L'intégration des agents IA dans le cycle de production n'est plus une expérimentation, mais une réalité opérationnelle à grande échelle. Dysmaths une application pour aider à apprendre les mathématiques et la géométrie lorsque l'on souffre de dyspraxie, dysgraphie https://dysmaths.com/ Application web pour aider les élèves de collège et lycée souffrant de dysgraphie et dyspraxie à faire des maths et de la géométrie Outils de dessin à main levée, géométrie précise (compas, rapporteur, règle) et opérations structurées (fractions, racines, puissances, symboles mathématiques) Export PDF et PNG avec conservation fidèle de l'échelle pour l'impression et la soumission des exercices Options d'accessibilité : police OpenDyslexic, personnalisations d'interface, import d'images et de PDFs Répond à un besoin réel : les outils standards ne sont pas adaptés aux difficultés de coordination et d'organisation spatiale en mathématiques IA ou réalité ? Par Amistory https://www.youtube.com/watch?v=PPYdAhBBF2I L'IA génère des contenus (images, voix, vidéos) de plus en plus indétectables Les arnaques au clonage de voix et deepfakes sont en forte hausse Les faux contenus viraux manipulent l'opinion à grande échelle Le faux n'est plus un accident, c'est devenu un système organisé La société entre dans une ère de doute généralisé sur le réel Comment s'informer quand le réel lui-même peut être simulé ? Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 12-13 mai 2026 : Lyon Craft - Lyon (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 19-20 mai 2026 : Green Code Challenge - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 27 mai 2026 : aMP Day Strasbourg 2026 - Strasbourg (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 4 juin 2026 : Workplace Intelligence Days - 1ère édition - Lyon (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 9 juin 2026 : France API 2026 - Paris (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 25-26 juin 2026 : Agile Tour Toulouse 2026 - Toulouse (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 12 octobre 2026 : Dev With AI - Paris (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

What if your database worked more like Git? Every change captured as an immutable event you can replay, instead of a single mutating row that quietly forgets its own history. That's event sourcing, and Chris May is back on Talk Python, fresh off our Datastar panel, to walk us through what it actually looks like in Python. We'll cover the core patterns, the libraries to reach for, when not to use it, and why event sourcing turns out to be a surprisingly good fit for AI-assisted coding. Episode sponsors Sentry Error Monitoring, Code talkpython26 Temporal Talk Python Courses Links from the show Guest Chris May: everydaysuperpowers.dev Intro to event sourcing e-book: everydaysuperpowers.gumroad.com Domain-Driven Design: The Power of CQRS and Event Sourcing: How CQRS/ES Redefine Building Scalable System: ricofritzsche.me DDD: www.amazon.com Understanding Eventsourcing (Martin Dilger): www.amazon.com Event Sourcing Explained using Football Video: www.youtube.com Why I finally embraced event sourcing and why you should too article: everydaysuperpowers.dev valkey: valkey.io diskcache: talkpython.fm eventsourcing package: github.com eventsourcing docs: eventsourcing.readthedocs.io John Bywater: github.com Datastar: data-star.dev Microconf: microconf.com Event Modeling & Event Sourcing Podcast: podcast.eventmodeling.org Python Package Guides for AI Agents: github.com Iodine tablets AI joke: x.com KurrentDb: www.kurrent.io Watch this episode on YouTube: youtube.com Episode #548 deep-dive: talkpython.fm/548 Episode transcripts: talkpython.fm Theme Song: Developer Rap

פרק מספר 515 של רברס עם פלטפורמה - באמפרס 91. רן תבורי, דותן ואלון מתכנסים לפרק באמפרס עמוס בחדשות טכנולוגיות, AI, שינויים בשוק התעסוקה, כלים חדשים למפתחים, והדילמה הנצחית של "אש או בוץ" בפרויקטים של קוד פתוח. [00:51] השקיעה של Stack Overflow רן פותח עם גרף שמראה את צניחת כמות השאלות הנשאלות ב-Stack Overflow לאורך השנים. האפקט ברור: מרגע שהבוטים שלנו מיצו את התוכן והחלו לתת לנו תשובות מיידיות, הטראפיק לאתר התרסק. דותן מציין שהירידה הכללית החלה עוד קודם, אך קפצה זמנית בתקופת הקורונה. [04:10] כולם מדברים על סקילים עולם סוכני הפיתוח (Agents) לא זז היום בלי סקילים (Skills). מקום מעולה להתחיל בו הוא האתר https://skills.sh/. דוגמה כיפית ושימושית מתוכו: סקיל לספריית Manim (של ערוץ היוטיוב 3Blue1Brown) שמאפשר ליצור אנימציות מתמטיות בקלות. הנה ציוץ הדגמה בטוויטר, והקישור לסקיל עצמו ב-Skills.sh. בעקבות הביקוש, נוצרה ממש תעשייה של מנועי חיפוש וספריות של סקילים: http://skills.sh (למקרה שפספסתם) https://context7.com/ https://skillsmp.com/ ריפו מעניין בגיטהאב: https://github.com/mhattingpete/claude-skills-marketplace [07:44] העתיד של ממשקי המשתמש ו-MCP Apps הסטנדרט החדש, MCP-apps, מאפשר להעביר רכיבי HTML אינטראקטיביים ישירות בתוך MCP (ולא רק טקסט). הפרויקט מובל בין היתר על ידי ליעד ועידו, מי שהקימו בעבר את https://mcpui.dev/. שווה לראות את סרטון ההדגמה ביוטיוב. ליעד השיק לאחרונה כלי מעניין נוסף בשם https://ora.run/ שמאפשר לכם לבדוק עד כמה האתר או הביזנס שלכם מותאם לקריאה על ידי סוכני AI (Agent-Ready). דיון פילוסופי קצר: איך ייראה עתיד ה-UX? חברות ענק כמו סיילספורס (Salesforce) כבר עברו להציע חוויית Headless מלאה שפונה קודם כל לאייג'נטים. [13:35] דונלד קנות', קלוד, והסקפטיות שנשברה פרופסור דונלד קנות' (Donald Knuth), מאבות מדעי המחשב, פרסם לאחרונה מאמר מרתק תחת הכותרת Claude Cycles. המסר המרכזי: קנות' התחיל כסקפטי מוחלט בנוגע ליכולות של כלי AI לכתוב קוד איכותי, אבל לאחר סדרת ניסויים, הוא מודה שהופתע לטובה. [15:52] פלטפורמות ניהול והרצה לאייג'נטים לכתוב סוכן זה קל, להריץ אותו בסביבה מאובטחת בסקייל ללקוחות זה סיפור אחר לגמרי. הפתרון? פלטפורמות Agent Hosting שצצות עכשיו בכל מקום: אנתרופיק (Anthropic) מציעים Managed Agents. גם OpenAI חזק במשחק עם פלטפורמת Frontier. ואמזון (AWS) הציגה את Bedrock AgentCore. [23:14] פיטורי AI ועתיד שוק התעסוקה התעשייה עוברת שינויים כואבים. חברות כמו Block ו-Coinbase מקטינות את מצבת כוח האדם ומצמצמות צוותים כפועל יוצא מהתייעלות מבוססת AI. למרות זאת, נשמעים גם סיפורים על חברות שמעדיפות כרגע לשכור מפתחים ג'וניורים זולים במקום לשלם על עלויות תפעול גבוהות של כלי AI. אז מה העתיד טומן בחובו? אולי לפחות נוכל לתת לרובוט לקפל לנו את הכביסה. [25:54] כלים לטרמינל (ההמלצה של אלון) כשהטרמינל עמוס באייג'נטים שרצים ברקע, כדאי לעשות סדר. אלון ממליץ בחום על https://cmux.com/, כלי טרמינל נוח במיוחד שמבוסס על Ghostty, עם תמיכה בטאבים ורטיקליים שמאוד מקלים על העבודה עם סוכנים מרובים (למרות שרן טוען אצלו זה קצת "התעייף" ונהיה אטי). [31:11] הפריצה לורסל (Vercel) ורסל סבלה לאחרונה מפריצת אבטחה (ההודעה הרשמית כאן), שבמהלכה כנראה דלפו מפתחות (API Keys) של לקוחות ונמכרו ברשת האפלה. אם עדיין לא עשיתם רוטציה למפתחות שלכם שם – זה הזמן. [32:41] פינת הקוד הפתוח של דותן: "אש או בוץ" (

This show has been flagged as Clean by the host. This show is about developing features for a web application. The technology used is ASP.Net WebForms , the programming language is Visual Basic .Net along with HTML and CSS and the development environment is Windows 11 running under a virtual machine in Linux, with Visual Studio and SQL Server as the database. Other tools used are Git , Github , Joplin and Dropbox , Google Gemini and a tool called Beyond Compare . ResourceRowControl.ascx.vb Public WriteOnly Property ResourceObject As Resource Set(obj As Resource) If obj IsNot Nothing Then HiddenResourceID.Value = CStr(obj.ResourceID) HiddenResourceTypeID.Value = CStr(obj.ResourceTypeID) Resource.Text = obj.ResourceName Type.Text = obj.ResourceTypeName Available.Checked = obj.ResourceAvailable End If End Set End Property Private Sub Available_CheckedChanged(sender As Object, e As EventArgs) Handles Available.CheckedChanged Dim objResource As New Resource With objResource .ResourceID = ResourceID .ResourceName = Resource.Text .ResourceTypeID = ResourceTypeID .ResourceAvailable = Available.Checked End With objResource.Add() End Sub ResourceRowControl.ascx Provide feedback on this episode.

Hoy te ofrecemos una gran variedad de cócteles con sabores a surf, garage, exótica, country punk o rhythm n’ blues. El primero es un Mai Tai en alta fidelidad servido por los californianos Shorty’s Swingin’ Coconuts, seguido por nuevos y próximos lanzamientos del sello de New Jersey, Hi-Tide Recordings. Tito Ramírez se alía con los holandeses Doctor Velvet y Mike Sanchez con el saxofonista Drew Davies, y nos llegan estupendas novedades flequilludas desde Melbourne, Tesalónica o Betxí.Playlist;(sintonía) SHORTY’S SWINGIN’ COCONUTS “Theme from Star Trek” (Mai Tai in Hi-Fi, Pt. II” EP)SHORTY’S SWINGIN’ COCONUTS “Dance girl dance” (Mai Tai in Hi-Fi, Pt. II” EP)THE BABALOONEYS “Soup surfer” (Goin’ for it)I. JEZIAK and THE SURFERS “The swell”THE VOLCANICS “Spin out” (In 3-D)GOONS “Rob, cheat, steal” (Never go back)THE UNTAMED YOUTH “The harem” (Git up and go)TIKIYAKI and THE HAWAIIAN BRASS “Let’s go for a ride” (Weekend in Waikiki)MAGIC SANDS “Hawai’i Kai” (Limon y Sal)DOCTOR VELVET “City jungle” (New breed)DOCTOR VELVET feat TITO RAMIREZ “Camino hacia ningún lugar”MIKE SANCHEZ with THE DREW DAVIES RHYTHM COMBO “Hey now” (Hey now)THE GNOMES “Magic man”THE SENCES “Best friend” (Can’t beat the Sences)LOS ALTRAGOS “Lo mejor de ti” (Necios y engreídos)JABATO “Vagabundo” (Guateque Taboo)OLD LADY “Good money, good time” (Tears around last call)JENNY DON’T and THE SPURS “Flying high”Escuchar audio

En este episodio Oz, Tito Neira y Humberto Arias (Senior PM en Microsoft) exploran cómo se está transformando el rol del Product Manager en la era del Product Engineer, los copilots y los agentes de IA. Humberto comparte desde adentro de Microsoft qué tareas del PM ya están automatizadas, qué nuevas skills ganan valor (AI First Mindset, vibe coding responsable, orquestar enjambres de agentes) y qué debe hacer hoy un PM tradicional para no quedarse fuera.00:00 Intro y bienvenida02:13 Mitos y Glass Wing de Anthropic06:26 ¿El PM está en peligro? El elefante en la sala07:58 De idea a MVP en el tiempo de un café10:13 Agentes, sub-agentes y enjambres12:19 Cómo entrevistar un AI PM hoy16:36 Tareas que un PM ya no hace18:40 Dos caminos: menos carga o más portafolio21:43 Analogía del cajero bancario22:49 Aplanamiento de organizaciones26:22 Git como fuente de verdad en SELLIT930:02 El jefe convertido en agente32:55 Vibe Coding responsable37:40 AI First Mindset y MCP Connectors42:39 Hardware del futuro: voz como interfaz44:40 Qué debe hacer hoy un PM tradicional47:24 Growth Mindset y cierre✩ CURSOS DISPONIBLES

Michael and Jake are joined by David Hemphill to discuss David's macOS app Gent, a task runner built on the "Ralph loop" pattern for AI-powered coding workflows.The conversation covers how Gent takes a project requirements document (PRD), breaks it into small tasks that fit within a single context window, and runs them sequentially or in parallel using copy-on-write clones and Git worktrees.We discuss our own evolving workflows with Claude Code, including plan mode, the "Grill Me" skill for stress-testing plans, managing context windows, and the /rewind command.Show LinksDavid HemphillGentRalph loopConductorPolyscopeChief"Grill Me" skillMatt Pocock / AI HeroSoloThe Eternal Promise: A History of Attempts to Eliminate ProgrammersLaracon

Early bird discounts for the San Francisco World's Fair, the biggest AIE gathering of the year, end today - prices will go up by ~$500 tonight so do please lock in ASAP!From near-universal AI tool adoption inside Shopify to internal systems for ML experimentation, auto-research, customer simulation, and ultra-low-latency search, Mikhail Parakhin joins us for a deep dive into what it actually looks like when a 20-year-old, $200B software company goes all-in on AI. We cover why Shopify has become much more vocal about its internal stack, what changed after the December model-quality inflection, and why the real bottleneck in AI coding is no longer generation, but review, CI/CD, and deployment stability.We also go inside Tangle, Tangent, SimGym, which are three major AI initiatives that Shopify is doing to make experimentation reproducible, optimization automatic, customer behavior simulatable, and search and catalog intelligence faster and cheaper at scale. Along the way, Mikhail explains UCP, Liquid AI, and why token budgets are directionally right but often measured badly, why AI-written code can still increase bugs in production, what makes Shopify's customer simulation defensible, and what he learned from the Sydney era at Bing.We discuss:* Mikhail's path from running a major Microsoft business unit spanning Windows, Edge, Bing, and ads to becoming CTO of Shopify* Why Shopify is talking more publicly about AI now, and why staying at the frontier has become necessary for the company* Shopify's internal AI adoption curve, the December inflection, and why CLI-style tools are rising faster than traditional IDE-based tools* Why Jensen Huang is directionally right on token budgets, but raw token count is still the wrong way to evaluate engineering output* Why the real unlock is not more agents in parallel, but better critique loops, stronger models, and spending more on review than generation* Why AI coding can still lead to more bugs in production even if models write cleaner code on average than humans* Why Shopify built its own PR review flow, and why Mikhail thinks most off-the-shelf review tools miss the point* How PR volume, test failures, and deployment rollback are becoming the real bottlenecks in the agent era* Why Git, pull requests, and CI/CD may need a new metaphor once code is written at machine speed* What Tangle is, and how Shopify uses it to make ML and data workflows reproducible, collaborative, and production-ready from the start* Why Tangle is different from Airflow, and why content-addressed caching creates network effects across teams* What Tangent is, and how Shopify is using auto-research loops to optimize search, themes, prompt compression, storage, and more* Why Tangent is becoming a democratizing tool for PMs and domain experts, not just ML engineers* Why AutoML finally feels real in the LLM era, and where auto-research still falls short today* Why Tangle, Tangent, and SimGym become much more powerful when combined into one system* What SimGym is, why simulated customers only work if you have real historical behavior, and why Shopify's data gives it a moat* How SimGym evolved from comparing A/B variants to telling merchants what to change on a single live storefront to raise conversions* Why customer simulation is so expensive, from multimodal models to browser farms to serving and distillation costs* How Shopify models merchant and buyer trajectories, runs counterfactuals, and thinks about interventions like discounts, campaigns, and notifications* Why category-level behavior is so different across commerce, and why ideas like Chinese Restaurant Processes are showing up again in practice* Shopify's new UCP and catalog work, including runtime product search, bulk lookups, and identity linking* Why Shopify is using Liquid AI, and why Mikhail sees it as the first genuinely competitive non-transformer architecture he has used in practice* Where Liquid already works inside Shopify today, from low-latency query understanding to large-scale catalog and Sidekick Pulse workloads* Whether Liquid could become frontier-scale with enough compute, and why Shopify remains pragmatic and merit-based about model choice* Who Shopify is hiring right now across ML, data science, and distributed databases* The Sydney story at Bing, why its personality was not an accident, and what Mikhail learned from deliberately shaping AI character early onMikhail Parakhin* LinkedIn: https://www.linkedin.com/in/mikhail-parakhin/* X: https://x.com/MParakhinTimestamps00:00:00 Introduction: Mikhail Parakhin, Microsoft, and Shopify00:01:16 Why Shopify Is Talking More About AI00:02:29 Internal AI Adoption at Shopify and the December Inflection00:06:54 Token Budgets, Jensen Huang, and Why Usage Metrics Can Mislead00:10:55 Why Shopify Built Its Own AI PR Review System00:12:38 AI Coding, More Bugs, and the Real Deployment Bottleneck00:14:11 Why Git, PRs, and CI/CD May Need to Change for Agents00:18:24 Tangle: Shopify's Reproducible ML and Data Workflow Engine00:21:19 Why Tangle Is Different from Airflow00:26:14 Tangent: Auto Research for Optimization and Experimentation00:30:07 How Tangent Democratizes Experimentation Beyond ML Engineers00:33:06 The Limits of Auto Research00:36:36 Why Tangle, Tangent, and SimGym Compound Together00:37:20 SimGym: Simulating Customers with Shopify's Historical Data00:42:47 The Infra Behind SimGym00:46:00 Why SimGym Gets Better with Real Customer History00:47:30 Counterfactuals, HSTU, and Modeling Merchant Trajectories00:51:55 CRPs, Clustering, and Category-Level Customer Behavior00:53:30 UCP, Shopify Catalog, and Identity Linking00:55:07 Liquid AI: Why Shopify Uses Non-Transformer Models00:59:13 Real Shopify Use Cases for Liquid01:03:00 Can Liquid Scale into a Frontier Model?01:09:49 Hiring at Shopify: ML, Data Science, and Databases01:10:43 Sydney at Bing: Personality Shaping and AI Character01:13:32 Closing ThoughtsTranscript[00:00:00] swyx: Okay. We're here in the studio, a remote studio, with Mikhail Parakhin, CTO of Shopify. Welcome.[00:00:08] Mikhail Parakhin: Thank you. Welcome.[00:00:10] swyx: I don't even know if I should introduce you as CTO of Shopify. I feel like you have many identities. Uh, you led sort of the, the Bing ML team, I guess, uh, uh, or ads team. I, I don't know, I don't know, uh, you know, it's, uh, people va-variously refer you as like CEO or, or, uh, I don't know what that, that, that said previous role at Microsoft was.[00:00:29] Mikhail Parakhin: Uh, that was... Yeah, my previous role w- at Microsoft was the-- I actually was the CEO of one of Microsoft's business units, which included, as I, you know, as we discussed, all the things that people like to laugh about, uh, including Windows and Edge and Bing and ads and everything.[00:00:47] swyx: Yeah, yeah. What a, what a, what a wild time.You've obviously, uh, done a lot since you landed at Shopify. Uh, one of the reasons I reached out was because you started promoting more sort of internal tooling, uh, primarily Tangle, but also a lot of people have seen and adopted Tobi's QMD, uh, and obviously, I think, uh, Shopify has always been sort of leading in terms of, uh, engineering.I think more-- it's just more recent that you guys have been more vocal about your sort of AI adoption. Is that, is that true?[00:01:16] Mikhail Parakhin: Well, I think AI tools in general are fairly recent development, uh, and we've-- Shopify, you know, at this stage of its development, we're developing AI in-in-house and other, uh, building tools that use AI and, you know, interfacing with the wider AI community, uh, you know, are on the sort of the, uh, runaway trajectory.So it just did by sort of natural byproduct. We, we talk about it more also. We just, uh, just even yesterday, Andrej Karpathy was famous in tweeting about, oh, are there some, uh, ways, uh, that, that you can organize your agents to store the data and then, uh, look up the data so that you don't have to research or, or lose context every- Yestime. And a little bit tongue in cheek, I tweeted that, “Hey, we've, we've done it much earlier, and we even have different approaches, Tobi and I.” Tobi, of course, is a big fan of QMD, and I'm more of a SQL, SQLite fan. But, uh, yeah, very similar things that we've already done here. The point is, yeah, we're very dynamic, you know, explosively growing company, and we have to be at the forefront of AI adoption, obviously.[00:02:29] swyx: Yeah. Yeah. Um, you, your team kindly prepared some slides actually that we were gonna bring up on to, uh, the screen. I think I can, I can screen share, and then we can kind of go through some of the shocking stats that maybe, maybe put some numbers to what exactly is going on. So here we have, uh- An internal AI tool adoption chart.What are we looking at here? What ?[00:02:54] Mikhail Parakhin: Yeah, this is very interesting statistics. Uh, this is number of daily active workers, you know, think of, uh, DAO, basically the active users of-[00:03:05] swyx: Yeah ...[00:03:05] Mikhail Parakhin: AI tool as a percentage of all the people in the company, right? And then- Yeah ... different AI tools. And, uh, you could see two things here is that one is the green is total.Uh, green is just total. So you could see that it approaches really % by now. It's hard not to do your job now without interacting deeply, at least with one tool. You could see another interesting thing is just as many people commented in December was the phase transition when suddenly models gotten good enough that, that everything took off and started growing.Uh, it, it was many people noticed that the thing is that small improvements accumulated into this big change in Sep- December roughly timeframe.[00:03:52] swyx: Yeah.[00:03:52] Mikhail Parakhin: The other thing I would claim you could see is that, uh, CLI-based tools and tools that don't require you to look at the code becoming more popular, and you could see, yeah, various versions of, uh, Cloud Code and Codex and Pi and internal development tools taking off.Uh, exactly, yeah, uh, and blue is our River, just internal agent for coding, where tools, uh, that require IDEs such as, uh, GitHub, Copilot or Cursor, they're not exactly shrinking, but they're not growing as fast. Like, uh, red, red line is, is the IDE kind of tools. So you could see that they're, they're not experiencing as, as fast of a growth.[00:04:37] swyx: As I understand it, basically, every employee has their choice, right? Of choose whatever tool you use, and then you're just kind of doing a, a daily sur-survey or something.[00:04:47] Mikhail Parakhin: Exactly. And, uh, we- Yeah ... the, the push is to get your job done, you can use any tool, and we effectively fund unlimited tokens for everybody.Uh, we, we do, we do try to control the models that, uh, people use, but from the bottom, not from top. Like we basically say, “Hey, please don't use anything less than Opus four point six.”[00:05:09] swyx: Oh .[00:05:10] Mikhail Parakhin: Some people, some people end up using GPT five point four extra high. Some people use Opus four point six. Um, uh, you know, uh, there are some, uh, there are plus and minuses in going for full one million context window versus not.But, uh, we try to discourage people from using anything less than that.[00:05:28] swyx: Yeah, yeah. Got it, got it. Uh, I mean, uh, that's, you know... The, the next chart here, it really kind of shows the expansion and the sort of December twenty twenty-five inflection, right? That, uh, people are using a lot of tokens. I think it's also really interesting that no one was kind of abusing it in twenty twenty-five.Like it was- Had comparatively, uh, to this year, there was almost no growth. I mean, it's still like, you know, probably, probably gave fifty percent.[00:05:56] Mikhail Parakhin: Yeah. This is just a different scale. It's still exponential- Yeah, yeah ...growth at just a different- ...rate of expansion. Uh, there was inflection point, and Sean, I would claim the, the super interesting part here is that you could see that the distribution becoming more and more skewed.Yes. The top percentiles grow faster. So that means- Yeah ...the people in the top ten percentile, they, their consumption grows faster than seventy-five and so forth. So, uh, the distribution skews more and more towards the highest users, which is... I don't know what it tells me. It's like it feels not ideal, to be honest.Or maybe it's okay. We'll see.[00:06:36] swyx: Why does it feel not ideal? Is, is it because of, um, quantity over quality, or what's the concern?[00:06:42] Mikhail Parakhin: Because take it to the limit. That means, you know, if, if this rate of separation continued- Ah, yes ...a year, there will be one person consuming all the tokens. So it's just, it's kinda strange.[00:06:54] swyx: Yeah, I mean, um, uh, I, I think internal like teaching and all that, uh, will, will help sort of distribute things more widely. But in, in the early days, of course, the people who are sort of more AI-pilled will obviously find more ways to use it than the people who are less AI-pilled. Maybe let's, let's call it that.I'll just, I'll just kinda quickly, uh, pause from the, the... You know, we will go back to the rest of the slides, but I just wanna, um, review, you know, there are a lot of CTOs of, of large companies like yourself where they're all considering some kind of token budget, right? Like I think it's something, something that Jensen Huang has been talking about, where like if your 200K engineer is not using 100K of tokens every year, like they're, they're underutilizing coding agents.Of course, Jensen Huang would say that, but like it seems a very quantity over quality approach and like some, some people are basically saying like, well, is this comparable to judging engineer quality by lines of code, right? Which we also know is like kind of flawed, but better than nothing. So I, I don't know if you have like a sort of management take here on, on how to view this kind of, uh, metrics.[00:08:02] Mikhail Parakhin: Well, I mean, you're, you're baiting me. I, I like... This is my favorite topic. Uh, if you let me, I'll probably talk for two hours on just this. I have a lot of things to say. Like I do think Jensen gotten a lot of bad press saying, “Oh, of course you're, you know, this, uh, the- ...the cake seller says you don't need enough cakes.”You know? Like, of course. Uh, but, uh, I actually, uh, think that's undeserved. I think he, he's actually right. Uh, I do think- He,[00:08:33] swyx: he's directionally correct.[00:08:35] Mikhail Parakhin: Yeah. Yeah. He's directionally correct for sure. Uh-[00:08:37] swyx: Who knows what the right number is? Yeah.[00:08:39] Mikhail Parakhin: The thing that I do Uh, want to say, and this is something that we learned through trial and error and very important is like two things.One is that it's not about just consuming tokens. Uh, you can consume tokens and, and in fact, the anti-pattern is running multiple agents, too many agents in parallel that don't communicate with each other. That's almost useless, uh, compared to just fewer agents and burns tokens very efficiently. Uh, setting up the right critique loop, especially with the high quality models, where one agent does something, the other one, ideally with a different model, critiques it, uh, suggests ways to improve it, the agent redoes it with this critique and, and so it takes much longer.So people don't like it because latency goes up. You know, they, they have to wait until this debate is happening. But, uh, the quality of the code is much higher. And another thing, just since you mentioned like, look, uh, uh, yeah, the overall budget is just like, uh, lines of codes. Lines of codes are exploding for everybody right now, or partially because AI is really mover balls, but partially just because AI can write a lot more code, you know, doesn't get tired.And so you have to have to have a very strong narrow waist during PR review. Otherwise, just the number of bugs will go through the roof. It's, uh, it's this unexpected consequence of the just volume trumping everything. I would claim by now good model writes code on average with fewer bugs than, than the average human.But since they write so much more of it, like more of it will make it into production. So you have to- You still[00:10:26] swyx: have[00:10:26] Mikhail Parakhin: more bugs. Yeah. Have to have a very rigorous PR reviews, also automated of course. But, uh, yeah, that to spend a lot budget there. Like this, this for me, for me, actually, the important metric is the ratio of budget spent during code generation versus, uh, spent, uh, expensive tokens like GPT, uh, five point four Pro or, uh, uh, Deep Think from Gemini, you know, checking on PR reviews.[00:10:55] swyx: Yeah, totally. Uh, I noticed in your chart you didn't have any review tools. Do you just use like, like let's say a Claude code to review tools? Or do you have another set of review tools like the Greptiles, the Code Rabbits, uh, Devin Reviews has a review tool. I don't know if you've had those specialist review tools.[00:11:13] Mikhail Parakhin: You are a little bit jumping on my store tool right now because the graphs I was only showing public tools. Uh, uh, the-- I haven't found a good PR review tool that, that does what I think should be done. And, uh, partially my, my thinking is because it's so... It just goes against both what people feel like emotionally they prefer and, uh, some of the, uh, you know, frankly Even business models that, that the companies run.At peer review tool, uh, time, you want to run the largest models. That means, I don't know, Codex or, or, uh, Cloud Code is not gonna cut it. You need to have pro-level models if you really want to, uh, stand the tide of bots from going into production. And you need us to spend a lot of time, the models taking turns, but you don't want, like, a big swarm of, uh, of, uh, agents.So in fact, you end up in a different dual-dualistic world where you generate not that many tokens. You, in fact, generate few tokens, but it takes f-a long time because these are expensive models taking turns rather than many, many agents trying to do many things in parallel. So that's, that's why I feel like I haven't found good tools, so we are using our own for peer review for now.[00:12:33] swyx: Yeah. Yeah. I mean, uh, I think a lot of companies are building their own, uh, especially to their needs, right?[00:12:38] Mikhail Parakhin: Mm-hmm.[00:12:38] swyx: Um, I, uh, you also have a chart here going back to the slides on, uh, PR merge growth, where we're now at thirty percent, uh, month on month rather than ten percent. Uh, and also the, the estimated complexity is going up.You know, this is productivity, right? ‘Cause y- presumably there's more stuff going into the code base and more, more features getting worked on. I'm curious about the backlog, right? Like the, the, the-- I actually don't mind a pro-level model taking an hour or two hours to review my PR, because I've dealt with humans who take a week to review my PR, right?And I keep pinging them on Slack, “Hey, hey, review my PR.” So, you know, I think there's some trade-off here where, like, it still doesn't make sense.[00:13:18] Mikhail Parakhin: Exactly. That, that's exactly m-my point. Uh, that on one hand, you can tolerate longer latencies at, uh, PR. On the other hand, like right now, the real problem is not in spending time waiting for PR.It's real problem is since there's so much more code than- Yeah ... uh, probability of at least some tests failing going up, and then you, like, keep de-failing, then you have to find the offending PR, evict it, retest it without that PR, and so deployment cycle becomes much longer. Uh, so it actually, in terms of the overall time to deploy, it's total time savings if you spend more time on a longer model, like thinking for an hour, because then, then you, you don't have to spend all that time during testing and rolling, you know, rolling back the deployment.[00:14:03] swyx: Yeah, totally. That's still worth it. You know, you don't look at the individual, look at the aggregate, and look at the, the, the change in the aggregate system.[00:14:11] Mikhail Parakhin: Exactly.[00:14:11] swyx: I'm kind of curious if, like, there's this PR mentality and, like, c-- the, the, the CICD paradigm will be changed eventually. Some people are like, obviously a lot of people want new GitHub, but I even wonder if, like, Git is the problem, right?Like, is that the bottleneck? Is the concept of a PR a bottleneck? Do you guys use stack diffs? I don't know if, uh, that's a, like, a merge queue stack diff type of thing.[00:14:34] Mikhail Parakhin: We, we use, we use Stacks, we u- we use Graphite. We worked with, uh, Graphite a lot. Uh, so we use Stack, uh, PRs. I think, uh, like that's clearly the overall CICD in general, and the interaction with the code repository right now is the, clearly the sort of the, the main issue and the bottleneck for us, uh, and highest top of mind.I would say we probably need a different metaphor or different whole design of how to process it in new agentic world. I haven't seen anything dramatically better yet. I, I think everybody right now is just trying to keep their head above the water ‘cause, ‘cause there, there's so many PRs and then everybody's CICD pipelines start creaking, the, the times are increasing, the number of bugs slipping by increasing, and you have to, have to clap on down.And so we are a little bit in this situation when we need to first stabilize that story and then start thinking, hey, what, what it could be a completely different and new world, which I haven't... I know some people working on it. I haven't seen something, like anything super compelling yet, but clearly the old thing were designed for humans will need to be morphed into something new.[00:15:53] swyx: One of the thing that I, I think about is kind of like the merge conflict is basically a global mutex on the whole system, right? And in, in hu- in human organizations, we do have something like that. It's the company standup. But like, other than that, it's like it's actually fitting for us to be somewhat decentralized, somewhat plugged into one stream of information source, but somewhat lossy.Like it's okay, you know, that, that not every delivery is like atomic consistency. Like we're not dealing with a database sometimes.[00:16:27] Mikhail Parakhin: This is a very good point, uh, because since humans don't write code too fast, you know that global mutex is not too bad. Once you-[00:16:36] swyx: Yes ...[00:16:37] Mikhail Parakhin: start writing code at the speed of machine, it becomes the, you know, the bottleneck.Then what do you do? Maybe, and I can't believe I'm saying this because I, I'm long-- lifelong opponent of, uh, microservices, and I always thought that was, like, a really bad idea. And now that you're saying it, like, maybe in new guys like microservices will make a comeback, you know, because then you, you can ship things independently in tiny things and, and the managing all that complexity automatically will be much easier.I don't know. Like, we'll s-- we'll have to see.[00:17:10] swyx: Yeah. I mean, I don't know what the Microsoft or, or Shopify thing is, but I, I read this paper from Google where they have a monorepo that deploys into microservices, right? And then, uh, the other concept that I think about a lot is the Chaos Monkey concept from, from Netflix.Being able to create, like, this robust system where, um, uh, you know, you, you have the service discovery, you have the, uh, the independent, independent microservices discovery and, and, uh, you know, probably going to be a fair amount of duplication. That's how an organic system sort of scales, uh, that, that you have that...I don't know how you call it. Slack? Robustness? Depend-- uh, d-duplication. I, I, I forget the-- I, I'm-- And this-- those-- these are not exactly the terms- Hmm ... I'm looking for, but I c-can't really think of the words. Okay. I was gonna go into Tangent and Tangle. Uh, so, uh, we, we sort of discussed the overall stats that, uh, Shopify has.Uh, but, you know, I, I think some, some pretty cool stuff that you guys are working on is your ML experimentation, uh, and your, your sort of auto tr-research training pipeline. Presumably you're much closer to this one because it's, it's a sort of personal hobby of yours. How, how would you explain them in, together?I thought we have a slide that, like, uh, has the s- the system diagram.[00:18:24] Mikhail Parakhin: Yeah. Tangle first and then Tangent as a-[00:18:27] swyx: Yeah ...[00:18:28] Mikhail Parakhin: as a thing on top of Tangle. And, uh, Tangle is the third generation, I claim, of, uh, systems of, uh, running any data processing, but a bit with a skew for ML experiments, but not necessarily. Any sort of data processing tasks where you need to iterate, share, and you have scale so that you want maximum efficiency.You know how, like, normally you would work, you would-- Imagine you're a data scientist or an ML practitioner, you would get Jupiter notebooks or, or maybe you would get, uh, you know, Pyth- your Python scripts, and you would manage the data, and you produce those TSV files, and you put them in some JFS or something.Then you would notice that, oh, it has this, uh, weird missing values. You go and write another script that, uh, goes and replaces them with, uh-[00:19:20] swyx: Ah ...[00:19:21] Mikhail Parakhin: dash S. And then, then you, then you run some, some, uh, “Oh, I need to filter bots.” And so you run some light GBM model that, uh, removes the bots. And then, then you like-- And then you, you kind of like get into shape, and then you start experimenting, and you run multiple experiments, and then you're like, “Oh my God,” like, “this experiment is worse.”You undo, and you cannot get to previous result. And like, “Ah, what did I do?” Like that. Again, then, then you finally like get everything working. Then you like start throwing it over the fence to production. You, you replicate it, those things don't work, and then sometimes you like don't notice that you forgot some feature naming and the, the features don't match.But then, like imagine you, you did everything, and then six months later you're like, have to repeat it because now there's more data, or you wanted to do another pass, and you're like, “What, what did I do?” Or like, or like, “This script crashes now,” or the, “the path has changed.” And then, then you're trying to, like you spend another month just doing ar- digital archeology on your own, you know, history, right?Now multiply that by many, many teams. Now imagine you got an intern that you wanna ramp up. Now you have to show that intern, “Oh, you know, look, here's the folder, there's the scripts, you know, ask your cloud agent to do, and then, uh, to, to figure it out.” And then cloud agent does something, and then you're, “Ah, yeah, right, right, it was the wrong folder.I forgot to tell you, I actually have this other thing I forgot myself.” And, and that's, that's the, like, the daily life we all, uh, all know it, uh, if, if you're a data scientist, machine practitioner, ma- machine learning practitioner or, uh, or even like any data managing, uh, person.[00:21:00] swyx: Yeah. So I, I used to do this, uh, f- uh, on the quant finance side, uh, in, in my hedge fund.So we did this before Airflow, and then, uh, obviously Airflow came along and, uh, then more recently Dagster, uh, I would say is like, in my mind, what I would use for that shape of problem, uh, where you had to materialize assets and create a pipeline.[00:21:19] Mikhail Parakhin: And that's, that's very good segue because... So Airflow is great, but Airflow is more about you, you have something and you wanna repeatedly run it in production on schedule.It's less about you as a team developing things and being able to share, and you grabbing the standard pipeline and saying, “Hey, I wanna change this tiny little component in the huge sea of data processing, and I don't wanna-- I wanna run ten experiments on this, and I wanna do hyperparameter optimization.”All that is very hard to do with Airflow. It's very easy to do with Tango. Tango is m- more about, it's everything about group of people Running experiments, it might be agents too nowadays. Uh, running experiments cheaply, collaborating, sharing results. Uh, you don't need to understand fully. You, you grab-- you clone somebody else's experiment or somebody else's pipeline, uh, run, uh, change small piece, run it, be, like, get it to production state, and then ship in one click.So then the... You don't have to port it into any other system to, to run in production. You can just run the same experiment. It's, it's fully production ready. And, and it's, uh, it has lots of... Again, as I said, it's third generation system. The original one was, I would claim there was Ether and then, uh, at least in my career, Ether was the first, first, uh, that pioneered this type of approach.And then there was, uh, Nirvana, which, uh, uh, at Yandex, which did kind of sec-second take on this. And now this one aggregates the, the learnings from all of those and, and Airflow as well to, to get to the state where you try it, it, it feels kind of magical. Uh, ‘cause now everything is based on content, uh, hashes.So even if the version changed, but if the output didn't change, nothing is being rerun. It's very efficient. If you... Multiple people start experiment that needs the same sort of data preprocessing, it's not repeated multiple times. It's automatically done only once. If you start ten experiments that all require, you know, some, some data preparation first as the first step, and you don't have to coordinate for that.Like, you don't have to know that other people are starting it. You now, it's very easy compos-, uh, composability, any language you can u- uh, you wanna use, and it's very visual. So you can see immediately, you can edit it easily, you can assemble small things with just even mouse clicks if you want to, and, uh, share, clone.And everybody knows also it's fully kind of static in the sense that we rerun it second time, it will exactly have the same results. Like, you will never have to do digital archeology. So full versioning and everything is also there.[00:24:06] swyx: Uh, so, so people can, uh... It's open source. Go to the GitHub repo and, and, uh, check it out.Uh, and it is also a really good, uh, blog post about it. I think all these is, like, really appealing. The, the, the, the thing that I think sells me the most about it is that, um, sort of development to production transition, right? Which I think, um, a lot of people haven't really solved that, uh, strictly, right?Like, we develop really, really well in, in Python notebooks, but then, you know, that's obviously not a sort of production ready process. I think that, like, any way in which that is solved, I think is, is very appealing. Then the other thing that you mentioned, which also raised my eyebrows, was content-based caching, which you mentioned is, is, um, you know, is ve-very much, uh, um, a sort of efficiency measure about, uh, you know, just like recalculation only on, on sort of content addressing Which I think makes sense.Uh, it surprised me that the savings could be this much, but maybe I just haven't worked at your scale where there's so much duplication, uh, that people just rerun because they change a single ID upstream.[00:25:10] Mikhail Parakhin: It does, yeah. But it's not only you rerun. The, the main savings are coming from the fact that you ran it, you got your job done, and you moved on.Then- Yeah ... somebody else in some department you don't know existed runs the same task, but on a newer version.[00:25:27] swyx: Yeah.[00:25:27] Mikhail Parakhin: Like right now, you can't, in, in most of the organizations, you can't even find out about it so that you can't even measure that you're spending that time twice, right? Here- Yeah ... if everybody's on Tango, that's detected automatically and detected that the output is the same.And then for that person, all it looks like is like experiment just suddenly moved, jumped forward, right? Uh, uh- Yeah ... so that's because, because the, there's network effect of multiple people helping each other.[00:25:51] swyx: Yeah. This is one of those things where it's designed to be a platform from the beginning rather than an individual developer's tool from the beginning, right?And, and everything's gonna streams down from there. That is the sort of Tango, uh, orchestrator, and it's, it manages jobs. We've seen a few versions of this, and this is obviously, uh, uh, the sort of, uh, unique approaches that you guys have, have, uh, figured out. And then there's Tangent.[00:26:14] Mikhail Parakhin: Yeah. And Tangent is basically an automatic auto research loop that can help and kind of do your work for you.Uh- ... you know, uh, effectively, effectively, Andrej Karpathy recently popularized it with auto research. Yes. Remember he said like he was, uh, speed running this, uh... Yeah, uh, you know the story. The, here we're basically bringing the same capability into Tango so that, uh, the, uh, Tangent can analyze it. It's just an agent that can run multiple experiments, figure out what can be changed, and keep on rerunning it, keep on modifying until, uh, maximizing some goal, some loss function, whatever you need to, to achieve.And in general, I would say if you're not using auto research-like approach in whatever you do, like literally whatever you do, then you're missing out. We saw at Shopify that taking like a wildfire, anything where you can put measurements can be done dramatically better. Our-[00:27:19] swyx: Mm-hmm ...[00:27:20] Mikhail Parakhin: uh, speed of, uh, templatization HTML, uh, completely new UX tem- uh, templatization of, uh, reducing latency for liquid themes.Uh, we-- Our, uh, search, uh, recently we moved from It's hard even, uh, quote from eight hundred QPS to forty-two hundred QPS with the same quality just by pure optimizations and not a research loop that kept running and changing code in our index serve on the same number of machines, just increasing the throughput.We, we managed to improve the quality of gisting and machine learning process. Uh, you know, gisting is the prompt compression technique that[00:27:59] swyx: allows for[00:28:00] Mikhail Parakhin: lower latency and, and lower and, uh, actually higher quality slightly. So like literally whatever different walks of life, and it doesn't have to be AI related.Uh, we, we had a reduction in, uh, storage because the agents would go and find data sets that clearly are derivative, uh, and then you don't need to store things twice. You know, we, we, we found somewhat embarrassingly that it was one of the largest tables was hashing random IDs into another random ID, and we literally- Oofput only one. So it was translating, yeah, two random IDs hashed[00:28:36] swyx: into[00:28:37] Mikhail Parakhin: each. So, so[00:28:37] swyx: it has access to the code as well, so it can, it can check the, like what, what the hell is it doing?[00:28:42] Mikhail Parakhin: So there, there cou- it could be run in two levels. You, uh, you know, at the superficial level, it could just use ex-existing components and, uh, reshuffle them.Uh, you know, like you can grab- Yeah ... uh, XGBoost, and you can grab some, some Py- PyTorch module, and then can grab some, you know, grab another tools and, and combine them. At a deeper level, since Tangle is all sort of CLI based underneath you, every, every component is a wrapped really CLI, uh, call and a YAML file, it can analyze code and create new components and, and, uh, keep on iterating as well.So, so you can, you can both have quick modifications of existing t- uh, pipelines with the, with components that are already there pre-baked, or you can create new components, uh, and-[00:29:29] swyx: Yeah ...[00:29:29] Mikhail Parakhin: keep iterating on those. So auto research is, again, this is probably the, the thing I was excited the most in the last two months happening, and we see it taking like, like totally like a wildfire.Just, uh, everybody, every day, every... well, every day, every minute, I would, uh, have somebody Slack message saying, “Oh, look how much better I made it.” And, uh, it's all throughout the research.[00:29:53] swyx: Is this democratized in some way in, in the sense that like is it your ML, uh, engineers and researchers doing this, or is it your regular PMs and software engineers also have the ability to auto-- to use Tangent?[00:30:07] Mikhail Parakhin: This is an awesome question. Like, Tango in general and Tangent in particular are extremely democratizing. Like they- Yeah ... they are the main tools for- ‘Cause I don't[00:30:15] swyx: need the details.[00:30:16] Mikhail Parakhin: Yeah. Exactly. Initially used by ML and AI engineers, but then literally, as you said, PMs are like the highest user right now is one of PMs on our org, uh, Sartak and he was, he was number one by, by usage of, of this ‘cause they're just, uh, energetic and knowledgeable, and now it, it unlocks a lot of capability where you don't have to co-change code manually.[00:30:39] swyx: I mean, I mean, because it kind of cuts out the ML, ML engineer from the process because the, the, the PMs have the domain knowledge and the ability to think about, uh, from first principles about, okay, what, what results do I want? And they can-- they even have the access to the data that, that needs to go in.So it's like in some ways, like this is the magic black box that we've always wanted for, for training and, and for, uh, I guess, uh, uh, hill climbing, whatever.[00:31:04] Mikhail Parakhin: It's basically cloud code for your AI development- ... uh, situation, right? Like now, now you don't have to know exactly how algorithms work. You can just, uh, bring your domain knowledge and expertise and product knowledge and iterate within Tangent until you've gotten the results that you need.[00:31:21] swyx: In my previous roles, every time that someone has pitched AutoML, you know, I've always been like, “Uh, this is not, this is not gonna work. It's, you know, it's, it's always gonna be a flop.” Somehow it's working now. I mean, presumably the answer is now we have LLMs and it's good enough, right? It's, it's an emergent property that we can do auto research, but like, it doesn't feel that satisfying that how come we didn't do this before, right?Like we just did like parameter search and like, I don't know. That's maybe that's it.[00:31:48] Mikhail Parakhin: Yeah. Bayesian optimization and hyperparameter optimization was, was the one that, or facet of AutoML that was used very actively, which incidentally also built into, uh, Tango. But, you know, I know Patrice Simard very well, and, uh, he was such a, uh, such a proponent of AutoML, and he put, like literally spent careers trying to democratize it.Without LLMs, it just turned out to be very hard. Like it, you, you would have flexibility within certain narrow domain, but it was hard to wider scale, and now with LLMs suddenly it's like magic wand, and so suddenly everybody- ... is an AutoML expert.[00:32:28] swyx: Yeah, I, I think it's multiple things, right? Like I'm, I'm just gonna bring up the, the, the chart again, right?Like LLMs can do the monitoring very well. That is the very potentially unbounded, super unstructured. It can do the analysis very well, it can do the... Uh, and basically it is much more intelligence poured into every single step. Uh, there's maybe nothing structurally changed about AutoML, but this is just m-more intelligent and more unstructured.[00:32:53] Mikhail Parakhin: Exactly.[00:32:54] swyx: Any flaws that you've run into? Like everyone is like drinking the Kool-Aid, oh my God, time savings, uh, you know, performance improvements. Like what, what, uh, issues have you have, uh, come up?[00:33:06] Mikhail Parakhin: This is really cool. It's not a solution to all the world's problems for sure. The limitations are usually the ones I-- And this is where we get into a bit of a subjective territory.Uh, I can only share what I've, I've seen so far, and I'm sure the situation, uh, is changing, and, you know, maybe after I say it, like many people will reach out and say, “Hey, what about this?” And you don't know that, and then, then we'll be probably right. But what I've seen is auto research is very good at doing kind of obvious things that you don't have bandwidth to do or you didn't notice or maybe you're not aware of like the-- some standard practices.It is not good at doing something completely out of distribution, something that, you know, you have to think for, for multiple days, uh, and, and do something like none of this. So, so it's, uh, I, uh, set an experiment once, uh, on, on my sort of, uh, hobby thing, and I let it run for, uh, ended up, uh, several weeks run, uh, you know, it's like full production kind of scale, so it, you know, slow runs and, and it ex-- it performed in the end, uh, over four hundred experiments, and only one was successful.I'm like, “Okay, that's, that's good.” But-[00:34:18] swyx: But it saved time.[00:34:19] Mikhail Parakhin: Yeah, I saved time. Like it, it was the, that thing. Yeah, if I, if I were doing four hundred experiments myself, my betting average, as I said, would have been much higher, I'm sure. But also, first of all, it would take me like three years to do four hundred experiments.And, uh, I didn't have to do them. Like the machines were just, uh, the price of electricity did that. So, and I got one improvement, uh, that in, uh, my, my-- Honestly, when I was starting that experiment, my thinking was to go and show that, “Hey, Andre, maybe you just don't know how to optimize.” And I was super smart because in, in my pro-problem, it was optimized for many years, and it was like fully improved.Uh, and I didn't expect it, you know, auto research to find anything at all. Yet it did. So instead of making fun of Andre, I ended up, uh, a big, big supporter. Yeah, that's exactly the tweet. Yes.[00:35:10] swyx: You and Toby really, really go back and forth on-online a lot, which is really funny. Uh, think of it as, as an eval for the optimalness of the code it's running on.Uh, it's almost like it reminds me of like a Kolmogorov complexity thing, but, uh, I guess it's-- there's some optimal thing that you're trying to sort of reduce down to, I guess. Um, and so, so you, you, you know, you should congratulate yourself that you had, uh, you know, uh, ninety-nine percent, uh, optimality.[00:35:36] Mikhail Parakhin: Exactly, yeah. I think Andre really deserves a lot of credit for popularizing this approach. This is, uh, this is incredibly, I think, powerful and cool and You know, the, uh, even him, him just mentioning it led to a lot of gains in a lot of places in the industry, so we should be thankful.[00:35:56] swyx: Yeah. I think he also has a just...I don't know what it is. Like, um, you know, it, it is a simple self-contained project that people can take and apply to other things, which is, is, is one thing, but also just the name. Just like somehow no one, no one managed to call their thing auto research. It's just naming things is very important. I think that that is mostly, uh, our coverage of Tango and, and, uh, Tangents.I think obviously, you know, there's a lot of, uh, ML infra at, at Shopify that people can, uh, dive into. We're about to go into SimGym, but before I do that, any, any other sort of broader comments around this whole effort? Like where is it, where is it leading to?[00:36:36] Mikhail Parakhin: As a segue to SimGym, like all those things start composing strongly.And, uh, you could see a huge unlock when you can look at each one of the tools and, and you see, oh, they're extremely useful. Uh, Tango is useful by itself. Auto Research is useful by itself. SimGym is useful by itself. If you combine all three, you create like synergetic effect. I think that's why we wanted to even, uh, cover them today is because this is something that if you go back even, you know, five years ago, would've been unthinkable.Uh, replicating that, uh, would, would be either incredibly costly or impossible, right? With probably thousands of people are required.[00:37:20] swyx: Well, we have serverless human, uh, serverless intelligence, right? Like, uh, so yes, you do have thousands of hu-- of, of intelligences, not just, not humans. And that's, that's close enough, right?Even if they're not AGI, they're, they're close enough to do the, the task that you need them to do. And, and, you know, that's, there's plenty for, for a lot of routine work, knowledge work. Okay, let's get into SimGym. Um, this is one of those things I, I was surprised to see actually it's apparently your, uh, one of your most popular launches, and I think something that, uh, I think Sim AI, I think Yunjun Park, who did the Smallville thing, there's a very small cottage industry of people trying to do like the simulate customer thing.I think a lot of people maybe don't super trust this yet because they're like, well, obviously they would just do what you prompt them to do, right? But maybe just think, uh, tell us about the sort of inspiration or origin story.[00:38:10] Mikhail Parakhin: That's exactly actually the thing I wanted to cover, because if you don't have the historical data, all you can do is prompt a-agents in a vacuum, and they will do exactly what you prompt them to do.In fact, when I first proposed it, and this is a bit of, um, my brainchild initially, if I, I can boast, even Toby said like, “But wouldn't they, they just repeat what, what you tell them?” And, uh, but I'm like, “Yes, except Shopify has decades of history of how people made changes and what there is, uh, there, what it resulted in terms of sales.”So now what we can do is we can-- we have this... It's not, it's a noisy data. There's a small, usually websites, uh, you know, like things, things are never in isolation. It's almost never AB experiment. It's always AA experiment when there's has two meanings, but basically, you know, in different time you run two different things.But if you aggregate in general, uh, like everything together, and you apply, uh, denoising and collaborative filtering like approach, you can extract a very clear signal. And then you can optimize your agents. And that's why it took so long. It took almost a year of that optimization of just us sitting and fiddling, and, and we had this internal goals of correlation of hitting-- internal goal was to hit zero point seven correlation with, uh, add to cart events, for example.Like that, that if we run real AB test experiment, that it should, it should go and, and rep-uh, replicate, uh, same sort of success that, that humans had or lack thereof. And it, it took forever, and I don't think that's easily replicatable because, uh, like who else would have that data? You have to have this historic, you know, decades, uh, worth of data.And now, now the, like the other thing you need is in-infrastructure and the scale, right? Because, uh, w- again, what we found, uh, stat sig results, you need to run a lot of simulations, a lot of agents, and, and it's-- Those are expensive things. Like you're, you're making actions in the browser because you want a real friction.You want to, to be able to get the image like of what humans will see because you wanna, uh, detect effects like, “Hey, if I make my images larger, will I have more sales or l- uh, fewer sales?” And like usually people's intuition here, by the way, is that I increase my images, I will have more because they look nicer.You know, designers all look sparse and big images. Like usually your sales tank, right? But, but, uh, you know, from HTML, all the characters look the same only the, the size tag looks different, right? So it's very hard. So you have to take visual information, you have to run this in simulated browser environment on the big farm and, and of course, you have to have, uh, like very, very expensive model, good model with multi-model model.So all this it's-- is what's taken so long and, uh, to share my personal fail a little bit there, Sean, is like, you know, we always had this bias to-- for like large company bias. You know, we always, uh, whenever you-- we do, we're like, “Hey, we'll run an experiment,” right? We make, make a change, and we will run an experiment and then, uh, see, uh, see which one's better or like, “No, this is worse,” and most of them are worse, so you discard it and keep iterating, hill climbing.And we're like, “Oh, like smaller merchants, they cannot get stat sig results. They cannot really run experiments simply because, you know, in a week there would be not enough data for them.” So we thought from this perspective. What we didn't realize is that most people don't have A and B, they just have one thing, and they need suggestions of What A and B should be.So, uh, we first build this, hey, we run simulation on two separate teams and, and, uh, say, “Hey, which one is better?” We then morphed it into, and very recently just released it, when you have just your site, your theme, we run over it and we say, “Hey, here's what predicted values of, of, uh, uh, conversions are, and here's how we think you should modify it to increase your conversions.”And then circling back to what you started with, the proof is in the pudding. Like, if we are not correlating with reality, like, people will not be using it. And, uh, thankfully, we see literally every day more users than the previous day. So, so right now, uh, right now- It's working. Yeah. I'm-- Right now my problem is how to pay for it all because the so our major thing is how to optimize the LLMs, do distillation, how to run the headless browsers, uh, and handful browsers, uh, uh, cheaper so that we can accommodate the increase in traffic.[00:42:47] swyx: Yeah. I, I understand that you, uh, you published a lot of technical detail at GTC, so I was just gonna bring it up a little bit. I think s- was this in, in con-conjunction with some kind of GTC presentation? Or something like that, right?[00:42:59] Mikhail Parakhin: Well, we, yeah, we, we did it in several place, but yeah, we had the engineering- Yeahblog, uh, as well. Yeah.[00:43:05] swyx: Yeah. So you're running, uh, GPT OSS. Uh,[00:43:08] Mikhail Parakhin: the, this is an older version. You know, now we run multimodal model. But yeah- Yeah ... GPT OSS, we still run GPT OSS as well for[00:43:15] swyx: And then you have the VMs, and you also have browser-based. I really like this one where it you said, “It violates almost every assumption that standard LLM serving is designed for.”And then you had like, basically orders of magnitude differences between everything.[00:43:29] Mikhail Parakhin: Exactly. Which is, which, uh, which was, you know, a bit of a challenge to implement, like when, like even simple things. Uh, be- since it violates all the assumptions, for example, multi-instance GPUs, like MIGs don't work as well.But we needed, uh, to get MIG to work because, ‘cause otherwise it's way too expensive. And so we had to deal with the, yeah, with, uh, lots of infrastructure and, and, uh, work with, uh, uh, Fireworks and CentML, uh, you know, to help with optimizations and browser-based, as you mentioned. Yeah, like, takes a village.[00:44:04] swyx: Okay. So there's a lot of like, I guess, experimentation in the infrastructure so far, and you've published more or less what you have here. I guess I'm, I'm less familiar with CentML. I, I don't do, uh, that much work in this, this part of the stack. But why was it the sort of preferred instance platform?[00:44:22] Mikhail Parakhin: There are really three probably top companies. There used to be, uh, uh- Three top companies, uh, at least I was aware of that did, uh, LM optimization. You know, together Fireworks and Santa ML, not necessarily in that order. Santa ML recently got acquired by NVIDIA. Uh, what they did is if you have a model and you want to optimize it to a specific prof-- uh, profile of usage, uh, they would go and do it.And, uh, we work with, with those companies, uh, this was work particularly in with Santa ML and NVIDIA to get them the best possible results out of it. And, and sometimes you, you have to retune depending on, like sometimes you want the maximum throughput, sometimes you want minimal latency, sometimes you want like the cheapest, right?And, yeah, or some combination. And so yeah, these are people who would come and help you.[00:45:14] swyx: I see. I see. Yeah, yeah. I'm familiar with these people for the LLM, you know, autoregressive stack. But the other interesting category of these optimizers is also the diffusion people, whereas like Fel and, you know, uh, Pruna recently has come up a lot as well, which I think is like really underappreciated, uh, at least by myself, because I, I thought, oh, all the workload would be LLMs, but actually there's a lot of diffusion as well.[00:45:38] Mikhail Parakhin: Exactly.[00:45:38] swyx: There's a lot here, so I, I, I... it's, it's, uh, it's, it's, it's hard to cover. But I, I do think like people underappreciate the importance of customer simulation, basically. I think this is something that I'm candidly still getting to terms with. Uh, you know, uh, you also-- your team also like prepared this, like, really nice diagram.Uh, I, I assume this is AI generated.[00:46:00] Mikhail Parakhin: Yeah, it looks-[00:46:01] swyx: Maybe it's not.[00:46:01] Mikhail Parakhin: Yeah, it looks, uh, Gemini-ish. Yeah, but, uh, uh, honestly, I, I don't know where, where the hell they generated. It looks, look, uh, looks like it's, uh, Google. But the interesting part, John, that, that, uh, we haven't covered, but I, I wanted to mention is if your store had previous customers, rather than it's a new store, you're like new merchant just launching things, it helps tremendously in just correlation and forecast.Yeah, we take your previous, uh, customer's behavior, and we create agents that replicate those specific distribution of, of customers that you get, and then we a- we apply those to your changes, and then that, that raised raw, you know, the re-- uh, just correlation with the add to cart events or to-- with conversion or whatever it, it, it may be, uh, quite dramatically.So, uh, replicating humans in general seems like an interesting, cool challenge.[00:46:58] swyx: As a shareholder, I think this is the-- like if people are Shopify shareholders, they should really deeply understand this because this is basically the moat. The, the more you use Shopify, the more it will just automatically improve, right?Like you're, you're doing the job for them.[00:47:13] Mikhail Parakhin: Yeah, that's what we started with. Like, uh- ... uh, otherwise, if you're just a startup, I wouldn't do it if, uh, you know, if it was my startup because Without the data, it, yeah, as, as you said, it's, it's exactly the case that, uh, whatever you say in prompt, that's, that's what the agents will be doing.[00:47:30] swyx: The statistician in me wants to like really satisfy the sort of, um, statistical intuition, I guess. Um, to me it's kind of, uh, the, the word that comes to mind is, um, ergodicity. Uh, so let's say a, a customer takes this path, customer takes this path, customer takes this path, right? Um, the... In my mind, the way I explain it is like, okay, here, here's the ninety-five percentile, here's the five percentile, and here's the median, right?Um, but to me, what SimGym is potentially doing is that it can, uh, modify... It can sort of model the sort of in-between sort of journeys as well, that, that maybe are dependent on the previous states. This may be like a very RL-type conclusion where like basically the summary statistics, if you only did naive AB testing, you only have the, the statistics at, at, at a certain point, and you only judge based on the sort of overall summary statistics.But here you can actually model trajectories. Does that make sense? Or-[00:48:31] Mikhail Parakhin: That makes total sense because like, well, that, that makes even more sense that maybe even you realize bec- because-[00:48:38] swyx: Okay. Please,[00:48:38] Mikhail Parakhin: please. Yes ... we do-- Yeah. The, so internally, uh, we have this system, we talked about it briefly once at NeurIPS.We have a huge HSTU-based system that models the whole companies, uh, and their possible paths. And like- Yeah ... what you are, what you are showing, like actually at any point of time, you can either model the user's behavior or you mo- can also think about, uh, the whole merchant as a company, as the entity that acts in the world.You can model that as well. And then you can do, can do counterfactuals. In your graph, like in your blue graph, uh, if you're... Imagine in the center there, uh, somewhere in the middle, you would have an intervention. I give that person a coupon, or I don't know, I send a personal thank you card, or give a discount in some- somewhere.And then you can, uh, then you can do forward rollouts from that counterfactual. So what would have happened with that intervention or without the intervention? And you can even ch- change where that intervention, uh, in time can happen, right? Like some- where, where in this journey. So we, we do this at the Shopify scale for our merchants, and then if we notice that something that they can be fixing, like there's a strong counterfactual, like we have Shopify policy, they basically get a notification like, “Hey, we think your...something is wrong with your-” I don't know, Canadian sales. Like, uh, it looks like it's misconfigured. Here's what you need to do. Or do you think like, uh, you have to set up this campaign with these parameters? And we do that at the buyer level to literally offer discounts or cashback or, or things to buyers.So this is-- I'm getting very excited. Like this is my sort of area of, uh, interest, I guess, and, and hobby. But being able to m-model something complex as human beings or companies and model counterfactuals on it, where you can have interventions in the future and optimize when to make intervention, what kind inter-- uh, what kind of intervention to make.It's such an unlock that previously was completely impossible. Like the-- it was, it was always dreamed of, but never... Like how would you even simulate it without LLMs or HTUs? I think very, very exciting times.[00:50:59] swyx: I just wanted to, uh, to maybe illustrate this. I, I'm not the best illustrator, but I, I am a conceptual statistics guy.And y-you know, you cannot just do this. Like this is a dimensionality AB test doesn't do, right? Like, uh, because it doesn't have the, the, the change over time, uh, stochastic nature, uh, and it doesn't have the sort of contextual like... Here's all the context to this point. Um, okay, cool. Um, that's SimGym.You're, you're gonna burn a lot of tokens on this thing. But you're, you're one of the, the only scale platforms in the world that can, uh, that can do this across a huge variety of workloads, right? I'm even curious on a sort of human, uh, research level of like, well, do, does retail behave d-differently from like clothing sales?D-does that behave differently from electronic sales? I, I don't know. I don't know what else you guys... The Kardashian shoppers, do they differ from like people who buy, uh, I don't know, cars and, uh, whatever.[00:51:55] Mikhail Parakhin: Well, very different, and different sensitivities and different modes of, uh, shopping and, and different levels of what's important.Now, to-totally, you can do aggregations at, uh, at a store level. You can do aggregations at a different, uh, category level. I don't know if, uh, you know, for our statisticians among us, I couldn't believe, but we-- recently we're looking at it, and we had to bring back, uh, CRPs, you know, Chinese restaurant process.It's a, like, way of aggregating and, like, naturally grow clustering. So across... Specifically to answer questions that, uh, like you were just posing on how, how if, if buyers behave different categories. And I'm like, “I haven't seen CRP since two thousand and one.” It's[00:52:37] swyx: so What? It's so- What is... No, I haven't, I haven't seen this.No. This is not in my training. Uh,[00:52:44] Mikhail Parakhin: but, but yeah, it, uh, uh, it actually, like the, the-- there was a very popular kind of theory, popular neurips HTML circles in early two thousands, uh, kind of nice. And now, now it has practical applications, uh- Yeah ... that we were resurrecting.[00:53:03] swyx: Yeah, amazing. Uh, I, I can see, I can see how this is like a, uh, a fun job for you where you get to apply all these things.Um, yeah, yeah, so super cool. Super cool. So, okay, so, so anyone who, who knows what CRPs are and has always wanted to use them at work, uh, they should, they should definitely join Shopify. Okay, so w-we have a lot and but I, I'm, I'm being mindful of the time. I, I do wanted to, to sort of cover some other things.Um, I-I'll give you a choice, UCP or Liquid?[00:53:30] Mikhail Parakhin: Liquid. I think, I think on UCP, you know, like UCP is very important for us and, and it just we are-- UCP, we have a structured, uh, discussions, and you can read about them, and we have, uh, blog posts, and we have a big release this week, in fact, like with our catalog.Oh,[00:53:46] swyx: okay.[00:53:46] Mikhail Parakhin: Uh, yeah,[00:53:46] swyx: but- Le-I mean, we, we can, we can discuss the, the, the release briefly because we'll release this after the-- after it's already announced so whatever. There's a catalog that you guys are doing?[00:53:55] Mikhail Parakhin: Yeah. So we are, we are- Okay ... we are bringing in capabilities of a whole, uh, Shopify catalog.Basically, you now you can search for products, you can do lookups by specific ID, you can do bulk lookups when you need to bring m-multiple products. You don't need to know in ad-in advance what you're trying to show or to sell or check out. Like, you can now, you can now have this decided at, at runtime, and this big area for investment for us for both non-personalized and personalized searches, trying to provide basically a win-window into whole universe of products that are being sold everywhere in the world.And Shopify is really not exactly, but almost like a super set of any-anything being sold. Now we are bringing it into UCP and, uh, and, uh, identity linking is another big thing for us, uh, so that you, you can use, uh, like Google or whatever, whatever identity you have, uh, they're minimizing friction.[00:54:56] swyx: Yeah. So[00:54:57] Mikhail Parakhin: yeah, big release for us.But Liquid AI of course we never talk about, and the problem might be more, more aligned with what we d-discussed previously on this chat.[00:55:07] swyx: Sure. The main thing that everyone understands about Liquid is that it is inspired by Worm, and I still don't know why. I'm curious on your explanation. I think you, you, uh, you can make things very approachable.And also I think like what is the potential of like the, the level of efficiency that you get out of Liquid?[00:55:23] Mikhail Parakhin: You- we all familiar with transformer architectures. And, uh, for the longest time, there was a competing architecture, it's called the state space models. So, so Sams, uh, you know, Chris, Chris Reyes, one of the pioneers and, and lots of startups, uh, trying to make those realities.They have, uh, significant benefits being main being, uh, being much faster and, uh, lower footprint and not quadratic in length, you know, sort of, uh, linear in, in, uh, in your context length. But with state space models- They never quite made it. Like they're used-- They have, uh, certain niches when they thrive, their hybrid architectures are useful, but they never quite made it.And liquid neural networks are, you can think of them as a next step, like, uh, sort of, uh, state-space model square. It's non-transformer architecture that's more complicated than sta-state space and really difficult to code if you-- if I'm being honest. But it's, um, very efficient. It's, uh, subline-- sub, uh, quadratic in, in length of your context.Uh, it's very compact way to represent things, and that's a liquid AI company. They... Their goal is to productize it, and very often you have this need, uh, when you need to have long context and small model, and you want to have low latency. Like in general, it's basically on par with transformers, and if you do hybrids with transformers, it's, it's even better.That's why we at Shopify, when we tried multiple and we constantly try multiple models, multiple companies, we found that for small, particularly with low latency applications, when you have low latency and/or if you need longer context lengths, liquid was the best. And so we still use the whole zoo and always like obviously test and use everything, uh, every open source model and, you know, it feels l

Las ofertas laborales de Product Engineer crecieron 53.6% en 2026. PostHog ($1.4B), Vercel ($9.3B) y Figma ($20B) ya dejaron de contratar developers tradicionales: lo que buscan son Product Engineers — o como Figma los llama, Product Builders. En este episodio, Oz y Tito Neira recorren qué es realmente un Product Engineer, por qué este perfil está unificando los roles de producto, diseño e ingeniería, y cómo la IA se volvió el acelerador imposible de ignorar.00:00 Intro y bienvenida00:43 ¿Qué es un Product Engineer?02:10 De ticket a producción sin ceremonias04:49 El problema de las dependencias en equipos06:30 ¿Puede un Product Owner ser Product Engineer?08:13 "Si tu trabajo no termina en Git, no existe"11:33 Dos tipos de developers14:40 Estadísticas del mercado laboral18:11 Empresas que lo hacen bien: PostHog, Vercel, Figma20:30 El developer que vive en la terminal23:26 Características del Product Engineer ideal27:05 Cuando la IA prioriza mejor que un humano29:41 ¿Es viable sin IA? La respuesta es no32:20 Recomendaciones para developers35:54 Superpoderes para el arquitecto de software39:09 Consejos para empresas42:24 Datos clave y reflexión final✩ CURSOS DISPONIBLES

This is episode 323, recorded on April 16th, 2026, where John and Jason dig into part one of the Microsoft Fabric March 2026 Feature Summary — including the GA of OneLake Catalog Govern for admins, the OneLake Catalog Search API as an MCP tool, workspace tags going GA, DLP policies extending to structured data in OneLake, branched workspace with Git integration and selective branching, the new connection reference variable type, Fabric CLI v1.5 with one-command deployments, the Fabric Remote MCP server, OneLake File Explorer reaching GA, and the preview of Fabric Runtime 2.0 with Spark 4 and Delta Lake 4. For show notes please visit www.bifocal.show

On this week's Security Sprint, Dave and Andy covered the following topics:Opening:• TribalHub Regional Tribal Technology Forums• WaterISAC H2OSecCon 2026. Virtual Event: 02 Jun, 11am-5pm ET Overview, Registration, Agenda, Speakers• Offensive AI: What Red Teams and Attackers are Doing Now - Gate 15Main Topics:Vercel April 2026 security incident Vercel 20 Apr 2026. Vercel said it identified unauthorized access to certain internal systems and initially found a limited subset of customers whose credentials were compromised. The company said the incident originated with a compromise of Context.ai, a third-party AI tool used by a Vercel employee, which then enabled takeover of that employee's Google Workspace account and access to some Vercel environments and non-sensitive-marked environment variables. Vercel said services remain operational, law enforcement has been notified, and customers who were not contacted are not currently believed to have had credentials or personal data compromised. Vercel is a cloud platform used for frontend hosting, serverless functions, and deploying websites, particularly those built with React or Next.js. It enables developers to easily build high-performance, edge-optimized applications. Key features include automatic Git integrations (CI/CD) for instant deployments, preview environments, and edge storage. • Vercel confirms breach as hackers claim to be selling stolen data • Breaking: Vercel Breach Linked to Infostealer Infection at Context.ai • Vercel's security breach started with malware disguised as Roblox cheatsWiz: 80% of cloud breaches are caused by basic mistakes - IT Pro - 13 Apr 2026 IT Pro reports that Wiz Threat Research found most cloud breaches in 2025 were driven by familiar security mistakes rather than entirely new vulnerability classes, with AI expanding the places where known risks can appear. The article frames the problem around scale, shared trust, and increasingly complex cloud and AI environments rather than exotic attack novelty. Target is cloud security teams, platform engineers, and enterprise risk leaders with Dig highlighting that basic exposure management, identity control, and configuration discipline remain the decisive factors in many modern cloud compromises. Fire As An Act Of Sabotage Guidance UK National Protective Security Authority 25 Sep 2024. The NPSA guidance outlines how to mitigate the risk of deliberate fire-setting used as sabotage against premises and infrastructure that may be attractive targets. Although not new, it remains operationally useful because it provides protective security and risk management guidance for owners and operators responsible for physical sites and critical functions. The relevance is heightened in an environment where sabotage, arson, and hybrid disruption are increasingly discussed alongside state and extremist threat models. From tabletop reality 10 gaps executive cyber exercises consistently reveal - SANS Institute - 2026 This analysis identifies recurring gaps observed during executive cyber exercises, including communication breakdowns and decision-making delays. It highlights the importance of realistic training scenarios to improve organizational readiness. The findings provide actionable insights for strengthening incident response at the leadership level. • Critical infrastructure resilience escalated threat navigation initiative - Canadian Centre for Cyber Security • Preparing for severe cyber threat why leaders must act now - NCSC UK • CISO Survey 2026: The State of Incident Response Readiness Quick Hits:• The State of Ransomware in Q1 2026 - Emsisoft • Safeguarding Our Data, Intellectual Property, and Technology from Non-traditional Collectors

Matt Bornstein speaks with Scott Chacon, cofounder of GitHub and CEO of GitButler, about why Git's user interface has barely changed since 2005, how GitButler is rethinking version control for both humans and AI agents, and what the "next GitHub" might actually look like. They cover parallel branches, agent-optimized CLI design, the future of code review, and why the best engineers of the future will be the best writers. Resources: Follow Scott Chacon on X: https://twitter.com/chacon Follow Matt Bornstein on X: https://twitter.com/BornsteinMatt Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Cisco posts urgent Webex Services warning Splunk issues fixes for Enterprise vulnerability Git identity spoof tricks Claude into approving bad code Get the show notes here: https://cisoseries.com/cybersecurity-news-cisco-webex-warning-splunks-enterprise-fix-git-spoof-tricks-claude/ Huge thanks to our sponsor, Conveyor Happy Friday. Hope there isn't a fresh security questionnaire sitting in your inbox right now. If there is, here's something worth knowing.  The teams that have fully automated their customer security reviews didn't just get a better trust center. They switched to an AI platform built for the whole workflow. Conveyor handles trust center, questionnaire automation, and self-serve for sales, all in one place, with AI keeping the knowledge base current so answers are always accurate. Learn why enterprise SaaS teams choose Conveyor at conveyor.com.

Episode 318 examines critical vulnerabilities and the evolving impact of AI on the security industry. The episode details a recent sophisticated impersonation and malware attack targeting open-source Slack communities, including their own, where attackers spoofed Seth's identity to distribute malicious links via Google Sites. The hosts express significant frustration with Slack's lack of built-in impersonation controls, comparing the flaw to the inherent trust issues in the Git protocol. A major portion of the discussion focuses on the "leak" of Anthropic's highly capable Mythos model and its potential to disrupt the market. They analyze how such frontier model announcements contribute to massive stock market volatility for traditional security firms while simultaneously creating an "intense echo chamber" regarding AI's ability to replace human practitioners. Referencing Thomas Ptacek's thesis, they debate whether AI agents will soon supplant human vulnerability research for common bug classes, shifting the human role toward high-level governance and "context infusion". Ultimately, the hosts advocate for autonomous defense and rigorous evaluation frameworks to manage "reasoning drift" and the exploding velocity of AI-generated code.

Is it time to replace GitHub in our workflow? We git into it. Plus, our favorite features in the new Linux 7.0 release.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:

An airhacks.fm conversation with Thorsten Hoeger (@hoegertn) about: discussion about migrating a German bank to AWS in 2012, early EC2 instances and the launch of AWS VPC for private networking, clicking the AWS console before discovering CloudFormation, CloudFormation released in 2011 with JSON-only templates, Hazelcast cluster synchronization bugs on single-core EC2 instances, multicast limitations in VPC and the transit gateway workaround, CFEngine from 1993 as a predecessor to declarative infrastructure management, Puppet and Chef and Ansible as configuration management tools, CloudFormation's declarative state reconciliation predating kubernetes by three years, CloudFormation's managed state versus Terraform's local state storage, three-way diff comparing new template and old template and physical resource state, drift detection and its limitations with default values, writing 3000 lines of CloudFormation JSON in Eclipse IDE, building a Jenkins plugin for CloudFormation lifecycle management, GitOps with Git servers and Jenkins for CloudFormation deployments, separating infrastructure changes from business logic changes in early setups, treating everything as a change in modern CI/CD pipelines, the origin of CDK at Amazon as an internal tool written in Java then rewritten in typescript, CDK beta participation through the AWS Hero program, CDK constructs and L1 low-level constructs mapping directly to CloudFormation resources, CDK synth phase serializing Java objects to CloudFormation JSON, Stacks as atomic deployment units in CDK, the trade-offs of splitting stateful resources into separate stacks versus single-stack deployments, AWS CloudFormation export and reference coupling between stacks, using AWS Parameter Store for loose coupling between stacks, CDK application as the project root with application code in subfolders, Terraform benefits for multi-provider scenarios like GitHub repos and on-prem routers, regulated industries and compliance benefits of cloud infrastructure as code, change management as a byproduct of Git-based infrastructure pipelines, serverless architecture similarities to application server and WAR deployment models, CDK asset system for versioning and pushing artifacts, CDK custom resource types and self-mutating pipelines as future topics, The CDK Book co-authored by Thorsten Hoeger and colleagues, Taimos GmbH consulting for AWS infrastructure Thorsten Hoeger on twitter: @hoegertn

Computer scientist Keith Winstein is an expert in how computers communicate. Computer networks create what he calls shared fictions – abstract realities, like a website or a Zoom call, that exist only because the computers on either end agree to act as if they are real. Unfortunately, today's networks lack a shared notion of a “computation,” which hurts market efficiency in cloud computing and frustrates efforts to hold tech companies accountable for the results of their algorithms. As computational power becomes concentrated in a smaller number of companies, Winstein advocates for a shared language of “computational truths,” defining computations precisely so results are reproducible and auditable. His research group hopes this will lead to greater transparency and accountability in the cloud and, ultimately, to greater confidence in the computations that companies do every day on our behalf. The truth matters, Winstein tells host Russ Altman on this episode of Stanford Engineering's The Future of Everything podcast. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile: Keith Winstein Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces guest Keith Winstein, a professor of computer science and electrical engineering at Stanford University (00:02:56) Why Choose Networking The appeal of the shared digital “fictions” created by connected computers. (00:04:22) The Internet's Impact The broader societal implications of networking technologies. (00:05:35) Computational Truth The concept of tracking how data is produced and verified. (00:09:18) Misaligned Cloud Computing How “pay for effort” models create inefficiencies in cloud systems. (00:13:51) Determining Computational Truth The need for verifiable computation that produces consistent results. (00:18:19) Computations & Accountability How identifying computations could improve trust in systems. (00:20:56) Collaborating Online Why latency challenges make online performance collaboration difficult. (00:24:38) Real-Time Performance Systems Creating a custom system for musicians to perform together online. (00:28:00) Latency vs. Bandwidth Why faster internet speeds don't necessarily reduce delay. (00:30:43) Eliminating Latency How buffering layers in software create unnecessary delay. (00:32:41) Balancing Audio Quality & Delay The different trade-offs for musicians, actors, and audiences. (00:34:20) Rethinking Computer Science Education The need to bring playfulness and interactivity back into learning. (00:35:46) The Xylophone-Based Class Teaching computation through real-time sound and music. (00:38:34) Future In a Minute Rapid-fire Q&A: optimism, truth in computing, and innovation. (00:41:01) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is a recap of the top 10 posts on Hacker News on April 08, 2026. This podcast was generated by wondercraft.ai (00:30): Git commands I run before reading any codeOriginal post: https://news.ycombinator.com/item?id=47687273&utm_source=wondercraft_ai(01:56): I ported Mac OS X to the Nintendo WiiOriginal post: https://news.ycombinator.com/item?id=47691730&utm_source=wondercraft_ai(03:23): Veracrypt project updateOriginal post: https://news.ycombinator.com/item?id=47686549&utm_source=wondercraft_ai(04:50): US cities are axing Flock Safety surveillance technologyOriginal post: https://news.ycombinator.com/item?id=47689237&utm_source=wondercraft_ai(06:17): Škoda DuoBell: A bicycle bell that penetrates noise-cancelling headphonesOriginal post: https://news.ycombinator.com/item?id=47687248&utm_source=wondercraft_ai(07:44): Microsoft terminates VeraCrypt account, halting Windows updatesOriginal post: https://news.ycombinator.com/item?id=47690977&utm_source=wondercraft_ai(09:11): They're made out of meat (1991)Original post: https://news.ycombinator.com/item?id=47688678&utm_source=wondercraft_ai(10:38): ML promises to be profoundly weirdOriginal post: https://news.ycombinator.com/item?id=47689648&utm_source=wondercraft_ai(12:05): LittleSnitch for LinuxOriginal post: https://news.ycombinator.com/item?id=47697870&utm_source=wondercraft_ai(13:32): OpenAI says its new model GPT-2 is too dangerous to release (2019)Original post: https://news.ycombinator.com/item?id=47684326&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

The expensive, challenging, and humbling journey with open source agents.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:

Today on the Ecomm Breakthrough Podcast, we're joined by a true expert at the intersection of technology, data, and e-commerce growth. Ellis Whitehead is the co-founder of DataBrill and a leading mind in PPC management, data science, and business intelligence space. With a PhD in automation and years of experience architecting smart technology for Amazon sellers, Ellis has helped brands leverage data-driven strategies to scale profitably and stay ahead of the competition. He's here to share how sellers can use advanced analytics and Ai to break through the seven-figure ceiling and unlock the path to eight figures and beyond. Ellis, welcome to the show! Highlight Bullets> Here's a glimpse of what you would learn…. Leveraging AI and data for scaling e-commerce businesses, particularly for sellers with seven-figure sales.Importance of establishing a proper data infrastructure before utilizing AI.The concept of a "data chain" consisting of four essential links: centralized data, capturing history, connecting disparate data sources, and constructing guardrails for AI.Challenges faced by e-commerce sellers regarding messy or disconnected data.The significance of capturing historical data for trend analysis and forecasting.The necessity of connecting various data sources to derive meaningful insights and metrics.The role of structured databases versus unstructured data storage solutions like shared drives.The impact of AI on decision-making processes and the importance of providing accurate context for AI tools.Recommendations for hiring the right talent to manage data infrastructure and AI integration.The critical need for a solid foundation before implementing AI to avoid compounding errors in business operations.In this episode, host Josh Hadley interviews Ellis Whitehead, co-founder of Data Brill, about how seven-figure e-commerce sellers can leverage AI and data to scale effectively. Ellis outlines a four-step “data chain” for success: centralizing data, capturing historical records, connecting disparate data sources, and building guardrails for AI. They discuss common pitfalls, the importance of solid data infrastructure, and actionable hiring advice for building in-house data teams. The episode emphasizes that AI is only as powerful as the data foundation supporting it, offering practical strategies for sustainable e-commerce growth.Here are the 3 action items that Josh identified from this episode:Prioritize Data Infrastructure:Invest in building a centralized, historical, and connected data warehouse before layering on AI. This is a full-time job—don't try to do it all yourself.Make Data-Driven Decisions:Use live, visual dashboards to monitor trends, market share, and leading indicators—not just lagging P&L statements. Let data guide your strategic focus.Leverage AI Only After Laying the Foundation:AI can scale your business—or your mistakes. Only deploy AI agents once your data is clean, structured, and governed by clear guardrails.Timestamp:00:00:00 Podcast IntroductionLeveraging AI and data for scaling e-commerce businesses.00:00:58 Guest IntroductionEllis Whitehead's background and expertise in data, PPC, and Amazon seller growth are introduced.00:02:00 AI Hype & Seller ChallengesDiscussion about the overwhelming AI chatter among e-commerce sellers and the feeling of being left behind.00:02:37 The Importance of FundamentalsEllis emphasizes sticking to business fundamentals despite rapid technological changes.00:03:11 Common Data Mistakes in E-commerceEllis introduces the “data chain” concept and outlines common mistakes sellers make with data and AI.00:05:07 Overview of the Four Data Chain LinksEllis lists the four essential links: centralized data, capturing history, connecting data sources, and constructing guardrails.00:07:29 Step 1: Centralizing DataDetailed explanation of why a structured database (like Postgres) is crucial versus using spreadsheets or shared drives.00:09:21 Technical Setup for Centralized DataDifferences between databases and shared drives, and why structure, speed, and reliability matter.00:11:38 Non-Technical Founders & Getting HelpAdvice for non-technical founders: learning, hiring, or consulting for proper data setup.00:15:14 Ongoing Maintenance CaveatEllis explains that data systems require ongoing maintenance due to changing APIs and data sources.00:16:45 Ways to Ingest DataDifferent methods for getting data into databases: APIs, manual downloads, and handling multiple currencies.00:19:15 Navigating Amazon API AccessChallenges and solutions for brands seeking Amazon API access, including using third-party services.00:21:45 Step 2: Capturing HistoryWhy historical data is vital for trend analysis, forecasting, and making informed decisions.00:24:27 Use Cases for Historical DataExamples of how historical data helps with leading indicators, seasonality, and strategic decision-making.00:26:30 Pitfalls of Ignoring TrendsDangers of relying on static data blocks and the importance of trend analysis for inventory and forecasting.00:29:10 AI Automation Cautionary TaleRisks of automating decisions without proper context and historical data.00:31:01 Tracking Keyword Popularity Over TimeHow tracking keyword trends can explain sales drops and inform campaign adjustments.00:33:24 Step 3: Connecting the DotsCombining disparate data sources to calculate advanced metrics and gain actionable insights.00:35:53 Practical Tactics for Data IntegrationHow to use database views, scheduled calculations, and file storage for efficient data analysis.00:37:05 Step 4: Constructing GuardrailsBuilding guidance and guardrails so AI can answer business questions reliably and avoid costly mistakes.00:39:06 Guardrails in Action: Use CasesExamples of how proper guardrails enable AI to deliver actionable, accurate reports and campaign strategies.00:43:12 Building In-House Data TeamsAdvice on hiring the right mix of technical talent or using consultants.00:44:30 Three Actionable TakeawaysSummary of key actions: hire for data roles, let data drive strategy, and only use AI after building a solid data foundation.00:47:38 Final Recommendations & ClosingEllis's final advice: start centralizing data in Postgres and set up guardrails for AI.00:48:07 Book RecommendationsEllis shares influential books: “Warren Buffett Accounting” and “1984.”00:49:30 Favorite AI Tools & WorkflowEllis describes his preferred AI tools and workflow: Claude, VS Code, TypeScript, Deno, Postgres, and git.What is Git? (00:50:19)Explanation of git as foundational versioning software for code and text files.00:51:22 E-commerce Influencer RecommendationEllis recommends following George Meressa for advertising and e-commerce insights.00:51:51 Where to Find Ellis WhiteheadInformation on how to connect with Ellis and Data Brill for further help.00:52:20 Podcast OutroClosing remarks and call to subscribe and review the podcast.Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough PodcastEmail Josh Had...

This show has been flagged as Explicit by the host. New hosts There were no new hosts this month. Last Month's Shows Id Day Date Title Host 4586 Mon 2026-03-02 HPR Community News for February 2026 HPR Volunteers 4587 Tue 2026-03-03 UNIX Curio #1 - Shell Archives Vance 4588 Wed 2026-03-04 HPR Beer Garden 11 - Belgian Scotch Ale Kevie 4589 Thu 2026-03-05 YouTube Subscriptions 2025 #15 Ahuka 4590 Fri 2026-03-06 Playing Civilization V, Part 9 Ahuka 4591 Mon 2026-03-09 A Bit of Git Lee 4592 Tue 2026-03-10 Happy by shower # 2 Antoine 4593 Wed 2026-03-11 Nuclear Reactor Technology - Ep 8 Generation Four Reactors Whiskeyjack 4594 Thu 2026-03-12 Hackerpublic Radio New Years Eve Show 2026 Episode 2 Honkeymagoo 4595 Fri 2026-03-13 WATER WATER EVERYWHERE! operat0r 4596 Mon 2026-03-16 Adding voice-over audio track created using text to speech on the movie subtitles Ken Fallon 4597 Tue 2026-03-17 UNIX Curio #2 - fgrep Vance 4598 Wed 2026-03-18 Recording good audio using open source tools Shane - StrandedOutput 4599 Thu 2026-03-19 Women in digital and games event Dave Hingley 4600 Fri 2026-03-20 The First Doctor, Part 5 Ahuka 4601 Mon 2026-03-23 How to be a better writer enistello 4602 Tue 2026-03-24 Hackerpublic Radio New Years Eve Show 2026 Episode 3 Honkeymagoo 4603 Wed 2026-03-25 On the Erosion of Freedom in Open Source Software HopperMCS 4604 Thu 2026-03-26 Quick Tips for January 20 26 operat0r 4605 Fri 2026-03-27 Lee locks down his wifey poo Elsbeth 4606 Mon 2026-03-30 My Nerdy Childhood: From Floppy Disks to Dial-Up Dreams Trollercoaster 4607 Tue 2026-03-31 UNIX Curio #3 - basename and dirname Vance Comments this month Past shows hpr3711 (2022-10-24) "Cars" by Zen_Floater2. m0dese7en said: "Additional details on cars" (2026-03-13 16:44:12) hpr4333 (2025-03-12) "A Radically Transparent Computer Without Complex VLSI" by Marc W. Abel. Marc said: "New online home for Dauug|36 and Dauug|18" (2026-03-25 15:18:15) hpr4424 (2025-07-17) "How I use Newsboat for Podcasts and Reddit" by Archer72. أحمد المحمودي said: "Not fixed" (2026-03-31 00:54:19) hpr4509 (2025-11-13) "HPR Beer Garden 5 - Heferweisen" by Kevie. Gan Ainm said: "Hefeweizen" (2026-03-04 06:47:39) Kevie said: "Thanks Gan" (2026-03-13 15:28:45) hpr4553 (2026-01-14) "Nuclear Reactor Technology - Ep 4 Less Common Reactor Types" by Whiskeyjack. Antoine said: "Were/are the designs patented?" (2026-03-18 12:41:35) Whiskeyjack said: "Reply to Antoine" (2026-03-19 03:31:50) Antoine said: "I will" (2026-03-21 02:30:29) hpr4565 (2026-01-30) "HPR Beer Garden 9 - Barley Wine" by Kevie. Aleman said: "Beer Garden" (2026-03-06 19:25:26) hpr4571 (2026-02-09) "Data processing retrospective" by Lee. Archer72 said: "previous generation" (2026-03-03 15:44:12) hpr4573 (2026-02-11) "Nuclear Reactor Technology - Ep 6 Thorium Reactors" by Whiskeyjack. Archer72 said: "Interesting series" (2026-02-28 16:59:15) Whiskeyjack said: "Reply to Archer72" (2026-02-28 23:06:46) Clinton said: "Modern situation." (2026-03-07 11:30:14) Whiskeyjack said: "Reply to Clinton" (2026-03-07 18:42:23) hpr4574 (2026-02-12) "UNIX Curio #0 - Introduction" by Vance. murph said: "Great show, looking forward to more." (2026-03-01 19:21:46) hpr4575 (2026-02-13) "Making First Contact" by Ken Fallon. Archer72 said: "Good to hear 73's" (2026-02-28 15:51:52) hpr4576 (2026-02-16) "Responce to Lee/Elsbeth eps" by operat0r. candycanearter07 said: "relatable episode" (2026-03-10 01:39:18) hpr4577 (2026-02-17) "HPR Beer Garden 10 - Scotch Ale/Wee Heavy" by Kevie. Kevie said: "Upcoming beers" (2026-02-26 18:14:16) hpr4583 (2026-02-25) "Nuclear Reactor Technology - Ep 7 Small Modular Reactors" by Whiskeyjack. brian-in-ohio said: "good shows" (2026-03-02 21:10:12) Whiskeyjack said: "Response to brian-in-ohio for HPR4583 Small Modular Reactors" (2026-03-03 23:38:55) hpr4584 (2026-02-26) "Recording a show, and crappy audio" by Archer72. Dave Lee (thelovebug) said: "Audio quality" (2026-02-27 08:33:24) Kevin O'Brien said: "The Zoom was perfect" (2026-02-27 17:29:43) Archer72 said: "Bad mic" (2026-03-03 15:08:13) jezra said: "false advertising! " (2026-04-03 17:28:05) hpr4585 (2026-02-27) "mpv util scripts" by candycanearter. Windigo said: "mpv fanclub" (2026-02-28 01:55:28) Windigo said: "Re: mpv fanclub" (2026-03-01 05:07:24) Archer72 said: "Second in mpv fanclub" (2026-03-01 08:52:41) candycanearter07 said: "updated script" (2026-03-01 22:35:38) This month's shows hpr4586 (2026-03-02) "HPR Community News for February 2026" by HPR Volunteers. candycanearter07 said: "41:40" (2026-03-01 23:39:18) Whiskeyjack said: "HPR Commnity News discussion on audio" (2026-03-03 23:11:25) hpr4587 (2026-03-03) "UNIX Curio #1 - Shell Archives" by Vance. Archer72 said: "Continuing series" (2026-03-03 15:15:19) xmanmonk said: "uuencode/uudecode on Solaris" (2026-03-05 01:47:53) Vance said: "Thanks, and Solaris" (2026-03-07 20:10:08) Jim DeVore said: "Thanks for the trip down memory lane!" (2026-03-17 01:19:46) hpr4591 (2026-03-09) "A Bit of Git" by Lee. candycanearter07 said: "anecdotal teaching is the best kind" (2026-03-09 04:58:24) hpr4592 (2026-03-10) "Happy by shower # 2" by Antoine. candycanearter07 said: "interesting!" (2026-03-10 04:20:16) Antoine said: "Sharing (response to candycanearter07)" (2026-03-21 02:27:17) hpr4593 (2026-03-11) "Nuclear Reactor Technology - Ep 8 Generation Four Reactors" by Whiskeyjack. Jim DeVore said: "Great series!" (2026-03-17 01:13:51) Whiskeyjack said: "Response to Jim DeVore" (2026-03-17 13:46:31) hpr4596 (2026-03-16) "Adding voice-over audio track created using text to speech on the movie subtitles" by Ken Fallon. folky said: "Interesting solution, but annoying " (2026-02-05 11:54:36) Carsten said: "Amazing project" (2026-02-25 00:29:08) candycanearter07 said: "interesting!!" (2026-03-16 13:38:03) hpr4597 (2026-03-17) "UNIX Curio #2 - fgrep" by Vance. Ken Fallon said: "Time to active use" (2026-03-05 05:58:31) L'andrew said: "Nice job explaining *grep features." (2026-03-18 03:34:11) candycanearter07 said: "informative" (2026-03-18 03:52:52) Vance said: "Expressions" (2026-03-20 18:16:09) hpr4598 (2026-03-18) "Recording good audio using open source tools" by Shane - StrandedOutput. Archer72 said: "Great tips!" (2026-03-19 10:39:24) Ole Aamot said: "GarageJam 6.0.1" (2026-03-24 01:50:51) hpr4600 (2026-03-20) "The First Doctor, Part 5" by Ahuka. Kevie said: "Great series" (2026-03-21 15:22:59) Kevin O'Brien said: "I think I will" (2026-03-21 21:23:38) Archer72 said: "Great series and 2nd continuation " (2026-03-21 22:35:05) hpr4605 (2026-03-27) "Lee locks down his wifey poo" by Elsbeth. Ken Fallon said: "Congratulations" (2026-03-18 11:09:45) Elsbeth said: "Thank you!" (2026-03-27 11:10:10) Trollercoaster said: "Congrats - and now we want all the fun puns!" (2026-03-27 12:58:38) Antoine said: "=)" (2026-03-29 22:39:06) ClaudioM said: "Congratulations to You Both!" (2026-03-30 13:22:43) Paulj said: "Congratulations" (2026-04-04 19:52:01) hpr4606 (2026-03-30) "My Nerdy Childhood: From Floppy Disks to Dial-Up Dreams" by Trollercoaster. Trey said: "Trip down memory lane..." (2026-03-30 14:24:54) xmanmonk said: "Great Episode!" (2026-03-30 16:23:43) Trollercoaster said: "Back to you..." (2026-03-31 08:24:58) Trollercoaster said: "Not to janitors" (2026-03-31 08:26:06) ClaudioM said: "Nerdy Nostalgia!" (2026-03-31 17:20:34) hpr4607 (2026-03-31) "UNIX Curio #3 - basename and dirname" by Vance. xmanmonk said: "Great episode!" (2026-03-31 14:19:12) Mailing List discussions Policy decisions surrounding HPR are taken by the community as a whole. This discussion takes place on the Mailing List which is open to all HPR listeners and contributors. The discussions are open and available on the HPR server under Mailman. The threaded discussions this month can be found here: https://lists.hackerpublicradio.com/pipermail/hpr/2026-March/thread.html Events Calendar With the kind permission of LWN.net we are linking to The LWN.net Community Calendar. Quoting the site: This is the LWN.net community event calendar, where we track events of interest to people using and developing Linux and free software. Clicking on individual events will take you to the appropriate web page. Provide feedback on this episode.

Mike & Tommy tackle Git best practices for Power BI teams, exploring how to reduce diff noise from slicer selections and matrix expansions, whether renaming visual folders is brilliant or risky, and how small teams can maintain clean, readable source control without losing their minds. They weigh practical tactics like commit checklists, folder naming conventions, and the trade-offs between tracking every click versus only intentional changes.Get in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Walt explains his mysterious nature, Bry rants, Tax mascots, Airport Plaza robbery, Prank gone wrong, Git ‘em is a narc.https://public.liveread.io/media-kit/tesd