Podcasts about railways

Hey, it's Katie and I want to welcome you to this special bonus episode. It'll be here for you completely ad-free for the next week so you can get a feel of what it's like to be a PREMIUM member. If you'd like an easy ad-free experience for all of our podcasts - that's over 200 episodes each month, then JOIN PREMIUM today at https://WomensMeditationNetwork.com/premium A peaceful train station in Switzerland surrounds you with soft movement and distant railway sounds. This calming travel ambience is ideal for focus, studying, or falling asleep. Let the gentle atmosphere transport you somewhere quiet. Love,

We've described some pretty fairly awful commutes on this show before. On today's very special "return-to-Africa" episode, all people wanted to do was get to work, but the weather got bad to the point where the road said "you shall not pass, your life is in danger". The train took a look at all this and said, "hold my beer”.On today's episode: you will learn how the location of today's story sounds toasty and hot to the uninitiated, but is actually one of the wettest places on earth; we will describe a claustrophobic and unenviable situation somewhere between the roller-skating in the back of the truck stunt from Jackass combined with the trash compactor scene from Star Wars; and I will actually say that no one choo choo chooses to be in today's kind of disaster, which I only call out early to ask for your forgiveness.And if you were listening on Patreon: you would learn about the US military's ambitions to turn its enemies into bee-coated, gay, diarrhea fanatics, and how they inspired Mikkos Cassadine's aims of destroying the world in the 1980s; we'll visit a place so rainy that you will have no choice but to assume I'm lying; and we'll walk through a list of all the different, awful things that can happen to your spine. Also, at the end of this episode, I'm going to announce the results of a naming contest we held for our hungry gorilla friend, and I will share the story behind it, which, fair warning, is extremely emotional.People describe travel by rail as a romantic way to see the landscape. Well, I don't know so much about that, but in this very special and intimate episode, a lot of people were brought closer together. I wanted to shout out Joyce Mokou Kontcheu (ask how to pronounce) who sent me my very first fan mail from Africa! That's a big deal for me. This was one of those rare occasions where a starter disaster perfectly sets the stage for a much worse secondary disaster. I call it a cascading infrastructure failure. She called it a careless disaster that could have been avoided with a little common sense. She truly hoped I would cover it, so yes, of course I did. Merci, Joyce.And I didn't have time to record a quick thing for our June Birthdays, so I wanted to shout out Charles Jewell and Kerry Ann Borthwick for another year around spinning around the sun in an otherwise cold and indifferent universe, wishing them a little birthday cheer, and thanks again to Hydra Corvi again for helping me with a little research on this one. From here we're back in my hometown just long enough to make a little fun of the World Cup before jetting off to beautiful Corsica to watch the beautiful game and have a high statical chance of getting very, very hurt. Spoiler, in the safety segment, I'm going to tell you what to do if not-only are you impaled on a pole, but suspended above the ground by it like a bug pinned to a wall. Good stuff.––––– THANK YOU. Most shows survive at the whim of production companies and corporate sponsors, built from the top down. Doomsday doesn't exist because some network exec believes in it – it exists because actual people do. It's built from the bottom up, and it's been my privilege to bring you these stories. Just you, me, and a microphone.  I don't do this for you, so much as I do this because of you. If you'd like to support the show at Buy Me A Coffee, or join the club over at Patreon for AD-FREE EPISODES, LONGER EPISODES, EXTRA CONTENT, all that good stuff All older episodes can be found on any of your favorite channels   Apple : https://tinyurl.com/5fnbumdw Spotify : https://tinyurl.com/73tb3uuw IHeartRadio : https://tinyurl.com/vwczpv5j Podchaser : https://tinyurl.com/263kda6w Stitcher : https://tinyurl.com/mcyxt6vw Google : https://tinyurl.com/3fjfxatt Spreaker : https://tinyurl.com/fm5y22su RadioPublic : https://tinyurl.com/w67b4kec PocketCasts. : https://pca.st/ef1165v3 CastBox : https://tinyurl.com/4xjpptdr Breaker. : https://tinyurl.com/4cbpfayt Deezer. : https://tinyurl.com/5nmexvwt  Follow us on the socials for more Facebook : www.facebook.com/doomsdaypodcast Instagram : www.instagram.com/doomsdaypodcast Twitter : www.twitter.com/doomsdaypodcast TikTok : https://www.tiktok.com/@doomsday.the.podcast Safety google off. We'll talk soon. And thanks for listening. Become a supporter of this podcast: https://www.spreaker.com/podcast/doomsday-history-s-most-dangerous-podcast--4866335/support.

There was more talking. Then the bell rang again, and Ruth fetched a cab. The children heard boots go out and down the steps. The cab drove away, and the front door shut. Then Mothercame in. Her dear face was as white as her lace collar, and her eyes looked very big and shining. Her mouth looked like just a line of pale red--her lips were thin and not their proper shape at all.

There was more talking. Then the bell rang again, and Ruth fetched a cab. The children heard boots go out and down the steps. The cab drove away, and the front door shut. Then Mothercame in. Her dear face was as white as her lace collar, and her eyes looked very big and shining. Her mouth looked like just a line of pale red--her lips were thin and not their proper shape at all.

There was more talking. Then the bell rang again, and Ruth fetched a cab. The children heard boots go out and down the steps. The cab drove away, and the front door shut. Then Mothercame in. Her dear face was as white as her lace collar, and her eyes looked very big and shining. Her mouth looked like just a line of pale red--her lips were thin and not their proper shape at all.

Father had been away in the country for three or four days. All Peter's hopes for the curing of his afflicted Engine were now fixed on his Father, for Father was most wonderfully clever with his fingers. He could mend all sorts of things.

They were not railway children to begin with. I don't suppose they had ever thought about railways except as a means of getting to Maskelyne and Cook's, the Pantomime, Zoological Gardens, and Madame Tussaud's.

They were not railway children to begin with. I don't suppose they had ever thought about railways except as a means of getting to Maskelyne and Cook's, the Pantomime, Zoological Gardens, and Madame Tussaud's.

Father had been away in the country for three or four days. All Peter's hopes for the curing of his afflicted Engine were now fixed on his Father, for Father was most wonderfully clever with his fingers. He could mend all sorts of things.

They were not railway children to begin with. I don't suppose they had ever thought about railways except as a means of getting to Maskelyne and Cook's, the Pantomime, Zoological Gardens, and Madame Tussaud's.

Father had been away in the country for three or four days. All Peter's hopes for the curing of his afflicted Engine were now fixed on his Father, for Father was most wonderfully clever with his fingers. He could mend all sorts of things.

In this episode of Between The Lines, Chelle and Leanne welcome Kate Solly back to the podcast to chat about her latest cosy crime novel, The Paradise Heights Miniature Railway Bust Up. Kate's books have become beloved for their blend of mystery, humour, community chaos and deeply relatable motherhood moments — and this latest instalment is no exception. This time, amateur sleuth Fleck Parker finds herself investigating strange goings-on at the local miniature railway, where expensive equipment has mysteriously disappeared. Between feeding a hungry baby, managing a cranky toddler, surviving school drop-offs and protecting her Wordle streak, Fleck somehow also finds time to investigate crime. Meanwhile, her best friend Trixie is dealing with mounting volunteer drama at the local craft shop, proving once again that small-town community groups can be just as chaotic as any murder scene. In our conversation with Kate, we discuss: Why cosy crime readers love quirky community settings Balancing humour with genuine mystery and darker themes Writing motherhood in a realistic, funny and relatable way The inspiration behind Fleck Parker's chaotic but lovable character Crafting communities, miniature railways and small-town dynamics Why amateur sleuths make such compelling protagonists As always, Kate brings warmth and humour to the conversation, and we found ourselves laughing through much of the episode. If you love cosy mysteries filled with heart, chaos, friendship and community drama, this episode — and this book — are such a fun time. About the Book The Paradise Heights Miniature Railway Bust Up is a cosy Australian mystery following amateur sleuth Fleck Parker as she investigates theft and suspicious behaviour at the local miniature railway. As Fleck juggles parenting chaos, community drama and mounting clues, she discovers that beneath the cheerful surface of Paradise Heights lies something far more sinister than anyone expected. About the Author Kate Solly is a writer, mother of six and author of Tuesday Evenings With the Copeton Craft Resistance and The Paradise Heights Craft Store Stitch-Up. Known for blending humour, heart and mystery, Kate writes cosy crime novels grounded in relatable family life, quirky communities and plenty of crafting chaos. Join Our Book-Loving Community Chelle and Leanne created Between The Lines because they love books and great conversations — and wanted to share both with you.

Voices of Forestry is back with a new episode! This month, Host Seth Stephenson talks with Dr. Nate Irby, the executive director of the Railway Tie Association, to talk about this important forest product. The two talk about how many ties can be found in the U.S., some issues the industry is facing, and the work being done to improve that industry. For more information on RTA or to see some of the data they have collected, you can visit https://www.rta.org/.Thank you to this month's sponsor, DDK Forestry & Real Estate. We appreciate their cotinued support of the show.You can find more music from Some Guy Named Robb/Robb McCormick on Spotify or by visiting https://www.sgnrobb.com/.For more information about the Arkansas Forestry Association visit arkforests.org.

Marlon Taylor, President of New York & Atlantic Railway (NYA), discusses his career journey in the rail industry and the path that led him … Read More

Trying to blanket ban speech using the senate,They released the footage of the police response to Henry NowakTrump talkin' 51st state again,Railway to executive bonuses!Brookfield Investment in FranceDRIPA needs to be repealed,Sign Up for the Full ShowLocals (daily video)Sample Showshttps://canadapoli2.locals.com/ Spotify https://podcasters.spotify.com/pod/show/canadapoli/subscribePrivate Full podcast audio https://canadapoli.com/feed/canadapoliblue/Buy subscriptions here (daily video and audio podcast):https://canadapoli.cm/canadapoli-subscriptions/Youtubehttps://www.youtube.com/c/CanadaPoli/videosMe on Telegramhttps://t.me/realCanadaPoliMe on Rumblehttps://rumble.com/user/CanadaPoli Me on Odysseyhttps://odysee.com/@CanadaPoli:f Me on Bitchutehttps://www.bitchute.com/channel/l55JBxrgT3Hf/ Podcast RSShttps://anchor.fm/s/e57706d8/podcast/rsshttps://LinkRoll.co Go here to discuss the show without algorithmic censorship. See you there!

James, Sam and Tom sit down and do a hobby update whilst Sam and Tom sit down with Josef from That Model Railway Guy

In episode 87 of Festpod: The Unofficial Rock Festival Guide, Neill, Rich, and Arran talk about this year's Download Festival - what's coming up, what's changed, and what we're looking forward to.Topics this episode:- Railway bans bins. We discuss East Midlands Railway's decision to ban festival-goers from bringing wheelie bins full of beer and camping gear onto their trains.- RIP Park Farm glass ban. We look into the Download rules, including the controversial ban on glass in the RIP Park Farm campsite. Can it be enforced?- Lineup Updates: We share the unfortunate news that Static X has cancelled their 2026 tour dates, including their Download appearance, due to serious medical issues. - The 2026 Map Reveal: We take a long look at the newly released festival map. We examine the massive expansion of the RIP areas and the brutally long walk facing families in the Mini Mosher and Quiet Camps. - Inside District X: We look at what's happening in District X, including the Ace of Spades tent, The Outpost, and the Arcade Bar. There are some changes and some new spots, too!- We review the stacked District X entertainment lineup, which features sets from Five, Electric Six, a DJ set from Creeper, Dick and Dom in the Doghouse, Token Grass, and Bongo's Bingo. - Are podcasts a good form of stage entertainment at music festivals? Would you rather see music or podcasts on stage at Download?- The Takeover Stage: A quick recap of the six unsigned bands kicking things off on Wednesday: 40,000 Leagues, Celavi, Pryma, DeadWax, Rxptrs, and Black Water County. - Our Top Band Picks: To wrap things up, we share our ultimate recommendations for the weekend. Hear why we are hyped to see Social Distortion, Scooter, Limp Bizkit, Linkin Park, Bad Omens, Caskets, A Day to Remember, and more! with our sponsor, Fat Frank's Camping Shop https://fatfrankscampingshop.comFind all our video podcast episodes in this playlist: https://youtube.com/playlist?list=PLlfyhNTZm6HBHjHm4kSORo_HykWXf0KGu&si=_5FbNi1-Rfe0sU1tPlease support us on Patreon! https://www.patreon.com/festpodIf you'd like to buy our merch, visit our merch store here: https://festpod.shopAll our links here: https://festpod.co.uk Hosted on Acast. See acast.com/privacy for more information.

The completed station was formally opened for use on 29 May 1854 to link London with the west of England and South Wales, reflecting the broader growth of rail transport during the mid-nineteenth ...

Send us Fan MailThis episode celebrates the contribution of young people in heritage railways ...both in the past and present day, as Alasdair Stewart and Producer Laura Raymond, sitting in for Sharon Gregory, visit three charming railways: Barton House in Norfolk, The Talyllyn Railway, as they mark the 75th anniversary as the  world's oldest preserved railway and Leighton Buzzard Railway.  Alasdair also talks to Michelle Bartram one the leaders of the young people's group at North Norfolk Railway. Young reporter Cynan Hughes enjoys a trip to the Talyllyn 75th Gala Weekend to travel on some of the night trains (along with dad Steve!)Please find links below for the organisations and Railways mentioned in this episode.The Talyllyn RailwayWe chat to Railwayman volunteer Chris Parrott in the Talyllyn Segment. You can find out more about his story in his book: Argentine Adventure is available from Amazon.Leighton Buzzard RailwayBarton House RailwayNorth Norfolk RailwayThis podcast is produced by Laura Raymond and presented by Alasdair Stewart and Sharon Gregory. Our 'Making Tracks' music is with kind permission of composer and musician Richard Durrant. It is a unique piece inspired by the rhythm of the historic rolling stock on the Ffestiniog Railway on the scenic journey from Harbour Station to Tan y Blwch. You can listen and download the full 'Tan y Bwlch' Ukulele Quartet here: Thank you to voice artist David King - for the Railway Ride outs voice over. Ukulele Quartet No. 1 "Tan y Bwlch"  Ukulele Quartet No. 1 "Tan y Bwlch" Richard Durrant · Single · 2019 · 3  songs.    

In the early nineteenth century, engineers discovered that steam power and iron rails could be combined to move people and goods faster than any horse or ox could. Within a few decades, railways had spread across every continent. Cities were reorganised around stations, clocks were synchronised, leisure and luxury were redefined, and entire economies began to run according to railway timetables. This was the Golden Age of the railways — a period when steam and steel transformed landscapes and fundamentally altered the way the world worked.  But how did a strange experimental machine become the backbone of modern life? How did railways reshape everything from holidays, to warfare, to time itself? And why, long after the steam age ended, does so much of modern life still run on railway logic? This is a Short History Of the Golden Age of Railways. A Noiser podcast production. Hosted by John Hopkins. With thanks to Christian Wolmar, a writer and broadcaster specialising in transport, and author of several books on the history of the railways. Written by Sean Coleman | Produced by Kate Simants | Production Assistant: Chris McDonald | Exec produced by Katrina Hughes | Sound supervisor: Tom Pink | Sound design by Oliver Sanders | Assembly edit by Dorry Macaulay | Compositions by Oliver Baines, Dorry Macaulay, Tom Pink | Mix & mastering: Cody Reynolds-Shaw Unlock the next two episodes of Short History Of… right now by subscribing to Noiser+. You'll also get ad-free listening and early access to shows across the Noiser podcast network, including Real Survival Stories and Sherlock Holmes Short Stories. Just click the subscription banner at the top of the feed, or head to www.noiser.com/subscriptions to get started. ⁠A Short History of Ancient Rome⁠ - the debut book from the Noiser Network is out now! Discover the epic rise and fall of Rome like never before. Pick up your copy now at your local bookstore or visit ⁠⁠noiser.com/books⁠⁠ to learn more. Learn more about your ad choices. Visit podcastchoices.com/adchoices

AP correspondent Donna Warder reports on a deadly suicide bombing in Pakistan.

Kenya's Court of Appeals issued a landmark ruling rejecting the government's decade-long effort to keep secret the $4.5 billion in China Exim Bank loan contracts used to finance the Standard Gauge Railway. The decision marks a major victory for civil society activists who have long argued that the project was plagued by corruption, opaque procurement practices, and unfavorable terms for Kenya. Eric & Geraud also discuss how a growing dispute between Niger and Benin over a Chinese-backed oil pipeline is exposing the intersection of resource politics, security risks, and Beijing's evolving role in Africa's energy sector. Finally, the discussion turns to China's new zero-tariff access for African exports, why many African governments may struggle to take full advantage of the opportunity, and how shifting global energy and trade dynamics are once again increasing the strategic importance of African infrastructure and commodities.

Now, there is a street in Dalkey that goes by the unusual name of ‘Atmospheric Road'.This name, however, is a nod to a most unusual train, the Dalkey Atmospheric Railway, that carried passengers between Dun Laoghaire and Dalkey in the 19th Century.Mia Sherwood Scully, Journalist and Historian joins Seán to discuss.

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

Iarnród Éireann has announced the building of four new train stations to welcome the arrival of the long-awaited Navan to Dublin railway.Joining guest host Ciara Doherty to discuss is Peadar Tóibín, Leader of Aontú and Brian Caulfield, Professor in the School of Engineering at Trinity College Dublin.

Hello, Beautiful...I'm so grateful you're here with me. The steady rhythm of a passing train creates a deeply calming and nostalgic ambience. These railway sounds offer a consistent white noise perfect for sleep, relaxation, or focus. Let the gentle movement of the train carry your mind into stillness. Love,

Last time we spoke about the New Fourth Army Incident. Across the Second Sino-Japanese War, the CCP entered after the setbacks of the 1930s, seeking to become a national leader in resistance while remaining cautious toward the Nationalist government. The 1936 Xi'an Incident reshaped politics, and by August 1937 KMT–CCP agreements defined a working arrangement: the CCP acknowledged KMT leadership and integrated its forces, while still pursuing political space and autonomy. As the war progressed, the CCP focused on defining its relationship with the KMT and keeping operational independence during cooperation. Mao Zedong managed this alliance by promoting a united front against Japan, yet protecting CCP revolutionary goals and internal control. The establishment of the Eighth Route Army and New Fourth Army marked this military reorganization. Throughout, the CCP feared that KMT collaboration with Japan could enable a peace settlement that would undermine communist legitimacy and restrict the party's future authority thereafter.   #202 The One Hundred Regiment Offensive Phase One Welcome to the Fall and Rise of China Podcast, I am your dutiful host Craig Watson. But, before we start I want to also remind you this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Perhaps you want to learn more about the history of Asia? Kings and Generals have an assortment of episodes on history of asia and much more  so go give them a look over on Youtube. So please subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry for some more history related content, over on my channel, the Pacific War Channel where I cover the history of China and Japan from the 19th century until the end of the Pacific War. Simultaneously with the friction between the Kuomintang (KMT) and the Chinese Communist Party (CCP), the Japanese were also working to take control of—and extract value from—most of the territory they had nominally conquered. Treating these two processes separately—"friction" on the one hand and "consolidation" on the other—does violence to the real difficulty of the CCP's dilemma: the Party often had to confront both problems at the same time. At certain moments, the CCP was effectively forced to wage a two-front struggle. Even so, if the worst of the KMT–CCP friction had already eased by 1941, the most serious and painful challenges posed by Japanese consolidation were still ahead. To recover anything close to reality, the two timelines have to be read together and placed on top of one another. The Japanese understood that consolidation could not be postponed, because much of the land behind the furthest reaches of their army was still only weakly under their actual control. In some places, order could be restored by relatively direct methods: rebuilding local administration and policy authority; repairing transportation and communications; enrolling Chinese personnel—usually, as it turned out, people of dubious reliability—as police or militia under puppet regimes; registering the local population; and requiring identity cards. In true old-style Chinese fashion, collective security practices were used widely. One form was the familiar bao-jia system, in one variant or another. Another was the so-called "railway-cherishing village": a village would be assigned a nearby stretch of track, and if residents failed to "cherish" it, they were held collectively responsible. Yet early Japanese weakness in northern China is vividly illustrated by an incident in the summer of 1938. Three young foreigners—vacationing from teaching in Peiping (Beijing)—were curious about events and about what people were doing. They loaded their bicycles on a southbound train, got off at Baoding, and rode west until they ran into Eighth Route Army detachments. In the early period of the war, commanders generally wanted to rely on more mobile forms of warfare. Mao, however, insisted on a strategy of de-escalation and dispersion: breaking the 8RA and New Fourth Army into small units as nuclei for combat, recruitment, political work, and base-area construction. Under this approach, few engagements could be truly dramatic in scale, and most were constrained by the need to survive. Each skirmish had to be carefully planned. The CCP would use local intelligence and the element of surprise so that a detachment could strike and withdraw before its limited ammunition ran out or before enemy reinforcements arrived. Small Japanese patrols and puppet units could be ambushed not only to seize weapons and other material, but also to inflict casualties. Active collaborators, or Japanese-sponsored administrative personnel, could be assassinated. Above all, Communist action aimed to disrupt transportation: mining roads; cutting down telegraph poles, stealing wire, and cutting rail lines; sabotaging rolling stock; and, at times, carrying off steel rails so that primitive arsenals could be supplied. Attempting derailments was also part of the effort. Destroying a bridge or a locomotive counted as a major achievement. Both the Communists and the Japanese understood that these tactics did not decisively shift the overall strategic balance. Still, they worked at other levels. For the Japanese, the result was a constant series of small wounds—painful, bleeding, and potentially infectious. Few areas in the countryside felt truly safe. Japanese field commanders documented growing frustration as they tried to eliminate resistance, restore administration, collect taxes, and prepare for more systematic and effective economic exploitation of conquered territory. Guerrilla warfare against the Japanese cannot be judged only in conventional battle terms—numbers of engagements, casualties, or territory occupied. It had to be evaluated politically and psychologically as well, exactly as Mao repeatedly emphasized. Since the CCP's wartime legitimacy depended on its patriotic claims, enough fighting had to be carried out to maintain credibility. Moreover, military success mattered for mobilizing the "basic masses," persuading wavering people to keep an open mind, and neutralizing opposition. As the logic put it, it was not that people always chose the side that was winning, but that few would ever join a side they believed was losing. One experienced cadre described the effect this way: Among the guerrilla units… there is a saying that "victory decides everything." No matter how hard it has been to recruit troops, supply the army, raise the masses' anti-Japanese fervor or win over the masses' sympathy, after a victory in battle the masses fall all over themselves to send us flour, steamed bread, meat, and vegetables. The masses' pessimistic and defeatist psychology is broken down, and many new guerrilla soldiers swarm in. But once the Japanese began to demand a heavy price for every engagement—whether the Communists won or not—this attitude began to change. In North and Central China, the Japanese earliest pacification sweeps created comparatively little trouble for the CCP. At first, the Japanese made few distinctions among Chinese forces. They simply tried to mop up or disperse them without regard to character. Over time, however, they realized that these sweeps actually made it easier for the CCP to expand. By the second half of 1939, Japanese methods became more discriminating. Chinese non-Communist forces would step aside while the Japanese hunted specifically for the 8RA, the N4A, and their local affiliates. The Japanese also made more direct appeals to non-Communist forces. According to Japanese army statistics, during the eighteen months from mid-1939 to late 1940, around 70,000 men from more or less regular Nationalist units in North China alone went over to the Japanese. The Japanese also reached informal "understandings" with several regional commanders whose forces together might have totaled as many as 300,000 men. This, of course, corresponded to what the CCP denounced as "crooked-line patriotism"—the "crooked-line" collaboration that preserved certain units so they could be used in future anti-Communist operations. When pacification efforts were intensified from late 1939 and throughout 1940, differences also appeared in the strategies Japanese armies used in North versus Central China. In North China, the approach relied heavily on military means, with political tactics limited largely to recruiting collaborators. In Central China, Japanese authorities did not hesitate to use military force, but they also attempted to supplement it with more comprehensive political and economic solutions by setting up tightly controlled "model peace zones." Although both approaches ultimately failed, they created enormous difficulties for Chinese Communists—until, in 1943, the Japanese were forced to ease off because the Pacific War against the United States became too burdensome. Careful reading of detailed intra-party documents suggests that repression also demobilized peasant support and terrorized populations into apathy, grudging acquiescence, or even active collaboration with the Japanese. In a locality already reduced from consolidated base status to guerrilla status, capacity and will were often too weak to administer complex reforms in systematic fashion. In other words, passive survival—defensive survival—was at least as important as what lay behind the heroic public images the Party projected. Systematic pacification in North China in late 1939 and 1940 radiated outward. It moved from areas held more or less firmly by the Japanese and their puppets into guerrilla and contested zones. The ultimate objective was to crush resistance or render it ineffective. The method was first to sweep the area clear of anti-Japanese elements, and then to establish a chain of interconnected strongpoints that could quickly reinforce one another. After that, puppet government would be expanded so it could take increasing responsibility for civil administration and "pacification maintenance," while Japanese forces repeated the initial steps further outward into contested territory. Violence was used selectively against individuals, groups, or villages accused of acts of resistance. This selective violence aimed to deter active participation in CCP-led programs, deprive Communist forces of a population willing to shelter them, and persuade informers to come forward. That was, at least, the theory of the strategy. In practice, the basic framework of the strategy depended on the main transport lines. Railways and roads—if properly fortified and protected—could separate resistance forces from one another and deny them one of their most effective weapons: mobility. These "cage" tactics (chiyu-lung, "jiu-lung") made it possible to enlarge pacified areas by "nibbling" outward, "as a silkworm feeds on mulberry leaves" (ts'an-shih). At the same time, the approach aimed to exploit North China's economy more effectively. To this end, the Japanese worked to improve and extend both railway and road networks. When the war began, in Shanxi the Cheng-Tai (Shijiazhuang–Taiyuan) and Tong-Pu (Datong–Tongguan) lines were metre-gauge, incompatible with the standard-gauge lines elsewhere in China—part of Yan Xishan's design to prevent deeper penetration into his province. By the end of 1939, the Japanese used forced labor to convert both lines to standard gauge. One benefit was the easier transportation of high-quality anthracite coal from the Qingxing mines (on the Cheng-Tai line) to industrial users in North China and Manchukuo. Of the newly constructed roads and railway lines, the most important was the Te-Shih line—from Dezhou in northeastern Shandong to Shijiazhuang. Construction began in June 1940 and finished in November, connecting the Tianjin–Pukou, Beiping–Hankou, and Cheng-Tai lines. This made it easier to move troops and transport raw cotton. Once the Te–Shih link was completed, the Japanese had direct connections between the point of their furthest advance at the elbow of the Yellow River and all major cities of North China, and beyond to Manchukuo. Communist sources began to speak of a "transportation war," noting with concern the moats and ditches, the blockhouses, and the frequent patrols protecting the lines. Both militarily and economically, these measures weighed heavily on forces led by the Communists in North China and on the populations under their control—especially the plains of central and eastern Hebei. One indicator of effectiveness was the rapid decline in "acts of sabotage" against North China railways in 1939 and the first half of 1940. A cadre in Jin-Cha-Ji reported in mid-1940: "The enemy has adopted a blockhouse policy, like that of the Jiangxi Soviet. They are spread like a constellation. In central Hebei alone, there are about 500, separated by one to three miles." Normal trading patterns were disrupted as Japanese or puppet occupiers took over administrative and commercial centers, and peasants found themselves caught between regulations imposed by the Communists on one side and those enforced by the other side. Finally, landlords, moneylenders, loafers, bandits—everyone who felt damaged by the new order inside base areas—could use pacification programs to try to recover influence or simply take revenge. Some became informers. After 8RA and local units were driven away, they could kill remaining cadres or activists and settle scores with the peasants who had supported them. Until the "first anti-Communist upsurge" was defeated, local elites and other disaffected elements might also seek support from Nationalists. It was even possible for an armed band to operate for several months inside consolidated regions of the CCP base, killing cadres as it went. Peng Dehuai later recalled this period in a way that underscored how pressure translated into wavering and collapse. Under the enemy's brutal pressure, in some districts the masses even hesitated or capitulated. From March to July 1940, large areas of the North China base were reduced to guerrilla regions. Before the "Cage-bursting battle",, they controlled only two county seats: Pingxun in the Taihang mountains and Pien-kuan in northwest Shanxi. Masses who previously had one set of obligations now had two—one toward the anti-Japanese regime and one toward the puppet regime. The situation in North China had not yet become a full crisis, but it was certainly serious. Action was needed to regain initiative. On 22 July 1940, Zhu De, Commander-in-Chief of the Eighth Route Army, Peng Dehuai Deputy Commander-in-Chief, and Zuo Quan Deputy Chief of Staff jointly issued the Preliminary Battle Order, laying out the strategic goals for the coming operation. The order stated: "To respond to the enemy's 'prison cage policy,' obstruct its advance toward Xi'an, create favorable conditions in the North China theater, and strike at the national resistance initiative, we have decided to take advantage of the concealment provided by tall summer millet and the rainy season to carry out a large-scale sabotage operation on the Shijiazhuang–Taiyuan railway (Zheng–Tai Line)." It required the participation of at least 22 regiments from the Jin-Cha-Ji Military Region, the 129th Division, and the 120th Division. The main objective was to "completely destroy key points along the Zheng–Tai Line" and to "cut the railway for a prolonged period." On 8 August, the headquarters of the Eighth Route Army issued the Operational Battle Order, further clarifying how forces would be deployed. The Jin-Cha-Ji Military Region was assigned to attack the eastern section of the Zheng–Tai Railway (from Niangzi Pass to Shijiazhuang). The 129th Division was assigned the western section (from Niangzi Pass to Yuci). The 120th Division was tasked with targeting the northern segment of the Tongpu Railway and the Fen–Li Highway. The order also required all troops to begin combat operations on 20 August, and emphasized that "the success of the campaign should be assessed primarily by the extent of damage inflicted on the Zheng–Tai Line." The operation was prepared under strict secrecy. Various elements of the Eighth Route Army conducted thorough preparations before the campaign. Reconnaissance teams, hidden and protected with the help of local villagers, penetrated deep into areas near the Shijiazhuang–Taiyuan railway to carefully map Japanese strongholds, enemy troop dispositions, and local terrain. At the same time, both military and civilian communities mobilized to stockpile grain, ammunition, and tools needed for railway sabotage; blacksmiths were organized to manufacture crowbars, pickaxes, and other essential equipment. Specialized military training covered demolition methods and techniques for dismantling railways, including tactics such as heating and bending steel rails. Civilian mobilization played a crucial role: militia and support teams took on tasks such as transport, medical aid, and coordination with military units. In Central Shanxi alone, more than 10,000 militia members were mobilized. The Eighth Route Army headquarters repeatedly stressed the need for operational confidentiality, stating: "Before the battle begins, the plan must remain strictly classified; until preparations are completed, the campaign objective may be disclosed only to brigade-level commanders." With the cover of dense summer millet, troops secretly assembled within their designated operational areas. Before the battle, the Japanese North China Area Army estimated the strength of the communist regular forces at about 88,000 men in December 1939. Two years later, they revised the estimate to 140,000. On the eve of the battle, communist forces had grown to between 200,000 and 400,000 men, organized in 105 regiments. By 1940, the growth had become so significant that Zhu De ordered a coordinated offensive by most of the communist regular units—46 regiments from the 115th Division, 47 from the 129th, and 22 from the 120th—against Japanese-held cities and the railway lines that connected them. According to the Communist Party's official statement, the battle began on 20 August.  On August 20, 1940, the rain didn't stop the campaign—it changed the battlefield. It slowed movement, blurred distance, and turned rivers and muddy roads into obstacles that could just as easily trap your own men as your enemy's. Along the districts bordering the Zhengtai Railway, the Eighth Route Army still moved, slipping through valleys and river crossings, bypassing Japanese posts, and positioning forces on both sides of the line as night settled in. By dark, the plan became a coordinated strike meant to hit the enemy before they could properly react. Across the entire Zhengtai Railway, attacks went out with timing designed to disorient Japanese defenders—so that their "first realization" arrived only after the railway itself was already being attacked and the window to respond effectively had slipped away.   A key portion of that strike fell to the right column of the Jin-Cha-Ji Military Region, centered on the 5th and 19th Regiments, with the mission of sabotaging the Niangziguan to Luanliu section. At 20:00 on August 20, part of the 5th Regiment infiltrated Niangziguan Village for the first time, overwhelmed the puppet troops stationed there, and seized the village by dawn. After that opening cut, the main force moved in to cover the engineers, destroy enemy fortifications, and blow up the Guandong Railway Bridge. When the sabotage was done, they withdrew from Niangziguan on their own initiative, leaving the enemy to deal with the destruction rather than being pulled into a long, grinding engagement.   That same night, at Mohe Beach along the Zhengtai line, another action unfolded. The 1st Company of the 1st Battalion of the 5th Regiment attacked the station and was immediately met with a counterattack by Japanese forces. By dawn on August 21, the company withdrew—an adjustment, not defeat—and then attacked again the same night after crossing the Mian River. This time the enemy retreated into barracks to resist more stubbornly, with nearly 1,000 Japanese troops holding Mohe Beach. Heavy rain had swollen the river and made foot crossing nearly impossible, but the attackers seized the village west of the station and held it. On August 22 afternoon, more than 400 Japanese troops counterattacked; the main force of the 5th Regiment hit from the north bank of the Mian River in a fire assault, killing more than 50 before withdrawing the 1st Company out of the fighting. The 19th Regiment, meanwhile, took Jucheng and Irrang stations, tightening the pressure on the railway corridor.   On August 23, 1940, the 5th Regiment recaptured Niangziguan and blew up the stone bridge east of the village, destroying the railway segment between Chengjialongdi and Mohetan. That night the 19th Regiment stormed Yirang Station and blew up the water tower and the railway, ensuring the disruption would not be temporary. From August 24 to 27, bridges near Yanhui—stone and wooden—were destroyed again and again. Under that continuous pressure, beginning on August 25, Japanese transportation along the Niangziguan to Luanliu section of the Zhengtai Road was cut off completely. Strongholds were left to fight more or less alone, unable to coordinate or move supplies the way they normally would.   While the right column worked the railway, other forces hit the system from different angles. The Central Column of the Jin-Cha-Ji Military Region—comprised of the 2nd, 3rd, and 16th Regiments—took responsibility for sabotaging the Zhengtai Road segment from Niangziguan to Weishui and for striking the Jingxing Coal Mine area. On the night of August 20, the 3rd Regiment launched coordinated attacks on the Gangtou old mine and the Dongwangshe new mine of Jingxing, and with miners assisting, the 1st Battalion quickly stormed the new mine and annihilated part of the enemy garrison. The rest withdrew into bunkers, resisting as best they could. By the afternoon of the next day, the entire enemy force had been wiped out. Afterward, major buildings in the mining area were destroyed and most materials were removed so that the mine could not resume production for more than six months. The 3rd Regiment also captured Jiazhuang, reinforcing the idea that sabotage here meant disabling not just lines of movement, but also the flow of resources.   Elsewhere, Japanese positions were disrupted in smaller, targeted strikes that still added up. After the Japanese stronghold at Nanzheng destroyed the railway between Nanzheng and Weishui, the 2nd Regiment took the eastern end fortress of the Faluling Railway Bridge, covered the engineers as they blew up a section of the bridge, and briefly occupied Caizhuang. The 2nd Battalion of the 16th Regiment attacked Beiyu on the night of August 20, annihilating most defenders, and on August 21 it covered the engineers to destroy the Beiyu Stone Bridge. Other units struck Didu and annihilated most defenders in Nanyu. By August 24, the Central Column had learned that more than 1,000 Japanese troops were stationed in Jingxing County, with additional reinforcements moving toward Nanyu and Didu. Their response was practical: detachments were assigned to watch and harass along the railway while the main force gathered in mobile positions—waiting for the next opening rather than charging blindly into concentrated strength.   Meanwhile, the left column of the Jin-Cha-Ji effort—from the 2nd Regiment of the Jizhong Garrison Brigade, the Military Region Special Service Regiment, and the Pingjinghuo Detachment—focused on sabotage from Weishui to Shijiazhuang. On the night of August 20, the Pingjinghuo Detachment attacked Yanfeng and blew up the railway. The Special Service Regiment moved with massed efforts as they destroyed power lines and highways from Yanfeng to Weizhou. On the night of August 22, the Special Service Regiment attacked Shang'an Station. On August 23, the 2nd Regiment stormed Touquan Station, captured two fortresses, then withdrew from the railway line; from August 25 to 27, they destroyed the highway connecting Pingshan, Huolu, Weishui, and Yanfeng.   While the main blow was falling along the Zhengtai Railway, the 129th Division was assigned raids on the western section. That area included the Japanese Independent Mixed Brigade No. 4 headquarters, a coal mine base at Yangquan, and support from Independent Mixed Brigade No. 9 from Yuci. These raids weren't only about destruction—they were meant to disorient, to create confusion over where the main pressure truly was. After the general offensive began at 20:00 on August 20, five companies of the 16th Regiment attacked Lujiazhuang Station and captured bunkers. Two guerrilla-operating companies in Yuci worked with engineers to destroy bridges between Lujiazhuang and Duanting. The 38th Regiment surprised Shanghu and Heshangzu stations, while the 25th Regiment captured Mashou Station and pushed Japanese troops toward Shouyang. The division's right-wing sabotage unit—28th and 30th Regiments of the newly formed 10th Brigade—took on sabotage on the Yangquan–Shouyang section, splitting routes on the night of August 20 to attack stations like Langyu, Zhangjing, Qinquan, and then striking additional positions with the 30th Regiment. Across that window, stations and strongholds such as Sangzhang, Yanzigou, Langyu, and Qinquan were taken, iron bridges were destroyed, and additional stations including Potou, Xinzhuang, Saiyu, Tielugou, Xiaozhuang, and Zhangzhuang were seized or disrupted.   As the western sabotage deepened, Japanese response hardened—but the ability to coordinate weakened. With the Zhengtai line sabotaged, the western section came under the 129th Division's control except for a few places such as Shouyang. Fierce assaults forced Japanese forces to lose contact with each other within days. Strongholds were attacked, besieged, and then annihilated as communication and coordination broke down. The 129th Division mobilized local people to destroy railway facilities, stations, and installations using demolition, burning, and flooding, moving materials so the railway and related infrastructure were effectively erased rather than merely damaged.   To cover these operations, the division occupied Shinaoshan with the 14th Regiment of the general reserve. Starting the morning of August 21, Japanese forces concentrated in Yangquan and attacked Shinaoshan daily. Enemy strength reportedly rose from more than 200 to more than 600, supported by bombing and strafing and the release of poison. The 14th Regiment held out until August 25, repelling repeated attacks, and by August 26 additional pressure came again as reinforcements increased. After six days and nights—and the annihilation of more than 400 enemy soldiers—the 14th Regiment withdrew from the main peak of Shinaoshan, continuing to contain the Japanese with smaller detachments while the main force shifted to another mission.   The first phase of sabotage had succeeded, but the campaign did not allow complacency. The Japanese strengthened their presence along the railway and launched frequent counterattacks, and Japanese divisions in southern Shanxi—including the 36th, 37th, and 41st—prepared to reinforce from the north. On August 26, the Eighth Route Army Headquarters issued instructions for a second phase: continue breaking through the road, concentrate superior forces, and annihilate Japanese units smaller than a battalion that were attacking or reinforcing. In line with that guidance, the Jin-Cha-Ji Military Region ordered the Jin-You Column to keep breaking through the road on August 27 for one or two days, while the 129th Division alternated daily in breaking through. Under sustained pressure, the western section of the Zhengtai Road was basically destroyed; transportation was effectively cut off except for a few towns such as Shouyang and Yangquan.   On September 2, orders were issued to conclude the Zhengtai Campaign starting from the 3rd and shift forces according to the second-step plan. As the Jin-Cha-Ji Military Region launched the Mengbei Campaign, the 129th Division shifted toward attacking invading Japanese forces, while other tasks—such as attacking the He-Liao Highway and recovering cities of He and Liao—were left for later. Beginning September 2, the Military Region deployed the 2nd, 5th, 16th, and 19th Regiments toward areas north of Meng County and Shouyang to recapture enemy strongholds. With the railway sabotaged, the Japanese main force north of Meng County shifted south to reinforce, weakening garrisons and spreading panic among the strongholds. As fierce offensives intensified, garrison troops began to waver. By the afternoon of September 5, Japanese troops at Xiashe, supported by troops from Shangshe, retreated to Shangshe and fled toward Meng County overnight. That night, the 19th Regiment arrived near Shangshe and, together with the Special Service Battalion of the 2nd Military Sub-district, pursued. The 1st Battalion of the 19th Regiment advanced into Shenquan and Putian to cut off the retreat route. By 9:00 AM on September 6 the enemy was surrounded in Xingdao Village, and after five hours of intense fighting most forces were annihilated. Survivors fled east to Luolizhang Mountain, only to be surrounded again by the 19th, 5th, and 16th Regiments. By the night of September 9, most Japanese forces had been wiped out, though more than 40 men broke through in dense fog and escaped into Meng County.   The siege continued through bitter episodes involving attacks and withdrawals under poison, with both sides paying heavily for every moment of progress. Eventually, on September 11, Japanese troops in Xiyan escaped back to Meng County, helped by more than 200 Japanese already present there. Meanwhile, the Japanese attempted to counter the pressure: on September 4 they sent more than 2,000 troops to reinforce Meng County and began a counterattack. On September 10, the Jin-Cha-Ji Military Region ordered the 19th and 5th Regiments to remain east and north of Meng County to coordinate with the 129th and 120th Divisions, while the rest prepared for new missions.   As fighting intensified around Zhengtai and Meng County, a parallel pressure campaign unfolded. To contain Eighth Route Army sabotage along Zhengtai, the Japanese assembled battalions from Independent Mixed 4th and 9th Brigades to strike the 129th Division. In response, the 120th Division began large-scale sabotage against the Tongpu Railway and major highways in northwestern Shanxi starting 20:00 on August 20. They captured enemy strongholds along rail and road lines, striking major bases such as Kangjiahui on the Xinjing Highway, where more than 50 Japanese and puppet troops were stationed, and also attacking other areas like Shishen, Lizhen, and Jingle. Ambushes were set to annihilate reinforcements arriving from different directions, and at 00:30 on August 21 the 2nd Battalion of the 4th Regiment attacked Kangjiahui and annihilated the defenders by dawn. Reinforcements arriving in cars were destroyed, and subsequent actions continued to expand the disruption.   Over more than 180 battles in northwestern Shanxi, the 120th Division annihilated more than 800 Japanese and puppet troops and captured or destroyed stations and strongholds including Kangjiahui, Yangfangkou, Pingshe, and Longquan. By disrupting the Tongpu Railway and transportation along the Xinjing, Taifen, and Fenli highways, they tied down Japanese forces and made it harder to reinforce Zhengtai. In practical terms, this meant the first phase of the Hundred Regiments Offensive—lasting about three weeks—ended on September 10 with major railway lines and motor roads attacked repeatedly. Roadbeds, bridges, switching yards, and installations were hit heavily; at the Qingxing coal mines, facilities were destroyed and production was halted for nearly a year.   By the end of that first phase, the campaign's logic had become clearer: once the Japanese leaned more heavily on a "cage-and-strongpoint" defense system, the same transport network that had supported their defense became less secure. When rail and road were repeatedly disrupted, strongpoints became more vulnerable—especially if Japanese units pulled out nearby detachments to respond to sabotage. So the campaign shifted from breaking transportation to attacking blockhouses and other strongpoints in contested areas, aiming to force Japanese forces back into well-defended garrisons and leave the countryside again contested by Communist forces. I would like to take this time to remind you all that this podcast is only made possible through the efforts of Kings and Generals over at Youtube. Please go subscribe to Kings and Generals over at Youtube and to continue helping us produce this content please check out www.patreon.com/kingsandgenerals. If you are still hungry after that, give my personal channel a look over at The Pacific War Channel at Youtube, it would mean a lot to me. From 20 August 1940, under secrecy and rain, units of the 8th Route Army infiltrated stations, captured villages, destroyed bridges, power lines, roads, mines, and stations across multiple columns. By early September the Zhengtai and related Tongpu transport routes were repeatedly severed, forcing Japanese troops to fight isolated strongpoints and hindering reinforcement. 

Claude deletes a company — and the internet immediately blamed the AI. But this story is really about backup design, credential management, and least privilege. An AI coding agent running Claude via Cursor deleted PocketOS's entire production database and all its backups in nine seconds. One bad design decision at a time, a startup built itself a disaster waiting to happen. Claude just happened to be the thing that set it off.Here's what you need to understand: the AI violated the principles it was given, and that's on Claude. But Claude never should have had access to do what it did. Credentials were sitting in a plain text YAML file. The production database and its backups lived on the same volume. No least privilege. No expiration on elevated permissions. And almost certainly, no backup recovery test — ever.In this episode, Curtis and Prasanna break down what actually went wrong with PocketOS, what Railway did to help recover the data, and what you need to do to make sure this never happens to you. Topics covered include backup isolation, the 3-2-1 rule, secrets management tools like AWS Secrets Manager and HashiCorp Vault, least privilege access, permission expiration, and credential scanning tools like TruffleHog.Chapters:0:00 — Intro: Meet the villain1:50 — Welcome and introducing "the French friend"3:48 — What Claude actually did to PocketOS7:20 — This is a backup story, not an AI story9:27 — The recovery: Railway, a weekend of chaos, and a lucky Twitter post12:31 — Your data is your responsibility — not your vendor's17:48 — Rule #1: Never store backups inside production20:37 — The real problem: credential management23:38 — Secrets management tools explained25:21 — Least privilege and why permissions need expiration dates34:59 — Finding exposed credentials with TruffleHog37:24 — Summary and takeaways

CMA CGM S.A. v. BNSF Railway Company

Send us Fan MailIn this episode Alasdair Stewart travels on an iconic Intercity 125 Class 43 rail tour from Euston to the north Wales coast . He meets up with travel journalist Tom Bright, Tom  is the news and features editor of Steam Railway Magazine. We hear how the love of telling stories and steam landed him a pretty ideal job.We have an update from Rother Valley Railway in Kent as they open a new station building in a million pound prjoect to link the village of Robertsbridge with the Kent and East Sussex Line and the mainline to London.Sharon Gregory reports back on her Railway Rideout to Crich National Tramway Museum in Derbyshire. The Young reporters meanwhile have been busy - George Woodward – tells how 'My Dad Saved a Railway' a model one but very much a Railway and Cynan Hughes rides on the Fairbourne scouting out the best places to photograph the upcoming Gala.Links to the Railway and organisations mentioned in this epsiode.National Tramway Museum in CrichRother Valley RailwayBranch Line Society RailtoursVintage Trains Fairbourne Miniature RailtoursDo follow us Facebook for photos from our adventures! This podcast is produced by Laura Raymond and presented by Alasdair Stewart and Sharon Gregory. Our 'Making Tracks' music is with kind permission of composer and musician Richard Durrant. It is a unique piece inspired by the rhythm of the historic rolling stock on the Ffestiniog Railway on the scenic journey from Harbour Station to Tan y Blwch. You can listen and download the full 'Tan y Bwlch' Ukulele Quartet here: Thank you to voice artist David King - for the Railway Ride outs voice over. Ukulele Quartet No. 1 "Tan y Bwlch"  Ukulele Quartet No. 1 "Tan y Bwlch" Richard Durrant · Single · 2019 · 3  songs.    

Bob argues that many Austro-libertarians (himself included, initially) have been too quick to dismiss the Trump administration's foreign and economic policy as mere incompetence or corruption, without grasping the strategic logic behind it. His thesis: the U.S. national security establishment sees China's rise as an existential threat and believes the window to act is closing fast, making the current flurry of aggressive moves less like random chaos and more like a desperate Hail Mary pass.Related:The Charts and Graphs Mentioned in this Episode: Mises.org/HAP549aThe Bob Murphy Show, "LEAKED: Trump's Secret Strategy Briefing": Mises.org/HAP549bCore Insights, "China Quietly Built a 10,400km Railway to Iran — The US is Terrified": Mises.org/HAP549cThe Tom Woods Show, "The Venezuela Propaganda, with David Stockman": Mises.org/HAP549dCelebrate Murray Rothbard's 100th birthday with a free copy of Anatomy of the State. Get yours at Mises.org/HAPodFree

Bob argues that many Austro-libertarians (himself included, initially) have been too quick to dismiss the Trump administration's foreign and economic policy as mere incompetence or corruption, without grasping the strategic logic behind it. His thesis: the U.S. national security establishment sees China's rise as an existential threat and believes the window to act is closing fast, making the current flurry of aggressive moves less like random chaos and more like a desperate Hail Mary pass.Related:The Charts and Graphs Mentioned in this Episode: Mises.org/HAP549aThe Bob Murphy Show, "LEAKED: Trump's Secret Strategy Briefing": Mises.org/HAP549bCore Insights, "China Quietly Built a 10,400km Railway to Iran — The US is Terrified": Mises.org/HAP549cThe Tom Woods Show, "The Venezuela Propaganda, with David Stockman": Mises.org/HAP549dCelebrate Murray Rothbard's 100th birthday with a free copy of Anatomy of the State. Get yours at Mises.org/HAPodFree

China saw steady growth in railway passenger trips in the first four months of the year. According to China State Railway Group, nearly 1.6 billion railway trips were made from January to April, up 6.8 percent from a year earlier.

learn about the French railway system

fWotD Episode 3296: Talyllyn Railway Welcome to featured Wiki of the Day, your daily dose of knowledge from Wikipedia's finest articles.The featured article for Thursday, 14 May 2026, is Talyllyn Railway.The Talyllyn Railway (Welsh: Rheilffordd Talyllyn) is a narrow-gauge railway in Wales, which runs for 7+1⁄4 miles (12 km) from Tywyn on the Mid-Wales coast to Nant Gwernol near the village of Abergynolwyn. Opened in 1865 to carry slate from the quarries at Bryn Eglwys to Tywyn, it was the first narrow-gauge railway in Britain authorised by Act of Parliament to carry passengers using steam haulage. Despite severe underinvestment, the line remained open, and in 1951 it became the first railway in the world to be preserved as a heritage railway by volunteers.Since preservation, the railway has operated as a tourist attraction, expanding its rolling stock through acquisition and an engineering programme to build new locomotives and carriages. In 1976, an extension was opened along the former mineral line from Abergynolwyn to the new station at Nant Gwernol. A major rebuilding and extension of Tywyn Wharf station took place in 2005, including a much-expanded facility for the Narrow Gauge Railway Museum, and in 2021 the railway was designated a World Heritage Site as part of the Slate Landscape of Northwest Wales.The fictional Skarloey Railway, which formed part of The Railway Series of children's books by the Rev. W. Awdry, was based on the Talyllyn Railway. The preservation of the line inspired the Ealing Comedy film The Titfield Thunderbolt.This recording reflects the Wikipedia text as of 00:18 UTC on Thursday, 14 May 2026.For the full current version of the article, see Talyllyn Railway on Wikipedia.This podcast uses content from Wikipedia under the Creative Commons Attribution-ShareAlike License.Visit our archives at wikioftheday.com and subscribe to stay updated on new episodes.Follow us on Bluesky at @wikioftheday.com.Also check out Curmudgeon's Corner, a current events podcast.Until next time, I'm neural Arthur.

BNSF Railway Company v. U.S. Dept. of Labor

On this day, 11 May 1894, the Pullman railroad strike began in Chicago following the firing of three workers the previous day, called by Eugene Debs' American Railroad Union (ARU).A month after it began, 400 ARU delegates from around the country met, and in defiance of Debs and their leadership agreed to boycott all Pullman railroad cars across the country in support of the workers in Chicago. The boycott began on June 26, when switchmen in Chicago refused to switch Pullman cars, and were fired. Their colleagues then walked out in their support.The strike then spread down various railroads until soon all 26 roads out of Chicago were stopped, as were all of the transcontinental lines which carried Pullman cars. At its peak it was the biggest strike in US history to date, involving over 250,000 rail workers across 27 states and territories. That said, the union weakened its base of support by refusing to admit Black members, which enabled employers to hire some Black workers as strikebreakers. Despite this, some Black workers helped strikers blockade train tracks around Chicago.Then the US government intervened, granting an injunction against all strike activities across the country, and brought in federal troops. Thousands of US soldiers joined state militia and deputy marshals paid by the rail companies to attack the workers, shooting dozens. Still, the workers fought back, and workers around the country organised to call a general strike to force Pullman into arbitration. But these efforts were blocked by union leaders and eventually repression broke the strike.This book tells its story, and that of other mass strikes in the US: https://shop.workingclasshistory.com/products/strike-jeremy-brecherOur work is only possible because of support from you, our listeners on patreon. If you appreciate our work, please join us and access exclusive content and benefits at patreon.com/workingclasshistory.See all of our anniversaries each day, alongside sources and maps on the On This Day section of our Stories app: stories.workingclasshistory.com/date/todayBrowse all Stories by Date here on the Date index: https://stories.workingclasshistory.com/dateCheck out our Map of historical Stories: https://map.workingclasshistory.comCheck out books, posters, clothing and more in our online store, here: https://shop.workingclasshistory.comIf you enjoy this podcast, make sure to check out our flagship longform podcast, Working Class History

The China-Europe Railway Express has made 130,000 trips by Saturday. The China State Railway Group said total cargo value has exceeded 520 billion U.S. dollars.

China's national railway network set records for passenger and freight traffic during the May Day holiday. From April 29 to May 6, passenger trips totaled 159 million, up 5.2 percent.

In May 1942, a team of Norwegian resistance fighters in occupied Norway were getting ready to blow up a railway carrying materials crucial to the German war machine. Led by Lieutenant Peter Deinboll, a local from the area, they set out to execute what the Allied forces saw as the top priority sabotage operation in Norway at that stage in the war. Should they fail, allied planes would carpet bomb the village, including Deinboll's hometown. Lars Bevanger speaks to Lieutenant Deinboll's nephew, Gunnar Deinboll Jenssen.Eye-witness accounts brought to life by archive. Witness History is for those fascinated by and curious about the past. We take you to the events that have shaped our world through the eyes of the people who were there. For nine minutes every day, we take you back in time and all over the world, to examine wars, coups, scientific discoveries, cultural moments and much more. Recent episodes explore everything from how the Excel spreadsheet was developed, the creation of cartoon rabbit Miffy and how the sound barrier was broken.We look at the lives of some of the most famous leaders, artists, scientists and personalities in history, including: the moment Reagan and Gorbachev met in Geneva, Haitian singer Emerante de Pradines' life and Omar Sharif's legendary movie entrance in Lawrence of Arabia.You can learn all about fascinating and surprising stories, like the invention of a stent which has saved lives around the world; the birth of the G7; and the meeting of Maldives' ministers underwater. We cover everything from World War Two and Cold War stories to Black History Month and our journeys into space.(Photo: Lieutenant Peter Deinboll Jr. Credit: Gunnar Deinboll Jenssen)

Your daily news in under three minutes. At Al Jazeera Podcasts, we want to hear from you, our listeners. So, please head to https://www.aljazeera.com/survey and tell us your thoughts about this show and other Al Jazeera podcasts. It only takes a few minutes! Connect with us: @AJEPodcasts on Twitter, Instagram, Facebook, and YouTube

As the Labor Day holiday draws to a close, China's railways are experiencing a peak in return travel, with passenger traffic remaining at a high level.

Railways crisscrossed across Wales. It was a network designed not for Wales, but through Wales. A network that tied valleys to ports, and ports to England. Follow us on social media: Instagram, Bluesky: @Welshhistorypod Facebook: https://www.facebook.com/welshhistorypodcast Please consider becoming a supporter at: http://patreon.com/WelshHistory Music: Celtic Impulse - Celtic by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100297Artist: http://incompetech.com/ © 2026 Evergreen Podcasts Learn more about your ad choices. Visit megaphone.fm/adchoices

Every now and then a new technology comes along that changes everything – electricity, computers, potentially AI. In mid-19th-century America, that technology was the steam locomotive. It knitted the US economy together, driving the nation's industrialisation during the Gilded Age. But along the way, it also caused one of the biggest financial crises in American history. FT Alphaville editor Robin Wigglesworth tells his co-host, FT columnist Gillian Tett, the story of the great railway bubble that ended in the Panic of 1873. It's also the story of the spectacular rise and fall of Jay Cooke, the greatest banker of his day, who lost a fortune betting on a railroad that would eventually span the North American continent – just not in time to repay its debts. Robin and Gillian discuss what lessons the financier's fate holds for the investors gambling on today's AI boom.Credits: New York Times Archive, Otto Herschan Collection/Hulton Archive/Getty Images, Hulton Archive/Getty ImagesFurther reading:Jay Cooke: Financier of the Civil War, by Ellis Paxson Oberholtzer (1907)Jay Cooke's gamble: the Northern Pacific Railroad, the Sioux, and the Panic of 1873, by M John Lubetkin (2006)Railroaded: The Transcontinentals and the Making of Modern America, by Richard White (2012)Pop! Why Bubbles Are Great For The Economy, by Daniel Gross (2007)A Fabulous Debt: The Epic Story of How Bonds Built the Modern World, by Robin Wigglesworth (2026 – forthcoming)To enjoy future episodes, be sure to subscribe to The Story of Money wherever you get your podcasts, also on the show's dedicated YouTube channel here: Hosts: Gillian Tett and Robin WigglesworthProducer: Lulu SmythSenior Producers: Michela Tindera and Laurence Knight Executive Producers: Flo Phillips and Manuela SaragosaOriginal music and sound design: Breen TurnerBroadcast engineers: Bianca Wakeman and Petros GiuompasisPodcast Development: Laura ClarkeFT Global Head of Audio: Cheryl BrumleyVideo editor: Josh Divney at Podcast DiscoveryRead a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

In today's lesson we discussed the following:   I feel very good today - Czuję się bardzo dobrze dzisiaj why - dlaczego Because today I did Yoga & Meditation and I feel fantastic - Ponieważ dzisiaj ćwiczyłem jogę i medytację i czuję się fantastycznie Do you understand - Czy rozumiesz Railway Station - Stacja kolejowa Please repeat - Proszę powtórzyć I am going to the railway station - Idę na dworzec kolejowy Please take me to the railway station - Proszę, zabierz mnie na dworzec kolejowy I am at the railway station - Jestem na stacji kolejowej We must buy a ticket - Musimy kupić bilet We go to the cash desk - Idziemy do kasy I would like a ticket - Chciałbym bilet What type of ticket - Jaki rodzaj biletu A ticket can be normal or with discount - Bilet może być normalny lub ze zniżką One normal ticket to Kracow/ Warsaw / Lodz / Gdansk - Jeden normalny bilet do Krakowa / Warszawy / Łodzi / Gdańska What class - Jaka klasa 1st class / 2nd Class / 3rd Class - Pierwsza klasa / druga klasa / trzecia klasa We can practice - Możemy ćwiczyć I am the cashier and you are the customer - Jestem kasjerem, a ty klientem A ticket to Warsaw please - Poproszę bilet do Warszawy What type of ticket - Jaki rodzaj biletu What time - Jaki czas What time is the next train - O której jest następny pociąg The next train is 1pm - Następny pociąg to 13.00 58zl please - 58zł proszę I thought it would be cheaper - Myślałem, że będzie taniej Second Class 48zl - Druga klasa 48zl Third class 30zl - Trzecia klasa 30zł I will be with the animals - Będę ze zwierzętami Will you pay by cash or credit card - Czy zapłacisz gotówką czy kartą kredytową? Normal train / Fast train / Express train / Intercity - Pociąg normalny / pociąg szybki / pociąg ekspresowy / intercity It must be that train - To musi być ten pociąg How long will the train take - Jak długo potrwa pociąg one hour fifteen minutes - godzina piętnaście minut  

In today's episode we discussed:   How do you feel - Jak się czujesz  Good and you - Dobrze, a ty  What is the topic today - Jaki jest dziś temat There is a very popular phrase - Istnieje bardzo popularne zdanie - Excuse me where is - Przepraszam, gdzie jest  Where is the Hotel Ibis - Gdzie jest hotel Ibis  Where is the Railway station - Gdzie jest dworzec kolejowy  Where is the Hospital - Gdzie jest szpital  Where is the closest Post Office - Gdzie jest najbliższy urząd pocztowy Please repeat - Proszę powtórzyć  Where is the Bus Station - Gdzie jest przystanek autobusowy  How can I get to the centre - Jak mogę dostać się do centrum  Take a Taxi - Wziąć taksówkę  How can I get to the Museum - Jak mogę dostać się do muzeum  I'm lost Man / Woman - Jestem zagubiony Mężczyzna / Kobieta  Where is the Pharmacy - Gdzie jest apteka Please go straight - Proszę idź prosto  or please turn left - lub proszę skręcić w lewo - Please turn right - Proszę skręcić w prawo  Where is the hotel Hilton - Gdzie jest hotel Hilton  Around 10 minutes on foot - Około 10 minut na piechotę  Please go straight, later turn right and straight again - Proszę iść prosto, potem skręcić w prawo i znowu prosto  I'm not from here - Nie jestem stąd  Where is the Museum - Gdzie jest Muzeum  We have a few museums in our city - W naszym mieście mamy kilka muzeów  Go straight to the end of the street and then turn right - Idź prosto na koniec ulicy, a następnie skręć w prawo  The museum is there - Muzeum jest tam   

This week, a permit to remove an attraction has been filed, a big timeline change is coming to an area of Disneyland, more updates to the Disneyland app, some rare discounted merchandise at the resort, we finish our discussion with Kevin Rafferty, and more! Please support the show if you can by going to https://www.dlweekly.net/support/. Check out all of our current partners and exclusive discounts at https://www.dlweekly.net/promos. News: A popular attraction in Disney California Adventure is moving towards extinction. Monsters, Inc. Mike & Sully to the Rescue! has had a permit filed with the city of Anaheim for demolition. In addition to the attraction, the Hollywood Lounge beverage stand is also included in the demolition permit. The timeline to start demolition is not until at least late 2027. - https://www.disneyfoodblog.com/2026/04/19/confirmed-this-disney-ride-will-permanently-close-in-2027/ We are just about a week away from the timeline expanding at Star Wars Galaxy's Edge. On April 29th, Batuu will welcome original trilogy characters like Darth Vader, Luke Skywalker, Han Solor, and Leia Organa. This has been a long time request from guests who want to interact with characters from across the trilogy in the Star Wars themed land. There is some criticism that the characters are not close enough to their appearance in the films, but we will have to wait and see. In addition, the area will have the classic music of John Williams playing from the movie score. - https://www.micechat.com/434704-disneyland-update-star-wars-land-glow-up-app-shake-up-toy-story-5-build-up/ https://www.laughingplace.com/disney-parks/photos-dok-ondar-changes-april-2026/ In the latest update to the Disneyland app, there have been some good quality of life improvements. Under My Visit, the Tip Board has been renamed to Wait Times & Showtimes, and My Day has been renamed My Plans. These updated names better reflect what can be found in each section. MyDisney Wallet has been added to the account settings where you can add your credit or debit card for payments. There still is no option to add a gift card. - https://www.micechat.com/434704-disneyland-update-star-wars-land-glow-up-app-shake-up-toy-story-5-build-up/ With the Disneyland 70th celebration coming to a close this summer, there are some good discounts to be had. Many 70th anniversary items are marked down up to 50%, including some Loungefly bags. In addition, there are some new Duffy and Friends items that have arrived at the resort from Aulani. Pins, a button up shirt, and more are available. - https://www.micechat.com/434704-disneyland-update-star-wars-land-glow-up-app-shake-up-toy-story-5-build-up/ A classic attraction is retuning to the Disneyland Resort. The Skyway is coming back! Ok, only as a straw topper, but still! Starting April 22, guests can add this to their collections. It features the classic rectangular, four person gondola from 1965. - https://www.laughingplace.com/disney-parks/disneyland-skyway-straw-clip/ SnackChat: New Menu Items - https://www.instagram.com/p/DXV432qlrJ3/?igsh=MWw5aDRwNHFybzZx Discussion Topic: Imagineer Kevin Rafferty Magic Journey: My Fantastical Walt Disney Imagineering Career - https://www.amazon.com/Magic-Journey-Fantastical-Disney-Imagineering/dp/1368020488 Marty Note pdf.pdf Marty's Marty Note pdf.pdf Marty's Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Join comedians Rachel Fairburn and Kiri Pritchard-McLean as they explore a shared passion, serial killers. Each episode the pair will talk all things murder and macabre and have a right laugh doing it. This is part 2 of the grim story of John Duffy and David Mulcahy, also known as the Railway Killers or the Railway Rapists. The two were convicted of murdering three women in the 1980s, as well as raping many more. Hang around for a delightful anecdote about losing your parents' ashes! Yes, there's some truly morbid murders before then, but it's worth the wait. Oh, and a very important message from Tim.

Scott and Wes break down a chaotic week in dev news — the Claude Code source leak, a nasty Axios npm supply chain hack, and Railway's private cache exposure — plus how to keep these nightmare scenarios from hitting your own projects. Show Notes 00:00 Welcome to Syntax! 00:55 Claude Code Leaked! Wes' X Post Apple Source Code Video 05:42 Burning through Claude Code token limits. Reddit Thread 08:57 Axios hacked! Step Security pnpm Supply Chain Security pnpm minimumReleaseAge 16:13 Pretext blew up! Pretext.js Demos Wes' Demo 27:24 Railway shared private cache. Railway Incident Report 31:54 Sick Picks & Shameless Plugs. Sick Picks Scott: Kindle Colorsoft Kids Wes: UGREEN 200W 8-Port GaN USB C Charger Block, Wyze Headphones Shameless Plugs Scott: Syntax on YouTube Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads