Podcasts about Heroku

[This episode from February 2024 was never published and recently discovered]In today's episode, Andrew kicks things off with a rant about tackling developer experience tasks at Podia, wrestling with GitHub actions, and Heroku deployment woes. Then the conversation takes a turn to the importance of debugging, the power of bash scripting, and the challenges of naming in programming, with Chris mentioning DHH's insights from a live stream. They discuss Chris's travel plans for RubyConf in Australia, other conferences coming up, and reminisce about their childhood love for trains and Thomas the Tank Engine.  The episode wraps up with Chris and Andrew sharing advice and tips on writing conference proposals (CFPs) and the value of diverse speaking styles and personalities for engaging an audience. Tune in now to hear more!LinksONCE/Campfiredebug.rbGitHub CopilotRubyConf Australia-April 11-12, 2024RailsConf 2024-May 7-9, 2024-Detroit, MISarah Mei-“What Your Conference Proposal is Missing”Ruby for All Podcast-Episode 50: The Art of Conference Speaking with Kevin Murphy[SFM] We like to party (YouTube)Ultimate Skyrim (YouTube)RailsConf 2023-Teaching Capybara Testing- An Illustrated Adventure by Brandon Weaver (YouTube)Chris Oliver X/TwitterAndrew Mason X/TwitterJason Charnes X/Twitter

Aaron is joined this week by Jesse Hanley, founder of Bento, to talk about building a seven figure business, why he feels less stress now than he did when he started, migrating from Heroku to Planetscale, and more.Sponsored by InterNACHI, Honeybadger, Bento, Vask, and NativePHP UltraInterested in sponsoring Mostly Technical?  Head to https://mostlytechnical.com/sponsor to learn more.Going to Laracon?  Sign up for the Mostly Technical Pre-Party!(00:00) - 5 TB of Data (11:12) - Laravel Live Japan (15:46) - Seven Figure Business (24:32) - Advice for Indie Hackers (28:57) - Pick Better Problems (32:50) - Friends of Bento (42:33) - Heroku to Planetscale (01:15:02) - What's Next Links:Jesse HanleySpeedshopJesse's Database School episodeTatamiDragonflyRedisShakeLaravel Live JapanDaniel Coulbourne

In 2011 Heroku defined the 12 factor app to remove emerging bottlenecks as developers tried to scale their output when they moved from building monoliths to microservices. In Platform Engineer we see a repeating pattern called the "8 Factor Platform Producers".  AI allows engineering teams to speed up but they face bottlenecks as platform capabilities are not scaling with that demand as they are often depending on a central platform engineering team to be built and maintained.To learn more about 8 Factor Platform Producers we invited Abby Bangser,  Founding Principal Engineer at Syntasso and CNCF Ambassador. She gave an amazing talk at KubeCon in Amsterdam and today walks us through the need of defining both consumers and producers for platforms to eliminate any emerging bottlenecks in Platform Engineering and allow an organization to reap the benefit of speeding up with AILinks we discussed:Abby's LinkedIn: https://www.linkedin.com/in/abbybangser/Abby's Kubecon Keynote: https://www.youtube.com/watch?v=8t0-5cvvMGM&list=PLj6h78yzYM2MXCOWSN9CqqID6OOvF7wxL&index=3012 Factor Apps: https://12factor.net/CNCF Whitepaper: https://cloudnativeplatforms.com/whitepapers/platforms/

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

Max Schoening is head of product at Notion, where he's been especially effective at getting designers and PMs to ship code, prototype in the terminal, and launch extremely successful AI products. He was previously a PM at Google, ran design at Heroku, was VP of Design (and a part-time engineer) at GitHub, and is a two-time founder. He's one of the most AI-forward product leaders out there and one of the deepest thinkers on how AI changes how we build and use software.We discuss:1. What's most worked in getting designers and PMs to embrace AI2. Why agency—not skills—is the thing that separates people who thrive from those who fall behind3. How the first 10% of every project is now “free,” and what that means for product development4. Max's “tiny core” theory of great products: iPhone multitouch, the GitHub pull request, Notion blocks, Dropbox's menu bar icon5. Why the SaaSpocalypse is overstated6. Why the amount of software has exploded but the quality hasn't, and why that gap creates opportunity—Brought to you by:WorkOS—Make your app Enterprise Ready, with SSO, SCIM, RBAC, and more: https://workos.com/lennyVanta—Automate compliance, manage risk, and accelerate trust with AI: https://vanta.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/why-cultivating-agency-matters-more—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Max Schoening:• X: https://x.com/mschoening• LinkedIn: https://www.linkedin.com/in/max-schoening• Website: https://max.dev—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Max Schoening(01:55) The origin story of designers coding at Notion(06:30) How much designers and PMs are shipping today(08:24) The balance between shipping code and strategic work(10:32) Why agency will help you thrive in the AI era(11:49) Examples of high agency at Notion(13:52) What we might lose as roles merge(15:56) Advice for developing agency(17:42) Malleable software explained(20:43) The Dieter Rams video and design philosophy(24:00) The SaaS apocalypse debate(28:25) How product building has changed in the past two years(30:27) What's next in how we build products(34:16) Token spend and ROI conversations(37:39) Getting people to change how they work(39:04) Max's AI stack(41:41) Which roles AI will transform next(44:26) When companies will start caring about ROI(48:38) Why Notion AI is so successful(51:47) How to ship more quickly while maintaining quality(56:40) Building taste through iterations(1:00:09) What matters most in building successful products(1:05:06) Using the jobs-to-be-done framework(1:07:28) Hot take on universal basic income(1:09:26) What Max would do with AGI(1:10:53) Contrarian corner(1:13:14) Failure corner(1:16:20) Advice for young people in Silicon Valley(1:19:20) Lightning round and final thoughts—Referenced: https://www.lennysnewsletter.com/p/why-cultivating-agency-matters-more—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

A new type of cyberattack is bypassing every security tool you've invested in — and it starts with a simple Microsoft Teams message. No malware. No exploit. No zero-day. Just someone pretending to be IT support. At the same time, new data shows 73% of ransomware attacks are now entering through VPNs, and small businesses are absorbing an average of $422,000 per incident. Meanwhile, KPMG just released its 8 cybersecurity priorities for 2026, sending a clear message to executives: the biggest risk isn't technology — it's leadership. On this episode of Security Squawk, Bryan Hornung, Randy Bryan, and Reginald Andre break down three critical developments every business leader needs to understand right now. This Week's Cybersecurity Breakdown 1. Microsoft Teams Hack (UNC6692 Attack Campaign) Hackers are impersonating IT support inside Microsoft Teams to gain access to enterprise environments. No software vulnerability exploited Targets C-suite and senior leadership (77% of victims) Uses legitimate platforms like AWS and Heroku to evade detection 2. VPNs Are Now the Front Door for Ransomware (At-Bay 2026 Report) New insurance data reveals a sharp increase in ransomware attacks targeting VPN infrastructure: 73% of attacks originate through VPNs 60% of victims had EDR deployed — and still got hit SonicWall vulnerabilities linked to a significant percentage of attacks Average loss: $422,000 for SMBs 3. KPMG's 8 Cybersecurity Priorities for 2026 A strategic warning for boards, CEOs, and executives: AI is now an attack surface Non-human identities (APIs, service accounts) are a major blind spot Supply chain attacks are becoming the primary entry point Cybersecurity is no longer an IT issue — it's a leadership responsibility The Bottom Line The biggest cybersecurity gap today isn't technical. It's leadership. You can't patch employee trust You can't rely on tools without oversight You can't delegate cyber risk and expect protection If you're running a business, this is required awareness. Support the show: buymeacoffee.com/securitysquawk Subscribe for weekly breakdowns of real-world cyber threats, ransomware trends, and executive-level security insights.

Your model only matters if it connects to the business. But when you're a data scientist learning MLOps on the fly, experimenting on live infrastructure is terrifying. Anastasiia Kulakova is an Amsterdam-based data scientist at JetLakes, a fast-growing energy and mobility startup. She shares her candid journey from Jupyter Notebook to production-ready ML. In this episode: • Why "we have the data" from stakeholders rarely means what you think • How to build your own MLOps learning sandbox without breaking production (GitHub Actions, Heroku, DigitalOcean) • The reality of being a data generalist at a startup — wearing every hat from model training to Scrum Master • How JetLakes uses predictive algorithms to balance the energy grid through EV charging optimisation • Virtual power plants: turning parked electric vehicles into grid-scale flexibility • Why the precision economy is coming to energy — and what that means for data teams

Chris and David welcome back Adam McCrea from Judoscale, to discuss the uncertainty around Heroku after Salesforce's announcement that it would stop taking new enterprise customers. Adam shares how the news landed in real time during a founder's retreat, and the conversation expands into what Heroku's apparent “maintenance mode” means for developers, pricing, autoscaling, platform alternatives, and the broader challenge of building durable developer businesses in the AI era. They also touch on Judoscale's upcoming “platform tour” and the value of smaller Ruby conferences. Hit download now to hear more! Sponsors:HoneybadgerJudoscaleLinks:Chris Oliver XAndrew Mason BlueskyDavid Hill LinkedInJudoscale- Remote Ruby listener giftAdam McCrea XAdam McCrea LinkedInJudoscaleRemote Ruby-Episode 163: Autoscaling Rails with Adam McCreaHeroku: What's Next by Jon Sully (Judoscale Blog)An update on Heroku by Nitin T BhatRenderLaravel CloudRBQ Conf, March 26-27, 2026, Austin, TXBlue Ridge Ruby, April 30-May 1, 2026, Asheville, NCRubyConf, July14-16, 2026, Las Vegas, NVRails World 2026, September 23-24, 2026, Austin, TXRuby Events 2026HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you. Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

Salesforce ha ufficialmente messo Heroku in manutenzione: niente più nuove feature, niente vendite enterprise. Vi racconto la storia della piattaforma che ha inventato il "git push" per deployare, e perché la sua morte ci dice qualcosa su come funziona il tech.Fonti e approfondimenti:- The Register: https://www.theregister.com/2026/02/09/heroku_freeze/- Heroku Blog: https://www.heroku.com/blog/an-update-on-heroku/- Lee Robinson — The Story of Heroku: https://leerob.com/heroku- Koyeb — Heroku's Free Tier Legacy: https://www.koyeb.com/blog/herokus-free-tier-legacy-the-shoulders-we-stand-on-15-years-laterLa mia app: https://play.google.com/store/apps/details?id=com.edodusi.coderoutine&hl=it-it00:00 Intro01:13 Come tre sviluppatori Ruby hanno inventato il deploy moderno03:54 Cosa ha ucciso Heroku06:57 Outro#heroku #salesforce #paas #cloud #deploy #ai

The OpenClaw bot asks Benedicte some “existential” questions. Benedikt ships their MCP.Benedicte is working on Jean-Claw for her upcoming talk when things get a bit existential. In the middle of setting up the YAML file, the bot halts on the “Who are you? Who am I?” step. She also used Claude to create a Queen Raae voice skill to help her write more like she actually talks.Benedikt shipped their MCP experiment, letting users generate broadcasts, use Liquid tags inside broadcasts, and somewhat create segments. And with Heroku's recent announcement, he's looking into alternatives once more.

This week we talk about multiple in-the-news topics like the SalesForce announcement that Heroku is in ~maintenance mode and we surface the big observability topic as I'm preparing to implement something basic for StaticBackend and since Morten already have this in his open source project we duscuss about ways to add this after the fct and some parts of tracing your system.

Mason Cosby sits down with Gillian Hinkle to discuss the complexities of marketing within a portfolio company. When an organization moves from a single product to a suite of services, marketers often struggle to build intentional sequencing. Gillian shares her approach to identifying customer behaviors and mapping the overlap between different buying committees.ㅤShe explains why you cannot go it alone: you must work with other teams to find commonalities in lead information and problem sets. A critical part of her strategy is to market to the internal sales team first. If sellers do not understand the deal cycle or how a new product addresses a specific pain point, they will not present it to their customers.ㅤGillian also details how to protect the customer relationship by validating data with product managers before launching a campaign. She emphasizes the importance of "small measures"—tracking observable behaviors and engagement rates in internal channels like Slack—to understand program success before revenue numbers come in.ㅤGuest BioGillian Hinkle is a seasoned marketing leader currently serving as the Senior Director of Product Marketing at Salesforce, with a focus on Heroku, a cloud platform as a service (PaaS). Her career trajectory transitions from an initial background in arts administration and education to technical B2B marketing leadership in the SaaS and cloud infrastructure sectors. Before joining Salesforce, Hinkle served as Director of Growth Marketing at Earnix.ㅤWhat We CoverUsing behavioral data to identify which customers are ready for expansion products.How to map the overlap between different buying groups—like marketing buyers versus data buyers—in a portfolio company.Why you must understand sales compensation before asking account managers to sell a new product.The strategy of "marketing to sellers" first: using enablement sessions to test if an offer is right for the current market.Protecting customer relationships by excluding unqualified accounts and validating pain points with product teams.Using Slack channels and lists to manage program execution and track internal engagement.The importance of reporting "small measures" and observable behaviors when revenue data is not yet available.ㅤResourcesHeroku: A cloud platform as a service (PaaS) supporting several programming languages.Slack: The primary communication tool Gillian uses for program management.

Today's guest is Gillian Hinkle, Senior Director of Growth & Digital Marketing for Heroku at Salesforce. Gillian brings extensive experience in enterprise growth strategy, digital operations, and the practical deployment of data and AI across complex marketing and revenue workflows. Gillian joins Emerj Editorial Director Matthew DeMello to explore how enterprise leaders can distinguish automation from true AI, design human-in-the-loop systems, and deploy generative and agentic tools responsibly inside real-world data environments. The conversation also examines how to reduce tool sprawl, strengthen data governance, and focus AI initiatives on high-impact workflows like lead qualification and customer service handoffs to drive measurable efficiency, improve employee engagement, and lower compliance and brand risk. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

This week we're joined by Peter van Hardenberg (PVH), director of the Ink and Switch research lab and co-author of the seminal Local First Software paper.Peter shares the origin story of local-first software, from his realization on a San Francisco train to his work at Heroku and beyond.We dive deep into Automerge, Ink and Switch's local-first sync engine built on CRDTs (Conflict-Free Replicated Data Types), exploring how it enables real-time collaboration while keeping data on your computer.We discuss the technical challenges of building distributed systems, the philosophy behind local-first software, and how projects like Key Hive are pushing the boundaries of decentralized data access.Peter also shares his vision for the future of computing, where software ownership and interoperability become fundamental principles rather than afterthoughts.https://www.pvh.cahttps://www.inkandswitch.comhttps://automerge.orghttps://github.com/automerge/automergehttps://github.com/pvh

In this episode I talk with Raphael Masson, CTO of Missive, and Craig Kerstiens from Crunchy Data. We cover bootstrapping Missive from a side project (Conference Badge), growing from 3 to 15 employees, migrating off Heroku, and why most developers underutilize Postgres.Links:MissiveCrunchy DataNonsense Monthly

In this episode, Chris and David Hill catch up on wild winter temperature swings, then dive into what Chris has been refactoring in Jumpstart to reduce merge pain, cut dependencies, and make upgrades smoother. The conversation branches into AI-assisted coding pitfalls and where AI shines, new web security headers that could simplify CSRF handling, and a promising new “old school Heroku on steroids” platform from Evan Phoenix called Miren, plus a few Hatchbox deployment learnings along the way. Hit download now to hear more!LinksChris Oliver XAndrew Mason BlueskyJudoscale- Remote Ruby listener giftDavid Hill LinkedInBlastoff Rails (submit a talk)RBQ Conf (submit a talk)Why GitHub Why? (YouTube-ThePrimeagen)Augment codeSec-Fetch-Site headerSec-Fetch-Dest headerSec-Fetch-Mode headerSec-Fetch-User headerMiren Developer Preview Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter

It's been nearly a decade since the last evolution of the PaaS platform, but AI has the potential to reshape and evolve this value concept. Let's explore that's possible. SHOW: 982SHOW TRANSCRIPT: The Cloudcast #982 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:[Mailtrap] Try Mailtrap for free[Interconnected] Interconnected is a new series from Equinix diving into the infrastructure that keeps our digital world running. With expert guests and real-world insights, we explore the systems driving AI, automation, quantum, and more. Just search “Interconnected by Equinix”.SHOW NOTESVercel v0Building Web Apps with just English and AI (Acquired podcast, Feb 2025)Vercel on The Cloudcast (2024)Vercel on The Cloudcast (2021)8 tools to build your own PaaS (2025)IS PAAS READY TO TAKE THE NEXT STEP? Where could PaaS evolve to now?Can new PaaS services abstract the developer, and just focus on business logic and business ideas? What languages or design patterns would be mandated? (web only, mobile only, web + mobile? )Can we template “best practices” enough to be reliable?Can we template compliances needed to handle financial transactions, customer data, etc.?Can troubleshooting become an automated service?Where was PaaS in the past? (Heroku, Google AppEngine, Cloud Foundry, Kubernetes)Language specificCloud specificAbstracting the infrastructure and securityFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

Scrappy ABM brings together host Mason Cosby and Yadin Porter de León, Director of Customer Stories and thought leadership over at Heroku, which is a part of Salesforce, to talk about going straight to the top of your target accounts without getting blacklisted.ㅤInstead of getting stuck in email blasts and third-party events that promise “rubbing elbows” with executives, Yadin shares a high-level framework that starts small with relationships your own C-suite already has, then builds proof points through web stories, webinars, podcasts, and thought leadership videos. The conversation walks through going top down from your CEO and bottoms up from directors and senior managers, doing the hard “eat your vegetables” work of segmentation, and mapping LinkedIn so you make it easy for leaders to say yes.ㅤThrough stories from Angel Med Flight, JetBlue, GE Healthcare, NASA Jet Propulsion Labs, Wells Fargo, Michael Dell, and a seven-figure deal, Mason and Yadin show how time and trust, podcasts, and truly helping individuals with their own goals can turn a focused ABM program into a powerful path to the C-suite.ㅤ

I'm back and I'm angry. My power went out, which caused my Internet to go down, which broke my favorite mug. And that's just the shit that happened before 7 AM. By 9 AM my doorbell was continuously chiming for no fucking reason. Join me in the struggle. We shall persevere. Tell me how your morning went by writing in to: podcast@searls.co. Here 4 U: Kudos to Adam Mcrea and Judoscale for solving my Heroku issue Kudos to me for separately solving my Heroku issue with Straight-to-Video ALVR for streaming SteamVR games Streaming flat games with Sunshine and Moonlight Aaron's puns, ranked AI's Dial-Up Era (and my take) Sandwich made the world's first immersive ad spot Coinbase CEO Stunt Exposes Prediction Market Vulnerability OpenAI and Microsoft resolve their issue without resolving their issue Amazon v. Perplexity BioWare reassures fans Mass Effect 5 is still coming The Outer Worlds 2 Eddy Burback - ChatGPT Made me Delusional

This is a recap of the top 10 posts on Hacker News on October 21, 2025. This podcast was generated by wondercraft.ai (00:30): ChatGPT AtlasOriginal post: https://news.ycombinator.com/item?id=45658479&utm_source=wondercraft_ai(01:53): Replacing a $3000/mo Heroku bill with a $55/mo serverOriginal post: https://news.ycombinator.com/item?id=45661253&utm_source=wondercraft_ai(03:17): Build your own databaseOriginal post: https://news.ycombinator.com/item?id=45657827&utm_source=wondercraft_ai(04:40): Foreign hackers breached a US nuclear weapons plant via SharePoint flawsOriginal post: https://news.ycombinator.com/item?id=45657287&utm_source=wondercraft_ai(06:04): Neural audio codecs: how to get audio into LLMsOriginal post: https://news.ycombinator.com/item?id=45655161&utm_source=wondercraft_ai(07:27): Wikipedia says traffic is falling due to AI search summaries and social videoOriginal post: https://news.ycombinator.com/item?id=45651485&utm_source=wondercraft_ai(08:51): LLMs can get "brain rot"Original post: https://news.ycombinator.com/item?id=45656223&utm_source=wondercraft_ai(10:15): 60k kids have avoided peanut allergies due to 2015 advice, study findsOriginal post: https://news.ycombinator.com/item?id=45652307&utm_source=wondercraft_ai(11:38): NASA chief suggests SpaceX may be booted from moon missionOriginal post: https://news.ycombinator.com/item?id=45655188&utm_source=wondercraft_ai(13:02): Apple alerts exploit developer that his iPhone was targeted with gov spywareOriginal post: https://news.ycombinator.com/item?id=45657302&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

As I'm building yet another software service business after having built and sold one back in 2019, I keep wrestling with a fundamental question that might sound simple but has profound implications: What do I actually own in this business?This episode of The Bootstraped Founder is sponsored by Paddle.comThe blog post: https://thebootstrappedfounder.com/the-ownership-paradox-what-do-you-really-control-in-your-software-business/The podcast episode: https://tbf.fm/episodes/416-the-ownership-paradox-what-do-you-really-control-in-your-software-businessCheck out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

How would you build a Heroku-like platform from scratch? This week we're diving deep into the world of cloud platforms and infrastructure with Anurag Goel, founder and CEO of Render.Starting from the seemingly simple task of hosting a web service, we quickly discover why building a production-ready platform is far more complex than it appears. Why is hosting a Postgres database so challenging? How do you handle millions of users asking for thousands of different features? And what's the secret to building infrastructure that developers actually want to use?We explore the technical challenges of building enterprise-grade services—from implementing reliable backups and high availability to managing private networking and service discovery. Anurag shares insights on choosing between infrastructure-as-code versus configuration, why they built on Go, and how they handle 100 billion requests per month.Plus, we discuss the impact of AI on platform adoption: Are LLMs already influencing which platforms developers choose? Will hosting platforms need to actively support agentic workflows? And what does the future hold for automated debugging?Whether you're curious about building your own platform, want to understand what really happens behind your cloud provider's dashboard, or just enjoy hearing war stories from the infrastructure trenches, this episode has something for you.–Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinRender: https://render.com/Render's MCP Server (Early Access): https://render.com/docs/mcp-serverPulumi: https://www.pulumi.com/Victoria Metrics: https://victoriametrics.comLoki: https://vector.dev/docs/reference/configuration/sinks/loki/Vector: https://vector.dev/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

In this episode, Chris and Andrew discuss the recent release of Rails 8 and the improvements in upgrading processes compared to previous versions. They dive into specific technical challenges, such as handling open redirects and integrating configuration options, and chat about Chris's recent experience with Tailwind's new Elements library, Bundler updates, and JSON gem changes.  They also touch on Heroku's evolving infrastructure and the potential benefits of using PlanetScale's new Postgres offerings. The episode concludes with a discussion about life without internet and Andrew's countdown to his upcoming sabbatical.  Hit download now! LinksJudoscale- Remote Ruby listener giftRails World 2025Tailwind Plus- ElementsInvoker Commands APIByroot's Blog post-What's wrong with JSON gem API?PlanetScaleHetznerHoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you. Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter

In this episode I talk with Adam McCrea, founder of Judoscale, an autoscaler for Heroku and other platforms. Adam built Judoscale as a side project in 2016 and ran it part-time for five years before going full-time. We discuss developer marketing challenges, the difficulty of measuring marketing attribution, and building sustainable businesses. We also compare notes on our respective developer tools.

In this episode of Remote Ruby, Chris and Andrew discuss the recent Google Cloud Platform and Heroku outages, sharing personal experiences of system impacts and recovery strategies. The conversation shifts to technical insights, including a deep dive into Rails ‘direct' routes and their routing helper capabilities. They also touch on the latest performance enhancements in Ruby 3.3, such as Embedded TypedData Objects and their impacts. Also, they explore parsing Ruby code with Prism and chat about  productivity hacks, upcoming RailsConf plans, parenting chaos, and dreams of launching their own MTV show. Hit the download button now! LinksJudoscale - Remote Ruby listener giftImplementing Embedded TypedData Objects (Rails at Scale)Supercharging Ruby with Embedded TypedData Objects (Ruby Stack News)Prism Ruby parserRailsConf 2025, Philadelphia, PA, July 8-10HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.JudoscaleMake your deployments bulletproof with autoscaling that just works.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you. Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter

In this episode, I talk with Errol Schmidt from Reinteractive about community involvement and sales strategies. Errol shares how he targets Salesforce by teaching their account executives about Heroku, positioning himself as the go-to expert. We discuss how developers are in sales whether they realize it or not, and the importance of relationship building.reinteractive

V 71. epizóde som sa rozprával s Jurajom Masárom, spoluzakladateľom Better Stack – jednej z najrýchlejšie rastúcich DevTools firiem v Európe, ktorá získala investície od fondu, za ktorým stojí aj zakladateľ Heroku. Juraj dnes riadi firmu v New Yorku z Prahy a v podcaste otvorene hovorí o tom, čo naozaj znamená budovať globálny biznis z Európy.V podcaste sme sa rozprávali o tom, prečo sa podľa neho neoplatí budovať globálnu firmu na Slovensku a aké sú najväčšie výhody i nevýhody podnikania v USA. Juraj otvorene priznal, že Better Stack musel otočiť produktový smer, hoci predtým získali investíciu. Zároveň priblížil, ako sa im darí škálovať tím – napríklad tým, že výber nových ľudí trvá aj 5 mesiacov, ale výsledok stojí za to. V podcaste nájdete konkrétny postup výberu, položené otázky a aj spôsob eliminácie uchádzačov.Prebrali sme aj tému CTO a CMO vo firmách – kto má kedy čo robiť, čo patrí do ich zodpovedností a ako udržať oba svety – produkt aj marketing – v rovnováhe. Juraj popísal aj svoj názor na AI vo vývoji softvéru a tiež to, prečo podľa neho senior programátorov tak skoro nenahradí. Zaujímavý bol aj pohľad na ich marketing – prečo v Better Stacku nevytvárajú tradičný marketingový tím, ale namiesto toho stavajú výkonný obsahový tím, ktorý má za cieľ pomáhať developerom po celom svete?Tento diel je plný skúseností, užitočných a hlavne praktických rád pre podnikanie, vďaka ktorým dokážete zlepšiť svoj biznis aj v prípade, že nie ste technologická firma. Užívajte!---------------------------------------------------------------------------Kapitoly:00:00:00 – Predstavenie hosťa00:01:16 – Prečo začať podnikať mimo Slovenska?00:04:51 – Ako sa podniká v USA a čo to prináša?00:11:02 – Ako škálovať firmu aj bez investorov?00:15:31 – Príbeh Better Stack00:22:59 – Ako získať špičkových vývojárov do tímu?00:35:15 – Kedy startup potrebuje pivot?00:45:01 – Vymení AI developerov?00:53:24 – Marketing, ktorý funguje01:08:50 – Ako rozdeliť zodpovednosť medzi CTO a CMO?01:11:09 – Founder mindset01:17:41 – Zmysel života podľa Juraja Masára---------------------------------------------------------------------------Viac z podcastov nájdete na:https://www.truban.sk/podcast/---------------------------------------------------------------------------Všetky spomenuté knihy a podcasty nájdete v článku na blogu:https://wp.me/p5NJVg-QQ---------------------------------------------------------------------------Podcast si môžete vypočuť aj na streamovacích platformách:● Spotify ▸ https://spoti.fi/31Nywax ● Apple podcast ▸ https://apple.co/3n0SO8F---------------------------------------------------------------------------● Najlepšie z podcastu na Instagrame ●https://www.instagram.com/truban.podcast/● Truban.sk ●https://bit.ly/3r1vYQJ ● Instagram ●https://www.instagram.com/truban/● Facebook ●https://www.facebook.com/miso.truban● LinkedIn ●https://sk.linkedin.com/in/truban

Julián Duque from Heroku joins me to explain and demo their new AI platform.Check out the video podcast version here https://youtu.be/BGqlLZHdRDsCreators & Guests Cristi Cotovan - Editor Bret Fisher - Host Beth Fisher - Producer Julián Duque - Guest You can also support my content by subscribing to my YouTube channel and my weekly newsletter at bret.news!Grab the best coupons for my Docker and Kubernetes courses.Join my cloud native DevOps community on Discord.Grab some merch at Bret's Loot BoxHomepage bretfisher.com (00:00) - Introduction (05:12) - Deep Dive into Heroku's AI Capabilities (14:23) - Heroku MCP server (28:27) - Describing MCP Tool Interactions (30:48) - DevOps Automation with Heroku MCP server (37:02) - Heroku AI and Future Prospects

Picture this. You've got a web app built with Rust and Solid.js. It started life running on a dusty on-prem server, but now it's time to move it to the cloud. The clock is ticking. You could take the well-worn AWS path: set up a VPC, configure subnets, attach an ALB, define IAM roles, and deploy with Fargate. Or you could try something different. In this episode of AWS Bites, we share the real story of migrating a monolithic containerized app to AWS App Runner. It promises to take your code, build it, deploy it, and scale it with minimal effort. But does it really deliver? We compare App Runner with Fargate based on hands-on experience. You'll learn where App Runner shines, where it gets in the way, and how we handled everything from custom domains to background job processing. You'll also hear when we would still choose Fargate, and why. If you've ever hoped for a Heroku-like experience on AWS, or you want to simplify your container deployments without giving up too much control, this episode is for you.AWS Bites is brought to you in association with fourTheorem. At fourTheorem, we believe serverless should be simple, scalable, and cost-effective — and we help teams do just that. Whether you're diving into containers, stepping into event-driven architecture, or scaling a global SaaS platform on AWS, our team has your back. Visit https://fourTheorem.com to see how we can help you build faster, better, and with more confidence using AWS cloud!

AI tools are transforming how developers write code, and although it's difficult to pinpoint how much code is now AI-generated code, estimates suggest it's between 20% and 40%, and this figure is poised to grow in the coming years. This evolution has given rise to a new coding paradigm in which developers act as directors, The post Vibe Coding at Heroku with Vish Abrams appeared first on Software Engineering Daily.

Heroku has been undergoing a major transformation, re-platforming its entire Platform as a Service (PaaS) offering over the past year and a half. This ambitious effort, dubbed “Fir,” will soon reach general availability. According to Betty Junod, CMO and SVP at Heroku (owned by Salesforce), the overhaul includes a shift to Kubernetes and OCI standards, reinforcing Heroku's commitment to open source. The platform now features Heroku Cloud Native Buildpacks, which let developers create container images without Dockerfiles. Originally built on Ruby on Rails and predating Docker and AWS, Heroku now supports eight programming languages. The company has also deepened its open source engagement by becoming a platinum member of the Cloud Native Computing Foundation (CNCF), contributing to projects like OpenTelemetry. Additionally, Heroku has open sourced its Twelve-Factor Apps methodology, inviting the community to help modernize it to address evolving needs such as secrets management and workload identity. This signals a broader effort to align Heroku's future with the cloud native ecosystem. Learn more from The New Stack about Heroku's approach to Platform-as-a-Service:Return to PaaS: Building the Platform of Our DreamsHeroku Moved Twelve-Factor Apps to Open Source. What's Next?How Heroku Is Positioned To Help Ops Engineers in the GenAI EraJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.

AI tools are transforming how developers write code, and although it's difficult to pinpoint how much code is now AI-generated code, estimates suggest it's between 20% and 40%, and this figure is poised to grow in the coming years. This evolution has given rise to a new coding paradigm in which developers act as directors, The post Vibe Coding at Heroku with Vish Abrams appeared first on Software Engineering Daily.

Pete Hamilton and Chris Evans are cofounders of Incident.io. Incident is an incident management tool. We discuss:How they think about brand and how it comes from their deep understanding of incident cultureLawrence's article asking for new macbooks that went viralGallows humor in incidents Why incident.io started on Heroku despite being an incident response platform—and why “shipping fast” mattered more than “scaling perfectly.”The benefit of building for users who are just like youHow Incident is using GenAIThis episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign-On and audit logs. Links:Pete Hamilton on Twitter Chris Evans on TwitterIncident Macbook articleThe flight plan that brought UK airspace to its kneesHow Netflix drives reliability across their organizationNote: this was recorded on 13th December 2024.

Hoje o papo é sobre uma engenharia. Neste episódio, conversamos sobre o histórico, as técnicas, as ferramentas e – talvez mais importante – as armadilhas quando o assunto é engenharia de plataformaVem ver quem participou desse papo: André David, o host que quer virar cozinheiro em alto mar. Ou não. Maurício “Balboa” Linhares, engenheiro de software e Hipster de longa data Vinny Neves, Líder de Front-End na Alura Paulo Alves, Coordenador da escola de DevOps da Alura Gui Santos, Fundador da Platform Rocks

News includes upcoming improvements to ex_doc for version navigation, the release of Phoenix Analytics 0.3.0 for plug-and-play application metrics, José Valim's detailed exploration of set-theoretic types for better library compatibility, German Velasco's demonstration of Elixir 1.18's enhanced type system, the beta release of the Ash Framework book on PragProg, and exciting developments in the FLAME ecosystem with AWS EC2 support, and more! Show Notes online - http://podcast.thinkingelixir.com/237 (http://podcast.thinkingelixir.com/237) Elixir Community News https://bsky.app/profile/david.bernheisel.com/post/3lffr6xdvq22r (https://bsky.app/profile/david.bernheisel.com/post/3lffr6xdvq22r?utm_source=thinkingelixir&utm_medium=shownotes) – ex_doc will soon feature a new button to navigate to the latest version's documentation when viewing older versions. https://x.com/mrpopov_com/status/1878817795049488421 (https://x.com/mrpopov_com/status/1878817795049488421?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix Analytics 0.3.0 released with improved support for Fly.io and Heroku deployments. https://github.com/lalabuy948/PhoenixAnalytics (https://github.com/lalabuy948/PhoenixAnalytics?utm_source=thinkingelixir&utm_medium=shownotes) – Plug and play analytics solution for Phoenix applications, offering embedded dashboard functionality. https://dashbit.co/blog/data-evolution-with-set-theoretic-types (https://dashbit.co/blog/data-evolution-with-set-theoretic-types?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim's article explaining how set-theoretic types will improve library backwards-compatibility in Elixir. https://www.elixirstreams.com/tips/elixir-118-type-system-changes (https://www.elixirstreams.com/tips/elixir-118-type-system-changes?utm_source=thinkingelixir&utm_medium=shownotes) – German Velasco's ElixirStream video demonstrating the improved type system changes in Elixir 1.18. https://pragprog.com/titles/ldash/ash-framework/ (https://pragprog.com/titles/ldash/ash-framework/?utm_source=thinkingelixir&utm_medium=shownotes) – Ash Framework book by Rebecca Le and Zach Daniel released in beta on PragProg, covering LiveView, auth, search, APIs, and notifications. https://github.com/phoenixframework/flame (https://github.com/phoenixframework/flame?utm_source=thinkingelixir&utm_medium=shownotes) – FLAME (Fleeting Lambda Application for Modular Execution) by Chris McCord enables dynamic resource scaling on Fly.io. https://github.com/probably-not/flame-ec2 (https://github.com/probably-not/flame-ec2?utm_source=thinkingelixir&utm_medium=shownotes) – FlameEC2 library extends FLAME functionality to AWS EC2 machines. https://bsky.app/profile/codebeam.bsky.social/post/3lfp4penmik2v (https://bsky.app/profile/codebeam.bsky.social/post/3lfp4penmik2v?utm_source=thinkingelixir&utm_medium=shownotes) – Code BEAM Lite London 2025 is on January 31, featuring Michał Muskała as speaker. https://alchemyconf.com/ (https://alchemyconf.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Alchemy Conf scheduled for March 31 - April 3 in Braga, Portugal. https://membrz.club/alchemyconf/events?tag=workshop (https://membrz.club/alchemyconf/events?tag=workshop?utm_source=thinkingelixir&utm_medium=shownotes) – Alchemy Conf workshops announced featuring Saša Jurić, Zach Daniel, and Andrea Leopardi. https://x.com/Alchemy_Conf/status/1879136370691862929 (https://x.com/Alchemy_Conf/status/1879136370691862929?utm_source=thinkingelixir&utm_medium=shownotes) – Additional announcement about Alchemy Conf workshop details. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Heroku is a cloud platform-as-a-service that enables developers to build, deploy, and manage applications. It was founded in 2007 and was acquired by Salesforce in 2010. The platform supports multiple programming languages, including Ruby, Python, Node.js, and Java, and has features such as automated scaling, database monitoring tools, and a streamlined deployment workflow. Vish Abrams The post Heroku and the Twelve-Factor App with Vish Abrams appeared first on Software Engineering Daily.

Corey Quinn welcomes Adam Zimman back to Screaming in the Cloud for a sponsored episode featuring Heroku by Salesforce. As Head of Product Marketing, Adam discusses after years of stagnation following its Salesforce acquisition. Recent investments and a dedicated team signal a renewed focus on developer experience. The duo explores Heroku's impact on modern app development, its role in popularizing the 12-Factor App model, and the decision to retire its free tier. Adam highlights key updates, including Kubernetes replatforming, .NET support, and AI tools for managed inference and agents. He also teases his upcoming book, Progressive Delivery, set for release next year.Show Highlights(0:00) Intro(1:01) Heroku sponsor read(1:39) How Heroku became resurgent(5:46) Heroku's legacy(9:53) Adam's thoughts on people's response to the free tier going away(10:55) Heroku's target customer(s)(13:51) Heroku sponsor read(14:19) How Heroku saves organizations money and developed over time(20:08) Heroku's re:Invent announcements(24:53) How modern-day developers have reacted to Heroku's resurgence(27:47) Where people can learn more about Heroku About Adam ZimmanAdam Zimman is Technologist and Author currently serving as the Head of Product Marketing at Heroku by SalesForce. Previously, he was a Venture Capital Advisor providing guidance on leadership, platform architecture, product marketing, and GTM strategy. He has over 20 years of experience working in a variety of roles from software engineering to technical sales. He has worked in both enterprise and consumer companies such as VMware, EMC, GitHub, and LaunchDarkly.Adam is driven by a passion for inclusive leadership and solving problems with technology. He is a co-author of Progressive Delivery: Build the right thing, for the right people, at the right time. His perspective has been shaped by a degree (AB) from Bowdoin College with a dual-focus in  Physics and Visual Art, an ongoing adventure as a husband and father, and a childhood career as a fire juggler.LinksHeroku's website: https://www.heroku.com/Adam's Bluesky: https://bsky.app/profile/azimman.bsky.socialAdam's Mastodon: https://hachyderm.io/@azAdam's LinkedIn: https://www.linkedin.com/in/adamzimman/Personal site: https://progressivedelivery.com/SponsorHeroku: http://heroku.com/

We compiled our favorite clips on developer tools and developer experience (DevX). We discuss why DevX has become essential for developer-focused companies and how it drives adoption to grow your product. Learn what makes developers a unique and discerning customer base, and hear practical strategies for designing exceptional tools and platforms. Our guests also share lessons learned from their own experiences—whether in creating frictionless integrations, maintaining a strong feedback culture, or enabling internal platform adoption. Through compelling stories and actionable advice, this episode is packed with lessons on how to build products that developers love. Playlist of Full Episodes from This Compilation: https://www.youtube.com/playlist?list=PL31JETR9AR0FV-46VR4G_n6xi4WdXEx-2 Inside the episode... The importance of developer experience and why it's a priority for developer-facing companies. Key differences between building developer tools and end-user applications. How DevX differs from DevRel and the synergy between the two. Metrics for measuring the success of developer tools: adoption, satisfaction, and revenue. Insights into abstraction ladders and balancing complexity and power. Customer research strategies for validating assumptions and prioritizing features. Stripe's culture of craftsmanship and creating “surprisingly great” experiences. The importance of dogfooding and feedback loops in building trusted platforms. Balancing enablement and avoiding gatekeeping in internal platform adoption. Maintaining consistency and quality across APIs, CLIs, and other resources. Mentioned in this episode Stripe Doppler Heroku Abstraction ladders Developer feedback loops Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence

What if you could scale your SaaS platforms effortlessly across diverse hosting services? Join us as we welcome Adam McCrea, the brilliant mind behind JudoScale, who takes us through his fascinating evolution from being a Rails developer to creating a cutting-edge autoscaling solution. Adam opens up about the technical challenges he faced while adapting JudoScale for platforms like Render, Fly, and Railway, and how Heroku's unique architecture initially shaped his approach. His journey is one of innovation driven by necessity, as JudoScale originated from a need to optimize costs more efficiently than existing solutions.Our conversation doesn't shy away from complexity; in fact, it embraces it. Adam shares his experiences of grappling with AWS integration, navigating the intricate maze of ECS, EC2, Fargate, and IAM, all driven by customer demand. We explore the strategic shift from metered billing to flat-tiered pricing and the hurdles faced while setting up a staging environment on Render, ultimately reaffirming Heroku's smoother experience. This episode promises valuable insights into the strategic decisions and architectural reimaginations that keep JudoScale ahead of the game.Adding a creative flair, we delve into the entertaining world of infomercial production, as Adam recounts his experience crafting a humorous Billy Mays-inspired ad for JudoScale. With the aid of AI tools like ChatGPT and Descript, Adam turned a fun concept into an engaging reality. As we wrap up, Adam shares his excitement for RailsConf in Philadelphia and the significance of fostering connections through digital networking. Whether you're a tech enthusiast or a developer seeking innovative scaling solutions, this episode is brimming with insightful takeaways and creative inspiration.Send us some love.HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showReady to start your own podcast?This show is hosted on Buzzsprout and it's awesome, not to mention a Ruby on Rails application. Let Buzzsprout know we sent you and you'll get a $20 Amazon gift card if you sign up for a paid plan, and it helps support our show.

In this episode of DevOps Diaries we cover some of the outstanding moments from DevOps Diaries in 2024. Hear about everything from artificial intelligence and Salesforce Flows, to Heroku and QA testing.You can expect snippets of conversation with: Julián Duque, Steve Fouracre, Alba Rivas, Dave Carroll, Jon Robinson, Todd Halfpenny and Andy Utkan. Thank you to all our guests this year for sharing wonderful insights and knowledge!All episodes from this year can be found on Gearset's YouTube channel as well as audio platforms.Learn more:- Deploy Salesforce Flows easily- Salesforce DevOps for enterprise organizations- Easy backup and restore solutions for Salesforce- How successful are teams using Gearset?About DevOps Diaries: Salesforce DevOps Advocate Jack McCurdy chats to members of the Salesforce community about their experience in the Salesforce ecosystem. Expect to hear and learn from inspirational stories of personal growth and business success, whilst discovering all the trials, tribulations, and joy that comes with delivering Salesforce for companies of all shapes and sizes. New episodes bi-weekly on YouTube as well as on your preferred podcast platform.Podcast produced and sponsored by Gearset. Learn more about Gearset.Subscribe to Gearset's YouTube channel: https://grst.co/4cTAAxmLinkedIn: https://www.linkedin.com/company/gearsetX/Twitter: https://x.com/GearsetHQFacebook: https://www.facebook.com/gearsethqAbout Gearset: Gearset is the leading Salesforce DevOps platform, with powerful solutions for metadata and CPQ deployments, CI/CD, automated testing, sandbox seeding and backups. It helps Salesforce teams apply DevOps best practices to their development and release process, so they can rapidly and securely deliver higher-quality projects. Get full access to all of Gearset's features for free with a 30-day trial.Chapters:00:00 Julián Duque — Heroku and Salesforce03:55 Steve Fouracre — Impact of AI on Salesforce Development08:02 Andy Utkan — Why Salesforce Developers should care about Salesforce Flows 10:58 Todd Halfpenny — The future role of Salesforce Developers15:12 Dave Carroll — The best learning method for Salesforce professionals19:38 Alba Rivas — How does Agentforce work?25:38 Jon Robinson — How to implement a good Salesforce QA strategy

This week, we cover Netflix's streaming hiccups, cloud earnings updates, Red Hat's CNCF donations, and the potential sale of Chrome. Plus, a few thoughts on parenting. Watch the YouTube Live Recording of Episode (https://www.youtube.com/watch?v=7qe9xOqN-Lk) 494 (https://www.youtube.com/watch?v=7qe9xOqN-Lk) Runner-up Titles The dog peed on it. Jamin's favorite Excel macros. Change up the noodles. 0.7 good tips there The tiniest of rebellions Win one for the stockholders Candor A datacenter with a gift shop. VP of Cables has cucumber water with VP of Monitors. You can't open source a monitor. Rundown Netflix Netflix's Boxing Event, Customer Acquisition vs. Churn Mitigation, Accounting for Events (https://stratechery.com/2024/netflixs-boxing-event-customer-acquisition-vs-churn-mitigation-accounting-for-events/?access_token=eyJhbGciOiJSUzI1NiIsImtpZCI6InN0cmF0ZWNoZXJ5LnBhc3Nwb3J0Lm9ubGluZSIsInR5cCI6IkpXVCJ9.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.AyMwbazpm5LR_zhwZiRLStIqxPaGuHbceNMyKVLcX4NNRg24VPow2YD-dCJbLx5RtePzQE87rXOA3LOTlPuRCJ07Z30HjhTordjCFnw8vz2mLtXe-oe4-It-_VoIvCnAutn5g1bP9rvIbWKvVcA0oteGHOEGMuIVZ7YDxghRvj6elT2Pz5fMcrwwjHKC3N5kIrZcxSTZVxFufWHx2FaYh6uelE8aVrzFOp6_VhvusKvvCkLI8rtRJKMyfLGMQRadts_RKnxXUB19eRcJgs1AiLUs2bmuSLUKvudnwpv3EimElaeKHUh9MqUljEGIXe89dgtImlpotzmvU0VKPy9cIg) Disney sets India Cricket Viewership Record for TV, streaming during World Cup (https://www.bmpsportsevents.com/blog-posts/disney-sets-india-cricket-viewership-record-for-tv-streaming-during-world-cup) Netflix Culture (https://jobs.netflix.com/culture) Earnings Amazon Reports Record $15.3 Billion Profit (https://www.nytimes.com/2024/10/31/business/amazon-q3-earnings.html?smid=nytcore-ios-share&referringSource=articleShare) Clouded Judgement 11.1.24 - Amazon, Google, Microsoft & Meta on AI and CapEx (https://cloudedjudgement.substack.com/p/clouded-judgement-11124-amazon-google?utm_source=post-email-title&publication_id=56878&post_id=150968391&utm_campaign=email-post-title&isFreemail=true&r=2l9&triedRedirect=true&utm_medium=email) Amazon Earnings, Robotics and Amazon's Expanding 1P Business (https://stratechery.com/2024/amazon-earnings-robotics-and-amazons-expanding-1p-business/?access_token=eyJhbGciOiJSUzI1NiIsImtpZCI6InN0cmF0ZWNoZXJ5LnBhc3Nwb3J0Lm9ubGluZSIsInR5cCI6IkpXVCJ9.eyJhdWQiOiJzdHJhdGVjaGVyeS5wYXNzcG9ydC5vbmxpbmUiLCJhenAiOiJIS0xjUzREd1Nod1AyWURLYmZQV00xIiwiZW50Ijp7InVyaSI6WyJodHRwczovL3N0cmF0ZWNoZXJ5LmNvbS8yMDI0L2FtYXpvbi1lYXJuaW5ncy1yb2JvdGljcy1hbmQtYW1hem9ucy1leHBhbmRpbmctMXAtYnVzaW5lc3MvIl19LCJleHAiOjE3MzM5MjMzMjUsImlhdCI6MTczMTMzMTMyNSwiaXNzIjoiaHR0cHM6Ly9hcGkucGFzc3BvcnQub25saW5lL29hdXRoIiwic2NvcGUiOiJmZWVkOnJlYWQgYXJ0aWNsZTpyZWFkIGFzc2V0OnJlYWQgY2F0ZWdvcnk6cmVhZCBlbnRpdGxlbWVudHMiLCJzdWIiOiJUSDM0Z1ZDeWh4V2dtbkFmdFhGbXVTIiwidXNlIjoiYWNjZXNzIn0.HO5sxW0eBQFKsqs38nWX6yVSp9OQh-tJNkNI7Nyib6zZxAbAEMMnfy2dJDBTZ4ZqZBXqfo5VqJhrBPhELzTg2M_rOrDWOaotGl1eqYHpBiPVdxuXBoXN6_ME7fut7d32Hr9FfAol8201Q3n6sOvQ7YBYyCDBJosEelNtWKICsg98WJ01Sd2EuZz-3XtA3gSziu7yhVsKX5cw_6sLtUPyyUwLaOqutRaJfvdhQVynvKmrgyX5OtlU60MmcwXrPWXDcptcesUUyAwzClRNIOIrSrPVvawNL66mJL24oyrbDFgUUJT4yVYHiuylb_JO1otCftQNhGkv0iOft8N0NPVpfg) Cloud market share shows vendors eyeing a $1T opportunity (https://siliconangle.com/2024/11/16/cloud-market-share-shows-vendors-eyeing-1t-opportunity/) Windows 365 Link is a $349 mini PC that streams Windows from the cloud (https://www.theverge.com/2024/11/19/24299789/microsoft-windows-365-link-device-cloud-pc) Going Open Source Red Hat to Donate Podman Along With Other Container Tools to CNCF (https://cloudnativenow.com/kubecon-cnc-na-2024/red-hat-to-donate-podman-along-with-other-container-tools-to-cncf/) Salesforce's Heroku platform open-sources Twelve Factor project (https://siliconangle.com/2024/11/15/twelve-factor-project-open-source-salesforce-kubecon/) Twelve-Factor App Methodology is now Open Source (https://12factor.net/blog/open-source-announcement) DOJ Will Push Google to Sell off Chrome to Break Search Monopoly (https://www.bloomberg.com/news/articles/2024-11-18/doj-will-push-google-to-sell-off-chrome-to-break-search-monopoly) Relevant to your Interests These are the passwords you definitely shouldn't be using (https://www.theverge.com/2024/11/13/24295543/most-common-passwords-list-2024) Datacenter Anatomy Part 1: Electrical Systems (https://semianalysis.com/2024/10/14/datacenter-anatomy-part-1-electrical/) New Apple security feature reboots iPhones after 3 days, researchers confirm (https://techcrunch.com/2024/11/14/new-apple-security-feature-reboots-iphones-after-3-days-researchers-confirm/) AI companies hit a scaling wall (https://www.platformer.news/openai-google-scaling-laws-anthropic-ai/) Invisible asymptotes — Remains of the Day (https://www.eugenewei.com/blog/2018/5/21/invisible-asymptotes) Clouded Judgement 11.14.24 - Market Tipping to Growth (https://cloudedjudgement.substack.com/p/clouded-judgement-111424-market-tipping) For the first time in 25 years, the number of software engineers dropped (https://x.com/mjovanc/status/1857720025563439295) The CNCF's plan to crowdfight patent trolls (https://www.runtime.news/the-cncfs-plan-to-crowdfight-patent-trolls/?ref=runtime-newsletter) Maybe Bluesky has “won” (https://anderegg.ca/2024/11/15/maybe-bluesky-has-won) Having 30,000 followers makes you a celebrity, UK advertising watchdog rules (https://www.theverge.com/2019/7/4/20682087/instagram-twitter-celebrity-30000-followers-advertising-standards-authority-uk) The Influence of Bell Labs (https://www.construction-physics.com/p/the-influence-of-bell-labs) Leaked Amazon memos identify critical flaws in the upcoming AI version of Alexa (https://fortune.com/2024/11/18/new-ai-alexa-latency-problems-echo-compatibility-uber-opentable/) RIP to RPA: The Rise of Intelligent Automation | Andreessen Horowitz (https://a16z.com/rip-to-rpa-the-rise-of-intelligent-automation/) Twenty is building an open source alternative to Salesforce (https://techcrunch.com/2024/11/18/twenty-is-building-an-open-source-alternative-to-salesforce/) Chips RISC-V — the CPU you didn't know you already have (https://adrianco.medium.com/risc-v-the-cpu-you-didnt-know-you-already-have-ff2f385f7ec6) Arm to Cancel Qualcomm Chip Design License (https://archive.md/FcXRW) The RVA23 profile is now ratified, so RISC-V gets satisfied (https://www.theregister.com/2024/10/23/rva23_profile_ratified/) Intel losses hit $16.6B as restructuring efforts take a toll (https://www.theregister.com/2024/11/01/intel_q3_2024/) Intel Was Just Dropped From the Dow (https://www.fool.com/investing/2024/11/05/intel-dropped-from-dow-djia-history-happen-next/) How much Apple Silicon improves with each release (https://appleinsider.com/articles/24/11/06/generation-gaps-how-much-faster-apple-silicon-gets-with-each-release) Nonsense Bojangles to install ordering kiosks across its system (https://www.restaurantdive.com/news/bojangles-grubbrr-installation-kiosks/732460/) 'Simpsons'-themed broadcast means Bengals-Cowboys won't be flexed (https://www.nytimes.com/athletic/5932410/2024/11/19/bengals-vs-cowboys-mnf-not-flexed-simpsons-alt-cast/?source=user_shared_article) Listener Feedback Deutsche Börse Cloud Exchange AG (https://en.wikipedia.org/wiki/Deutsche_B%C3%B6rse_Cloud_Exchange_AG) LibreLinkUp Status Bar a VS Code extension to display good glucose level in the status bar (https://marketplace.visualstudio.com/items?itemName=borkod.librelinkup-vs-code-extension) Conferences CfgMgmtCamp (https://cfgmgmtcamp.org/ghent2025/), February 2rd to 5th. DevOpsDayLA (https://www.socallinuxexpo.org/scale/22x/events/devopsday-la) at SCALE22x (https://www.socallinuxexpo.org/scale/22x), March 6-9, 2025, discount code DEVOP SDT News & Community Join our Slack community (https://softwaredefinedtalk.slack.com/join/shared_invite/zt-1hn55iv5d-UTfN7mVX1D9D5ExRt3ZJYQ#/shared-invite/email) Email the show: questions@softwaredefinedtalk.com (mailto:questions@softwaredefinedtalk.com) Free stickers: Email your address to stickers@softwaredefinedtalk.com (mailto:stickers@softwaredefinedtalk.com) Follow us on social media: Twitter (https://twitter.com/softwaredeftalk), Threads (https://www.threads.net/@softwaredefinedtalk), Mastodon (https://hachyderm.io/@softwaredefinedtalk), LinkedIn (https://www.linkedin.com/company/software-defined-talk/), BlueSky (https://bsky.app/profile/softwaredefinedtalk.com) Watch us on: Twitch (https://www.twitch.tv/sdtpodcast), YouTube (https://www.youtube.com/channel/UCi3OJPV6h9tp-hbsGBLGsDQ/featured), Instagram (https://www.instagram.com/softwaredefinedtalk/), TikTok (https://www.tiktok.com/@softwaredefinedtalk) Book offer: Use code SDT for $20 off "Digital WTF" by Coté (https://leanpub.com/digitalwtf/c/sdt) Sponsor the show (https://www.softwaredefinedtalk.com/ads): ads@softwaredefinedtalk.com (mailto:ads@softwaredefinedtalk.com) Recommendations Brandon: Silo Season 2 (https://www.googleadservices.com/pagead/aclk?sa=L&ai=DChcSEwiDr7fyuOmJAxX3Sf8BHXDxOLYYABAAGgJtZA&ae=2&co=1&gclid=Cj0KCQiAi_G5BhDXARIsAN5SX7oulKQPevGaYaSaUDENHbWyKOcMu4Fmlc4iCckvLOeL6efJ5O2cjGwaAhrNEALw_wcB&ohost=www.google.com&cid=CAESVeD2KJUTEM8UiN83N5t9ZLDm6pVzs_bp0Nv22irf8c10iQpHCSaeMICL3a5Z0KW71vqjmjtEZN-nmHWD5NzWkGS6PAdJQ7nzZWHjww4Bd4X7JwFb9yk&sig=AOD64_1o6vDN1m33XOCeIfBmYKhiq2cH7Q&q&adurl&ved=2ahUKEwjckLDyuOmJAxUbw_ACHZelJwUQ0Qx6BAgpEAE) Matt: Followup - Spotify Premium limits audiobooks to 10 hours a month

Hi, Spring fans! Happy Spring Boot 3.4.0 release day to those who celebrate! Today I'm joined by both Terence Lee, from Heroku, and my friend DaShaun Carter, and we talk about platforms, buildpacks, and more. #heroku #paas #buildpacks,

Today Laura and Kevin chat with Wesley Beary. Wesley is a tech wizard who has been reshaping the developer experience for over 15 years, turning complex API and open source challenges into solutions loved by developers worldwide. We talk about how do you think organizations can prioritize APIs tools better to enhance internal developer productivity. We hear about Wesley's philosophy on balancing the need for speed, security, with simplicity when building tools and platforms for developers. As an angel investor, Wesley explains how he identifies promising startups that are building for developers or focused on improving the developer experience. We learn the differences between Salesforce and startups approach developer tooling and internal processes. We also learn what's next for developers. Lastly, we find out if Wesley really likes all this. Check this one out!Wesley Beary is currently shaking up the tech world as a Founding Engineer at Anchor, a dev-friendly platform that provides private CAs for internal TLS encryption. It is making HTTPS certificates easy to get on servers, allowing developers to focus on building rather than managing security. Here's a blog Wesley wrote about how Anchor developed a CLI and the tools that helped.Wesley's many career highlights include his maintenance of Ruby's excon gem (over 485 million downloads) and suite of fog gems for cloud services. He also led the design of the Heroku public API and played a key role in publishing the pioneering HTTP API Design Guide. Wesley's insights have guided countless developers in building better APIs.Wesley is more than an open source expert; he's a community builder, having organized dev meetups and delivered keynote addresses at conferences for Upstream, ArrrrCamp, and Ruby.

Sam and Ryan talk about how frameworks and infrastructure evolve with each other, using Next.js as a representative example. They discuss how hosting providers like Heroku have always imposed certain constraints on apps, what features those constraints enable hosting providers to support, how burdensome those constraints are across different frameworks, and how frameworks that add infra-specific APIs can best communicate the costs of those APIs and benefits they enable.Timestamps:0:00 - Intro3:03 - Heroku and the Twelve-Factor App7:39 - GitHub Pages and static sites13:57 - Serverless and JAMstack17:30 - Vercel and CDNs, self-hosting, and Next.js19:00 - How framework APIs can nudge an app towards a particular hosting solution23:09 - What constraints does Next.js impose on your app (e.g. middleware doesn't run node), and what benefits do those constraints give you?36:13 - How Next.js APIs are motivated by wanting to tease apart static and dynamic code, in an attempt to support the needs of any web app with a single stack40:33 - What is the relationship between frameworks and infra?47:37 - How can frameworks that add infra-specific APIs best communicate the costs of those APIs and the benefits they enable?Links:The Twelve-Factor App

In this episode of the Convergence podcast, host Ashok Sivanand sits down with Wes Beary from Anchor.dev. Wes shares his journey, from helping shape Heroku's engineering culture to his work today in API design and encryption as a service. Discover how Wes' experience at Heroku and his open-source contributions have shaped his views on building delightful developer experiences and empowering engineering teams. He also talks about the importance of uniformity in API design, fostering a strong engineering culture, and scaling development teams while preserving their core strengths. Unlock the full potential of your product team with Integral's player coaches, experts in lean, human-centered design. Visit integral.io/convergence for a free Product Success Lab workshop to gain clarity and confidence in tackling any product design or engineering challenge. Inside the episode: Wes Beary's transition from game development to cloud infrastructure and open-source contributions The creation of Heroku's command-line interface (CLI) and API development process Best practices in API design and the importance of consistency for developers Insights into fostering engineering culture and maintaining it as teams grow The role of Anchor.dev in simplifying encryption for engineering teams without dedicated security resources Lessons from Heroku's rise to success and what makes a platform as a service valuable Balancing innovation, team culture, and enabling versus gatekeeping in engineering organizations Mentioned in this episode: Anchor.dev – Get started with Anchor.dev at https://lcl.host/ Heroku – Platform as a service, acquired by Salesforce Kobo Readers Fog – Wes Beary's open-source project for cloud API integration Subscribe to the Convergence podcast wherever you get podcasts including video episodes to get updated on the other crucial conversations that we'll post on YouTube at youtube.com/@convergencefmpodcast Learn something? Give us a 5 star review and like the podcast on YouTube. It's how we grow.   Follow the Pod Linkedin: https://www.linkedin.com/company/convergence-podcast/ X: https://twitter.com/podconvergence Instagram: @podconvergence  

In the latest episode of the "Giant Robots On Tour" podcast, hosts Rémy Hannequin and Sami Birnbaum welcome Marc G. Gauthier, a solopreneur and startup coach, who shares his journey from software development to becoming the founder and developer of The Shadow Boxing App. Marc describes how his interest in software engineering began at a young age with QBasic and evolved through various leadership roles at companies like Drivy (now Getaround) and Back Market. His early passion for gaming led him to learn coding, and over time, he naturally transitioned into management roles, finding excitement in organizing and leading teams while maintaining his love for building products. During the episode, Marc discusses the challenges and intricacies of scaling startups, emphasizing the importance of balancing speed and reliability in software development. He recounts his experiences in leadership positions, where he faced the dual task of managing rapid team growth and maintaining software efficiency. Marc also shares insights into the startup ecosystem, noting that most startups struggle to achieve success due to a combination of market timing, team dynamics, and resource management. His own venture, The Shadow Boxing App, represents his attempt to return to hands-on coding while leveraging his extensive experience in startup coaching and advising. Marc also touches on the role of AI in the future of software development, expressing cautious optimism about its potential to augment human workflows and automate repetitive tasks. He advises current and aspiring developers to embrace AI as a tool to enhance their capabilities rather than a replacement for human ingenuity. Marc concludes by highlighting the importance of realistic expectations in the startup world and the need for continuous learning and adaptation in the ever-evolving tech landscape. Getaround (https://getaround.com/) Follow Getaround on LinkedIn (https://www.linkedin.com/company/getaround/), Facebook (https://www.facebook.com/getaround), X (https://twitter.com/getaround), YouTube (https://www.youtube.com/getaround), or Instagram (https://www.instagram.com/getaround/). Back Market (https://www.backmarket.com/en-us) Follow Back Market on LinkedIn (https://www.linkedin.com/company/back-market/), Facebook (https://www.facebook.com/BackMarketCom), X (https://x.com/backmarket), or Instagram (https://www.instagram.com/backmarket). The Shadow Boxing App (https://shadowboxingapp.com/) Follow Marc Gauthier on LinkedIn (https://www.linkedin.com/in/marcggauthier/). Follow thoughtbot on X (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Transcript: RÉMY:  This is the Giant Robots Smashing Into Other Giant Robots podcast, the Giant Robots on Tour series coming to you from Europe, West Asia, and Africa, where we explore the design, development, and business of great products. I'm your host, Rémy Hannequin. SAMI: And I'm your other host, Sami Birnbaum. RÉMY: If you are wondering who we are, make sure you find the previous podcast where we introduced the Giant Robots on Tour series by throwing random icebreakers at each other. And find out that Jared likes it when someone takes the time to understand someone else's point of view. Joining us today is Marc G Gauthier, a Solopreneur and Startup Coach. Marc, you used to be VP of Engineering at Drivy, now known as Getaround, and also Director of Engineering at Back Market. You also have been a coach and advisor to a startup for over a decade. Currently, your current adventure is being the Founder and Developer of The Shadow Boxing App available on the Apple App Store. We always like to go back to the start with our guests. Everyone has a story, and we are interested in your journey. So, Marc, what led you into the world of software engineering in the first place? MARC: Hello. Well, happy to be here. And, yeah, I started getting into software development quite a long time ago. I actually learned software development with QBasic when I was something like seven. And, from there, I just kept on learning, learning, and learning and got into school for it, then worked in different startups, and then moved into more leadership position management. And I'm now, like, coaching people and building my own product. What do you want to get? Because it's broad. I've been doing it for quite a while. Like, I don't think the QBasic days are that insightful. The only thing I remember from that time is being confused by the print comment that I would expect it to print on my printer or something, but it didn't; it just printed on the screen. That's the only thing I have from back then. SAMI: Why at seven years old? And I'm taking you back too far, but at seven years old, I was probably collecting Pokémon cards and possibly like, you know, those football stickers. I don't know if you had the Panini stickers. MARC: Oh yeah, I was doing that as well. SAMI: But you were doing that as well. But then what drove you at that age? What do you think it was that made you think, I want to start learning to code, or play around with the computer, or get into tech? MARC: [laughs] Yeah. Well, I remember, back then, I really wanted a computer to play games. Like, I had a friend who had a computer. He was playing games, and I wanted to do that. So, I was asking my mom to have a computer, and she told me, "Yeah, you can have one." And she found a really old computer she bought from a neighbor, I think. But she told me like, "I don't know anything about it. So, you have to figure it out and set it up." And she just found someone to kind of help me. And this person told me to, like, take the computer apart. She taught me a bit of software development, and I kind of liked it. And I was always trying to change the games. Back then, it was way easier. You could just edit a sound file, and you would just edit the sound file in the game, so yeah, just learning like this. It wasn't really my intent to learn programming. It just kind of happened because I wanted to play video games really. SAMI: That's really cool. It's really interesting. Rémy, do you remember how...how did you first get...do you remember your first computer, Rémy? RÉMY: My first computer, I think I remember, but the first one I used it was, first, a very long time ago. I discovered that it was an Apple computer way, way later when I discovered what Apple was and what computers were actually. And I just remember playing SimCity 2000 on it, and it was amazing. And we had to, you know, cancel people from making phone calls while we were on the computer because of the internet and all the way we had to connect to the internet back then. And after that, just, I think, Windows 95 at home. Yeah, that's the only thing I can remember actually. Because I think I was lucky, so I got one quite early. And I don't really remember not having one, so I was quite lucky with that. And so, I was always kind of in the computer game without being too much [inaudible 05:02] [laughs]. SAMI: Yeah, I think that's similar to me as well. Like, it's interesting because my initial introduction to computers would have been watching my older brothers kind of play computer games and actually being told to get out the room, or like, you know, "We're busy now. Don't bother us." And then, what actually happened is when they left the room, I managed to play what they were playing, which was the first ever GTA. I don't know if anyone ever played this, but it is so cool if you look back on it. You could probably find emulators online, but it was, like, a bird's eye view, like, way of operating. And it was probably also that drive where you get frustrated on a computer because you want to do something, so, like you were saying, Marc, where you went to edit the sound files because you want to change something. You want to do something. I definitely think that is something which I felt as well is that frustration of I want to change this thing. And then, that kind of gets into well, how does it work? And if I know how it works, then I can probably change it. MARC: Yeah. And once you figure out how things work, it's also really exciting. Like, once you figure out the initialization file on Windows, like, you can edit, like, what level is unlocked right away. It's kind of cheat codes but not really. And there are some really fun ones. Like, I would edit sound files for racing games. And, usually, it's just a base sound file, and then they would pitch shift the sound to make it sound like an engine. So, if you record your voice, it's just really funny. RÉMY: So, Marc, you mentioned moving to management positions quite early. Do you remember what made you do this move? Was it for, like, a natural path in your career, or was it something you really wanted from the first part of your career as a developer? What happened at this moment? MARC: Yeah, that was not completely planned. Like, I don't think I really plan my career precisely. It's just something that happens. So, I joined Drivy after, like, I was already a software engineer for, like, five years at that point. I joined as a lead backend engineer. I did that for three years. And after three years, the company went from...I think there was, like, three software engineers to a dozen. There was a need for more structure, and the CTO, at the time so, Nicolas, wanted to focus more on products. And it was hard to do both, like do the product side, the design, the data, and do the engineering, the software, and so on. So, he wanted to get a bit away from software engineering and more into product. So, there was a gap in the organization. I was there. I was interested to try, and I was already doing some more things on the human side, so talking to people, organizing, internal communication. I kind of liked it. So, I was excited to try, give it a try. It was really interesting. I found that it was a different way to have an impact on the team. I just kept doing it. And my plan was to keep doing it until I'm bored with it. And I'm still not bored with it, even though you kind of miss just actually building the software yourselves, actually coding. So, that's also why I'm trying something different right now with my mobile app adventure. SAMI: Right. So, on the side, you've got this Shadow Boxing App, which, in my dedicated research, I downloaded and had a go with it. MARC: Did you actually try it, or did you just click around? SAMI: I did a proper workout, mate. I did. I put myself as, like, the absolute beginner. I did it on my MacBook Pro. I know it's built for iPad or iPhone, but it still worked amazingly well. And it kind of reminded me why I stopped doing boxing because it's hard work. MARC: [laughs] Yeah, it is. SAMI: It's not a gimmick this thing, right? So, it's like, the best way to describe it is it's essentially replacing if I was to go to the gym and have a trainer who's telling me kind of the moves to make or how to do it, then this kind of replaces that trainer. So, it's something you can do at home. It was really cool. I was surprised, actually. I thought, at the beginning, it's not going to be that interactive, or it won't actually be as hard or difficult as a workout, and it really was. So, it's, yeah, it was really cool, really interesting to try it. And going into that, you say you wanted to get back more into coding, and that's why you are doing this kind of, like, app on the side, or it allowed you to kind of do a bit more coding away from the people management. You've been involved in a lot of startups, and I actually often get...as consultants, when we work at thoughtbot, we get a lot of people who come with different startup ideas. When you look back at all the startups you've been involved with, do you think more startups are successful than those that fail? Or have you seen a lot of startups...actually, people come with these great ideas; they want to build this amazing product, but it's actually really hard to be a successful product? MARC: I think it's [inaudible 10:22] how to have the right idea, be at the right spot at the right time, build the right team, get enough momentum. I think most startups fail, and even startups that are successful often can be the result of a pivot. Like, I know companies that pivoted a bunch of times before finding any success. So, it's really hard actually...if I take my past four companies, only two are still alive. Like, the first two went under. Actually, there's even more companies that went under after I left. Yeah, it's just really hard to get anything off the ground. So, yeah, it's complicated, and I have a lot of respect for all the founders that go through it. For The Shadow Boxing App, I worked on it for the past three years, but I'm only working on it almost full-time for the past two months. And it was way safer. I could check the product-market fit. I could check if I enjoyed working on it. So, I guess it was easier. I had the luxury of having a full-time job. Building the app didn't take that much time. But to answer your question, I think, from my experience, most startups fail. And the ones that succeed it's kind of lightning in a bottle, or, like, there's a lot of factors that get into it. It's hard to replicate. A lot of people try to replicate some science, some ideas. They go, oh, we'll do this, and we'll do that. And we use this technique that Google uses and so on, but it's never that straightforward. SAMI: Yeah, I'm so happy you said that because I think it's a real brutal truth that I'd also say most of the startup projects that I've worked on probably have failed. Like, there's very few that actually make it. It's such a saturated market. And I think, I guess, in your role as advising startups, it's really good to come in with that honesty at the beginning and to say, "It's a big investment if you want to build something. Most people probably aren't successful." And then, when you work from that perspective, you can have, like, way more transparent and open discussions from the get-go. Because when you're outside of tech...and a lot of people have this idea of if I could just get an app to do my idea, I'm going to be the next Facebook. I'm going to be the next, you know, Amazon Marketplace. And it just kind of isn't like that. You've got these massive leaders in Facebook, Amazon, Google, Netflix. But below that, there's a lot of failures and a massively saturated market. So, yeah, just, it's so interesting that you also see it in a similar way. MARC: What I saw evolve in the past 10 years is the fact that people got more realistic with it. So, maybe 10 years ago, I would have people coming to me with just the most ridiculous idea, like, you know, I'll do Airbnb for cats. And really think, yeah, I just need a good idea, and that's it. But now I feel like people kind of understand that it's more complicated. There's way more resources online. People are more educated. They also see way more successes. Failures are also a bit more advertised. We saw a bunch of startups just go under. It feels like every month I get an email from a tool I used in the past saying, "Oh, we're shutting down," and so on. So, I think it's not as bad as 10 years ago where weekly I would have just people asking me, "I want to build this app," and the app would be just the most ridiculous thing or something that would be really smart, but it's really like, "Oh, I want to do, like, food delivery but better than what exists." It's like, yeah, that's a really good idea, but then you need...it's not only software. There's logistics. There's so much behind it that you don't seem to understand just yet. But, as a coach, so, what I'm doing is I'm helping startups that are usually before or after series A but not too large of startups just go to the next stage. And people are really aware of that and really worried. Like, they see money going down, market fit not necessarily being there. And they know, like, their company is at risk. And especially when you talk to founders, they're really aware that, you know, everything could be collapsing really quickly. If they make, like, three really bad decisions in a row, you're basically done. Obviously, it depends on the company, but yeah, people are more aware than before, especially nowadays where money is a bit harder to get. Let's say two years ago, there was infinite money, it felt like. Now it's more tight. People are more looking at the unit economics precisely. So, people need to be more realistic to succeed. RÉMY: What's the kind of recurrent struggle the startups you coach usually face? Apparently, it quite changed in the past decade, but maybe what are the current struggles they face? MARC: It really depends. It's kind of broad. But, usually, it would be, let's say, a startup after their first round of funding, let's say, if you take startups that are looking for funding. So, you usually have a group of founders, two to four, usually two or three, that are really entrepreneurs that want to bootstrap some things. They're builders. They're hacking things together, and they're really excited about the product. And, suddenly, fast forward a few years, they're starting to be successful, and they have to lead a team of, you know, like, 50 people, 100 people, and they weren't prepared for that. They were really prepared to, like, build software. Like, especially the CTOs, they are usually really great hackers. They can, like, create a product really quickly. But, suddenly, they need to manage 30 engineers, and it's completely different, and they're struggling with that. So, that's a common problem for CTOs. And then, it creates a bunch of problems. Like, you would have CEOs and CTOs not agreeing on how to approach the strategy, how to approach building a thing. What should be the methodology? Something that worked with 3 engineers around the table doesn't work with 50 engineers distributed in 5 countries. And if it's your first time being a CTO, and often founders of early-stage startups are first-time CTOs, it can be really hard to figure out. MID-ROLL AD: Are your engineers spending too much time on DevOps and maintenance issues when you need them on new features? We know maintaining your own servers can be costly and that it's easy for spending creep to sneak in when your team isn't looking. By delegating server management, maintenance, and security to thoughtbot and our network of service partners, you can get 24x7 support from our team of experts, all for less than the cost of one in-house engineer. Save time and money with our DevOps and Maintenance service. Find out more at: tbot.io/devops. RÉMY: In your past companies, so you've been VP and CTO. So, in your opinion, what's the best a VP or a CTO can bring to a scaling startup? What are your best tips to share? MARC: I guess it depends [laughs], obviously, like, depending on the stage of the company, the size of the company. For instance, when I was at Drivy, at some point, the most important thing was scaling the team hiring, and so on. But, at some point, we got acquired by Getaround, and the priorities got shifted. It was more like, okay, how do you figure out this new setup for the company and the team? Like, what is good? What is bad? How do you communicate with the team? How do you get people to stay motivated when everything is changing? How do you make sure you make the right decisions? And then, when I joined Back Market, Back Market when I joined, I had a team of a bit less than 12 engineers reporting directly to me. And after a bit more than a year, I had 60, and I hired most of them. So, here the challenge was just scaling insanely fast. Like, the company is really successful. Like, Back Market is selling refurbished electronics in a mission to, you know, provide a viable alternative to buying new electronics. So, it's basically, do you want a smartphone that is both cheaper and more ecologically viable? And most people would say yes to that. So, a company is insanely successful, but it's really hard to scale. So, at that point, the role was, okay, how do you make sure you scale as well as possible with a lot of pressure while still leaving the team in a state that they're able to still build software? Because it's just really chaotic. Like, you can't, like, 5X your team without chaos. But how do you minimize that but still go really fast? SAMI: Yeah. So, not only did I try that Shadow App. I actually went on that Backup website. What's it called? It's not called Backup. What's it called again? MARC: Back Market. SAMI: Back Market. Thank you. Yeah, it was really cool. I checked my old iPhone SE from 2020, which I've kept for about...over three years, I've had this iPhone. And they said they would give me $72 for it, which was really cool. So, it sounds like a really cool idea. MARC: That's something we worked on, which is, basically, if you have any old phones in your drawer, it's a really bad spot for them. And so, there's a service. You go on the website. You say, "I have this, I have that; I have this, I have that." And either we buy it from you, or we just take it away from you, and we recycle them, which is much better than just having them collect dust. SAMI: Yeah, no, it's a great idea. What interested me when you were speaking about kind of these different positions that you've been in, I was almost expecting you to talk about maybe, like, a technical challenge or code complexity difficulty. But, actually, what you've described is more people problems. And how do we scale with regards to people, and how do we keep people motivated? So, I guess using that experience, and this might be counterintuitive to what a lot of people think, but what do you think is the hardest thing about software development? I know there could be many things. But if you had to pick something that is the most difficult, and maybe we can all have an answer to what we think this is, but starting with you, Marc, what do you think is the hardest thing about software development then? MARC: What I saw is how do you build something that works for enough time to bring value to the customers? So, it's easy to hack something together pretty quickly and get it in front of people, but then it might not be reliable. It might break down. Or you could decide to build something perfect and spend, like, two years on it and then ship it, and then it's really stable, but maybe it's not what people want. And finding this balance between shipping something fast, but shipping something that is reliable enough for what you're building. Obviously, if you're building a health care system, you will have more, like, the bar will be higher than if you build, like, Airbnb for cats. Finding this balance and adjusting as you go is really hard. So, for instance, when do you introduce caching? Because, obviously, caching is hard to do right. If you don't do it, your site will be slow, which can be okay for a time. But then if you introduce it too late, then it's really hard to just retrofit into whatever you already have. So, finding the right moment to introduce a new practice, introduce a new technology is tricky. And then, like, I talked a lot about the people, and it's also because I spent quite a bit of time in leadership position. But, at the end of the day, it will be the people writing the code that gets the software to exist and run. So, having people aligned and agreeing on the vision is also key because unless I'm the only developer on the project, I can't really make all decisions on things that are going to get built. So, figuring out how to get people motivated, interested in just building in the same direction is really important. It's really easy. Like, one thing with Drivy, when I was there, that was really fun to see, like, many people have this reaction, especially the more senior people joining the company. They would see the engineering team, and they were really, really surprised by how small it was because we were being really, really efficient. Like, we were paying really close attention to what we would work on. So, kind of technology we would introduce would be quite conservative on both to really be able to deliver what is the most important. So, we were able to do a lot with, honestly, not a lot of people. And I think this is a great mark for success. You don't need a thousand people to build your software if you ask the right question, like, "Do I need to build X or Y?" and always having these discussions. RÉMY: What's your opinion on that, Sami? SAMI: Yeah, I guess it changes. Like, for example, today, the hardest thing about software development was just getting Jira to work. That has literally ruined my whole day. But I've found, for me, what I find is the most difficult thing to do is making code resilient to change. What I mean by that is writing code that's easy to change. And a lot of that, I guess, we try to work on at thoughtbot, as consultants, is following kind of design principles and best practices and certain design patterns that really make the code easy to change. Because that, I think, when I'm writing code is the biggest challenge. And where I feel when I'm working with our clients one of the biggest things they can invest in, which is difficult because there's not a lot of visibility around it or metrics, is ensuring that code that's written is easy to change because, at some point, it will. And I've also worked on systems which are bigger, and when you can't change them, conversations start happening about the cost of change. Do we rewrite it from the ground up again? And that opens a whole different can of worms. So, that, for me, I think, is definitely one of the hardest things. How about yourself, Rémy? RÉMY: I don't know about the most difficult. I mean, there are many things difficult. But I remember something that I had to put extra effort, so maybe it was one of the most difficult for me. When I started being a consultant, when I joined thoughtbot was to understand what's the boundary between executing and giving an advice? So, basically, I discovered that when you're a consultant, but it works also when you're a developer in a team, you know, you're not just only the one who is going to write the code. You're supposed to be also someone with expertise, experience to share it and to make the project and the team benefit from it. So, at some point, I discovered that I should not just listen to what the client would say they want. Obviously, that's what they want, but it's more interesting and more difficult to understand why they want it and why they actually need, which could be different from what they want. So, it's a whole different conversation to discover together what is actually the necessary thing to build, and with your expertise and experience, try to find the thing that is going to be the most efficient, reliable, and making both the client and the customers happy. MARC: Yeah. And as software engineers, it's really easy to get excited about a problem and just go, "Oh, I could solve it this way." But then you need to step back and go, "Well, maybe it doesn't need fixing, or we should do something completely different." At some point, I was working with a customer service organization. In their workflows, they had to go on, let's say, five different pages and click on the button to get something to do one action. And so, what they asked for is to have those five buttons on one single page, and so, they could go, click, click, click, click, click. But after looking at it, what they needed is just automation of that, not five buttons on the page. But it's really easy to go, oh, and we could make those buttons, like, kind of generic and have a button creator thing and make it really fancy. When you step back, you go, oh, they shouldn't be clicking that many buttons. SAMI: Yeah, that makes so much sense because just in that example...I can't remember where I read this, but every line of code you write has to be maintained. So, in that example where you've got five buttons, you're kind of maintaining probably a lot more code than when you've got the single button, which goes to, I don't know, a single action or a method that will handle kind of all the automation for you. And that's also, you know, driving at simplicity. So, sometimes, like, you see this really cool problem, and there's a really cool way to solve it. But if you can solve it, you mentioned, like, being conservative with the type of frameworks maybe you used in a previous company, like, solve it in the most simple way, and you'll thank yourself later. Because, at some point, you have to come back to it, and maintain it, change it. Yeah, so it makes a lot of sense. And, Marc, you said you started when you were 7, which is really young. Through that amount of time, you've probably seen massive changes in the way websites look, feel, and how they work. In that time, what's the biggest change you actually think you've seen? MARC: The biggest thing I saw is, when I started, internet didn't exist or at least wasn't available. Like, I remember being at school and the teacher would ask like, "How many people have a computer at home?" And we'd be like, two or three people. So, people didn't have internet until I was like 14, 15, I'd say. So, that's the biggest one. But, let's say, after it started, they just got more complicated. Like, so, the complexity is getting crazy. Like, I remember, at some point, where I saw I think it was called Aviary. It was basically Photoshop in the browser, and I was just insanely impressed by just the fact that you could do this in the browser. And, nowadays, like, you've got Figma, and you've got so many tools that are insanely impressive. Back then, it was just text, images, and that's it. I actually wrote a blog post a few years ago about how I used to build websites just using frames. So, I don't know if you're familiar with just frames, but I didn't really know how to do divs. So, I would just do frames because that's what I understood back then, again, little kid. But it was kind of working. You were dealing with IE 5 or, like, I remember, like, professionally fixing bugs for IE 5.5 or, like, AOL, like, 9, something ridiculous like this. So, building a website just got way easier but also way more complicated, if that makes sense. Like, it's way easier to do most things. For instance, I don't know, like, 20 years ago, you wanted a rounded corner; you would have to create images and kind of overlay them in a weird way. It would break in many cases. Nowadays, you want rounded corners? That's a non-topic. But now you need, like, offline capabilities of your website. And, in a lot of cases, there's really complex features that are expected from users. So, the bar is getting raised to crazy levels. SAMI: Yeah, I always wonder about this. Like, when you look at how the internet used to be and how people develop for the internet, and, like you're saying, now it's more complex but easier to do some things. I don't know if as developers we're making things harder or easier for ourselves. Like, if you look at the amount of technology someone needs to know to get started, it grows constantly. To do this, you have to add this framework, and you need to have this library, and maybe even a different language, and then, to even host something now, the amount of technologies you need to know. Do you think we're making things harder for ourselves, or do you think easier? MARC: Well, I guess there's always back and forth, like, regarding complexity. So, things will get really, really complex, and then someone will go, "Well, let's stop that and simplify." That's why, like, I'm seeing some people not rejecting React and so on, but going a simpler route like Rails has options like this. There's people using HTMX, which is really simple. So, just going back to something simpler. I think a lot of the really complex solutions also come from the fact that now we have massive teams building websites, and you need that complexity to be able to handle the team size. But it's kind of, then you need more people to handle the complexity, and it's just getting crazy. Yeah, honestly, I don't know. I'm seeing a lot of things that feel too complex for...like, the technology feels really complicated to accomplish some things that should be simple or at least feel simple. But, at the same time, there are things that got so simple that it's ridiculous like just accepting payment. I remember, like, if you wanted to accept payment on a site, it would be months of work, and now it takes a minute. You just plug in Stripe, and it works. And it's often cheaper than what it used to be. So, it's kind of...or deploying. You mentioned deploying can be really hard. Well, you don't need to have a physical server in your room just eating your place up to have your website, your personal website running. You just push it to Vercel, or Heroku, or whatever, or just a static page on S3. So, this got simpler, but then, yeah, you can get it to be so much more crazy. So, if you host your static website on S3, fairly simple. But then if you try to understand permissions on S3, then, you know, it's over. RÉMY: I don't know if it's really in the path of our discussion. I just wanted to ask you, so this is the on tour series, where we...so, usually, the Giant Robots podcast used to be a little bit more American-centric, and this on tour is moving back to the other side of the Atlantic with, again, Europe, West Asia, and Africa. You've been part of a company, Drivy, which expanded from France to neighboring countries in Europe. What could you tell our listeners about how to expand a business internationally? MARC: That's a tough question, especially in Europe. Because I know looking from the outside, like, if you're from the U.S. and you look at Europe, it feels like, you know, a uniform continent, but really, it's very different. Like, just payment methods are different. Culture is very different. For instance, when I was working at Back Market in France, one of the branding aspects of Back Market was its humor. Like, we would be making a lot of jokes on the website, and it would work really well in France. Like, people would love the brand. But then you expand to other countries, and they just don't find that funny at all. Like, it's not helping at all, and they're expecting a different tone of voice. So, it's not just, okay, I need to translate my own page; it's I need to internationalize for this market. I guess my advice is do it country by country. Sometimes I see companies going like, oh, we opened in 20 different countries, and you go, how even do you do that? And spend some time understanding how people are using your product or, like, a similar product locally because you would be surprised by what you learn. Sometimes there's different capabilities. For instance, when Drivy went to the UK, there's so much more you can learn. There's the government database that you can look up, and it really helps with managing risk. If people are known to steal cars, you can kind of figure it out. I'm simplifying a bit, but you can use this. You don't have that in France because we just don't have this solution. But if you go to Nordic countries, for instance, they have way more electric vehicles, so maybe the product doesn't work as well. So, it's really understanding what's different locally and being willing to invest, to adapt. Because if you go, okay, I'm going to open in the Netherlands but you don't adopt the payment methods that are used in the Netherlands, you might as well not open at all. So, it's either you do it properly and you kind of figure out what properly means for your product, or you postpone, and you do it well later. Like, right now, I'm struggling a bit with my app because it's open. So, it's on the App Store, so it's open globally. And it's a SaaS, so it's simpler, but I struggle with language. So, it's in French and English. I spoke both of this language, obviously, French better than English. But I think I'm doing okay with both. But I also built it in Spanish because I speak some Spanish fairly poorly, and I wanted to try to hit a different market like the Mexican market that are doing boxing quite a lot. But the quality doesn't seem there. Like, I don't have the specific boxing lingo, so I'm contemplating just rolling it back, like, removing the Spanish language until I get it really well, maybe with a translator dedicated to it that knows boxing in Spanish. Because I work with translators that would translate, but they don't really know that, yeah, like a jab in boxing. In Spanish, they might also say, "Jab." They won't translate it to, like, [inaudible 38:31]. SAMI: Yeah. At thoughtbot, we have one of our clients they wanted to release their app also internationally. And so, we had also kind of a lot of these problems. We even had to handle...so, in some languages, you go from left to right, right to left. So, that kind of also changed a lot of the way you would design things is mainly for people who are going from left to right. I mean, that's thinking kind of more Europe, U.S.-centric. And then, you could be releasing your app into a different country where they read the other direction. So, yeah, a lot of this stuff is really interesting, especially the culture, like you're saying. Do they find this humor funny? And then, how do they translate things? Which, in my head, I think, could you use AI to do that. Which is a nice segue into, like, the mandatory question about AI, which we can't let you go until we ask you. MARC: [laughs] SAMI: So, okay, obviously, I'm going to ask you about your thoughts on AI and where you think we're headed. But I've seen something interesting, which I don't know if this is something that resonates with you as well. I've seen a bit of a trend where the more experienced developers or more senior developers I talk to seem to be a bit more calm and less concerned. Whereas I would consider myself as less experienced, and I feel, like, kind of more anxious, more nervous, more jumping on the bandwagon sort of feeling of keeping an eye on it. So, I guess, with your experience, what are your thoughts on AI? Where do you think we are headed? MARC: That's a big question, and it feels like it's changing month to month. It feels way more interesting than other trends before. Like, I'm way more excited about the capabilities of AI than, like, NFTs or stuff like this. I'm actively using AI tooling in my app. I was using some AI at Back Market. So, it's interesting. There's a bunch of things you can be doing. Personally, I don't think that it's going to, like, make programming irrelevant, for instance. It will just change a bit how you will build things just like...so, we talked about what changed in the past. For instance, at some point, you would need a team of people moving around physical computers and servers and just hooking them up to be able to have a website. But now, most people would just use a cloud provider. So, all those people either they work for the cloud provider, or they're out of a job. But really what happened is most shifted into something different, and then we focused on something different. Instead of learning how to handle a farm of servers, we learned how to, I don't know, handle more concurrency in our models. And I think when I look back, I feel like, technically, maybe, I don't know, 70%, 80% of what I learned is now useless. Like, I spent years getting really good at handling Internet Explorer as a web developer. Now it's just gone, so it's just gone forever. And it feels like there's some practice that we're having right now that will be gone forever thanks to AI or because of AI, depending on how you look at it. But then there'll be new things to do. I'm not sure yet what it will be, but it will create new opportunities. There are some things that look a bit scary, like, or creepy. But I'm not worried about jobs or things like this. I'm a bit concerned about people learning programming right now because, yeah, there's a lot of hand-holding, and there's a lot of tools that you have to pay to get access to this hand-holding. So, if you're a student right now in school learning programming and your school is giving you some AI assistant, like Copilot or whatever, and this assistant is really good, but suddenly it goes away because you're not paying anymore, or, like, the model change, if you don't know how to code anymore, then it's a problem. Or maybe you're not struggling as much. And you're not digging deep enough, and so you're learning slower. And you're being a bit robbed of the opportunity to learn by the AI. So, it's just giving you the solution. But it's just, like, the way I use it right now, so I don't have an assistant enabled, but I usually have, like, a ChatGPT window open somewhere. It's more like a better Stack Overflow or a more precise Stack Overflow. And that helps me a lot, and that's really convenient. Like, right now, I'm building mostly using Swift and Swift UI, but I'm mainly a Ruby and JavaScript developer. So, I'm struggling a lot and being able to ask really simple questions. I had a case just this morning where I asked how to handle loading of images without using the assets folder in Xcode. I just couldn't figure it out, but it's really simple. So, it was able to tell me, like, right away, like, five options on how to do it, and I was able to pick the one that would fit. So, yeah, really interesting, but yeah, I'm not that worried. The only part I would be worried is if people are learning right now and relying way too much on AI. RÉMY: Well, at least it's positive for our job. Thank you for making us believe in a bright future, Marc. MARC: [laughs] RÉMY: All right. Thank you so much, Marc, for joining us. It was a real pleasure. Before we leave, Marc, if you want to be contacted, if people want to get a hold of you, how can you be contacted? MARC: There's two ways: either LinkedIn, look up Marc G Gauthier. Like, the middle initial is important because Marc Gauthier is basically John Smith in France. My website, which is marcgg.com. You can find my blog. You can find a way to hire me as a coach or advisor. That's the best way to reach out to me. RÉMY: Thank you so much. And thank you, Sami, as well. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have any questions or comments, you can email us at hosts@giantrobots.fm. You can find me on social media as rhannequin. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening, and see you next time.  AD: Did you know thoughtbot has a referral program? If you introduce us to someone looking for a design or development partner, we will compensate you if they decide to work with us. More info on our website at: tbot.io/referral. Or you can email us at: referrals@thoughtbot.com with any questions.

Paul Orlando is back to talk about his book titled “Why Now?” You may remember Paul from his last appearance (a fan favorite) talking with Jerod about complex systems & second-order effects. Paul's book, “Why Now?” explores the concept of timing and the importance of understanding the ‘why now' in business and product development. We discuss timing examples from the book that were either too early or too late (such as the first video phone and car phones), the need to consider both technological advancements and user demand when assessing timing, the significance of timing in the success of companies like Apple and the launch of the iPhone, Uber and Heroku, and more. Also, join our Slack community for a chance to get a signed copy of Paul's book.

Adam Gross is one of the masters of product-led growth (PLG). Most recently, Adam was Vimeo's interim CEO. Before Vimeo, Adam was CEO of Heroku, which he joined after selling his startup, Cloudconnect in 2013. Additionally, Adam has held executive leadership roles at Salesforce and Dropbox, and has been an active angel investor & advisor to companies, including Buildkite, Cribl, and Tailscale. In Today's Episode with Adam Gross We Discuss: PLG Tactics from Dropbox, Heroku and Salesforce: What were Adam's biggest takeaways from his time at Salesforce? How did it shape his growth mindset? What did Adam learn about customer acquisition at Dropbox? What would Adam most like to change about growth today? Product-Led Growth: The Fundamentals: What is growth? What is it not? What do founders get wrong about growth? Why does Adam think PLG is not for everybody? What do most great PLG businesses have in common? How are value propositions segmented in PLG? How can startups transition from individual to enterprise clients? Why does Adam think startups doing paid acquisition sub $100M aren't actually PLG? The Secrets to Optimizing Growth Channels: What are the most common reasons fast-growing companies plateau? How does Adam advise founders on diversifying channels? What are the biggest mistakes founders make when scaling into enterprise? How should startups do effective product marketing in horizontal products? What is emotive & strategic marketing? How should startups balance both? How Angel Investing Changes How You View Companies: What are Adam's top 3 pieces of advice for founders? What does Adam mean when he says you are either hiring a poet or a librarian? What are the biggest mistakes founders make when hiring? What was Adam's biggest investment miss? What did he learn from it?