Podcasts about lovable

In this episode, we explore the impressive milestone of Lovable reaching $500 million in annual recurring revenue and discuss the platform's capabilities in project building. We also examine alternative solutions for those looking to scale their tech projects and highlight the importance of choosing the right tools for different skill levels and business needs.Chapters00:00 Lovable's Milestone02:01 AI Hustle School Community04:00 Building Projects with Lovable06:00 Exploring Alternatives to Lovable Our AI Hustle Skool Community: https://www.skool.com/aihustleGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode we speak to Jonathan Grahl.  Jonathan is the Team Lead of Infrastructure at Lovable where he oversees the platform stack the company runs on. We talked about Kubernetes, Sandboxes and Chocolate.   Do you have something cool to share? Some questions? Let us know: - web: kubernetespodcast.com - mail: kubernetespodcast@google.com - twitter: @kubernetespod - bluesky: @kubernetespodcast.com   News of the week OpenTelemetry is a CNCF Graduated Project CNCF TAG Elections KubeCon India Kubernetes Community Days global events Links from the interview Lovable Cilium Etcd Cloudflare Sandbox Agent Sandbox (Kubernetes) OpenClaw Claude OpenAI Bun toolkit for Javascript gVisor Firecracker Kata Containers Vitess

A VC fell asleep for 30+ minutes during a founder's pitch. The round stillclosed.This week on Bricks, Bucks & Bytes, we traded the VC horror stories founders never forget, debated whether the hottest AI startups are just "reselling tokens," and brought on three founders fresh off funding rounds: Guy Saxelby (Earlytrade, $25M total raised), Adrian Rhaese (EnvioTech, €1M pre-seed) and Ben Waters (LightTable, $22M Series A).Tune in to find out about:✅ Why Patrick calls Lovable, Cursor and Vercel "resellers of tokens" and what that means for their valuations✅ How Earlytrade automates construction payments, with 10% of revenue already running with zero humans✅ The streetlight startup saving cities 80% on energy while mapping how a whole city moves✅ Dustin's no-mercy pushback on what it actually takes to be a "platform for pre-construction"Listen now on Spotify and YouTube. #bricksandbytes #bricksbytes #bricksbucksandbytes #aec #construction#constructiontech #ai #vcChapters00:00 Intro01:10 VC Horror Stories Founders Never Share07:05 The Weirdest VC Behaviour We've Seen13:01 Why AI Costs Are Eating Your Margins19:15 Will AI Companies Ever IPO?25:49 How to Find Early Product-Market Fit33:24 Expanding Internationally: What Actually Works40:09 Where Construction Tech Innovation Happens45:48 The Growth Playbook for the Next 5 Years56:56 The Hardest Lessons of Entrepreneurship58:11 Why Timing Beats Everything in Business58:19 How Perception Shapes Professional Success59:10 Why Being Eccentric Is a Branding Advantage01:02:07 Where Tech Meets Construction01:04:09 AI That Actually Manages Construction Projects01:10:10 Why Pre-Construction Is Where the Money Is01:16:16 Mastering the Critical Path

AGENDA: 00:00 – SpaceX Launches the Largest IPO Roadshow in History at $1.77T Valuation 05:00 – Did Elon Break the IPO Playbook? The High-Risk Pricing Strategy Explained 12:00 – Will SpaceX Create a New Generation of Venture Billionaires? 17:00 – OpenAI Files to Go Public as the AI IPO Race Officially Begins 19:00 – Sam Altman's Vision: Why AI Is Becoming Always-On Infrastructure 22:00 – Apple Admits Defeat on Siri and Turns to Google AI 25:00 – Uber Cuts 23% of HR as AI Reshapes White-Collar Work 31:00 – Founders Revolt Against VCs: The Fundraising Horror Stories Going Viral 38:00 – Lovable Hits $500M ARR: The Rise of the 100-Person Billion-Dollar Company 48:00 – Elon's Masterstroke: Why the Cursor Acquisition Could Be the Deal of the Year  

One of the biggest problems of vibe coding? Securely keeping the project up to date and sharing it with your team to make it actually useful. And there's a new solution that does just that, Codex Sites. With a few simple prompts, you can turn vibe coded throwaway apps into working pieces of software that your team can share. We put AI to work on Wednesday and show you how to get the most out of Codex Sites. Codex Sites: The Lovable and Replit Killer? A hands-on Guide to Codex Sites -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Codex Sites vs Static File SharingLive Dashboards and Automated WorkflowsBuilding Internal Apps With Codex SitesReal-Time Data Integration in CodexAgent Layer and Role-Based Access ControlCodex Sites vs Replit, Lovable, BoltDynamic Business Insights and CollaborationCodex Sites Secure Team Sharing LimitationsAutomations and Custom Skills in CodexFuture of AI Native Business ToolsTimestamps:00:00 The future of work automation03:43 Free daily newsletter highlights08:29 Managing audience momentum dashboard12:04 Pulling stats and data access14:48 Creating dynamic web tools16:18 Editing video collaboration challenges21:09 Comparing coding platforms like Replit25:47 Future of Business Analytics Tools27:11 Introducing the Start Here series32:35 Updating old content ideas34:53 Streamlining team efficiency with AI37:02 Episode use cases overviewKeywords: Codex sites, OpenAI, AI dashboards, live software, file sharing, business automation, dynamic data, ChatGPT business, agentic system, Chrome integration, MCP servers, skills, plugins, Copilot Scout, internal dashboards, data analysis, role based access control, data governance, enterprise AI tools, site hosting, live app builder, prompt driven apps, automations, Replit alternative, Lovable competitor, full stack app builder, dynamic business context, annotation feature, nontechnical teams, BI dashboards, Kanban tracker, evergreen content, live indicators, audience momentum dashboard, sub agent, responsive design, visual design, parallax feature, actionable insights, version control, dynamic deliverables, artifact, demo over memo, knowledge work, IT security, internal URL sharing, AI native workflow, internal business tools, real time updates, start here seriesSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

AI Ready: Ahmad Ghabboun Ahmad Ghabboun built a Demo Day–winning AI product during his MSIS program — after arriving with no plans to work in AI at all. He breaks down how his mindset shifted, how his design background made him a stronger prompter, and how to build AI fluency that actually holds up in interviews. Useful for students and early-career professionals trying to get AI-ready without faking it. Ahmad Ghabboun is a Master of Science in Information Systems (MSIS) 2026 Graduate at the UW Foster School of Business. Before Foster, he spent roughly fifteen years in UX and product design, building web applications for startups. At Foster he built several generative-AI tools in his coursework, including Synapse, which won Best Business and Tech Product at the MSIS Demo Day. He is targeting product management and technical product roles. What you'll learn Why naming the specific AI model you use — and justifying it — matters more in interviews than saying "I use AI" How a design background translates into sharper, more technical prompts How to keep a human in the loop so AI assists your judgment instead of replacing it Why AI's tendency to agree with you makes human and second-model pushback essential How to stay current with fast-moving tools without trying to learn everything The difference between a productivity mindset and a learning mindset in school Key moments The third-quarter AI classes that moved AI from "not on my list" to his career focus The origin of Synapse: manually juggling answers across Gemini, Claude, and a third model How Synapse runs a dual-model validation and a judge step to flag gaps for technical PMs Why interview proctoring now detects AI use — and what a "perfect" AI answer signals to interviewers Ethan Mollick's "jagged edge" and why it shifts with every model release Resources mentioned Lovable; Replit; Gemini; Claude; ChatGPT; Jira; Azure DevOps; GitHub; Ethan Mollick's "jagged frontier" of AI capability.

I denne episode af EDB 5.0 får vi besøg af Janne Jul Jensen, medstifter af Henosia. Janne er uddannet softwareingeniør og har en ph.d. i UX. Hos Henosia er hun med til at udvikle et vibe coding værktøj til app generation, hvor medarbejdere beskriver den løsning, de har brug for, og Henosia så bygger applikationen for dem, så arbejdsgange kan automatiseres og manuelt arbejde reduceres. I episoden taler vi om, hvordan vibe coding kan give medarbejdere mulighed for selv at bygge værktøjer til opgaver som KPI-rapportering, rejseafregning og interne workflows, mens IT stadig sætter rammerne for datasikkerhed, datakilder, adgang og go live. Vi kommer omkring sikkerhed, integrationer til eksisterende systemer, adoption i organisationer og hvordan Henosia arbejder med deres AI agent, datalag og fremtidige funktioner. Shownotes 00.00-13.06: Introduktion til Janne, vibe coding og Henosia 13.06-46.39: Sikkerhed, integrationer, adoption, use cases, proces for at bygge, sammenligning med Lovable og Claude Code samt pivotering fra B2C til B2B 46.39- 52:28: Fremtiden for Henosia, datalag og persongalleri Vært Mathias Mengesha Emiliussen

What happens when AI becomes a requirement instead of an advantage?   In this episode, Erik Van Horn sits down with Shaina Denny to discuss how artificial intelligence is transforming franchising, why most brands are still underutilizing it, and what franchisors and franchisees should be doing right now to stay competitive.   Shaina shares how she uses Claude and ChatGPT in her own business, why she expects employees to be 10x more productive with AI, and how franchisors can leverage projects, SOPs, living brand manuals, and knowledge systems to scale more efficiently.   They also discuss the difference between using AI as a simple content generator versus using it as a strategic business tool that improves operations, hiring, training, and execution.   Whether you're a franchisor, franchisee, executive, or entrepreneur, this conversation will challenge how you think about productivity and the future of work.   In this episode you'll learn: ✅ Why 10x productivity is becoming the new expectation ✅ How franchisors are currently using AI ✅ Claude vs. ChatGPT vs. Lovable vs. Replit ✅ How to build better SOPs with AI ✅ The right way to create a company knowledge base ✅ Why most AI-generated work still falls short ✅ How to think about AI adoption inside your organization  

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

If you've ever shouted “just let me talk to a person” at a chatbot, this one's for you. Jeannie Walters is joined by special cohost Brandon McGovern, Senior Director of Customer Experience at HP, to pressure-test the biggest question in AI customer service right now: how do we automate without breaking trust?We start with a headline that feels like a warning label. Norse Atlantic Airways offers dirt-cheap tickets, but customers say there's a catch: customer support is so locked behind tech that getting help can become impossible. We unpack why this isn't simply a “tech problem,” but a governance and leadership problem. When companies remove phone numbers, skip the escape hatch, and ignore high-emotion journeys like refunds and disruptions, they don't just frustrate people, they create financial harm and open the door to fraud.Then we zoom out to the enterprise reality. Cisco's line that adopting AI is “like surgery without the drugs” is painfully honest, and it frames the messy middle many CX teams are living through. We talk about why rushing to automate tasks can amplify mistakes, how to redesign workflows around outcomes, and why “faster” is the wrong North Star compared to what's now possible. Along the way, we dig into authenticity, rising customer expectations, and why AI is killing the illusion of fine print as customers use their own tools to read policies and push back.If you're leading CX, contact centers, or digital support, you'll leave with practical guardrails for pilots, measurement, and intent selection. Subscribe, share this with a teammate, and leave a review with the biggest AI question you're wrestling with right now.About Brandon McGovernSenior Director of Customer Experience at HPUnderstanding your customers isn't enough. I build the systems that turn that understanding into outcomes.I'm a Senior Director of Customer Experience at HP, leading enterprise-wide measurement, analytics, and operations that enable the company to understand and act on customer sentiment in real time. I oversee a global Voice of the Customer ecosystem capturing tens of millions of signals annually, translating them into product, service, digital, and brand strategy decisions across the business.My work has delivered double-digit NPS improvements and material revenue impact by shifting CX from a reporting function to an operational and strategic capability - powered by data, automation, and applied AI.Beyond enterprise implementation, I build with AI hands-on - personal projects in game design, product prototyping, and workflow automation using Claude, Lovable, and other tools. Building outside my domain teaches me where AI actually breaks down, which makes me a better architect of AI-powered operating models at work.I bring engineering depth coupled with business leadership (MBA, MS in Electrical Engineering, Stanford executive education), and I specialize in building scalable CX platforms, driving cultural change, and aligning executives around customer-led transformation. Follow Brandon on LinkedIn: https://www.linkedin.com/in/brandonmcgovern/Articles Mentioned:- Norse Atlantic Airways Offers Dirt-Cheap Tickets. There's a Catch (Wired) -- https://www.wired.com/story/norse-airlines-ftc-complaints-ai-scams/- Cisco exec says adopting AI is like 'surgery without the drugs' (Business Insider) -- https://www.businessinsider.com/cisco-ai-adoption-customer-service-2026-5- Dissatisfied: Three-fourths of AI customer service rollouts are a letdown (The Register) -- https://www.theregister.com/ai-ml/2026/05/13/ai-customer-service-bots-get-rolled-back-at-74-of-firms/5239800Enjoyed the show? Subscribe, share with your team, and leave a quick review to help others find us. Leave your review at ratethispodcast.com/xact.Want to ask a question? Visit askjeannie.vip to leave Jeannie a voicemail! (And don't forget to follow Jeannie Walters, CCXP, CSP on LinkedIn!)

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Lovable's remarkable growth as it hits $500 million in annual recurring revenue and discuss Apple's exciting new features in AI and vibe coding. Additionally, we cover insights on upcoming IPOs for major AI players and my personal journey of vibe coding an iOS app.Chapters00:00 Lovable's Growth Highlights00:52 Apple's AI Features03:52 Perplexity's IPO Plans09:35 Updates on OpenAI and Tools for Humanity14:04 Google DeepMind's AI Accelerator20:06 My Vibe Coding Experience Show LinksSelfpause our Vibe coded app: https://apps.apple.com/us/app/selfpause-daily-affirmations/id1518538414Get the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiHow I Grow and Scale My Business with AI: https://www.skool.com/aihustleGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode #260 features Maxine Minter — Founder and General Partner of the Pre-Seed Venture Capital Fund, Co Ventures. Maxine reflects on growing up between Australia, Japan and Europe, speaking Japanese before English, and raised by a fiercely entrepreneurial single mother. Vidit and Maxine explore her childhood, the influence of her grandparents, executive coaching, the idea of “generative ambition”, and the lessons learned from building companies, backing founders and how and why she started her own VC fund, Co Ventures. They also discuss the specifics of how the best Aussie founders go global, the realities of venture capital, AI, partnership, importance of play, and why the biggest opportunities often come from stepping outside the boxes others expect you to fit into. Please enjoy exploring your curiosity. ________ Get in touch with us via email at contact@curiositycentre.com Join our stable of commercial partners including the Australian Government, Google, KPMG, Vanta, Allens, Macquarie Capital, City of Sydney and more.  Show notes and more episodes here Follow us on LinkedIn, Twitter and Instagram Get in touch with our Founder and Host, Vidit Agarwal directly here Contact us via our website ________ The High Flyers Podcast features in-depth interviews with the world's most influential figures in business, tech, finance, government and sport. Launched in 2020, it has ranked in the global top ten for past three years, with listeners in 27 countries and over 200+ episodes released, and featured in Forbes, Daily Telegraph, and at SXSW. Our guests include -- Malcolm Turnbull (Prime Minister of Australia), Jason Collins (Head of BlackRock, Asia Pacific), Brad Banducci (CEO, Woolworths), Michael Schneider (CEO, Bunnings), Elena Verna (Head of Growth, Lovable), David Haber (a16z Partner), Jodie Auster (Uber's Global Head of Travel), Rob Giglio (CCO, Canva), Jean-Michel Limieux (CTO, Shopify and Atlassian), Stevie Case (CRO, Vanta), John Haddock (CBO, Harvey), Mark Suster (Partner, Upfront Ventures), Niki Scevak (Partner, Blackbird), Craig Tiley (CEO, USA Tennis), Jeanne DeWitt Grosser (COO, Vercel), Paul Bassat (Partner, Square Peg), Bowen Pan (Creator, Facebook Marketplace), Peter Varghese (Secretary of Foreign Affairs, Australian Government), Sam Sicilia (CIO, Hostplus), Jack Zhang (CEO, Airwallex), Tim Doyle (CEO, Eucalyptus), Sukhinder Singh Cassidy (CEO, Xero), Sanjeev Gandhi (CEO, Orica), Philip Green (Australia's Ambassador/High Commissioner to India), Vivek Bhatia (CEO, MUFG), Cristina Cordova (COO, Linear) and more.

Joe Polish sits down with Strategic Coach Founder Dan Sullivan and The CEO of CEG Worldwide John Bowen to explore the research-backed framework behind their new book, The Greater Game — a 100x blueprint that reveals why only 5.4% of Entrepreneurs are playing a completely different game than everyone else. Together they unpack the shift from Founder-dependent businesses to scalable ecosystems, the finite-vs-infinite game divide, and why AI is less a technological revolution and more a cognitive one. Here's a glance at what you'll discover in this episode: The number that reveals whether you're winning or losing the only game that matters... and why 94.6% of Entrepreneurs are optimizing a game that's already coming to an end (you've probably already done 10x without calling it that — what you do next is the whole point) Dan Sullivan's quiet observation after 52 years and 7,000+ Entrepreneurs... the exact moment a successful person stops growing isn't failure — it's something far more seductive, and almost no one catches it in themselves (the first exercise he runs at Strategic Coach is designed to show you you've already crossed the line once) Joe typed a question into AI and got back the most brutal case study in modern business history... Blockbuster, Kodak, Borders, Toys "R" Us — and the one invisible shift every company on that list missed before it was too late (this isn't a technology story — it's a thinking story) Why John Bowen started three new companies on his 70th birthday... and the dashboard he and Dan built for roughly $2,000 that a top vendor quoted them $50,000 a year to provide (his tech team called after the first meeting and said "we'll just build it and give it to you tomorrow") The four-hour version of something that used to take Dan Sullivan four weeks... and what it reveals about the only AI upgrade that actually changes your trajectory (this isn't about using AI more — it's about using it in the right direction entirely) What Joe Polish teaches Genius Youth Members that no business school has ever covered... and why writing handwritten postcards in an age of AI might be the single highest-leverage thing you do this week (the killer app of 2026 is not what anyone is selling you) If you'd like to join world-renowned Entrepreneurs at the next Genius Network Event or want to learn more about Genius Network, go to www.GeniusNetwork.com. Show Notes: The Book: The Greater Game and the 5.4% Dan and John's new book — published by Hay House and instantly a #1 Amazon bestseller — grew out of a 25-year research partnership to study what separates the highest-performing Entrepreneurs from everyone else. Their research across 7,000+ Entrepreneurs found that 94.6% are still optimizing the game they're in — while only 5.4% are architecting a completely different one. The book maps out exactly what those 5.4% are doing. The book's central premise: "Every system that got you here is optimized for a game that's coming to an end." From 10x to 100x: Dan's Framework Dan has been coaching Entrepreneurs to 10x since the 1990s — starting with an exercise where he had Clients identify when they were one-tenth of where they are today. Everyone in his program had already done 10x without labeling it that way. When he challenged a Client who said they couldn't go 10x in three years, the Client responded they could do it in 15 — and then voluntarily suggested doing it again. That's when the 100x idea crystallized. Dan's thesis: give yourself a long enough time horizon, use AI as a genuine collaborator, and constant growth becomes the natural state — not the exception. The Four Levels of The Greater Game Level 1 — Foundation for Freedom: Vision, security, and financial confidence. Getting off the couch. Level 2 — Energy for Expansion: Motivation and IP development. Dan has built an extraordinary amount of intellectual property; John and Joe have too. Level 3 — Platform / Ecosystem: Moving from Founder-dependent to a scalable system. John's own company grew 58% while writing the book — by walking the talk of this level. Level 4 — Agency: Creating markets. Courage, commitment, and building an ecosystem where you're generating the category itself. Finite vs. Infinite: What the Game Shift Really Means Finite game: competing for market share, managing dependencies, staying indispensable personally, reacting to market pressure. Business value: 3–5x EBITDA. Infinite game: designing an ecosystem, multiplying unique genius through others, engineering your own absence, redefining the market. Business value: multiples that reflect systems, not the Founder. Joe's examples (finite → infinite): Blockbuster → Netflix, Kodak → Apple, Borders → Amazon, taxi companies → Uber, Toys "R" Us → Lego. The pattern: finite players optimize the current game; infinite players keep changing what the game is. Dan's real-world example: Paul Van Dyne came to Strategic Coach planning to retire at 65. He went on to take his engineering firm from #40 to #1 nationally in nine years through M&A — and now plans to build his gourmet coffee shop inside one of his medical centers. AI as a Cognitive Revolution Dan's framing: AI isn't a technological revolution — it's a cognitive revolution. He compares its impact to the introduction of zero in mathematics, which made economics, double-entry bookkeeping, and science possible. Practical example: Dan used to need four weeks to structure a new book. With AI, the same work takes four hours. He now writes a new book every quarter. John's vibe-coding story: his Team built the entire Greater Game Dashboard for roughly $2,000–3,000 using Lovable — after being quoted $50,000/year from a top vendor. They own the code and iterate freely. Joe's counterpoint: the killer app today is being fully human — knowing how to bond, connect, and think for yourself. "Write with your hands, think with your brain."  The Greater Game Dashboard John built this free interactive tool at TheGreaterGameDashboard.com to put the book's framework into action. The 15-minute assessment shows you exactly where you stand relative to peers and the 10 Greater Multipliers. The dashboard automatically calculates what your company is worth to a buyer today — and shows how each improvement raises that number. Dan calls it the greatest tool he's seen in 52 years of coaching Entrepreneurs. Monthly updates include an Entrepreneur Pulse confidence index. Useful whether you ever intend to sell or not — knowing your number changes how you invest in your business. Building Great Teams: Cast, Don't Hire Dan's principle: Strategic Coach treats itself as a theater company — with backstage and front-stage roles. They don't hire for jobs, they cast for roles. Every new hire is there to free up someone already in the company. Babs Smith built the Strategic Coach Team around Dan from the start — several Team members have now been with the company 20–30+ years. Beware the Founder-as-salesperson trap: if you're great at selling, you'll hire the wrong people — you'll confuse their excitement for the role with fit for the role. John, Joe, and Dan all find talent primarily through communities — mastermind groups, Genius Network, Strategic Coach — rather than ads. Great people seek out great people. Dan's upcoming book (Hay House): Casting Not Hiring. IP as a Strategic Asset Dan has had 82 thinking tools patented by the US Patent Bureau (none rejected), with 75 more pending. Each patent is a borrowable asset — you can borrow up to half the appraised value, creating a private intellectual property bank. Joe Polish's company operates as an ESOP — all Team members become equity owners after a vesting period, creating a true ownership culture without requiring employees to buy in upfront. Genius Youth and the Human Connection Advantage Joe's Genius Youth program focuses on skills AI can't replicate: human connection, handwritten notes, cold plunges, cooking and hospitality, ethical influence. Joe's 2026 Genius Network Annual Event —  features Peter Diamandis and Steven Kotler (Authors of We Are as Gods), live robots, and a mystery musician on 300M+ albums. Resources: The Greater Game (Book) — Dan Sullivan & John Bowen The Greater Game (Audiobook) — narrated by Gord Vickman, Hay House Business TheGreaterGameDashboard.com — free 15-minute assessment & company valuation tool 10xTalk Podcast — Subscribe — 10xTalk.com 10xTalk on Apple Podcasts Strategic Coach — Dan Sullivan's coaching program Genius Network — Joe Polish's community for elite Entrepreneurs Joe Polish's Genius Network Annual Event CEG Worldwide (John Bowen) — research and coaching for financial advisors Cleator Ghost Town, Arizona — Joe's 40-acre ghost town & the Cleator Bar and Yacht Club Inside Strategic Coach Podcast — Episode on Hiring — Dan Sullivan & Shannon Waller AI Killed the Modern Company (Video) — Peter Diamandis & Salim Ismail Why Microsoft AI Chief Predicts AI Automation of White-Collar Work in 18 Months — Fortune / Mustafa Suleyman

Feature Differentiation Is Dead. Here's What Actually Wins Now. When AI writes 80-plus percent of your code, the feature advantage you spent years building can be replicated in a day. Elena knows this better than most - she spent 15 years running growth at Dropbox, Miro, SurveyMonkey, and Amplitude, then joined Lovable and watched the old playbook stop working in real time. At Lovable, $400M ARR and 200 people, no titles, shipping multiple times a day, the rules are different. In this session, she breaks down what replaced feature moats, why she fired herself from her own VP job to go back to being an IC, and what it actually looks like to run a company at this velocity. You'll learn: Which moats still hold - network effects, data, brand, security and compliance - and why hardware is harder to copy than software ever was Why freemium is now a marketing budget line item, not a cost problem, and how Lovable's LinkedIn Premium partnership is converting at double digits What "no titles, everyone ships" looks like in practice, including a 20-year-old engineer pushing back on a VP's pricing page PR Why the next career flex isn't climbing to VP - it's becoming a high-power IC who builds what used to take a team of dozens How to build context for your AI so it actually replicates your thinking instead of producing average output for everyone This is for you if: You're a founder or growth leader trying to figure out what your actual moat is when feature differentiation keeps evaporating You're in management and quietly wondering if you'd be better off getting your hands dirty again You're trying to understand how an AI-native company actually operates day to day, not just in theory

AI isn't coming.It's already here — and it's creating millionaires in real time.In this powerful episode of Inside the Vault, Ash Cash sits down with Justin Burns to break down exactly how AI is shifting wealth in 2026 — and why the people who move NOW will dominate the next decade.While 300 million jobs are projected to be disrupted globally, a new class of builders is quietly launching AI-powered apps, software tools, and automation systems that are generating real income — fast.This isn't theory.This episode shows you how to:• Identify the AI opportunity gap • Turn a simple idea into a profitable app • Use tools like Claude, Lovable, Replit & Stripe • Build without hiring expensive developers • Launch a Minimum Viable Product (MVP) • Move from consumer to creator in the AI economy • Protect your code & ownership • Prepare for the Agentic AI eraJustin literally builds an AI-powered app LIVE on the show — proving that simplicity, when paired with the right framework, can lead to serious wealth.The question is simple:Will AI replace you…Or will you use AI to replace your income?This is the greatest wealth transfer opportunity of our lifetime.

Hannah Hoffmaster went from a self-described two-out-of-seven in technical skill to building multi-agent AI tools in a single year at Foster. This episode is for anyone — technical or not — trying to understand what genuine AI fluency looks like and how to build it. Hannah Hoffmaster is a student completing the one-year MSIS program at the University of Washington Foster School of Business. She came to the program with some knowledge of statistics and R, but little coding experience. Through her coursework — including Prof. Leo Bousioux's AI and Generative AI in Business class — she developed the ability to design and build AI-powered tools, including a charity comparison platform and an ADHD-focused scheduling app. She describes experimenting with AI as something she now does for fun. We covered alot of ground in this episode: How to think about AI as a build tool when you have no coding background Why "trust but verify" is the core discipline of working with AI, and how to operationalize it How to design a multi-agent workflow around the parts of a task you don't want to do What a deliberate, build-first job search looks like in a fast-moving field How to stay current as tools change — by building, researching versions, and talking to peers Why holding your career goals loosely can be an advantage in an uncertain market Resources mentioned: GiveWise (Hannah's project); Offload and the "Nudge" chatbot (Hannah's project); Claude Code; Supabase; GitHub; Vercel; Lovable; ChatGPT; Gemini; Codex; Prof. Leo Bousioux's AI and Generative AI in Business course; Foster's AI club.

Most growing companies are held together by spreadsheets that nobody fully understands — built by someone who left three jobs ago, maintained by someone who doesn't know why it exists, and quietly critical to daily operations. In this episode, Jeff Mains sits down with Garrett Fritz, co-founder of MetaCTO, a fractional CTO firm that helps mid-market companies transform outdated operational processes into custom, scalable software.Garrett breaks down why so many organizations are trapped in the "if it ain't broke, don't fix it" mindset, how AI has lowered the barrier to custom software without eliminating the need for expertise, and when it actually makes sense to build your own tool versus buying off-the-shelf SaaS. He also shares how internal tools can evolve into white-labeled revenue generators — and the most common mistake founders make when they try to take that leap too fast.Whether you're drowning in manual processes, questioning your SaaS spend, or wondering how to implement AI responsibly, this episode delivers a practical, no-hype roadmap.Key Takeaways4:37 — **The #1 operational inefficiency Garrett sees:** Hundreds or thousands of employees running mission-critical operations on a spreadsheet built a decade ago by someone who's since been promoted — and nobody knows why it has the formulas it has. 6:15 — **What "turning spreadsheets into apps" actually means:** MetaCTO embeds in the business, decodes the spreadsheets, understands the workflows, and builds working software that can replace the internal process — or be taken to market as a SaaS product. 7:54 — **Profitable from day one:** Because Garrett and his partner came with a thick Rolodex from 15–20 years in tech leadership, MetaCTO launched with clients already lined up — no burning cash to find product-market fit. 13:27 — **70% of AI POCs never see the light of day:** The excitement dies when teams realize how much effort is involved. MetaCTO's focus is getting those 90%-done prototypes all the way to the finish line. 18:34 — **Build custom vs. buy SaaS — the real decision framework:** After 2–4 weeks embedded in a business, MetaCTO looks at licensing costs, actual feature utilization (often just 2% of the SaaS product), man-hours wasted, and growth trajectory to determine the ROI break-even point. 28:25 — **Niches win:** SaaS isn't dead — it's narrowing. The companies gaining ground are building hyper-specific tools for specific industries (think: Procore, but only for commercial plumbers) where the UI, reports, and workflows are built around exactly how that niche operates. 31:33 — **The #1 mistake when productizing internal software:** Not talking to the second customer. Your problems aren't always everyone else's problems. Validate outside your organization before building for market, or you risk six months of rework when the deltas turn out to be core to the platform. 33:40 — **How to actually quantify the ROI of custom software:** Bake usage analytics into every product from day one. Track utilization, time on platform, transactions processed, and revenue generated — then compare to the man-hour cost baseline captured during discovery. 39:14 — **Responsible AI implementation starts with one rule: Resist "Accept All."** Don't grant admin tokens to AI agents for convenience. Suffer through permissions early so you don't face irreparable reputation or business damage when a bad actor exploits an over-permissioned agent. 41:22 — **The smartest first step for any leader feeling stuck:** Use AI tools like Replit to build a prototype with fake data. Don't try to connect it to real systems — just use it to force yourself through the problem-solving process. Come to the conversation with a working wireframe and you'll skip weeks of expensive discovery.Tweetable QuotesAt the heart of it is some Excel spreadsheet that some employee made 10 years ago — and it is critical to the operation." — Garrett Fritz"70% of AI proof of concept projects have never seen the light of day. It's pretty common to get excited about something and then realize, oh, this is a lot more effort than we thought." — Garrett Fritz"You can't just give a layman a chainsaw and expect to be a carpenter. A little bit of finesse and experience goes a long way." — Garrett Fritz"The niches win. The companies gaining ground are building hyper-specific tools for specific industries — where the UI, reports, and workflows are built around exactly how that niche operates." — Garrett Fritz"We never build it and run away. And as you can imagine, anyone who's created a piece of software has never said 'I'm done' either." — Garrett Fritz"Resist 'Accept All.' Give the AI admin access for convenience, and you're one bad actor away from irreparable damage to your business." — Garrett Fritz"AI is most valuable when it's applied to real business friction — not just trendy experiments or chatbots. Nobody needs another one of those." — Jeff MainsSaaS Leadership Lessons1. Familiarity is the enemy of efficiency. The "if it ain't broke, don't fix it" mentality keeps organizations locked in spreadsheet-driven operations for years — sometimes decades. The pain point has to get big enough to justify change, but by then the cost of switching is enormous. Don't wait for a crisis to modernize.2. The barrier to custom software has dropped — but expertise still matters. AI tools like Replit and Lovable have made it possible for non-developers to prototype software. But there's a massive gap between a 90%-done prototype and a production-ready, secure, maintainable application. Knowing what you're doing still matters.3. Don't buy features you'll never use. Most enterprise SaaS customers use 2% of the product's functionality — but pay for 100% of the license. When your team is only using 2% of the product and only 50% of the people who should be using it actually are, you're compounding inefficiency at every layer.4. Build for the second customer before you build for the market. If you think your internal tool has market potential, validate it with people outside your organization before investing further. Your problems are not automatically everyone else's problems. The cost of discovering core delta requirements after six months of development is enormous.5. Measure everything from day one. Custom software that doesn't have baked-in usage analytics is a black box. You can't demonstrate ROI, you can't justify ongoing investment, and you can't make intelligent roadmap decisions. Instrument every product with utilization metrics, transaction data, and performance monitoring from the start.6. AI governance isn't optional — it's the first conversation. The most dangerous thing you can do is grant your AI agents broad permissions during development and never revisit it. Treat AI like a junior employee: define its scope, limit its access, and require human approval for anything with downstream consequences. Someone always has to be the final buck.Guest Resourcesgarrett@metacto.comhttps://metacto.com/https://www.linkedin.com/in/grfritz/https://www.linkedin.com/in/grfritz/Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

20+ AI agents in daily production. 2.25M sessions. 614 meetings booked by a single agent. Millions of interactions across the stack. Amelia Lerutte, Chief AI Officer at SaaStr, and Jason Lemkin, Founder and CEO of SaaStr, take you behind the scenes of the AI agent stack running SaaStr every day, with live demos of the actual backends. In this session, they go deep on the top agents in production: 10K, SaaStr's AI VP of Marketing, built on Replit with 1,000+ commits in 4 months QB, the AI VP of Customer Success, managing 150+ customers end-to-end Annie, the AI Event Producer running saastrannual.com (46K+ lines of code) Amelia AI, the inbound agent on Qualified that booked 614 meetings and handled 402,000 chats for SaaStr Annual alone Agentforce, reviving the leads humans never followed up with Ava (Artisan) for warm outbound on the B leads humans won't touch Monaco for fully cold outbound that fills its own funnel with lookalikes You'll also hear the honest stories: the day Annie sent emails from a prohibited address, why Replit and Lovable versions of the same agent come to different conclusions, why the traditional CSM role is dead, and how headless Salesforce + Replit is the fastest path to your first real agent. The biggest takeaway: don't put AI on your A leads. Put it on the B leads your humans won't follow up with. That's where the real revenue is. Recorded live at SaaStr AI Annual 2026. Part of The Agents series.

In dieser Podcastepisode stelle ich drei KI-Tools mit ihren jeweiligen Stärken vor. Alle drei Tools – Langdock, Lovable und Hermes – um die es in dieser Podcastepisode geht, nutze ich häufig in meiner täglichen Arbeit. Welche Anwendungsfälle es gibt, erfahren Sie, wenn Sie sich den Podcast anhören. Ich erkläre Ihnen außerdem, worauf es beim Einsatz von KI im Alltag und im Unternehmen wirklich ankommt. Shownotes: https://digital4productivity.de/drei-ki-tools-drei-unterschiedliche-staerken Youtube-Video: https://youtu.be/kjovrSFfW7k In diesem Podcast geht es um produktive Digitalisierung mit: iPad Microsoft 365 Online- und Hybrid-Events Grundsatzthemen und Trends Mehr Informationen unter https://digital4productivity.de jekel & team Immanuelkirchstrasse 37  D-10405 Berlin Tel:       +49 30-44 0172 99 Mobile: +49 170-93 170 93 E-Mail:  t.jekel@jekelteam.de Web:     https://digital4productivity.de/

“Blessed are the poor in spirit, for theirs is the kingdom of heaven. Blessed are those who mourn, for they will be comforted. Blessed are the meek, for they will inherit the earth. Blessed are those who hunger and thirst for righteousness, for they will be filled. Blessed are the merciful, for they will be shown mercy. Blessed are the pure in heart, for they will see God. Blessed are the peacemakers, for they will be called children of God. Blessed are those who are persecuted because of righteousness, for theirs is the kingdom of heaven. “Blessed are you when people insult you, persecute you and falsely say all kinds of evil against you because of me. Rejoice and be glad, because great is your reward in heaven, for in the same way they persecuted the prophets who were before you. ' Matthew 5:3-12

#Podcast #mundial #lenovo #tecnologia #anker PLAYLIST Rolones: https://acortar.link/syEyR7En este video hablamos de IA, tecnología y tendencias digitales: desde Lovable y la postura del Papa León XIV ante la inteligencia artificial, hasta el regreso de “Desde el Teclado” de Matuk. También te contamos sobre el examen en línea de la UNAM, qué pasa con tus cuentas digitales al morir y las novedades de Anker y Lenovo rumbo al Mundial.00:00 Inicio y rola01:17 Patrocinios01:43 Comentarios y “windowseando”06:44 ¿Conoces a Lovable? Te contamos10:29 El Papa León XIV y su postura ante la IA22:57 Regresa “Desde el Teclado” de Matuk27:41 Examen de la UNAM en línea28:39 Hereda tus cuentas y productos digitales al morir31:03 Desde Manhattan, todo lo nuevo de la marca Anker52:13 El Mundial se vive con la tecnología de Lenovo01:08:30 Final

Third-Party Risk Management (TPRM) has historically been a tedious, 200-page paper exercise that felt like being catapulted back to 1979. But AI is changing that.In this episode, Ashish sits down with Igor Andriushchenko (CISO at Lovable) and Jasper Mills (CEO of Ethira) to discuss the collision of TPRM and AI.We dive into the hidden risks of Shadow AI, exploring the chaos that ensues when non-technical teams spin up unauthorized AI tools without security oversight. Jasper and Igor explain why the future of vendor risk involves treating AI agents like a contracted workforce, managing their lifecycles, and preparing for the 2027 era of "agent-to-agent" negotiations where humans are entirely removed from the loop.We also cover the impact of DORA (Digital Operational Resilience Act) regulations, the Build vs. Buy debate for AI security tooling, and how to use autonomous agents to finally automate tedious vendor questionnaires.Guest Socials -⁠⁠ ⁠⁠Igor's Linkedin + Jasper LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:00) Jasper and Igor's Backgrounds (Athira and Lovable) (04:00) Why Traditional Third-Party Risk Management is Abysmal (06:20) DORA Regulations and the Collision of AI and Compliance (11:30) Using AI to Automate Vendor Assessments and Questionnaires (16:30) The Build vs. Buy Debate for AI TPRM Tools (22:30) Shadow AI: "Giving a Kindergarten a Nuclear Bomb" (25:30) Using AI Agents for Automated Vendor Discovery and Inventory (28:30) 2027: The Future of Agent-to-Agent Negotiations (30:40) Treating AI Agents Like a Contracted Workforce (34:10) Enforcing Contractual Accountability through AI Guardrails

On this episode, I speak wtih Léonard Boussioux — Assistant Professor, Foster School of Business; Adjunct, Paul G. Allen School of Computer Science & Engineering, UW. PhD, MIT (machine learning & operations research). Co-founder of Universal AI. "Professor Leo," as his students call him, is a leader in AI education, research, experimentation, and adoption. He and I are on the Foster AI Taskforce, and sat down for this conversation in August of 2025. Leo rejects the career advice you've heard your entire life: pick a lane, specialize, go deep. His counter-argument is that AI now lets you be — in his words — a specialist of everything. In this conversation, we dig into what that actually means for MBA students, career switchers, and anyone trying to figure out how to use AI without offloading their thinking to it. We cover how Leo teaches non-coders to ship working products in a single class session, how he uses six different AI models to plan a vacation (and why), the new category of jobs emerging around human-AI collaboration, and why the people who panic about AI are usually the ones who haven't played with it yet. 3 Key Takeaways 1. Drive the AI. Don't delegate to it. The students who get worse at thinking are the ones who treat AI as a ghostwriter. The ones who get sharper treat it as a collaborator — pushing it in specific directions, rejecting outputs, iterating.  2. Build something this weekend. Reading about AI is not learning AI. Leo's students — most with zero coding background — ship working websites and games in a single class. Vibe coding tools like Lovable have collapsed the gap between idea and prototype to minutes. If you're an MBA recruiting into product, strategy, or consulting, the ability to prototype your own thinking is now a baseline skill, not a bonus. 3. The new jobs are at the human-AI seam. Automation creates a new category of work: deciding where humans belong in the loop, designing the workflows, catching the 5% of edge cases that have outsized consequences. Moderator, orchestrator, AI workflow consultant — these roles barely existed two years ago. Position yourself there. Learn more about Leo at https://leobix.com, or on LinkedIn.

Europe's startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number The post The European Startup Scene appeared first on Software Engineering Daily.

Europe’s startup ecosystem is maturing rapidly, with companies like Revolut, Lovable, and Legora demonstrating that world-class technology businesses can be built and scaled on the continent. While the US remains the dominant force in venture-backed software as home to the largest markets, the deepest capital pools, and the most ambitious exit culture, a growing number The post The European Startup Scene appeared first on Software Engineering Daily.

Bodie Grimm from the Kilowatt podcast talks about vibe coding news sources.Featuring Tom Merritt and Bodie Grimm.Link: Why Work is Starting to Look Medieval by Sierra LaDukeA note from Bodie:I remixed my project so people can play around with it without affecting my original version: https://kilowatt-curator-clone.lovable.appI also figured out how to let others Remix a project on Lovable. Sign up for a Lovable account and log in. Go to the following link: https://lovable.dev/projects/20673798-2b68-4188-96ff-37dcfa6a9f35. Click the Remix button at the top right. It will take you to the prompt/preview page to edit the the new "Remixed" site.I also created a brief (video and audio) explainer if you want to add it to the end of the episode:https://drive.google.com/drive/folders/1bmb71gyGsH8paiHiAHd2ejoOLpxn8Iro?usp=share_link-Bodie Hosted on Acast. See acast.com/privacy for more information.

No Braincast 634, Carlos Merigo, Cris Dias, Hiago Vinícius e Luiz Yassuda discutem o vibe coding, a nova febre da IA que promete permitir que qualquer pessoa crie aplicativos, dashboards, automações e protótipos apenas descrevendo o que quer. A conversa passa por Claude, Codex, Lovable, Replit, Bolt, Cursor, Manus, low-code, SaaSpocalipse, token maxing e a fantasia do “unicórnio de uma pessoa só”. Afinal, estamos diante de uma revolução criativa, em que mais gente pode transformar ideias em produtos, ou de uma fábrica de gambiarras em escala industrial? Também entram no papo os riscos de segurança, vazamento de dados, dependência das big techs, código ruim, Shadow IT, empresas tentando substituir times inteiros por IA e a importância de repertório, critério e bom gosto num mundo onde executar ficou mais fácil, mas saber o que pedir continua sendo o grande desafio. No Qual é a Boa, ainda tem Cinemático sobre Obsessão, jogos como Crimson Desert e The Last Caretaker, o Anti-Authoritarian Toolkit, IA em Curso, The Traitors e Momento Faustão. -- CONHEÇA OS CURSOS DA ESCOLA DE IA DA PUCPR https://posdigital.pucpr.br/areas/escola-de-ia?utm_source=podcast&utm_medium=braincast&utm_campaign=pucpr_externo_leads_ativacao-1_escola-ia&utm_content=audio_atributo_26-05-17 -- 04:17 PAUTA 05:37 O que é vibe coding 08:31 Origem e ferramentas 09:52 É programação mesmo 14:50 SaaSpocalipse e limites 19:59 Dilema do monstro 25:30 Token maxing e tralha 27:50 Low code e democratização 30:37 Agentes e checagem 34:10 Programadores e IA 34:52 Autocomplete e Vibe Code 38:52 Hype e corrida da IA 39:56 Segurança e dados 41:45 Automação pessoal útil 43:55 SaaS pequeno vs grande 46:07 Sites leves sem WordPress 49:57 Canva e custos ocultos 57:09 Dependência e mediação 59:45 Legado corporativo e suporte 01:02:57 Habilidades e formação 01:11:40 Bom gosto e repertório 01:12:46 Curiosidade como profissão 01:15:03 Educação e base teórica 01:18:00 A febre dos prompts 01:18:50 QUAL É A BOA 01:28:56 Toolkit anti autoritário 01:34:38 Cupom IA em Curso 01:35:24 Reality The Traitors 01:40:06 Momento Faustão -- ✳️ TORNE-SE MEMBRO DO B9 E GANHE BENEFÍCIOS: Braincast secreto; grupo de assinantes no Telegram; e episódios sem anúncios!

Your Friday May 22nd sports talk with Bill Murphy and Danny Boy Reginald.

Steve Holmberg is one of our favorite recurring guests, and this time, he showed up with something nobody expected. Over the holidays, Steve went from zero coding experience to a fully functional AI-powered app called Field Check, built using the vibe coding platform Lovable. The app connects retail store associates directly to brand product and marketing teams through AI-driven chats that go deeper than any survey can — and he built the whole thing in about a month. In this conversation, Steve walks through exactly how he did it, what he wishes he'd known before starting, and why AI is "a tool masquerading as a solution." He also deployed Field Check live on the Second Nature Slack channel to surface what the audience actually wants — and the results are fascinating. Plus: a halftime check-in on Second Nature's 2026 predictions, what happened at Sea Otter with Chinese bike brands, why Adidas is having a moment, and the 32-inch wheel debate that nobody asked for. Show Notes: Popfly for Creators: https://popf.ly/secondnaturecreators Popfly for Brands: https://popf.ly/secondnaturebrands Steve Holmberg: https://www.linkedin.com/in/stephenholmberg/ Insight Accelerator: https://insight-accelerator.com/ Lovable: https://lovable.dev/ Field Check: https://fieldcheck.app/ Supabase: https://supabase.com/ Cloudflare: https://www.cloudflare.com/ Github: https://www.github.com 2026 Predictions Episode: https://www.youtube.com/watch?v=k0G3OpDVnyA Steve's Prediction Post: https://www.linkedin.com/posts/stephenholmberg_2026-sport-and-outdoor-industry-trends-activity-7414293417557626880-AXy7 Trailcon: https://www.trailconference.com BPC - Brand, Product, Content Jason Fried - David Senra Podcast: https://www.youtube.com/watch?v=BdDCtMA1gSw Adventure Bucket List: https://reachinternationaloutfitters.com/collections/state-bucket-lists Join us on LinkedIn: https://www.linkedin.com/company/second-nature-media Meet us on Slack: https://www.launchpass.com/second-nature Follow us on Instagram: https://www.instagram.com/secondnature.media Subscribe to our newsletter: https://www.secondnature.media Subscribe to the YouTube channel: https://www.youtube.com/@secondnaturemedia

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl

I need to be loved, but I want to be lovable.You can find a transcript of this video and over 900 more devotions like this one on our website at PeaceDevotions.com.If you find value from these devotions we'd encourage you to support our ministry. You can support us by praying for our pastors, sharing and commenting on our videos, or by donating at https://peacedevotions.com/donateConnect with us on social media, our website, or get these emailed to your inbox.Facebook: https://www.facebook.com/PeaceDevotions/Instagram: https://www.instagram.com/peace_devotions/YouTube: https://www.youtube.com/channel/UC2pFo5lJV46gKmztGwnT3vAWebsite: https://peacedevotions.com/Email List: https://peacedevotions.com/emailYou can also add Peace Devotions to your Flash Briefing on Amazon Echo Devices.https://peacedevotions.com/echo/

One of the hardest questions people carry is this: “If people really knew me… would they still stay?” Most of us spend our lives trying to prove we're lovable through performance, approval, success, or image. But Galatians 4 reminds us that the gospel does not begin with us becoming lovable enough for God. It begins with God setting His love on us through Jesus. In Christ, we are not merely tolerated. We are adopted.Paul's use of adoption language in Galatians 4 would have shocked the Roman world. Adoption was not sentimental. It was legal, permanent, and identity-changing. An adopted son received the full rights, inheritance, and family name of the father. That means your standing before God is not fragile or probationary. You are not trying to earn your place at the table. Jesus already secured it through His life, death, and resurrection. The cross was not just about forgiveness. It was about belonging.This week, before your phone, email, schedule, or anxiety tells you who you are, begin each day by sitting with Galatians 4:6–7. Slow down long enough to let this truth reshape your heart: “I am no longer a slave, but God's child.” Don't just read it quickly. Pray through it. Repeat it. Let it become the loudest voice in your life this week.Because of Jesus, you do not have to earn God's love or prove your worth. In Christ, you have already been welcomed into the family of God.

C&R have fun talking full NFL Schedule Release! Are you surprised at what the league decided regarding social media teams & Vrabel/Russini? A quarterback who is must-watch TV joins the Netflix 'Quarterback' show. The Cavs win & 'OLD-SCHOOL WHEN 50 HITS' celebrates Gronk's Bday! Lovable goofballs take centerstage. Plus, how stars are affected when someone very famous watches them courtside!See omnystudio.com/listener for privacy information.

Covino & Rich have a yo so fiesta with 'OLD-SCHOOL WHEN 50 HITS!' They celebrates Gronk's Bday! Lovable goofballs take centerstage. What doesn't Rich understand? Plus, how stars are affected when someone very famous watches them courtside!See omnystudio.com/listener for privacy information.

Chandler Bolt realized that much of what his sales managers did at Selfpublishing.com can be done better by good AI. Within a month, he built a first version with spiked sales. Now he's replacing all his managers. This is his guide to doing it well. Chandler Bolt is the founder and CEO of SelfPublishing.com, an education company that helps entrepreneurs and experts write, publish, and market books. Over the past decade, the company has helped publish more than 7,000 books and grown into an eight-figure business. Today, Chandler is focused on using AI tools like Lovable to build internal systems that improve sales, operations, and management at scale. Sponsored byZapier More interviews -> https://mixergy.com/moreint Rate this interview -> https://mixergy.com/rateint

In this episode, Ryan sits down with Denver local and yoga enthusiast Will, creator of the new community tool Denver Yoga Finder. What started as a personal search for the right yoga studio after moving from Philadelphia turned into a lightweight but powerful platform helping Denver residents discover yoga studios by neighborhood, style, and class type. The conversation explores: Why Denver's wellness culture inspired the project How AI and "vibe coding" tools like Perplexity, Claude, and Lovable made building the app fast and accessible The surprising diversity of yoga styles in Denver Ryan's personal yoga transformation journey after years of Brazilian Jiu-Jitsu injuries Favorite Denver taco spots, cheesesteaks, and outdoor lifestyle perks Will walks through the functionality of the tool, including: Interactive neighborhood map Filtering by yoga styles and heated classes Studio Instagram and website integrations Favorites and discovery features for new Denver residents Ryan shares how yoga dramatically improved chronic pain issues, including severe plantar fasciitis, after years of martial arts training. He also celebrates completing his 700th yoga class milestone. The episode closes with classic Denver food talk, including praise for Patzcuaro's, cheesesteak debates, and the unique joy of skiing and golfing in the same weekend. Topics Covered Denver yoga culture Wellness communities Vibe coding and AI-assisted app development Lovable and Perplexity workflows Hot yoga and recovery Denver neighborhoods Restaurant recommendations Startup creativity and lightweight tools Community-focused software projects https://realgooddenver.com/ https://denveryogafinder.info/

Episode 300 is here!

https://youtu.be/aQyHwoGfy50 Max Kryzhanovskiy, President and CEO of MOS Creative, is driven by a desire to set an example for his children and show what's possible through technology, persistence, and innovation. As the leader of a tech-forward agency that builds websites, apps, and AI-enabled platforms, Max helps businesses move from idea to execution by creating digital products that solve real problems and scale over time. We explore Max's MVP Framework — Define the problem, Determine target market, Prototype the product, Build the MVP, Test and obtain feedback, Iterate — a practical approach for transforming ideas into scalable digital products. Max explains why founders should avoid overbuilding too early, how AI is accelerating prototyping and development, and why businesses must balance automation with authentic human connection. — Drive Growth Using AI Agents with Max Kryzhanovskiy  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Max Kryzhanovskiy, the President and CEO of MOS Creative, a company that builds websites and apps that drive growth. They were also the first company in Baltimore to launch a mobile site. Welcome to the show, Max.  Thank you for having me.  Let me ask you this—what is a mobile site? Is it a mobile phone site, or is it something different?  I mean, now it probably doesn't matter as much anymore, because everybody obviously has a website that works on a smartphone screen—or a responsive websites. But before mobile websites came out—or I should say, when smartphones first came out—we had to adjust for smaller screens. We were all used to bigger screens on a computer, and then once we started having different screen sizes come out before responsive, we were the first company to have a mobile website in Baltimore. And we actually built a web application specifically to create them ourselves, and then also went to market to offer it to other clients as well. So a mobile website is just like it sounds, a website that’s specifically designed for mobile.  That’s cool. So it sounds like you are very much a tech-forward company, and you are at the edge of technology. And as we were logging on, you said that you would be recording this on your phone because you actually have AI agents running on your computer. Does that mean you have AI agents as part of your team? What kind of agents do you have? Is it still an experiment, or is it already in execution mode?  It's in execution mode, but we're always experimenting. We like to think we're ahead of the curve, but with AI, we're all experimenting to a certain extent, right? Something new comes out, we try it out, see if it works, and see how it can be applied to your business—what kind of outcomes it can give you. So I'm all about AI. It's amazing. It's an amazing tool. But I think AI is becoming a lot more than we thought it was going to be—and also a lot less at the same time. Meaning, when AI launched—for example, when ChatGPT came out to the broader market—I mean, obviously AI had been around for a while—but when ChatGPT launched its chatbot platform publicly, we were amazed by how much work it could done. So it went from zero to a hundred. “Oh my God, it can do all of this,” right? But now, for example, with the more recent models—4.5, 5.0—the improvements are much smaller.  It's not a hundred percent or a thousand percent better anymore. Now it's maybe five or ten percent better, but the cost keeps increasing. I just read somewhere that even Claude said Claude Code won't be included much longer as part of the regular plan. So now it's only in the $200 higher-tier plan, plus you have to buy additional tokens. So it's really becoming more like, “Hey, yeah, we can do this for you—but you're going to end up paying something similar to what you'd pay a team.” At first, it was more like, “Let's get into the market. Let's get a lot of people interested.” But now, obviously, they have a lot of money behind them—investors, VCs, public market pressure—and they need to bring in revenue. So I think things are going to change very soon. AI is going to become a lot more expensive because the infrastructure and resources it requires are expensive. So eventually, those costs are going to be passed on to users. Yeah. And I noticed that ChatGPT started to do some ads as well. They’re probably going to go that direction, and who knows what that’s going to bring. But that's not our topic today. Today, it's about something else—frameworks. But before I go to the framework question, I'd like to ask you: what is your personal “why,” and how are you manifesting it at MOS Creative? Well, I'm a family man, so my “why” is to see my kids grow up to be amazing human beings—and hopefully to show them a great example of what can be accomplished in sports and in business. So my “why” is also to be a good person. Success can mean different things to different people, but for me, I love the hunt to get to a certain level of success. And then it's kind of like—us as humans, or at least a lot of people—we reach a certain level of success and we don't really celebrate it. It's more like, “Okay, let's get to the next level.” So my “why” is to show my kids that anything is possible if they really want it. Why I got into this space—it was exciting. You could see how quickly technology was moving, the kind of innovation that was possible, and it excited me. So that was one of the main reasons I got into technology. But the other reason was because I was in a different business, and we created technology that helped us grow. And I thought, “Oh wow, this is a completely different way to scale a business.” So technology became the direction we took. Yeah, I love it. I think inspiring our kids is a huge driver for many people, and it totally makes sense. Technology is exciting. I'd like to switch gears here and ask my other common question on this podcast, because this podcast is all about frameworks—business frameworks—how we can help listeners understand things, simplify things, and see different perspectives. So my question to you is: what is your favorite shortcut to success—or framework? And I don't mean “shortcut” in a negative sense, but rather a framework that allows you to understand things differently, make decisions, serve clients, and create valuable outcomes. Whatever it is—something that has worked for you, and is simple enough that you can explain it to listeners in three to five steps. Well, I believe in always being open to learning. It's not specifically a framework—it's more of a mindset: understanding that we don't know everything, especially now, with how quickly things are changing. I mean, a lot of people say that AI is going to make humanity a little dumber than we are. But actually, I learn a lot from it as well. If I'm doing something and I think, “Oh, this is a great way to speed up the process,” then I use it. So let's say, for example, a client asks me a question. There are different ways to approach it. If I already know the answer because I have specific experience with it, I can answer it, right? That doesn't always mean the answer is going to be correct.  I can research it, or I can get an answer from AI and then verify it through research and experience to make sure the outcome is actually what it says it's going to be. The learning part is making sure you're always open to figuring out whether the steps you've taken before are the right steps—or whether they can be optimized. I'm a big believer that everything can be optimized, especially now. There's almost no question that can't be answered quickly. Maybe there are some deep philosophical questions—but for the most part, especially in business, work, or even life, you can get answers very quickly. For example, I had a kind of vertigo-type feeling, and I was wondering what exactly it was. I entered specific prompts into ChatGPT, and it actually broke things down really well for me. Then I went to a doctor. First, I checked with a friend of mine who's a nurse, and she said, “This is probably what you have.” And she started asking me questions. I thought, “This is funny—these are exactly the same questions ChatGPT asked me.” And her husband said, “You know what? That proves that medicine is basically a set of questions. As you answer one question, it leads to the next.” So it's like a dynamic questionnaire. And by the time I got to the doctor, I already had a good idea of what it potentially was, and I knew what questions to ask so I could understand the next steps to fix it.  Yeah.  So what I'm saying is there’s always a way to improve. I'm a big believer in that. It doesn't matter what you're doing, because in this age, everything moves very fast—regardless of the business you're in. That's true. It's interesting that you say ChatGPT can answer any question. It's true—sometimes it hallucinates, but it still gives you an answer. Yesterday, I went to a presentation, and the president of Great Game of Business talked about this. He said, “Today, the answer is everywhere. So it's not a lack of answers—it's a lack of good questions.” So what we really have to come up with are good questions to ask. That's the bigger challenge now—not finding the answer. And I thought that was a really interesting insight. I agree. It's the same thing, right? It relates to prompts as well. If you have a good prompt, you're going to get a better answer. If you ask a good question, you're going to get a better answer. So yeah, I agree with you. Listen, AI isn't a complete solution, but it's a huge help—especially if you're just starting out. Yeah. So what drives your business? Is it technology? Is it trends? Is it something else? What drives it?  It's kind of a mix between technology and growth marketing. What that means is we work with clients all the way from ideation to scaling. We've also had several clients successfully exit. So clients come to us and say, “I have an idea. How do I take it to the next step?” Obviously now, there are AI builders and AI platforms that can help take a high-level idea and turn it into some kind of prototype—or at least a basic flow. But ideally, we work with clients from the idea stage all the way through design, development, launch, and driving traffic to the product. So the perfect client fits into that category. They might have an idea for a web application, mobile application, or software product.  They come to us and they're not really sure what the next steps are—or they've done some research For example, I spoke to a prospective client the other day. She worked with a developer who tried to build the product using an AI builder. For some reason, something didn't work out, and now she's back at square one. So now we have to review what she actually wants to build, determine the best approach, and figure out what phase one, phase two, and phase three should look like. So that's kind of how we work. For our clients, it's not just, “Let us develop it for you.” It's also about the creative side, the messaging, and the user experience. It's about making sure that when someone downloads the app—or visits the website or web application—it serves its purpose. It's a problem-solving product. It needs to solve a problem so users keep coming back again and again. And then we help grow it to new audiences. That's when it starts to scale and become exponential. Does that make sense? Yeah. So I’m wondering, you work from the idea forward, or you work from the outcome backwards? What’s the approach?  That's a great question. Not everyone knows the outcome right away. When someone has both an idea and a clear outcome, it works better, right? Because then you can help them get to that outcome. But overall, the outcomes are usually very high-level. You know: “I want to build this web application or software because I'm targeting this audience.” Okay—but what does that really mean? What problem are you solving? To be honest with you, ninety percent of people don't really know what problems they should be solving at the initial stage. So, talking about frameworks, we work with them to define which problems they should solve first. Because most startups—or even profitable companies trying to add new technology into their workflow or business—often don't know what one or two problems they should solve for the MVP before going all in. Yeah. Okay, so step one is to define the problem. What's step two?  Make sure you have the right audience for that problem. That's a big issue. A lot of times, people try to serve everyone. You don't want to go too broad, and you don't want to go too narrow. If you go too narrow, you're going to hit a ceiling before you even go to market.  So you determine the audience for the problem you're trying to solve, right?  Correct.  And then what's the next step?  Once you determine the audience and define the problem, the next best step is to create some kind of prototype and actually take it to that audience to test for product-market fit. Meaning: get feedback. Again, it doesn't have to be a fully working product. But go to that audience and get feedback like: “Yes, this solves my problem,” and “Yes, I would pay for it.” Or even better—for them to actually exchange some money to join a waitlist or gain access to an early version of the product, so they can test it and provide feedback. That's the best-case scenario. Because once you have that input, it becomes much easier to make adjustments. It doesn't matter whether those adjustments are in the design or in the actual working product—you're refining it for that niche audience. Yeah, that makes sense. So you design the prototype or minimum viable product, then you test it and get feedback. Then what do you do?  Well, I want to clarify something. Designing a prototype and having a minimum viable product can be two separate things.  Okay.  You can design a prototype. Again, it can be designed in Figma, using an AI builder, or even just as a workflow or user flow. Obviously now, things are a little different because you can build prototypes much faster. That doesn't mean they're going to be production-ready. But a minimum viable product is usually focused on solving one or two specific problems for that market. It's a problem-solving product that actually works—meaning it's much closer to being production-ready. Yeah.  So those are two separate things. There's a very big difference between them.  Yeah, because now you have vibe coding, and with tools like Lovable—or whatever platform you're using—you can create a prototype quickly. But it's not necessarily going to work, and then you still have to build the actual working product. Correct. Yes, I agree. Then you test it, expose it to the target market, and gather feedback. And then what do you do? Do you iterate? What's the next step? You iterate, yeah. So at that point, ideally, you have product-market fit, you've received great feedback from users, and—best-case scenario—they've even paid you some money. Then you either expand on what has already been built, or you go all in: invest more money into it and start building a production-ready product. And once you have that, you may realize that you also need to improve the user interface. That happens a lot—especially if you vibe-coded it. The output usually isn't the best when it comes to user interface design or user experience. So you may need to redesign the interface, properly develop it, and then take a production-ready application to market. And then it goes back into the cycle of iteration. Meaning, you keep gathering feedback. This is why I often recommend not adding too many features in the beginning. Focus on one or two core features—one or two main user flows within those features. That's it. Forget about everything else. Yeah. And then you can add features later.  You can always add features later. Most of the time, if you add too many features in the beginning, you'll probably end up cutting at least 40% of them because people just won't use them. And I'm not talking about core features like sign-up, sign-in, forgot password, onboarding, authentication—that kind of stuff. Obviously, you need those. But you still have to figure out who your audience is. Do you need SMS login? Do you need email login? Do you need both? Do you need social logins? You have to make sure you clearly understand your audience—but you don't need everything all at once. You may eventually need all of it, but not in the beginning. Yeah, that's true. So you've worked with other businesses, which means you're primarily a business-to-business agency, right?  Business-to-business, business-to-government—we've also built business-to-consumer apps as well. But usually, our client is a business-to-business.  Yeah. So here's my question: In B2B, how do you gain people's trust so they'll even engage with your product? I understand there's a funnel—but how do you get businesses into the top of that funnel? How do you create that initial trust so they engage? What does it take? Many things. Content helps, obviously. Creating content like this, creating videos—I create videos on a regular basis talking about what's out there, what's possible, what's good, what's bad. Kind of the everyday life of an agency, and the type of work we do. We also post projects on different directories and platforms. A lot of previous clients come back to us, and we get many client referrals. We rank pretty well for SEO and AEO, so a lot of people find us through ChatGPT. Especially because that's one of the services we offer. People find us when searching for things like “best app developers” or “best website designers” in our specific area. We're not targeting nationwide rankings—that's much harder and a much longer-term strategy. But in our area—Maryland, Howard County, Columbia—we rank very high.  And what does it take to rank high in AEO—in AI search?  It's the same approach we take to rank in Google. Google obviously owns Gemini, and now there's Google AI Overview. It's really a real-estate play. If you have a website that's properly structured for Google—with some adjustments for semantic search, like adding question-and-answer content to every page, especially product and service pages—you improve your chances significantly. You also need a properly configured robots.txt file with clear descriptions, so when search crawlers reach your site, they can immediately understand the structure and know where to go. When you see sources cited in AI search, that's exactly what those systems are reading from your site.  You also need the right technical setup: Your website has to be fast. You need proper H1, H2, and H3 structure across the site. So overall, it's about having a properly structured website. If you follow strong SEO fundamentals, with additional improvements specifically for AEO and GEO—because now it's not just SEO anymore, it's SEO, AEO, and GEO—you'll usually appear in ChatGPT, Google AI Overview, Gemini, Perplexity, and other AI search tools. And your Google Business Profile and Google Maps listing are properly optimized—which has changed a lot recently on Google's side as well—you'll also show up more often in local AI search results. So isn't it true that AI search looks for different kinds of signals than traditional SEO? I've heard, for example, that backlinks are less important in AI search than they used to be. They're not as important for AI search, but backlinks still carry a lot of weight. Again, you have to think about this as two separate systems, right? There's Google Search—with Google AI Overview and featured snippets—and then there's Google Maps. You don't need a website just to appear on Google Maps. You mainly need a properly optimized Google Business Profile. And you can still show up in AI search that way. Having a website does help, because it sends another signal to Google, but it's not as critical. The most important thing—and I'll answer your question for both cases—is consistency and structure. For Google Maps, if you have a properly maintained Google Business Profile with constant updates—blog posts, videos, photos, and business updates—that teaches Google AI what your business does. So you want updated product pages, images, descriptions, and location details if you're location-based.  All of that educates Google, which helps you rank higher on Google Maps. And like I said, Google Maps ranks very well in AI search. Now, if you also have a website, that's even better. And on your website, it helps to embed your Google Map as well, because that reinforces another signal from Google Maps. For example, some of our clients have multiple locations, so we include Google Maps with all their locations on the site—and that helps. Then you also create location pages, just like you create product pages or service pages. Google—and AI systems in general—don't really rank entire websites. They rank individual pages. That's why top-of-funnel content is usually blog posts or educational content answering someone's problem. Then that written or video content leads users to a service page or product page. That's basically how it works. Does that make sense? Yeah, that's very interesting. So if I want to increase my AI ranking… one of my clients told me that if your clients post about you on Reddit, that can be really powerful and help drive AI search visibility. Is that true? Reddit and Quora are very powerful. Very powerful. They rank very high. Listen, I'll give you a simple example that anybody can use. If you go to Quora or Reddit and look at the questions people are asking—for example, let's say you search for “app development”—you can filter by questions and literally see what people are asking. If you answer those questions in a natural way, related to your service or product, and include a backlink—not in a salesy way, but naturally—that's a very strong backlink. And speaking of backlinks: they're still relevant. Maybe they don't carry as much weight as they used to, but they're still very valuable.  Because when Google or AI systems evaluate content—and when you search in ChatGPT, Claude, or Gemini and see sources—those sources are essentially citations and backlinks. So if your website has strong citations and is properly structured, it absolutely helps you get discovered. You just need to make sure everything is set up correctly so Google—or any other search system—understands what your content means. But yes, to answer your question directly: Reddit and Quora are excellent for visibility because they're high-authority websites with massive traffic and very strong domain ratings. Yeah. That’s great. So Google Maps, Reddit, Quora, they are big drivers. That’s great.  Huge drivers. I mean, listen, there are many others—but social media has become huge over the past two years. Before, if you made a Reel on Instagram, you wouldn't be able to find it through Google search. But in the past couple of years, they opened that up. Why do you think they did that? Because they understand the value of content. Just like YouTube—where you can find videos through specific keywords—they want Instagram videos to be discoverable through Google Search and AI search. And then those searches lead people back to their platform. If someone who isn't already an Instagram user discovers content they like—a creator they like—they may sign up for Instagram because of it. So yeah, all of this ties back to backlinks and discoverability. It's really about how you use those backlinks. I mean, YouTube has been a huge driver for people looking for answers or trying to learn almost anything. So yeah, that's kind of how it works. It's one big spiderweb. Yes. It’s interesting. So basically, the more content I have and the more content other people post about me in credible sites, whether it’s Reddit, Quora, YouTube, social media, and they all point to my website or web pages, then the more it’s going to be discoverable by AI. That’s kinda makes sense.  You're definitely going to become more discoverable. But again, if it's just “Steve Preda,” that alone may not be valuable unless someone is specifically searching for your name. Now, if people are responding to or discussing how to apply a specific framework—and someone is searching for that framework that relates to your content—then it becomes relevant. Does that make sense?  Yeah. Yeah, understand. Yeah. Absolutely. Let me ask you this. If you could have a magic wand and fix one thing inside your company in the next 12 months, what would that be?  That’s an interesting question. I don’t know. I think I'd be very interested in applying more AI agents so they can help drive the business and support more growth. Overall, I just want healthy growth—making sure we're happy with the work we're doing, and that our clients are happy with the work we deliver. Because that leads to better outcomes, longer-term relationships, and healthier growth for the company. I mean, my ultimate goal at some point is probably to grow the company and eventually sell it. If we're happy with what we're doing, and our clients are happy with the work we're delivering, I think that growth will happen organically. Yeah. And what do you need to make the company sellable in your perspective?  Having strong, scalable systems—and AI is going to help with a lot of that.  So do you believe that a company with only AI employees—at the extreme—could still become a very valuable company? No, I'm not saying we should rely only on AI, and I'm definitely not planning to let go of any employees. What I'm saying is that AI can help with certain smaller tasks that sometimes get missed or forgotten. That's a perfect fit for AI. For example, even during conversations—if a project manager is handling several clients at once—we usually need updates on what was discussed. Yes, AI can record the conversation, but more importantly: what are the actionable next steps? And from those action items, what has already been completed, and what still needs to be done? Those are the kinds of things AI agents can help with—tasks that don't necessarily require a human. That way, time isn't wasted and can instead be used more effectively to make sure things are getting done and that we're reaching the outcome you mentioned earlier. What is your opinion about controlling AI agents? What is the level of risk? Not just about someone maybe doing a prompt injection and kind of hijacking your agents, but losing control of the agents in terms of complexity. So do you see a risk there that someone could kind of unleash these agents and somehow not be able to control them, or the quality of their work? Could they not control that? Or something changes and the agents get impacted—maybe a software update or something like that? Is this a thing, or is that not a concern? I think there should definitely always be guardrails. For example, right now we're building a platform with AI to gather RFPs, review them, score them, and actually create outputs—like the structure of the RFP. But before they get submitted, an actual person reviews them. I think there should always be final approval by a human—unless it becomes such a perfect system. I mean, it's software, right? At a certain point, can something go wrong? Yes. Especially with updates—unless you own the full process from beginning to end. Yeah, I think there's always a risk, but there's always a risk with software.  There should definitely be some guardrails, no doubt about it. I don't think it should be the last step before a human approves it and actually—for this RFP example—submits the response to whatever platform. I think a human should always review and approve it to make sure everything is working properly. But I think you can save a lot of time. For example, instead of us doing two or three RFPs a month, we can do ten or fifteen. I mean, the quality isn't really changing. It's structure. It's answering what they're asking for. So if it fits the criteria we're looking for, we still spend time reviewing it. I mean, we got an RFP the other day that was 150 pages. It would probably take two days just to read it. And at a certain point, you're like, “You know what? This isn't a good fit.” So it saves time. It just creates more efficiency. But there should definitely be guardrails and structure for sure, and a human should be involved in the loop. That I agree with you on. Okay. It's a big topic. One of the thoughts is that at some point AI is talking to AI. Like in hiring—you see these big recruiting companies using AI to filter resumes, and then applicants use AI to write resumes that fit what the filters are looking for. And at some point, the authenticity or credibility of those resumes begins to fade because it's all prearranged. So then the whole purpose of filtering employees starts to diminish. Do you think this kind of thing might happen with RFPs too? Maybe. Very possible. I wouldn't be surprised if it's not happening already. Yeah, I mean, it's definitely very possible. There are already several platforms that find RFPs. They work a little differently. We're building specifically for our own purpose. I do want to document the process to kind of show, “Hey, here's what can be done.” But yeah, it's very possible, for sure. Listen, if you're relying on a regular process to get a job, then you're probably not going to get the job. There are a lot more people looking for work right now. I don't know if you heard about Microsoft—and I think Tesla too—but companies are letting people go left and right. Microsoft is offering long-term employees buyouts. And by long-term employees, I mean people who are probably older and maybe not as knowledgeable or experienced with AI.  It's like, “Hey, let us buy you out so you can retire a little earlier.” So this is happening. If you're going through the same regular hiring process as everyone else, you're competing against 500 or 1,000 other people for the same job. Obviously, it's an employer's market right now, not an employee's market. If you're trying to get a job, it shouldn't just be through the regular process. It should be through people you know. Networking is going to have even more value. Personal connections matter, and people knowing, “Hey, this person actually spoke to me the right way.” You should also know how to use AI, because that's going to give you an edge in getting a job. But actually speaking to someone should happen through networking and connections. Yeah, that's my feeling too—that human interaction is actually going to increase dramatically in value. Because authenticity… that's really the only way to verify authenticity: being face-to-face with someone, a real physical person. That's fascinating. Yeah. But I'll tell you—like I said, I post videos on a regular basis. My mom asked me the other day, “Max, are you using AI, or is it really you?” I said, “No, it's really me. It's not AI.” So it's funny because AI is getting so good that you're not always sure what's real anymore. And even with RFPs—it's not just about submitting proposals or resumes. Personal and human connection is going to become more valuable than ever. If I personally knew every buyer putting out an RFP, I'd rather talk to them directly, one hundred percent. Because it becomes a completely different process.  Yeah, that's spot on. Love it. So, great information. I love the framework: define the problem, determine the audience, create a prototype, build the MVP, test it, and then iterate. That's how you build a digital product—whether it's a website or an app. So if you're out there looking for a solution, Max Kryzhanovskiy and MOS Creative may have the solution for you. So if people would like to connect with Max Kryzhanovskiy and MOS Creative, where can they reach you? People can reach us through our website: www.moscreative.com. They can also find me on LinkedIn under Max Kryzhanovskiy or MOS Creative. They can fill out a form on our website or email us at info@moscreative.com. Fantastic. So if you want an AI-driven platform, definitely reach out to Max. So Max, thank you for coming and sharing your ideas. And I love that you have such a strong vision for AI and that you're actively experimenting within your company, which means your clients will benefit from that as well. And if you enjoyed this conversation, then stay tuned, because every week a successful entrepreneur comes on the show and shares their ideas and frameworks. So thanks for coming, Max—and thank you for listening. Thank you. Important Links: Max's LinkedIn Max's website Max's email: info@moscreative.com

Nintendo raised the Switch 2 price to $500 amid a global memory shortage. ShinyHunters forced Canvas offline during finals season. Researchers found 5,000+ insecure vibe-coded apps, Mozilla credits Mythos for 423 Firefox bug fixes in April, and France escalates its Musk probe. Nintendo says it will increase the price of the Switch 2 globally on September 1, from $450 to $500 in the US, and the price of the original Switch in Japan (Bloomberg) Instructure disables its Canvas edtech platform, used by thousands of schools, universities, and companies, amid a data extortion attack claimed by ShinyHunters (Krebs on Security) Researchers: 5,000+ web apps built using AI coding tools like Lovable, Base44, and Replit have little to no authentication, and ~40% exposed sensitive data (Wired) Mozilla says Anthropic's Mythos Preview and other AI models helped it identify and ship 423 Firefox security bug fixes in April, compared to 31 a year earlier (TechCrunch) French prosecutors escalate an investigation into Elon Musk and X, focused on alleged algorithmic manipulation and sexual deepfakes, to a criminal probe (CNBC) Longreads Anthropic co-founder Jack Clark explains why there's a 60%+ chance of AI systems autonomously building their successors by 2029 and the consequences of automated AI R&D (Import AI) How Delta SkyMiles and airline loyalty programs turned carriers into fintech companies with wings, and why most airlines couldn't survive without them (NY Mag) Learn more about your ad choices. Visit megaphone.fm/adchoices