Podcasts about gcp

GCP with Andy, Geoff, Ben & Chris Wilson review New Generation Wrestling's 18th Anniversary Show from Hull City Hull, Hull. As we chat about the show and the pubs of the day in the City!!Support this podcast at — https://redcircle.com/graps-and-claps-podcast/donations

Innovation isn't about funding, it's about how organisations are built and led. Progress comes from cutting bureaucracy, empowering mission-led teams, and asking the right questions to unlock bold breakthroughs. This week, Dave, Esmee and Rob are joined again by André Loesekrug-Pietri, Chair and Scientific Director of the Joint European Disruptive Initiative (JEDI, Europe's ARPA) to explore how Europe can turn moonshot ambitions into reality by building the right people, culture and operating models for future-shaping organisations. TLDR00:41 – Introduction01:14 – Hang out: Esmee returns and the missing API has been found!05:14 – Dig in: Staying in step with global innovation12:57 – Conversation with André Loesekrug-Pietri1:02:26 – Roland Garros tennis, and unlocking creative energy GuestAndre Loeskrug-Petri: https://www.linkedin.com/in/andrepietri/X: @eurojediwww.jedi.foundation HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Vertex AI SearchとかRAGとか、一体何に使えるんだ？

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Life sciences are at a turning point, where scientific innovation, regulatory pressure, and patient expectations collide with unprecedented advances in data, AI, and digital platforms. IT is no longer a supporting function but a critical driver of how therapies are discovered, developed, scaled, and delivered safely and at speed.This week, Dave and Rob kick off the Life Sciences mini‑series with Thorsten Rall, Global Industry Lead for Life Sciences at Capgemini, to exploring the current state of the sector, the key themes shaping the episodes ahead, and what it takes to drive better patient outcomes. TLDR00:30 – Introduction to Life Sciences and co‑host Thorsten Rall04:37 – Hang‑out: Navigating Waterloo Station07:50 – Deep dive with Thorsten Rall into the Life Sciences landscape28:03 - What are the main challenges in the sector and main themes45:31 – BBQ season is starting HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/with co-host Thorsten Rall: https://www.linkedin.com/in/thorsten-alexander-rall-b232185/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

William and Eyvonne discuss recent tech news, including the growing political and community opposition to AI data centers driven by fears over power and water usage. They also analyze the “AI Chip War” as hyperscalers such as AWS and Google invest in specialized silicon for training and inference.  Episode Links: Amid backlash, O'Leary Digital CEO... Read more »

Lots of people move to the cloud; it's common. In fact, it's very common to hear customers who are being asked to migrate their workloads to a cloud vendor for a variety of reasons. You might not agree, but often there is some reason to move to the cloud. Sometimes it's even moving from one cloud to another, just because one of the big three (AWS, Azure, GCP) seems more attractive this year than the one from last year. When you move, do you size your system for the peak? 80% of the peak? Perhaps there is another goal for which you design. Do you worry about ever being under-provisioned and letting customers have a slower system? Or do you ensure you never hit the peak, which increases costs? Read the rest of Over or Under Provisioned

India's GPU footprint is on track to grow 40x by 2030, from ~50,000 today to a couple of million. That number is bigger than any public forecast. Sharad Sanghi has the unusual standing to make it: he built Netmagic into India's most significant datacenter business, and he's now running Neysa, the only neo cloud in India that Semi Analysis has rated, backed by Blackstone.In this episode of Intelligent Indians, Rajinder Balaraman and Sharad cover:1. Why neo clouds exist as a category, and what hyperscalers structurally can't do for one market 2. The ITQ case study: how to define ROI before infrastructure 3. The three infra mistakes that quietly cost AI teams 10x their compute spend 4. Why power, not GPUs, is the real bottleneck, and why 50% of India's data centre capacity sits in one city 5. What  India's AI Mission could actually unlock in the next phaseIf you're building AI infrastructure in India, tracking the space as an investor, or working on policy in the area, this is the operator view. From someone whose entire balance sheet depends on getting the call right.Chapters 00:00 India's AI Moment The Big Picture02:00 Welcome Introducing Sharath of Neysa03:30 How He Built India's First Data Centre with NetMagic06:00 How ChatGPT Sparked the Idea for Neysa18:00 India is 2nd Largest AI Consumer 21:00 50,000 GPUs Today. 2 Million by 2028 24:30 Neysa vs AWS, GCP, Azure 28:00 Why Indian Banks Are Early AI Adopters31:30 Financial Services, Healthcare, Manufacturing 35:00 PhonePe, Perfios, Hungama - Real AI Use Cases in India38:30 Why Most AI Projects Stay in Pilot and Never Reach Production52:30 GPU Obsolescence Risk — How Neysa Manages It55:00 Healthcare, Education, Agriculture — Where Founders Should Build58:30 IIT Bombay and the Bharat Gyan Project1:01:00 Why India Needs to Keep Its AI Talent at Home1:04:00 Why He Refused to Flip the Company Outside India1:06:30 What It Takes to Make India the AI Research Capital of the World

Just wrapped a great conversation with Woon Ho Jung, CTO - Cloud Native, Commvault, at Google Cloud Next 2026 and this one hit a nerve. Everyone is talking about multi-cloud, AI pipelines, and scaling data.But almost no one is talking about what's quietly breaking underneath it all. Data protection. We got into what's really happening inside enterprises today.Teams assume replication and retention policies are enough. They're not.At scale, across billions of objects, things get messy fast. Gaps show up where you least expect them.That's where the big announcement comes in. Clumio is going deeper with Google Cloud. Clumio for GCP is not just another backup solution. It's a rethink of how you protect cloud-native data, especially inside Google Cloud Storage where most AI and analytics pipelines live today.What stood out to me:- Protecting data at massive scale is still an unsolved problem for many teamsNative tools give a false sense of security- Resilience in the AI era needs a completely different approachIf you're building on Google Cloud right now, this is something you need to pay attention to. This is not about backup. This is about trust in your data layer.#data #ai #commvault #security #googlecloudnext #api #google #theravitshow

今回のGCP Houseは、5月12〜14日にサンフランシスコ・サンマテオで開催された、世界最大級のB2B SaaSカンファレンス「SaaStr AI Annual 2026」の振り返り回。GCP投資先経営陣とともに現地入りした湯浅・工藤の2人が、昨年からの変化と、米国最前線で起きていることの本質を議論しました。工藤執筆の現地レポートもぜひ合わせてご覧ください。「米国AI駆動GTMの最前線 ー SaaStr AI 2026から見えた成長への渇望」https://x.com/_mayumayu13/status/2056147908706332895?s=20昨年のSaaStrを覆っていたのは、「AIで何ができるかわからない。でも、とにかくやらなければ」という焦りでした。一方、今年は明らかに次のフェーズへ。AIは“アシスタント”ではなく、“エージェント”として業務に組み込まれ、GTM・CRM・組織のあり方そのものが書き換わり始めています。今回の収録で繰り返し語られたキーワードが、「Speed is Moat」の進化。1年前は、“とにかく速く動く”という抽象的な焦りとして語られていたものが、今では「何を、なぜ、どこまでエージェント化するのか」という具体戦略とセットで議論されるようになっていました。■概要Vercel・Anthropicの実装事例に見る、AI-Native GTMの解像度スポンサー席を塗り替えた、新興AI-Native CRM企業の本気度“見えないインフラ”として浮上するセマンティックレイヤー元LinkedIn幹部が語る、「3年で人員1/3」という組織変革への意識シリコンバレーの外側では、米国はまだそこまで変わっていないという見落とされがちな論点日本のスタートアップ／事業会社にとって、この構造変化はどんな機会になるのか現地で見た景色と温度感を、これから半年〜1年の戦い方を描き直すための材料として持ち帰っていただけたら嬉しいです。■プロフィールGCP パートナー 湯浅エムレ秀和GCP プリンシパル 工藤真由

What your team uses when you aren't looking? What apps and AI tools are people at work using without telling the IT team? In this episode of the Cloud Do You Do podcast, Revolgy's Ashley Saunders talks with Chase Doelling from our partner, JumpCloud, about unapproved software and Shadow AI. People want to get their tasks done faster, so they try out new AI tools without checking first with their IT department. The risk is that they might be putting private company information directly into public systems. Chase explains why unapproved AI is different from older software issues, and why blocking websites doesn't solve the problem. What you'll find in the episode: Data risk: AI learns from whatever information you type into it, which creates security gaps that regular software doesn't. Hidden costs: How companies end up paying for the same software multiple times because different teams might buy their own tools. A better approach than blocking: Why it works better to guide people toward safe options instead of just blocking access. Getting a clear view: How JumpCloud tracks browser use and login paths to show exactly what apps are running. We are Revolgy - a global cloud partner. Our cloud engineers and architects provide professional and managed services for your projects on GCP and AWS. In a nutshell, we help to make life digital-native companies, SMBs and corporates in the cloud easier. Check our website revolgy.com for more information.Make sure to follow Revolgy on Spotify, Linkedin, and X.Thanks a lot for listening, and see you next time!

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Today's most pressing challenges arise from the collision of rapid technological change with deepening economic inequality, weakening democratic systems, geopolitical instability and accelerating climate pressure, leaving world leaders wrestling with how to govern and solve these deeply interconnected crises.This week, Dave, Esmee and Rob are joined by Dex Hunter-Torricke, Founder & President The Center for Tomorrow to explore how tech can solve world macro issues. TLDR00:33 – Introduction00:40 – Hang out: The Boys on Amazon Prime final episode (spoilers) 06:02 – Dig in: How to solve world macro issues? 07:45 – Conversation with Dex Hunter-Torricke 44:52 – Writing a book and meeting world leaders GuestDex Hunter-Torricke: https://www.linkedin.com/in/dextb/https://www.centerfortomorrow.com/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Can a machine beat a Wall Street journalist to the punch? In this episode of the Cloud Do You Do podcast, Revolgy's Štěpán Kaiser sits down with Gelu Sulugiuc, a former financial journalist for Bloomberg and Reuters who traded the newsroom for the startup world. Gelu shares his incredible journey from growing up in communism to building and successfully selling his first AI startup, PLX AI, to Thomson Reuters. Today, he's building his next company on Google Cloud. His team creates custom tools that scan financial reports and catch the big news the second it drops. In this episode: Why building specialized, in-house AI models is more accurate and cost-effective than relying on massive LLMs like Claude. How automation is freeing up journalists to do real investigative work. The future of algorithmic trading for everyday investors. Plus, a hilarious story about Gelu's first day working in Denmark. We are Revolgy - a global cloud partner. Our cloud engineers and architects provide professional and managed services for your projects on GCP and AWS. In a nutshell, we help to make life digital-native companies, SMBs and corporates in the cloud easier. Check our website revolgy.com for more information.Make sure to follow Revolgy on Spotify, Linkedin, and X.Thanks a lot for listening, and see you next time!

A palate cleanser before we dive back into Frontier Wrestling Alliance television: an interview with the comedy icon of the FWA, Dirk Feelgood.This episode's alternative titles:-I Waved At Her For Two Hours StraightCosmic CometsCamp Of PainCovered In Barbed WireI Don't Even Know Who That Fella IsI Wanted To Be Dean MalenkoAndrew IceDICK FeelgoodCharismatic Neon GoofballI'm On Call!Paul, Just Kill MeTerry Funk With No Underpants OnThe Dance Floor of PCWWhat A PlonkerAural HallucinationsWell, That's Gonna Be RubbishRichard And JudyHow Much Are You Getting For This?I Don't Have Any Whistles!Do You Think This Guy's A Butler?People Want To Make MoneyThat Promo Did Me InHow Many Times Can You Powerbomb Chris Egan?Why Do You Want To See Pictures Of Me?A monthly show from the people behind Must See Matches, The Arn & Eddie Experience and GCP, and there's two things you can do about it...If you want to follow along, head to the FWA Files channel on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@TheFWAFiles⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Listen to Dirk's podcast, The Movie Mixtape: https://open.spotify.com/show/0QVKAjmbIAWTdsFgJuiQDZ?si=60235acb237c42f5Twitter/Bluesky/YouTube: @FWApodIG: @FWA.podlinktr.ee/FWApodKieran: @kieraneditsEddie: @EddieSideburns/KawadaKicksBestAndy: @oggypart3Dirk IG:@DirkFeelgood/@the_moviemixtape

**Our listeners can get 30% off OpenMetal-hosted private clouds and bare-metal servers with the code below**Promo Code: RICHOMI30Discount: 30% off clouds and bare metal hardwarePromotion Page:

GCP goes to BWR Riot Rumble 2026 with Andy & Geoff as they chat about the show, the beers and hotels of the day.Support this podcast at — https://redcircle.com/graps-and-claps-podcast/donations

What if you could run 35 AWS services locally in under 25 milliseconds, using just 13 megabytes of memory, with a single Docker command and no cloud bill? That's exactly what Floci does.In this episode, Frank Delporte talks with Hector Ventura, the creator of Floci, a free and open-source cloud emulator built with Quarkus and GraalVM native compilation. Hector walks us through why he built it when LocalStack dropped its open-source community edition, how AI tooling helped him accelerate development of new service integrations, the challenges of keeping GraalVM happy with third-party libraries, and the road ahead for Azure and GCP support.If you're a developer who wants fast local testing, a DevOps engineer writing Terraform, or a student learning cloud without the cost, Floci is worth a look!Guest: Hector Ventura Foojay Author page LinkedInLinks On Foojay: Introducing Floci: A High-Performance, GraalVM-Powered AWS Emulator Floci project site Floci on GitHub Migrate from LocalStackContent00:00 Introduction of topic and guest01:48 What is Floci?02:15 How Floci compares to LocalStack03:01 Why Hector started Floci04:02 Floci emulates the cloud APIs05:02 How additional services got integrated with AI assistance06:31 Meaning of the name Floci07:07 Why Quarkus and GraalVM as the starting point for Floci09:35 How Floci starts up very fast and only uses a low amount of memory12:18 GraalVM can be hard with some libraries or frameworks14:02 What is needed to use Floci14:56 The challenges to support AWS, Azure, GCP and finding contributors20:24 Funding Floci21:04 How data is persisted in Floci22:37 Verifying Floci versus the "real" APIs with compatibility tests23:56 In the future: UI for Floci25:04 Biggest challenges while creating Floci25:32 Functionality compared between Floci and LocalStack and migrating28:15 Feedback from the Floci users28:58 Long-term plans for Floci29:59 Biggest surprises during the development of Floci31:00 Best use-cases for Floci32:12 In the next releases...33:31 How to get started with Floci35:00 Conclusion

Starting an AI company is all about spotting a real problem and using AI to solve it in a smarter, faster way than what's out there today. It's less about having the perfect idea and more about starting focused, learning fast, and building something people actually want.This week, Dave, Esmee, and Rob are joined by Gijs van de Nieuwegiessen and Tijn van Daelen, founders of One Horizon AI, to explore what it really takes to start and build an AI‑native company TLDR00:32 – Introduction00:55 – Hang out: Why Dutch names can be a real tongue-twister02:00 – Dig in: Exploring how an AI-native culture fits with human-to-human interaction13:35 – Deep dive with Gijs van de Nieuwegiessen and Tijn van Daelen1:01:54 – Following AI: Bloopers, reflections, and field hockey with the kids GuestGijs van de Nieuwegiessen: https://www.linkedin.com/in/nieuwegiessen/Tijn van Daelen: https://www.linkedin.com/in/tijn-van-daelen-495986131/Open source repo: https://github.com/onehorizonai/ink HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Dr. Roger Nelson, founder of the Global Consciousness Project, shares how random number generators placed around the world respond when millions of people focus on major events like 9/11, elections, and celebrity deaths. Dr. Nelson started at Princeton University's Engineering Anomalies Research Lab studying whether human consciousness could affect sensitive electronic equipment. They found 15% of people could change random number generator behavior through intention alone. People who could do it were free of conditioning that says you can't. The effects were tiny but accumulated over years of data collection. This led Dr. Nelson to create the Global Consciousness Project in 1997. He placed random number generators around the world to see if major global events would affect them when millions share the same emotional state. The network started with 3 devices and grew to 60-70 by the end of the formal experiment. GCP 2.0 now has 1600 random number generators sending data to cloud servers. The results were undeniable. Across 500 events, the data showed a seven sigma deviation with odds of three parts in a trillion against chance. But what shocked researchers most was the timing. On 9/11, changes started four hours before the first plane hit. Major earthquakes showed patterns eight hours before they struck. Dr. Nelson's explanation: the global mind had premonitions. Liz and Dr. Nelson discuss why random number generators are vulnerable to consciousness when other devices aren't, what the noise problem means for interpreting results, and why some elections produced strong effects while others showed nothing at all. Guest: Roger Nelson, PhD Global Consciousness Project: https://global-mind.org/results.html GCP 2.0: gcp2.net | RNG Observer Buy my Books HERE⁠⁠⁠⁠Newsletter ⁠⁠⁠ |Patreon | Buy me a coffee More at: https://www.wtfjusthappened.net/ Society for Scientific Exploration Conference 2026 June 17–21, 2026 Denver Marriott Westminster Hotel in Westminster, Colorado Join Us Forever Family Foundation Love Knows No Death Summer Grief Transformation Retreat 2026 July 24 @ 4:00 pm - July 26 @ 5:30 pm Chester, Connecticut Join us! Full Show Notes

Today our Packet Pushers team assembles to discuss whether the grass is greener on the NetOps or DevOps side of the telemetry fence. William of The Cloud Gambit, Scott of Total Network Operations, and Ned and Kyler of Day Two DevOps discuss the difficulties and differences of getting telemetry and state from devices across different... Read more »

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

Open Source is giving AI a real boost, making it easier and faster for organisations to build and experiment with new ideas. As adoption grows, these open ecosystems are helping businesses move quicker, stay flexible, and unlock value with more confidence.This week, Dave, Esmee, and Rob are joined by Richard Harmon, VP & Global Head of Financial Services at Red Hat to explore how Open Source is shaping AI, from mainframes to Kubernetes, and from regulation and sovereignty to a future of AI agents writing code. TLDR00:25 – Introduction00:52 – Hangout: Deep democracy training and “what instrument are you?”03:19 – Dig in: Open‑source culture and AI, do they complement each other?10:02 – Conversation with Richard Harmon51:12 – Sitting in the chair and trying to keep up with AI GuestRichard Harmon: https://www.linkedin.com/in/richardlaurenharmon/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Neste episódio, Guilherme Goulart e Vinícius Serafim analisam casos reais e tendências que colocam em xeque a segurança digital e física no Brasil. Você vai descobrir como criminosos burlaram um sistema de reconhecimento facial em condomínios de Porto Alegre usando engenharia social, expondo os riscos do teatro da segurança, do solucionismo tecnológico e da hipossuficiência técnica dos consumidores. Em seguida, você vai entender o que está por trás do lançamento do modelo Mitos da Anthropic — classificado como perigoso demais para uso público —, e por que os resultados práticos com o Firefox e o cURL geraram ceticismo no meio da cibersegurança, levantando questões sobre propaganda de IA, governança, regulação e concorrência no mercado de inteligência artificial. Neste episódio, você também acompanha a análise da lei 15.397, que atualizou crimes digitais no Brasil com penas mais severas para furto qualificado digital, cessão de conta laranja e fraude eletrônica — e por que, sem investimento em capacidade investigativa, isso pode ser apenas populismo penal. Além disso, são discutidas duas vulnerabilidades críticas no Linux (CVE Copyfile e Dirty Frag) com exploits já circulando antes da correção, e como a IA pode acabar com o anonimato na internet ao identificar autores por fingerprint de texto com apenas 125 palavras. Os temas de privacidade, proteção de dados, LGPD, segurança ofensiva, pentest e infraestrutura em nuvem permeiam toda a conversa. Assine o Segurança Legal na sua plataforma favorita, siga o perfil nas redes sociais e avalie o podcast para ajudar a ampliar o alcance deste projeto independente de conteúdo sobre segurança da informação. Você também pode apoiar diretamente pelo Apoia.se (apoia.se/segurancalegal) ou simplesmente indicar o podcast para colegas e amigos — cada compartilhamento faz diferença. Entre em contato pelo e-mail podcast@segurancalegal.com ou pelo Mastodon, Instagram, Bluesky, YouTube e TikTok. Esta descrição foi realizada a partir do áudio do podcast com o uso de IA, com revisão humana.  Visite nossa campanha de financiamento coletivo e nos apoie!  Conheça o Blog da BrownPipe Consultoria e se inscreva no nosso mailing Shownotes Polícia prende suspeitos de invadir e furtar apartamentos de alto padrão em Porto Alegre; grupo usava fraude em reconhecimento facial Polícia desarticula grupo de criminosos que furtava apartamentos de luxo via redes sociais Atualização do Código Penal para alguns crimes digitais Will AI end anonymity? I tested it I can never talk to an AI anonymously again Anthropic's most dangerous AI model just fell into the wrong hands Unauthorized group has gained access to Anthropic's exclusive cyber tool Mythos, report claims It’s a myth that you need Mythos to find bugs: Open source models can do it just as well Filme: Quebra de Sigilo (Sneakers) BC Protege Livro – Sob a sombra da suástica: a França ocupada Filme – Viagem ao mundo dos sonhos Artigo – Em louvor ao Teatro da Segurança Imagem do episódio: The Ancient Days, Willia, Blanke

The SaaSpocalypse marks the end of traditional CRM with manual data entry, rigid interfaces, and seat‑based software no longer make sense in an AI‑driven world. Success now depends on outcome‑focused plumbing: intelligent orchestration that delivers results, not screens.This week, Dave, Esmee, and Rob are joined by Hannah Datz, Americas Vice President of CRM at ServiceNow, to unpack the major announcements from ServiceNow Knowledge 2026 in Las Vegas and explore how AI is accelerating the SaaSpocalypse and driving a fundamental shift in the future of CRM. TLDR00:34 – Introduction 00:54 – Hang out: Happy Password Day and emerging threats 06:46 – Conversation with Hannah Datz 57:20 – From tennis excitement to the best burger ever GuestHannah Datz: https://www.linkedin.com/in/hannahdatz/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Eyvonne and William sit down with Joseph Nicholson, a Network Operations Engineer with NTT DATA, to share how public speaking transformed his career and technical experience. Joseph went from a terrifying ten minute lightning talk at AutoCon 2 to presenting 45-minute sessions at conferences like NANOG. Together they discuss how conversations in conference halls influenced... Read more »

In this episode, Garima Kapoor, co-founder and co-CEO of Min.io, shares insights into how storage infrastructure is evolving in response to AI, cloud, and enterprise needs. She offers a clear view of the market dynamics, innovative trends, and the strategic role of open-source technology in shaping the future.Key topics:The origins and motivation behind Min.io's developmentHow data growth influences storage strategies and the shift toward hybrid and private cloudsThe impact of AI on storage infrastructure and workloadsCompetitive landscape with giants like AWS, Azure, GCP, and the rise of Neo CloudsThe importance of open standards for application portability and data gravityEvolving customer adoption: from open source developer community to enterprise salesThe role of AI in accelerating product development, coding, and organizational decision-makingHow AI's rapid evolution is shifting the fundamentals of skills and fundamentals for engineersFuture market opportunities: exponential growth in storage needs driven by AI and IoTTimestamps:00:00 - Introduction to Garima Kapoor and Min.io00:31 - Motivation behind starting Min.io & market needs for object storage01:07 - The founding story and personal drivers for creating Min.io02:13 - Data growth drivers and the importance of data proximity over cloud location03:05 - Business landscape: cloud vs. on-premises and hybrid environments04:01 - Data migration challenges and promoting application portability06:10 - Early product-market fit through open source and developer community growth07:19 - Enterprise adoption journey from open source to cloud-native architecture08:17 - Customer acquisition strategies blending bottom-up developer growth and enterprise sales09:27 - Competing with Amazon, Microsoft, Google in the cloud storage space11:33 - Impact of AI on storage: demand, infrastructure evolution, and market timing12:51 - Min.io's advantage in AI workloads due to cloud-native architecture13:21 - Penetration of AI in storage: training, inferencing, and data utilization15:01 - AI for enterprise applications: storage, models, and data lakes16:26 - Neo Clouds and their role in GPU-optimized storage architectures18:58 - The increasing demand for object storage driven by AI and data creation21:02 - The effect of AI coding tools on product development speed and engineering skills23:36 - Internal AI-driven solutions for operational efficiency24:44 - The role of AI in reducing reliance on SaaS tools and infrastructure security27:22 - Managing costs and building for the future in AI investment and storage29:01 - The opportunity cost of tokens and AI-driven productivity gains31:00 - Skills for early engineers in an AI-enabled future33:32 - Min.io's next steps and market expansion plans34:36 - The paradigm shift: every business becoming AI and data-driven by 2026Resources & Links:Connect with Garima Kapoor:⁠Min.io Official Website⁠⁠Garima Kapoor - LinkedIn⁠⁠OpenAI⁠⁠NVIDIA GDC Announcements on Object Storage⁠⁠Nataraj's previous interview on startup infrastructure⁠⁠LinkedIn⁠⁠Twitter⁠

AI is only as strong as the data beneath it, and as it moves into the core of the enterprise, fragmented, duplicated, and poorly governed data is no longer hidden in the background, it's amplified, exposed, and impossible to ignore.This week, Dave, Esmee, and Rob are joined by Edward Calvesbert, VP Product Management for IBM watsonx AI & Data Platform, to dig into the foundations of enterprise AI, from data silos and the SaaSpocalypse to lakehouse architectures and agent‑driven workflows. TLDR00:17 – Introduction 00:55 – Dig in: The big data unlock for AI14:02 – Conversation with Edward Calvesbert57:58 – Hiking Mount Rainier near Seattle GuestEdward Calvesbert: https://www.linkedin.com/in/ecalvesbert/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Las Vegas, you're officially on the record during Google Cloud Next 2026. The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas.This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond.Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones. Dave, Rachel, and Rob close out their conversation with Cliff Krimmel, Head of Customer Engineering for Banking at Google Cloud, diving into the changing stack and the rise of the agentic development platform. TLDR00:32 – Day 3 kicks off01:25 – Hang out: Travel-ready tips05:18 – Dig in: The Agentic Data Cloud09:00 – Conversation with Cliff Krimmel32:05 – Closing with burgers & hotdogs GuestCliff Krimmel: https://www.linkedin.com/in/ckrimmel/ HostsDave Chapman:   https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:   https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:   https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:   https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:   https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:    https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Las Vegas, you're officially on the record during Google Cloud Next 2026. The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas.This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond.Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones. Dave, Rachel, and Rob discusse the highlights of Google Cloud Next 2026! TLDR00:24 – Introduction01:14 – Hang out: Progress from Google Cloud Next 2025 to Google Cloud Next 202608:15 – Dig in: Executive overview of this year's key themes31:38 – Closing remarksHostsDave Chapman:   https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:   https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:   https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:   https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:   https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:    https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

At Google Cloud Next in Las Vegas, Keith sits down with returning guest Woon Jung, CTO of Clumio, to break down their new GCS backup launch — and why "replication" and "backup" are not the same thing. They dig into the real failure domains that catch GCP-native organizations off guard, the operational reality of multi-cloud [...]

Las Vegas, you're officially on the record during Google Cloud Next 2026. The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas.This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond.Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones. Dave, Rachel, and Rob continue their conversation with Gina Fratarcangeli, Managing Director and NA GSI Leader, exploring how partners are redefining their role, from funding models and board‑level conversations to shaping the agentic enterprise blueprint. TLDR00:24 – Day 2 kicks off!00:40 – Hangout: impressions from GCN '26 at the Mandalay Bay Convention Center and Dave denting his stuff05:26 – Dig in: What announcements our roving reporter spotted09:54 – Conversation with Gina Fratarcangeli32:41 – Texas brisket on paper BBQ GuestGina Fratarcangeli: https://www.linkedin.com/in/gina-fratarcangeli/ HostsDave Chapman:   https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:   https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:   https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:   https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:   https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:    https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Las Vegas, you're officially on the record during Google Cloud Next 2026. The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas.This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond.Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones. Dave, Rachel, and Rob continue their conversation with Khulan Davaajav, Product Marketing Manager, Generative Media Models at Google about the rise of creative AI and how it's redefining human expression in the age of intelligent tools. TLDR00:24 – Day 2 on it's way!00:40 – Hangout: Producer distracted and Rob's red eye03:31 – Highlights: Roving reporter's media related announcements07:16 – Conversation with Khulan Davaajav34:24– Mongolian BBQ GuestKhulan Davaajav: https://www.linkedin.com/in/khulandav/ HostsDave Chapman:   https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:   https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:   https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:   https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:   https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:    https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Eyvonne Sharp and William Collins speak with Sif Baksh, Principal Solutions Architect at Tines, to discuss the power of automation. Sif shares some personal stories of how he has been able to use automation to innovate and modernize networking operations. They also discuss the importance of learning AI and using it as a tool, how... Read more »

Las Vegas, you're officially on the record during Google Cloud Next 2026.The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas. This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond. Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones.Dave, Rachel, and Rob kick off the event with Mark Steel, Director of Retail Industry, EMEA at Google Cloud, diving into the rise of Agentic Commerce and how AI agents are redefining the relationship between brands, retailers, and consumers.TLDR00:24 – Guest introduction and this week's key themes00:52 – Hangout: new podcast equipment and roving reporter Rachel Belmonte05:52 – Dig in: what to expect from Google's announcements11:41 – Conversation with Mark Steel39:10 – Favourite BBQ picks GuestMark Steel: https://www.linkedin.com/in/marksteel220/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Las Vegas, you're officially on the record during Google Cloud Next 2026. The #RealitiesRemixed podcast team is back at GCN'26, recording live from the Strip, where bright lights collide with big ideas.This week, we're swapping roulette wheels for real talk, hosting live conversations with Google leaders who are redefining what's next across AI‑first enterprise transformation, agentic AI, data, sovereignty, security, and beyond.Expect sharp insights, bold opinions, and future‑shaping conversations, delivered straight from Las Vegas to your headphones. Dave, Rachel, and Rob continue the conversation with Dominic Cody, Global Director of Technology, Distributed Cloud, about the Sovereign Edge and reclaiming control in the age of Agentic AI. TLDR00:24 – Guest introduction and this week's key themes00:40 – Hangout: AI on the Expo floor and roving reporter Rachel Belmonte01:24– Dig in: what to expect from Google's announcements05:36– Conversation with Dominic Cody31:28 – Favourite BBQ picks GuestDominic Cody: https://www.linkedin.com/in/dominiccody/ HostsDave Chapman:   https://www.linkedin.com/in/chapmandr/Rachel Belmonte:  https://www.linkedin.com/in/rachel-belmonte-63550358/Rob Kernahan:   https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:   https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:   https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:   https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:    https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Realities Remixed, formerly know as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.As AI accelerates the shift from networks to ecosystems, organisations face a growing tension between fast‑moving technology and slower, socially driven organisational change. Success in the “Never Normal” will depend less on intelligence itself and more on leadership qualities, judgement, narrative, trust, and the ability to create space for corporate explorers to build the Day After Tomorrow, not just optimise today.This week, Dave, Esmee, and Rob are joined by Peter Hinssen, keynote speaker, author and lecturer and co-founder of nexxworks to explore how leaders navigating through rapid change, focused on transforming uncertainty into opportunities for growth and innovation.. TLDR00:41 – Guest introduction and overview of this week's theme 01:02 – Hangout: Episode 200! 06:25 – Dig in: Deep dive into the pace of change 14:17 – Conversation with Peter Hinssen on adaptive organisations and leadership styles 55:10 – Continuing the conversation about Tech 1:11:20 – Travel to Taiwan, Silicon Valley, and China GuestPeter Hinssen: https://www.linkedin.com/in/phinssen/https://www.peterhinssen.com HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Dan Nathan and Guy Adami discuss major tech themes and trades, focusing on dispersion in mega-cap tech and the recent underperformance and rebounds of the “Mag 7.” They examine Microsoft's AI positioning and Azure deceleration amid ongoing capacity and power constraints, contrasted with rivals (AWS, Oracle, GCP) picking up demand tied to OpenAI. The conversation highlights Michael Burry's view that software-stock declines have been amplified by software credit stress and may be overextended, with Oracle as a key example given its steep drawdown and elevated CDS. They also cover Apple's AI strategy, reliance on Gemini, WWDC expectations to improve Siri, and key technical levels amid headline sensitivity. Finally, they assess Intel's sharp rally tied to a reworked CHIPS deal and Nvidia involvement, and preview Netflix earnings with a mixed technical setup and potential upside. Show Notes Trading Post Monday April 13th (Cassandra Unchained) Top of the Morning (Axios) —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media

對很多工程師來說，最麻煩的事情不是寫程式，而是要「把產品順利上線」。從環境設定、部署流程，到各種技術細節，往往比開發本身還花時間。 今天這位大來賓，他在大學時期就注意到這個問題，並從自己的實務經驗出發，把它變成一個解決問題的產品。透過新創加速器的幫助，他正式踏上創業之路，現在更獲得矽谷創投的青睞。在今天的節目，我們將請他來聊聊這段從畢業專題走向科技新創的歷程，也聽聽他一路走來的關鍵選擇與經驗分享。 歡迎今天的大來賓： Zeabur 雲端部署平台創辦人 林沅霖 - - - - - -- - - - - - 【寶博朋友說千萬粉絲專屬社群頻道 Discord 開張啦

In an era where technological progress reshapes power, security, and prosperity at unprecedented speed, societies face a defining choice: adapt incrementally or reinvent boldly. How can breakthrough and disruptive technologies enable strategic leapfrogging, transforming long‑term ambition into real‑world impact amid a rapidly shifting global landscape.This week, Dave, Esmee, and Rob are joined by Andre Loesekrug-Pietri, Chair and the Scientific Director of the Joint European Disruptive Initiative (JEDI, the European ARPA), to explore the ambition behind and what it would take for Europe to stop reacting to technological change and start shaping it. TLDR00:30 – Guest introduction and overview of this week's conversation01:35 – Team Dig in: Is Europe falling behind on competitiveness?13:52 – Conversation with Andre Loesekrug-Pietri1:00:19 – Traveling to Japan with the French president GuestAndre Loeskrug-Petri: https://www.linkedin.com/in/andrepietri/X: @eurojediwww.jedi.foundationHostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Vibe coding: give AI a description of what you want, the model writes the code, you ship it, and then you hope for the best. It works great for side projects, but it can fall apart the moment you point an AI agent at production infrastructure. Today, William and Eyvonne sit down with John Capobianco,... Read more »

This week on Group Chat, Zach White returns and the crew goes deep on Nike's earnings disaster and why the brand has become culturally irrelevant. From losing China to getting outpaced by Salomon and Arc'teryx, the guys break down what went wrong and whether the Swoosh can recover. OpenAI just acquired TBPN, a tech podcast, for a reported $200M+ after only 18 months. The crew breaks down why 70K viewers of the right people is worth more than millions of the wrong ones, and what it means for the future of media acquisitions. Kanye sold out two nights at SoFi for the first time in LA since 2019. 45 songs, Lauryn Hill, Travis Scott, and $18M on night one alone. Is cancel culture officially dead? The crew debates what it means when the most controversial artist in the world sells out the biggest venue in the city. Plus: the Artemis moon launch, the moon landing conspiracy debate (the crew votes), a GCP fan builds a SaaS product from a pod tip, Dee builds a baseball swing analyzer on a Duffy boat, and Venice is the new West Hollywood.

There is a war being fought right now — through you, around you, inside you. A war so vast and so subtle that most of humanity will live and die without ever knowing it existed. Vadim Zeland called them pendulums — invisible energy structures created by collective human thought that feed on your attention, harvest your emotional energy, and keep your frequency locked in a band that serves their survival instead of yours.   In this episode of The Reality Revolution, I expose the full architecture of the Pendulum War using data from the Schumann Resonance monitoring record, the Global Consciousness Project at Princeton, HeartMath Institute electromagnetic research, and Rupert Sheldrake's morphogenetic field experiments — all converging on the same extraordinary conclusion.   The evidence is staggering. Anomalous Schumann spikes correlating with mass synchronized dreaming events. GCP data showing global coherence effects that are not bounded by geography. HeartMath research proving the human heart generates an electromagnetic field that synchronizes other people in proximity without their knowledge or effort. And Zeland's framework — derived from his background in Soviet quantum physics — revealing that pendulums have convinced most people their inner state is a response to external conditions rather than a generator of them. This is the pendulum's masterwork. And it is a lie.   Coherence is the weapon. Your inner state is the cause. And the moment you embody that truth, the pendulum loses you forever. This episode includes a full activation sequence with deep trance induction, heart coherence technique, and a broadcast-mode energetic shift designed to purge pendulum residue and restore your original signal.    

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.One of the most fundamental challenges in modern computing is the growing hardware–software mismatch. As Moore's Law slows and performance gains no longer come “for free,” software built on Turing‑era, sequential assumptions struggles to keep pace with today's highly parallel, heterogeneous hardware. That disconnect is now a central constraint on innovation.This week, Dave, Esmee, and Rob are joined by Peter Richards, advisor and AJ Guillon, founder of YetiWare, to explore why this mismatch persists, what it means for organizations today, and how emerging approaches may redefine the relationship between software and hardware in the years ahead. TLDR00:35 – Introduction01:04 – Hang out: How deep can we go, and what is the history of the compute era?07:00 – Dig in: The power demands of LLMs, data centers, scale, size and potato chips15:35 – Conversation with Peter Richards and AJ Guillon55:31 – Spring cleanup with a chainsaw and cycling GuestPeter Richards: https://www.linkedin.com/in/peter-richards-3b99688/AJ Guillon: https://www.linkedin.com/in/ajguillon/ HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.In a world defined by constant change, leaders must evolve from rigid hierarchies to emotionally intelligent, empowering leadership. By fostering adaptability, continuous learning, distributed leadership, and a culture of curiosity, organisations become better equipped to navigate technological disruptions, such as AI, with resilience and innovation.This week, Dave, Esmee, and Rob are joined by Jana Werner and Phil Le-Brun, Executives in Residence at AWS to explore what it really takes for organisations to thrive in a world of continuous transformation, and why rigid hierarchies, control, and over-designed change programmes so often get in the way.  TLDR00:42 – Guest introduction and overview of this week's theme01:26 – Team dig-in: A new cycle of change is on it's way19:27 – In‑depth conversation with Jana and Phil57:37 – Octopus playlist and case study highlights GuestJana Werner: https://www.linkedin.com/in/janawerner1/Phil Le-Brun: https://www.linkedin.com/in/phillebrun/https://www.theoctopusorganization.com/ A Guide to Thriving in a World of Continuous Transformation HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

Sweet Sixteen coverage, and much more in this episode of The GCP. Tune in.Remember, bet with your head, not with your heart; Always. -GCSIGN UP for the GSNC VII: https://www.ginsportsnetwork.com/tgcFollow us on Twitter! @ginpros#GSN #GCP #GINPROSLIVE STREAM SCHEDULE: VARIABLE: Follow our Social Media Outlets!@gincollege@gincommish/IG/Twitter - John J. IITony Minarik - CBB InsiderFIND THE GIN & TONIC SERIES HERE - https://www.youtube.com/channel/UCTAcezOkWmUotUyAK1CzE_Q@ginsportsnetwork - Main@ginpros - Twitter@ginpromotions - Parent IG@gincollege - IGNational Problem Gambling Helpline: 1-800-522-4700www.linktr.ee/ginproswww.ginsportsnetwork.comwww.ginpros.orgwww.facebook.com/ginproswww.patreon.com/ginprosGIN PROS. 2026 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit ginpros.substack.com

William Collins and Eyvonne Sharp invite Skylar Sands, Senior Automation Engineer at World Wide Technology, to discuss what it means to integrate AI into the daily workflow in a meaningful way. Together they break down the shift in the automation engineer's role now that AI can instantly generate the “toolkit” of Python, Ansible, and Bash,... Read more »

Realities Remixed, formerly known as Cloud Realities, launches a new season exploring the intersection of people, culture, industry and tech.Technology and AI are rapidly transforming marketing, challenging old assumptions and shifting the focus toward analytics‑driven precision, smarter segmentation, and multimodal content. As attention becomes harder to capture and creative workflows evolve, the real advantage lies in adapting for an AI‑first environment, especially by rethinking overlooked segments like the mid‑market and being far more specific in strategy and execution. This week, Dave, Esmee, and Rob are joined by Carlos Corredor, founder and CEO of Condor, to discuss the difference between value metrics and vanity metrics in modern marketing and why many organisations still struggle to connect marketing activity to real business results. TLDR00:32 – Introduction00:59 – Hang out: Staying healthy06:10 – Dig in: The valuable to measure with metrics17:52 – Conversation with Carlos Corredor47:15 – Looking ahead to the World Cup football GuestCarlos Corredor: https://www.linkedin.com/in/carlos-corredor-condor/HostsDave Chapman:  https://www.linkedin.com/in/chapmandr/Esmee van de Giessen:  https://www.linkedin.com/in/esmeevandegiessen/Rob Kernahan:  https://www.linkedin.com/in/rob-kernahan/ProductionMarcel van der Burg:  https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman:  https://www.linkedin.com/in/chapmandr/ SoundBen Corbett:  https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett:   https://www.linkedin.com/in/louis-corbett-087250264/ 'Realities Remixed' is an original podcast from Capgemini

In this sponsored episode, FluidCloud co-founders Sharad Kumar and Harshit Omar sit down with William and Eyvonne to discuss how FluidCloud tackles multi-cloud portability. They detail how FluidCloud acts as a cloning platform that scans an existing cloud or VMware environment, extracts complex infrastructure configurations (including compute and storage, as well as firewall rules and... Read more »

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