Podcasts about replit

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. 

SaaStr 859: The $257 Employee: What Agents That Actually Work Look Like Right Now with Replit's CEO and Founder Everyone's talking about agents. Almost nobody is running them the way Jason Lemkin is. At SaaStr, two AI employees - 10K, the autonomous VP of Marketing, and QB, the AI VP of Customer Success - ran a significant chunk of this event. They emailed 331 investors with individually researched, personalized outreach. They proactively contacted 100-plus sponsors and filed their own self-assessment of where they fell short. They built dashboards, ran campaigns, and tracked 10 years of attendee data without sleeping, complaining, or asking for a coffee break. The tab: $257 a month on Replit.  In this session, Jason sits down with Amjad Masad, Founder and CEO of Replit, to pull apart how this actually works - and what it tells us about where agents are headed next.  You'll learn: Why monorepo architecture matters more than most people realize - and how putting everything in one place gives your agents global context they can't get when apps are siloed How Replit built a self-improving loop where an internal agent analyzes every user interaction nightly, generates prompt changes, ships them as A/B tests, and improves the product autonomously What "context compaction" actually means in practice, why Replit's is better than the generic version, and how to think about long-running agents that never reset Why the gap between agent hype and agent reality is at its widest right now - and what the next capability jump looks like by Q3/Q4 Why Replit users were six months ahead of Silicon Valley on agents, and what that early edge looks like when you apply it to marketing, customer success, and operations This is for you if: You tried an AI tool six months ago, it disappointed you, and you haven't gone back - this session will show you how much has changed You're a founder or operator trying to understand what a real, production-grade agent looks like versus a chatbot with a fancy name You're thinking about the economics of AI replacing or augmenting headcount and want a concrete data point, not a thought experiment

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.

Nebius Chief Revenue Officer Marc Boroditsky talks with TITV Host Akash Pasricha about customer demand variations, lessons learned from past Blackwell hardware implementations, and the impending rollout of Nvidia's Vera Rubin chips. We also talk with Stephanie Palazzolo about the public release and security workarounds built into Anthropic's Claude Fable 5, Michele Catasta about Replit's internal performance benchmarks and token routing efficiencies, and Anissa Gardizy about OpenAI's multi-billion dollar negotiations to lease a net-new 10-gigawatt data center site in Ohio. Lastly, we speak to Cory Weinberg about how Nasdaq landed the SpaceX IPO listing.Articles discussed on this episode: https://www.theinformation.com/articles/openai-talks-lease-10-gigawatt-ohio-data-center-backing-nvidiahttps://www.theinformation.com/newsletters/ai-agenda/anthropics-new-model-targets-power-users-cuts-ai-rivalsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:42 - Anthropic Releases Claude Fable 5 Model12:32 - Developer Reactions to Anthropic's Fable 521:09 - OpenAI in Talks to Lease 10GW Ohio Data Center30:59 - How Nasdaq Landed SpaceX's Mega-IPO40:37 - Demand Outlook on Nvidia's Vera Rubin Chips

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  

Dalla AI WEEK, in questo episodio live di Intelligenza Artificiale Spiegata Semplice, Pasquale Viscanti intervista Michele Catasta, Presidente di Replit e una delle figure italiane più influenti nel panorama internazionale dell'Intelligenza Artificiale e dello sviluppo software.Michele è tra i protagonisti della rivoluzione del Vibe Coding, il nuovo approccio alla programmazione che permette di creare applicazioni, automatizzare processi e sviluppare prodotti digitali dialogando con l'AI in linguaggio naturale. Un cambiamento che sta ridefinendo il ruolo degli sviluppatori e aprendo nuove opportunità per imprenditori, manager e professionisti.Nel corso della conversazione, Pasquale e Michele approfondiscono come l'Intelligenza Artificiale stia trasformando il modo in cui vengono costruiti software e startup, il futuro delle competenze digitali, l'evoluzione degli agenti AI e il ruolo che piattaforme come Replit avranno nella democratizzazione della tecnologia.Pasquale Viscanti e Giacinto Fiore ti guideranno alla scoperta di quello che sta accadendo grazie o a causa dell'Intelligenza Artificiale, spiegandola semplice.Puoi iscriverti anche alla newsletter su: https://www.iaspiegatasemplice.it

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.

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

Resgate seus $20 dólares para testar o Replit, a ferramenta nº 1 de vibe coding do mundo aquiNOVOS CURSOS DA ARENA - Claude Code e Cursor! Assine a Arena com R$100 OFFAssista ao Papo na Arena no Web SummitBom dia! ☕️O Aíquis segue curtindo o verão europeu, agora em Paris com o João Fonseca e o Arthur puxa mais um giro das notícias que mais chamaram a atenção nessa semana com olhar de produto (e acidez).No programa de hoje, temos:Anthropic protocola IPO e lança modelo novo do ClaudinhoSam Altman responde sobre a corrida de IPO com a AnthropicGustavo Vitti e a tese de que estamos trabalhando MAIS, não menosMeta lança assinaturas pagas no Instagram, Facebook e WhatsAppMacBook Neo, a aposta da Apple pra brigar no preçoOs "Jogos Olímpicos do anabolizante" e a obsessão de Silicon Valley por peptídeosSiga a Product Arena nas redes:Instagram⁠⁠Substack⁠⁠Linkedin

This episode is a rebroadcast of Auren's appearance on the GTMnow podcast ---------------------------------------------------Auren Hoffman (Flex Capital) joins the GTMnow podcast to share some of the most contrarian takes in tech today, from why AI moats are gone, to why your next VC meeting will be with a bot, to why AI is secretly going to trigger a baby boom.In this episode:Why Auren runs 500+ AI agents to source deals, and what that means for founders raising capitalThe "agent-to-agent" meeting prediction: by end of 2026, first VC conversations will be fully automatedWhy every software moat has been "blown up" and what Salesforce, LinkedIn & DocuSign need to do to surviveThe OpenAI x The Hustle acquisition breakdown: why it's the smartest (and cheapest) distribution play in AIWhy missing a great deal is 10x more painful than making a bad one, Auren's honest VC mistake frameworkThe baby boom thesis: why AI, IVF, self-driving cars & cheaper energy could reverse the fertility declineWhy companies won't sign yearly SaaS contracts anymore, and what that means for every B2B founderAuren Hoffman is the founder of NQB8, Flex Capital, SafeGraph, and LiveRamp. He's an early backer of Replit, Perplexity, Rippling, Vercel, Coinbase, Chime, and AppLovin.Max's socials: https://x.com/hackitmaxhttps://www.linkedin.com/in/maxaltschuler/Auren's socials:https://x.com/aurenhttps://www.linkedin.com/in/auren/https://auren.substack.com/GTMnow: https://gtmnow.com

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.

Состояние DevOps в 2026: Platform Engineering, AI-агенты и что стало с junior-инженерами. Собрались на кухне с тимлидом системного юнита из большой компании - поговорить что и как сейча. О ЧЁМ ВЫПУСК • DevOps vs SRE в 2026: где проходит граница и почему «you build it, you run it» иногда создаёт больше проблем, чем решает. • War story: упавший Kubernetes во время корпоратива с пивом - классика первых K8s-внедрений. • Момент, когда DevOps ломается: 600 сервисов, 3600 пайплайнов и почему каждый новый инженер пишет 3601-й. • Platform Engineering: зачем нужна платформа, что такое метаплатформы и как устроены слои внутри крупной компании. • Junior + AI = middle: что изменилось с приходом AI-ассистентов и сколько теперь занимает обучение DevOps. • AI в работе DevOps прямо сейчас: мультиагентные помощники для расследования инцидентов, внутренние vs внешние модели. • Реальные AI-катастрофы 2025-2026: Replit дропнул базу и бэкапы, сервис аренды машин ушатал прод. • Multi-agent flow: refiner + архитектор + автономный бот, PR за час вместо недели. • Тимлидам: не носи инженерам PR, которые ты навайпкодил за вечер. • Что реально учить в 2026: Linux, сеть, Kubernetes, один язык программирования и AI-грамотность. • Знать базу vs спрашивать AI: почему без фундамента ты не поймёшь, куда тебя модель направляет. ГОСТЬ В гостях - Андрей Волхонский, руководитель юнита System в Центре разработки инфраструктуры Авито. 13+ лет опыта: от Windows-DevOps в TravelLine и Kaspersky до платформенной инженерии в большой продуктовой компании. ССЫЛКИ

Resgate seus $20 dólares para testar o Replit, a ferramenta nº 1 de vibe coding do mundo aquiSemana de Produtos do Itaú: IA que facilita, humanos que transformamBom dia! ☕️Aíquis foi curtir o verão europeu e o Arthur resolveu trazer um formato diferente, com um giro das notícias que mais chamaram a atenção. No programa de hoje, temos:Papa vs. IAMais layoff na conta da IARobotáxi atolando em enchenteSnapchat prepara o lançamento do seu novo óculos inteligenteO CEO mais valioso do mundo virando influencer de comida de ruaA lista dos álbuns mais escutados da históriaSiga a Product Arena nas redes:InstagramSubstackLinkedin

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!

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

In this solo episode of the Franchise Secrets Podcast, Erik Van Horn breaks down how he's actually using AI inside his businesses — not for hype, but to create real leverage, save time, and scale expertise.   Erik shares:   * how he built custom AI systems to manage email workflows, * an AI-powered SOP generator he created live with mastermind members, * why most people are overcomplicating AI, * and how he's turning 15+ years of franchise knowledge into scalable tools like "Ask Erik."   He also explains why authentic leadership and real human perspective will become even MORE valuable in an AI-driven world.   Whether you're a franchisee, franchisor, investor, or entrepreneur, this episode will challenge the way you think about AI, productivity, and scaling yourself without simply working more hours.   Topics discussed:   * AI workflows for entrepreneurs * SOP automation * Replit, Claude, Granola & AI agents * Scaling expertise with AI * Content creation systems * Authenticity in the AI era * Franchise growth & operational leverage   Timestamps: 0:00 — AI, Ranching, and Why Erik's Neighbor Never Steps Foot in His Culver's 1:05 — What's Happening Around the Ranch 6:39 — Why Erik Doesn't Chase Every AI Tool (And What He Does Instead) 8:17 — How He Cleared 800 Emails With a Custom AI Agent 11:20 — Live-Building an SOP Generator in 30 Minutes 14:41 — FranchiseTribe.com and How to Use the SOP Tool Right Now 19:38 — Introducing Ask Erik: AI Trained on 20 Years of Franchise Expertise 25:03 — The Blunt Self-Assessment Every Franchise Buyer Needs 28:06 — How Erik Creates Content That Still Sounds Like Him 34:52 — Return to Real: Why Authenticity Wins in the AI Era   Connect with Erik Van Horn:

YouTube and Snap settle over social media addiction in schools claim, Replit resolves Apple App Store dispute, OpenAI launches new personal finance feature. MP3 Please SUBSCRIBE HERE for free or get DTNS shows ad-free. A special thanks to all our supporters–without you, none of this would be possible. If you enjoy what you see youContinue reading "US Bureau of Labor Statistics Data Shows AI Affects Job Losses – DTH"

85% of AI teams will hit a serious production failure this year. The only thing separating them from the 15% who don't? Evals.After nearly two decades of building AI systems at Microsoft, Facebook, and Dropbox, Ameya Bhatawdekar is now Field CTO at Braintrust, the AI observability platform used by Airtable, Notion, Stripe, Dropbox, Vercel, Cloudflare, Lovable, and Replit.We discuss a shift that most teams underestimate. The winners in AI are not just shipping faster. They are building systems that behave predictably, improve continuously, and earn user trust over time. As traditional monitoring breaks down in a probabilistic world, observability now requires learning how an AI system reasons, not just how it performs. This leads to a new paradigm where agents are no longer just executing tasks, but also analyzing and debugging other agents.The episode also traces the evolution of machine learning itself. From feature engineering to deep learning to transformers , each leap increased capability and reduced control. Evaluation is now where control sits.Ameya is clear on one point. Moving fast with weak evaluations feels like velocity, but it compounds into technical debt, unpredictable failures, and ultimately a loss of user trust. The teams that win are the ones that invest early in rigor, especially in understanding context, which is quickly becoming the hardest and most critical layer in AI systems.If you are a founder or engineer moving beyond the demo phase and trying to build durable, high-quality AI systems, this episode will change how you think about shipping.0:00 — Trailer00:55 — What's Braintrust?05:01 — What agents are shipping today07:54 — What evals look like in practice for Notion & Zapier09:44 — Evals vs Classic monitoring11:33 — Who is the Field CTO?16:35 — What goes wrong when agents fail18:26 — Agents analyzing other agents24:17 — Evals are existential in vibecoding25:52 — Ship fast with weak evals or slow with strong evals?25:41 — What makes enterprises trust an LLM?29:25 — Do AI startups know how good their product is?30:23 — 3 ML systems: Microsoft, Dropbox, Meta36:30 — How the 2017 transformer paper changed everything38:20 — All algorithms are predicting the next word43:40 — What LLMs will do in 1 year-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

A security agency tested 5,000 apps built with Lovable, Replit, Base44 and Netlify. Every single one had vulnerabilities — including apps that were live, charging customers, and handling personal data. Sophia Matveeva is joined by Rags Vadali — former Google engineer, Meta product lead who launched Instagram filters to 600 million people, and CEO of AI startup Floto — for an honest expert conversation about what AI tools can and cannot do for your product right now. You'll learn: Why a product can look finished while being fundamentally unsafe What VCs now do when they see a vibe-coded product Why Apple is rejecting AI-built apps from the App Store When to call in developers in the age of AI (and why what they do for you has changed) This is not an episode about why AI tools are bad. It is about knowing where the line is — so you can use them on the right side of it. Resource mentioned in this episode: Wired: Thousands of Vibe-Coded Apps Expose Corporate and Personal Data on the Open Web Ready to build your tech product the right way? Book a call: https://calendly.com/sophia-matveeva/new-meeting Timestamps: 00:00 - Introduction: VC walks away from vibe-coded startup 02:36 - Security breach: 5,000 AI-built apps had vulnerabilities 05:00 - The iceberg problem: What's hidden below the surface 08:35 - Every single app had security issues exposed 11:09 - Who gets sued: The platforms or the founders? 13:09 - VCs rejecting vibe-coded apps during due diligence 15:29 - Apple cracking down on AI-generated apps 18:21 - The maintenance nightmare: Adding features breaks everything 24:46 - What kind of engineer you actually need now 29:53 - Building isn't the constraint anymore - sales and marketing are 34:35 - Engineers' role is now strategic, not operational Free AI Mini-Workshop for Non-Technical Founders: Learn how to go from idea to a tested product using AI — in under 30 minutes. Get free access here: techfornontechies.co/aiclass Follow and Review: We'd love for you to follow us if you haven't yet. Click that purple '+' in the top right corner of your Apple Podcasts app. We'd love it even more if you could drop a review or 5-star rating over on Apple Podcasts. Simply select "Ratings and Reviews" and "Write a Review" then a quick line with your favorite part of the episode. It only takes a second and it helps spread the word about the podcast. Listen to our podcast on: Apple Spotify YouTube Audible Pandora Transcript: https://www.techfornontechies.co/blog/303-before-you-build-with-ai-what-every-non-technical-founder-needs-to-know

Deputy Bureau Chief of Finance Cory Weinberg and Mostly Metrics author CJ Gustafsson join TITV Host Akash Pasricha to break down how OpenAI stands to gain over $5 billion from the upcoming Cerebras IPO through unconventional "penny warrants". We then explore exclusive reporting from Aaron Holmes on Microsoft's renegotiated revenue-sharing deal with OpenAI and how the tech giant has already doubled its $13 billion investment. Next, Rocket Drew provides updates on the Musk-OpenAI trial featuring testimony from Satya Nadella and Ilya Sutskever, followed by Replit's Michele Catasta on the new "VibeBench" for AI coding models. We wrap with Stephanie Palazzolo discussing Thinking Machines' high-profile research preview of real-time interaction models.Articles discussed on this episode: https://www.theinformation.com/articles/openai-making-billions-just-promising-buy-suppliershttps://www.theinformation.com/articles/openai-save-97-billion-2030-latest-microsoft-dealhttps://www.theinformation.com/articles/microsoft-recouped-double-13-billion-openai-investment-revenueSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Cerebras IPO: OpenAI's $5B Potential Windfall14:25 - Exclusive: Microsoft Recoups OpenAI Investment26:11 - Musk vs. OpenAI: Nadella & Sutskever Testify30:20 - Replit President on Benchmarking Coding Models40:21 - Thinking Machines Teases New AI Interaction Model

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Tyler Cloutier, founder of Clockwork Labs and creator of SpaceTimeDB. They explore how SpaceTimeDB functions as more than just a database—it's essentially a distributed operating system that merges server logic with data storage, enabling real-time applications and time-travel capabilities. The conversation ranges from the technical architecture of databases and operating systems to the philosophy of distributed systems, touching on everything from Unix and Linux to how SpaceTimeDB could revolutionize AI-generated software deployment. Tyler explains how their system reduces the complexity of building real-time applications, makes deployment simpler for both humans and AI agents, and why games like their MMORPG BitCraft Online drove them to create this new infrastructure. They also discuss the future of the internet, the role of bots in gaming, and how SpaceTimeDB fits into the broader landscape of cloud computing alongside tools like Cloudflare, Vercel, and Docker. For more information, visit spacetimedb.com or check out Clockwork Labs on GitHub and Twitter.Timestamps00:00 Stewart introduces Tyler Cloutier, founder of Clockwork Labs, discussing the origin of SpaceTimeDB's name inspired by Einstein's theory and its time travel capabilities that store all operations indefinitely05:00 Tyler explains SpaceTimeDB as more of an operating system than a database, using tables instead of file systems while running code in a sandboxed environment with full atomic properties10:00 Discussion of how SpaceTimeDB replaces both Node.js and Postgres by merging web server and database functionality, eliminating separate deployment concerns15:00 Tyler explains JavaScript execution through Chrome's V8 engine and JIT compiling, leading to Node.js creation for server-side JavaScript development20:00 Explanation of stateless web servers versus stateful game servers, and why games require in-memory state management for real-time performance25:00 Tyler introduces reducers and real-time subscriptions, questioning why more applications aren't real-time when state changes should update immediately30:00 Discussion of Facebook as essentially a text-based MMO, comparing social media architecture to game server requirements and the need for unified systems35:00 Tyler explains ACID properties in databases: atomic, consistent, isolated, and durable, using game item trading examples40:00 Comparing SpaceTimeDB to smart contract systems without cryptocurrency or global consensus, positioning it as a smart database with centralized trust45:00 Tyler reveals SpaceTimeDB uses 43% fewer tokens than Postgres for AI-generated applications, making it valuable for vibe coding platforms50:00 Conversation shifts to bots in games and proof-of-human concepts, with Tyler proposing biometric systems and discussing potential in-person gaming applications55:00 Closing discussion about tracking AI-driven traffic through UTM parameters and finding SpaceTimeDB at spacetimedb.comKey Insights1. SpaceTimeDB is fundamentally a database that runs application code directly inside it, combining what traditionally required separate systems like Postgres and Node.js. Users compile their application logic into WebAssembly or JavaScript and upload it to run within the database itself. This architecture provides high performance because the entire server backend operates inside the database environment. The system also features time travel capabilities, storing every operation and change to data persistently and indefinitely, allowing users to set application state back to any earlier point in time. This makes SpaceTimeDB more accurately described as an operating system rather than just a database, where the abstraction is that everything is a table rather than a file.2. The inspiration for SpaceTimeDB came from building BitCraft Online, an MMORPG where all players exist in a single persistent world and rebuild civilization together. Traditional MMO backends required complex custom solutions to handle real-time state, with game servers storing state in memory and periodically writing to databases. This complexity existed because games cannot afford the latency of constantly delegating to distant databases like traditional web applications can. SpaceTimeDB solved this by making the database fast enough to handle real-time requirements directly, eliminating the need for separate game servers. This same performance advantage that benefits games also applies to web applications, which is why SpaceTimeDB evolved from a game-specific tool to a general-purpose platform.3. SpaceTimeDB functions as a distributed operating system where each database acts like a process in an actor model system, similar to Erlang or Scala Akka. Databases can send messages to other databases and be spawned across a cluster for horizontal scaling. This represents an overlay operating system running on top of Linux rather than competing with it, providing a distributed abstraction across many machines while Linux handles device drivers and hardware support. The vision is for the cloud to function as a single enormous computer running one operating system, where developers simply publish their programs without managing separate services, deployment, routing, networking, or persistence infrastructure.4. The real-time capabilities of SpaceTimeDB address a fundamental limitation in how most web applications work today. Traditional web servers are stateless, delegating all state to databases and accepting network round-trip latency for each request, which is why users often must refresh pages to see updates. SpaceTimeDB allows queries to be subscribed to, maintaining open connections that stream changes whenever query results update. This makes applications like Discord, Facebook, or banking systems naturally real-time without requiring page refreshes. The historical accident that more things are not real-time represents a problem SpaceTimeDB solves by unifying the web world with the game world's real-time requirements.5. SpaceTimeDB implements ACID properties—Atomic, Consistent, Isolated, and Durable—ensuring database operations are reliable and safe. Atomic means operations either fully happen or not at all, preventing issues like item duplication in games when trading between players. Consistent means declared invariants like unique usernames are always enforced. Isolated means concurrent operations do not interfere with each other. Durable means changes persist even if computers restart, with varying levels from in-memory on one machine to disk storage across multiple geographic locations. These properties are managed through reducers, functions inspired by React Redux that fold changes into application state incrementally.6. For AI and large language models, SpaceTimeDB offers significant advantages in building and deploying applications. Testing showed that creating applications with SpaceTimeDB uses 43% fewer tokens compared to Postgres implementations, costs less, has fewer bugs, and is easier to extend. This matters because the primary cost for vibe coding platforms is tokens. As more software gets written in the next twelve months than ever before, there is insufficient focus on infrastructure required to run all this AI-generated software. SpaceTimeDB positions itself as ideal for LLMs to target because of its simplified deployment model where developers just publish code and the system handles everything behind the scenes.7. SpaceTimeDB can be understood as a smart contract system without cryptocurrency or global decentralized consensus. Like blockchain smart contracts, it executes code with atomic, consistent, isolated, and durable properties, but avoids the expense and slowness of requiring all computers worldwide to agree on everything. Instead, it offers centralized trust where users trust Clockwork Labs not to modify deployed contracts, rather than the trustless but extremely costly blockchain approach. This makes it functionally similar to Cloudflare's durable objects but with full relational database capabilities. The system exists before the networking layer where Cloudflare operates, handling deployment, server, and database functions while Cloudflare could provide DDoS protection in front of it.

How Replit's VP of Ops Runs an AI-Native Company (And Why Most Enterprises Are Still Getting It Wrong) Jonathan Eide has built and scaled operations at Meta, Coinbase, and now Replit, the company that's quietly become one of the fastest-growing AI-native businesses on the planet. In this conversation, he breaks down what's actually different about running a company where every function builds their own software, why "vibe coding" is too lightweight a term for what's coming, and the hiring shift every CXO will face in the next 24 months. If you're a CXO trying to figure out how to move your org from "we use Copilot" to genuinely agentic operations, this is the playbook. What we get into: The two archetypes Replit hires for, and why the "perfect candidate" has both Why Jon hasn't written a Linear ticket by hand in months (and what replaced it) The internal AI analyst tool that replaced an entire junior analyst function How Replit killed Google Slides internally with a self-updating, self-populating weekly wins deck Why measuring AI productivity by tokens or time saved is the wrong move (Theory of Constraints, applied) The hiring shift: from screening interviews to "build me a demo before we talk" What enterprise adoption actually looks like at Zillow, Atlassian, and old-school PE-backed manufacturers Why Replit's "plan" is to not have a plan, and why vision/mission still has to be rock solid The single-person companies hitting tens of millions in ARR Rapid fire: the deeply held belief about AI that Jon thinks will be gone by 2028 Chapters: 00:00 Intro 01:16 Jonathan Eide  02:00 What's fundamentally different about running ops at an AI-native company 04:20 How every function at Replit is building its own tools 07:35 Killing PowerPoint: the internal weekly wins deck built in Replit 11:35 Source of truth, guardrails, and avoiding the "everyone builds an app" sprawl 14:20 Can large or legacy companies actually adopt this operating model? 18:15 Why measuring tokens and time saved is the wrong way to track AI productivity 22:20 How Jon redesigned his interview process for AI-native hiring 25:35 Are AI-native companies hiring fewer people, or different people? 28:25 Why "AI native" will disappear as a hiring filter 29:15 Growing at Replit's pace: planning when the market resets every two weeks 32:24 Replit's three user segments: hobbyists, prosumer entrepreneurs, enterprise 34:41 Surprising businesses being built on Replit (and the single-person unicorn thesis) 37:30 The plan is to not have a plan: vision vs short-term flexibility 40:36 Rapid fire: the belief about AI that will be destroyed by 2028 41:32 Rapid fire: the one AI buzzword Jon would ban from Replit meetings 42:27 Rapid fire: what Jon is most optimistic about About Jonathan Eide: Jonathan is VP of Operations at Replit. Prior to Replit, he held senior operating roles at Coinbase and Meta, leading data and operations functions through hypergrowth phases at both. About the AI CXO Podcast: The AI CXO Podcast helps CXOs get actionable insights into how to navigate AI's reshaping of the business landscape. New episodes drop weekly. Subscribe for more conversations with operators building the AI-native enterprise. https://www.youtube.com/@productfaculty  #AI #Replit #CXO #VibeCoding #EnterpriseAI #AINative #AIAgents #FutureOfWork #ProductLeadership #Operations

In MobileViews Podcast 609, Jon Westfall and I discuss the evolving landscape of AI tools and consumer technology. I start by highlighting that the Google AI Plus subscription is now shareable via the Google One family plan, which I consider a helpful step in bridging the "AI divide" for households. Then we revisit the recurring topic of the rising trend of "vibe coding," with Jon explaining how he used AI to generate a custom workout-tracking web page to completely replace a subscription fitness app he had been using. We discuss how this newfound accessibility to coding could threaten small companies that rely on micro-subscriptions for minor app features, while also warning of the severe security risks of amateur coders leaving hardcoded secrets on platforms like Replit and Lovable. Additionally, I noted using Google's NotebookLM to cross-reference our 2026 podcast transcripts with the Techmeme River news, which surfaced highly relevant updates for us, like the extended FCC deadline for foreign-made drones and routers. Jon and I also explore the practical and social implications of AI in the workplace, particularly focusing on an article I found about the trend of "whispering" to AI instead of typing happening in offices. Jon shares how rambling to ChatGPT's voice mode for five minutes helps him rapidly prototype structured op-ed pieces, essentially using it as a highly effective sounding board and editor. However, we both point out that this shift toward voice interactions makes open-plan offices—an environment I already strongly dislike—even more distracting, making modern workspaces resemble call centers. To round out the episode, we tackle a few persistent societal myths. Jon draws on his background teaching educational psychology to debunk the concept of preferred learning styles, and I acknowledge that the popular 10,000 steps a day health goal is largely an outdated pedometer marketing gimmick, even though I still try to hit that target to stay active.

Ryan Burke, VP of Worldwide Sales at Crogl, joins Sam Jacobs, AJ Bruno, and Asad Zaman on the new economics of enterprise cyber risk. Topics include Anthropic's Mythos model, AI for the security operations center, why vibe-coded apps are far more likely to have security issues, why Claude Design tanked Figma's stock, and what the Elon Musk versus OpenAI lawsuit signals for AI governance. Key takeaways: AI has crashed the cost of running sophisticated attacks, putting nation-state-grade tooling in the hands of low-skill operators. As Ryan Burke, VP of Worldwide Sales at Crogl, put it on Anthropic's Mythos model: "Mythos has lowered the cost to like the dollar menu equivalent of...running an attack...so more people can do it." Enterprises are staring down a multi-year patching backlog that runs from now until the end of time. Non-technical teams in finance, ops, and HR are shipping internal tools using Replit and Claude, and almost none of them are securing what they build. Ryan Burke flagged the research: "vibe-coded software is almost 3 times as likely to have security issues." When the employee who built the agent quits, the agent stays behind with no owner, no documentation, and quiet access to systems it never should have had in the first place. For founders eyeing an exit, security has joined revenue, IP, and hitting your numbers as a non-negotiable diligence pillar. As Ryan Burke explained: "lack of security can kill an acquisition...a fourth pillar now is you're secure." Acquirers like JPMorgan Chase will not buy a fintech startup that turns into a vector for attackers to walk straight into their environment. The market case for NRR-fortress legacy SaaS may be weaker than the last decade made it look. As Asad Zaman, CEO of Sales Talent Agency, argued: "there was a generation of software companies that had signs that they had really good customer relationships...but their customers felt more like prisoners." If AI makes switching cheap and a new generation of software actually delights users, the moats around system-of-record incumbents start to compress fast. Connect with the hosts and guest:  Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/  Guest: Ryan Burke, VP Worldwide Sales at Crogl - https://www.linkedin.com/in/ryan-burke-bos/ Topline is more than a YouTube Channel:  Subscribe to Topline Newsletter: https://toplinemedia.substack.com/  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Introducing Ryan Burke 03:14 Anthropic Mythos and Cyber Risk 04:20 How Attackers Use AI at Scale  07:00 Dollar Menu Attacks Explained  10:41 AI for the Security Ops Center  14:53 Why Claude Tanks Figma's Stock  18:30 Sam's Advice on Falling Stocks  20:50 Are Legacy SaaS Companies Back?  24:04 The Vibe-Coding Risk Surface  27:56 Quiz Pro: Cybersecurity Edition  33:46 Replit Apps Inside Enterprises  40:18 Security as the M&A Fourth Pillar  44:17 Personal Data and Digital Legacy  47:24 Bulls vs Bears: Elon vs OpenAI  52:03 Will ServiceNow Hit $32B?

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

In this episode of Growing Your Firm, host David Cristello sits down with Isaac Perdomo, founder of Opzer, to discuss the practical application of AI and low-code/no-code tools in modern accounting practices. Isaac shares real-world case studies of firms drastically reducing manual workloads and increasing their capacity for high-value advisory services.   In this episode, we explore: - The "Window of Opportunity": Why the next 12–24 months are critical for firms to adopt AI before the market settles. - Beyond the Hype: Moving from generic ChatGPT use to building bespoke internal tools with Claude, Replit, and Lovable. - The 15-Minute Close: How a case study firm reduced bookkeeping time from 2 hours to 15 minutes per client using automated workflows. - Quality Assurance (QA) with AI: Using LLMs as an additional layer of review for financial statements and deliverables. - Vibe Coding & Custom Apps: Why the era of the "30-app tech stack" is evolving into firms building their own specialized internal software. - Sentiment Analysis: How to use meeting transcripts from tools like Granola or Fireflies to detect client dissatisfaction before they churn.   Key Tools Discussed: - Automation Engines: Make.com, n8n, and Zapier. - AI Models: Claude and ChatGPT. - Prototyping: Lovable and Replit. - Meeting Intelligence: Granola, Fireflies, and Fathom.   Featured Guest: Isaac Perdomo  

In this episode, Susie Arnett, the Director of Wellness Programming at Six Senses, shares her real-world take on vibe coding and what AI tools actually deliver when a non-technical person tries to build a digital product. She explains why the promise that anyone can build anything is overstated, what platforms like Replit are useful for, and where she sees the real opportunity for hotels to give teams time to invent.Read Susie's post on this on LinkedIn here A few more resources:If you're new to Hospitality Daily, start here. You can send me a message here with questions, comments, or guest suggestionsIf you want to get my summary and actionable insights from each episode delivered to your inbox each day, subscribe here for free.Follow Hospitality Daily and join the conversation on YouTube, LinkedIn, and Instagram.If you want to advertise on Hospitality Daily, here are the ways we can work together.If you found this episode interesting or helpful, send it to someone on your team so you can turn the ideas into action and benefit your business and the people you serve!Music for this show is produced by Clay Bassford of Bespoke Sound: Music Identity Design for Hospitality Brands

Want to build an AI side hustle? Get the free AI Side Hustle Crash Course: https://clickhubspot.com/lkb Episode 821: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to Replit founder Amjad Masad ( https://x.com/amasad ) about growing 100x in one year.  — Show Notes:  (0:00) 2.5M to 250M in 1 year (10:28) the darkest hour (17:00) pivot, pivot, pivot, until it hits (28:19) companies exploding with Replit (33:05) Amjad's business ideas (38:44) "we are in the singularity" (51:24) best business biography  (53:23) getting on Joe Rogan (57:00) slowing down under pressure (1:11:08) Vercel scandal (1:13:35) lifestyle upgrades of being a billionaire — Links: • Replit - https://replit.com/  — Check Out Sam's Stuff: • Hampton (joinhampton.com): My community for founders. Average member does $25m/year. Many of the guests are members. Get after it...apply: http://joinhampton.com/mfm — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com  • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies! Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC • I run all my newsletters on Beehiiv and you should too + we're giving away $10k to our favorite newsletter, check it out: beehiiv.com/mfm-challenge My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano /

SaaStr 853: The Agents #004: Tragedy Apps, Too Many AI SDRs, and Why Your Next Hire Should Report to an Agent Your AI SDR pitches are getting better, but your AI PR pitches are getting you blocked. Jason and Amelia break down why the gap between good and great agents is the difference between pipeline and the spam folder. Then they introduce "tragedy apps," the term for products that had every advantage in the AI era and blew it. Descript had the customers, the product, and the timing, and froze. Replit waited 10 years for its moment and seized it. The lesson: catching up isn't enough if you're not building something new. Plus, the SaaStr team built an AI API Report Card that grades every major SaaS API on how agent-friendly it is (Stripe got the only A+, Marketo got a C, and no, they're not surprised). Jason and Amelia also get honest about running 4-5 AI SDRs from different vendors, why they'll probably have 6 by year end, and why single-vendor consolidation isn't the answer yet. And the wildest part: their AI VP of Marketing, 10K, now generates 3 actionable campaign ideas a day, runs autonomous campaigns on weekends, and might be a better boss than either of them. They're seriously hiring a human marketer whose primary manager would be the agent. Not a joke. Not a thought experiment. A real job posting. Finally, if your team is resisting AI, stop worrying about change management. Just hire one senior person who's all-in on agents and let the rest sort itself out.

"Not a creative"?

Amjad Masad is the Co-Founder and CEO of Replit, one of the leading "vibe-coding" platforms. Under his leadership, Replit has raised a total of $922 million in funding, recently raising at a whopping $9 billion valuation. Replit has over 50 million registered users and is used by employees at 85% of Fortune 500 companies. Replit's revenue jumped from $10 million to $100 million in nine months, and the company is on track to reach $1BN in ARR by the end of 2026. AGENDA:  00:00 — Why Coding Models are Hitting a Performance Plateau 07:21 — Is Most of the Value of Replit Not Anthropic Model Quality? 10:04 — Why Did Replit Decide to Not Build Their Own Model, Like Cursor Did? 11:58 — Why Product Quality Must Always Beat Cost Optimization 14:51 — How Do Replit Choose Which Model To Route To For Different Tasks? 24:43 — The SaaS Apocalypse: Why it is Fair and Just? 29:55 — What Will the Cost of Tokens Be in 5 Years? 31:09 — Is Cursor Dead? Debunking the Twitter Narrative 33:36 — Are IDEs Dead? 35:54 — Should Students Still Study Computer Science? 42:47 — Are US Companies Using CCP Subsidised Open-Source Chinese Models 56:59 — What Do No Founders Know About True Product-Market Fit  

In this episode, Ray Cochrane unpacks Anthropic’s Mythos model and the Treasury’s emergency meetings with Wall Street, then digs into Apple’s vibe-coding crackdown and a gaming-anxiety study that hit way too close to home. Also covered: Verge’s solid-state motorcycle, UBTech humanoid robot sales jumping 23-fold, Japan’s first osmotic power plant, Finland’s permanent nuclear waste vault, Ghostty landing in Ubuntu, Cloudflare’s EmDash CMS, and a Claude Code skill that talks like a caveman. – Want to start a podcast? It’s easy to get started! Sign up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show by framing Anthropic’s new Mythos model as the AlphaGo moment for cybersecurity. From there, the episode moves through Apple’s pushback against AI-generated apps, a gaming anxiety study with a deeply personal hook, a series of “first to ship” energy and robotics wins out of Finland, China, and Japan, and several developer-tool stories that show how quickly the economics of software are shifting. Mythos, the Detection Ceiling, and Wall Street’s Emergency Response Anthropic’s Mythos model has Wall Street rattled. Operating autonomously, Mythos found and demonstrated the exploitation of a 27-year-old TCP SACK bug in OpenBSD, an operating system famous for being one of the most security-focused on the planet. Per Anthropic’s red team, over 99% of the vulnerabilities Mythos has identified remain unpatched. The researchers’ conclusion is blunt: “the moat in AI cybersecurity is the system, not the model.” The policy response moved fast. On April 7th, Treasury Secretary Bessent and Fed Chair Jerome Powell pulled the CEOs of Goldman Sachs, Citi, Bank of America, and Morgan Stanley into Treasury headquarters on short notice. All four banks are now testing Mythos internally. Treasury CIO Sam Corcos is also seeking direct access. Anthropic is gating distribution through Project Glasswing, a limited-access program with JPMorgan, Apple, Google, Microsoft, and Nvidia. Cochrane comes down firmly behind Anthropic’s gated approach. Because a 5.1-billion-parameter open model can apparently recover the core analysis chain for the OpenBSD flaw, this capability is not locked behind Frontier Compute. He wants the critical infrastructure hardened before the public gets keys. However, he also notes the bigger lesson is about human wisdom: people offloading all their thinking to AI lose out on the wisdom that makes any of these tools genuinely useful. Apple Bans Vibe Coding Apps from the App Store Apple has been quietly pushing back against what people are calling “vibe coding” apps. Replit, Vibecode, and an app called Anything all run AI models on the phone and produce working software that runs inside the host app. Apple cites Guideline 2.5.2, in effect since 2017, which requires apps to be self-contained. Replit and Vibecode had their App Store updates blocked. Anything was pulled in late March, briefly restored on April 3rd, and then pulled the same day again. The forcing function is volume. App Store submissions jumped 84% in a single quarter as vibe coding tools flooded Apple’s review queue with AI-generated apps. Cochrane thinks Apple is justified, given the security issues swirling around the Vibe coding ecosystem. Even a beautiful diamond gets lost in a sea of sand, and that flood is exactly what Apple is trying to manage. The company behind Anything is now pivoting to iMessage, desktop, and Android. Playing Video Games to Win Is Linked to Higher Anxiety Cochrane gets personal on this one. Through high school and his early 20s, he was deeply addicted to League of Legends. His dad teased him about it constantly. In the last few years of that addiction, his body would go ice cold and shake every ranked match before. His partner identified it as a panic attack. The moment that happened, he quit. Today, he no longer shakes. The new study lines up with his experience. Researchers Kayleigh Watters and Mikael Rubin at Palo Alto University analyzed a publicly available database of 13,464 adult gamers, most of whom primarily played League of Legends. Players who game to win show higher generalized anxiety but actually play fewer hours, since performance pressure pushes them out. Players who game to relax show strong links between social anxiety avoidance and more hours played. The study appeared in the Journal of Affective Disorders. The headline framing of “playing to win makes you anxious” misses the point. The real finding is more interesting: gaming for avoidance and gaming for competition are both warning signs, for different reasons. Cochrane notes that the League of Legends community’s toxicity has been a running joke for years, and this study suggests the game’s structure may have been manufacturing the anxiety that fueled it. Sponsor: GoDaddy Economy hosting is $6.99/month, WordPress hosting is $12.99/month, and domains are $11.99. Both hosting plans include a free domain, professional email, and SSL certificate. Go to geeknewscentral.com/godaddy for the best pricing and to directly support this independent show. Verge Motorcycle: World’s First Production All-Solid-State Battery Cochrane filled his tank for $60 today, which made this story land especially hard. His mom has driven electric for years and patiently manages a 90-mile real-world range. The next-generation answer is already shipping. Verge Motorcycles, a Finnish company, is the first production vehicle of any kind with an all-solid-state battery. Their 2026 bikes ship in Q1 with a pack from Donut Lab, another Finnish outfit spun out of Verge. The numbers are bonkers. The pack delivers an energy density of 400 Wh/kg, roughly double that of current Tesla cells. It sustains 100kW charging, hits full charge in about 5 minutes in the lab and 12 minutes on the actual bike, and the long-range version covers 600 kilometers (about 370 miles) per charge. Toyota, QuantumScape, and Samsung SDI have all been telling us that solid-state is coming in 2027 to 2030. A Finnish motorcycle company shipping in Q1 2026 just embarrassed them all. UBTech Humanoid Robot Sales Jump 23-Fold UBTech dropped its 2025 annual earnings on April 1st. Humanoid robot revenue hit 820 million yuan, roughly $119 million USD, up 2,203% from 35.6 million yuan the year before. Unit sales went from 3 robots in 2024 to 1,079 in 2025. Shares jumped 14% on the announcement. The customer list is a real industrial deployment: BYD, Foxconn, Geely, FAW-Volkswagen, and Audi. The flagship is the Walker S2, with UBTech targeting 5,000 units in 2026 and 10,000 in 2027. Cochrane is honest about what this means. He does not think we are heading for an extinction event, but worker displacement is a real concern. The US has no universal income or universal healthcare. The people affected are not white-collar managers. They are everyday line workers who already make the least on the ladder. Work efficiency reportedly doubles when these robots arrive, which is a company-side win, but the humans they replace are not getting half a year of gardening leave to retrain. He invites the listener to take on this one directly. Japan Switches On Asia’s First Osmotic Power Plant In August 2025, Fukuoka’s Seawater Desalination Center quietly opened Asia’s first osmotic power facility. It generates about 880,000 kilowatt-hours per year, enough for roughly 220 homes. It is only the second operational osmotic plant in the world, after Mariager, Denmark, in 2023. Osmotic generation uses a salinity gradient: fresh water on one side of a membrane, salt water on the other, and the pressure difference spins a turbine. The clever part is what Fukuoka does with desalination brine. Instead of regular seawater, the plant uses concentrated brine left over from the desalination process. This amplifies the salt gradient and squeezes more energy out of the same membrane. The result is a closed-loop partnership: the desalination facility produces drinking water and leaves brine behind, the osmotic plant turns the brine into electricity, and that electricity runs the desalination facility. Every desalination plant on Earth produces brine, so if Fukuoka’s co-located model works, the same pattern could be replicated across hundreds of plants worldwide. Japan’s Luna Ring Solar Moon Proposal Goes Viral Again Shimizu Corporation’s Luna Ring concept is making the rounds again. The pitch: a 6,800-mile belt of solar panels around the Moon’s equator, beaming microwave power back to Earth. Project lead Tetsuji Yoshida has long argued that a full ring could eliminate fossil fuel dependence entirely. The proposal first surfaced in 2013, has no funding, no government endorsement, and no concrete cost estimate. Shimizu has not put any active development behind it. Cochrane finds the concept fun every time it resurfaces. However, this would have to be a worldwide effort in the truest sense, with treaties, a new generation of launch economics, and microwave power transmission at a scale nobody has demonstrated. Beaming the power back to Earth has always been one of the biggest practical holdbacks. The Luna Ring is inspirational, but not shipping. Finland’s Onkalo Nuclear Waste Vault Opens Finland’s Onkalo facility is the world’s first permanent deep geologic repository for spent nuclear fuel. Operated by Posiva, the facility is buried about 430 meters down in 1.9-billion-year-old bedrock. It is designed to hold up to 6,500 tons of spent fuel and operate until the 2120s. The construction costs about €1 billion, with operating and closure adding roughly €4 billion more before the program is done. The catch is that radioactivity remains dangerous for hundreds of thousands of years. Edwin Lyman, director of nuclear power safety at the Union of Concerned Scientists, warned that the copper canisters will eventually corrode, with different scientific opinions on how fast. Geologic disposal remains “fraught with uncertainties,” and we have never validated an engineered system across a 100,000-year time frame. The bet is that the rock and copper outlast the radioactivity. Cochrane sees Onkalo as time-buying rather than a final answer. It is more of a bank holding spent fuel while science catches up. He prefers it to Japan’s ongoing approach of releasing tritium-treated water from Fukushima Daiichi into the Pacific, even though the dilution is well below WHO drinking water guidelines. Burying the waste in an insurmountable containment strikes him as the more honest answer to a problem nobody knows how to truly solve. Ghostty Terminal Lands in the Ubuntu Repos Ghostty 1.3.0 is now available in Ubuntu 26.04 LTS’s universe repository. The install is simply `sudo apt install ghostty`, no PPAs, no Snap, no Nix, no building from source. Ghostty was created by Mitchell Hashimoto, co-founder of HashiCorp. It is GPU-accelerated, uses native Swift on macOS and native GTK4 with libadwaita on Linux, and supports tabs, splits, profiles, ligatures, and the Kitty graphics protocol. Cochrane recently caught Hashimoto on a podcast, where he walked through his agentic coding workflow. Ghostty is being actively built using AI harnesses like Claude Code and Codex. Hashimoto told a story in which Codex fixed a six-month-old bug in 45 minutes, for a total API cost of $4.14. Personally, Cochrane uses WezTerm, but he is excited to see Ghostty become more widely available with a native UI rather than Electron. Borgo: Rethinking Go Using Rust Analytics India Magazine profiled Borgo, a programming language by developer Marco Sampellegrini (GitHub: alpacaaa). Borgo is statically typed with Rust-like syntax, but it compiles to Go and uses the Go runtime and garbage collector. It includes sum types (Option and Result), pattern matching, and full compatibility with existing Go packages. Notably, it removes Rust’s borrow checker and lifetimes entirely. Borgo is not new. It first appeared on Hacker News in 2023, with a RustLab talk in 2024. The 2026 angle is a renewed look at it through the lens of AI coding agents, since type-rich languages like Rust have been showing outsized productivity gains. Cochrane is a fan of Rust and stands by the borrow checker, but he enjoys these exploratory languages for what they reveal about what developers actually want. Caveman: A Claude Code Skill That Cuts 65% of Tokens Developer Julius Brussee built a Claude Code skill called Caveman that forces Claude to respond in stripped-down fragments. No articles, no “just,” no “really,” no pleasantries, no hedging. The tagline is “why use many token when few token do trick.” Across 10 real dev tasks, Caveman mode averaged 294 tokens per response, compared to 1,214 in normal mode. That is a 65% drop in output tokens. The project is MIT licensed with three intensity levels: lite, full, and ultra. Cochrane stumbled across the project online and shared it with a classmate who had been complaining about token costs. The classmate now insists that “the caveman is the only way to live.” Cochrane has not made the switch, but the bigger point lands. If a community plugin can cut 65% of tokens without correctness regressions, the labs are shipping verbose-by-default and charging users for the privilege. He suspects verbose output makes models feel more trustworthy, even when the token math says otherwise. Cloudflare Launches EmDash as a WordPress Successor Cloudflare released EmDash on April 9th, an open-source, MIT-licensed, TypeScript-based CMS pitched as the spiritual successor to WordPress. The big flex is that it was built in 60 days using AI coding agents. EmDash runs on Astro 6.0, either on Cloudflare’s edge platform or on a standard Node.js server. The plugin security model uses sandboxed Dynamic Workers with explicit permissions, addressing the architecture flaw that Cloudflare says causes 96% of WordPress vulnerabilities. Cochrane could not resist pointing out the irony of the name. The em dash has become the trademark giveaway that an AI was involved in writing. He has reservations about whether EmDash will succeed. WordPress is extremely hard to unseat, plenty of “WordPress killers” have come and gone, and the ecosystem is twenty-plus years deep. He is curious to see what comes next but not optimistic. Google Open-Sources the DESIGN.md Format Google Labs open-sourced the DESIGN.md format used by Stitch, their AI UI design tool. DESIGN.md is a declarative file capturing a project’s design system, colors, typography, and spacing in a way AI agents can read and apply. Cochrane has tried Stitch personally and finds it impressive at producing web designs. He has also seen DESIGN.md-style files already start appearing in repositories. He sees this kind of file becoming a new paradigm for agentic design, alongside robots.txt and llms.txt. However, he worries about a side effect. If everyone uses the same standardized format and the same AI tools, the web could become a homogeneous set of sites that all look the same. He is enthusiastic about the standardization but hopes designers continue to push for genuinely unique work. A 13-Liter PC With a Water Loop Built Into the Case Geeky Gadgets covered a build by “Visual Thinker”, a 13-liter mini-ITX case with custom SLA-printed water distribution plates built directly into the chassis. Instead of traditional soft tubing, plates channel coolant between the CPU and GPU blocks and are sealed with TPU and silicone molds. The case supports a full-size GPU and an SFX power supply. No thermal benchmarks, parts list, or pricing have been published. It is a one-off you cannot buy. Cochrane sees this as a sign of where PC building has gone in 2026. Modern mid-grade GPUs run nearly every recent game, so raw performance is no longer the differentiator. He likes seeing builders lean into design and craft rather than just stuffing the most powerful parts into a box. He admits he is the traditional type and built his own machine to maximize parts, but the design-first direction is a healthy evolution for the hobby. To close out the show, Cochrane recommends Pocket Casts as a podcast app. He finds it picks up new episodes very quickly. Big thanks to GoDaddy for over twenty years of keeping this show on the air, and a reminder that every promo code use is like writing a check to the show. The post Mythos: Cybersecurity’s AlphaGo Moment #1862 appeared first on Geek News Central.

Amjad Masad and Haya Odeh, cofounders of Replit, join South Park Commons Partners Aditya Agarwal and Ruchi Sanghvi to share what it really took to build one of AI's most important platforms, and how it's now enabling a new wave of million-dollar founders.Amjad breaks down why traditional ideas like ICP are breaking down in the age of AI, what's actually changing under the hood of modern models, and why we may be reaching the limits of prompting. Haya shares the origin of “Seek Pain”—Replit's most counterintuitive cultural principle—and how a relentless focus on what's not working drives better products, faster learning, and stronger teams.Amjad Masad: https://www.linkedin.com/in/amjadmasad/ Haya Odeh: https://www.linkedin.com/in/haya-odeh-b0725928/ Ruchi Sanghvi: https://www.linkedin.com/in/rsanghvi/ Aditya Agarwal: https://www.linkedin.com/in/adityaagarwal3/ South Park Commons: https://www.linkedin.com/company/southparkcommons/Apply to SPC: https://www.southparkcommons.com/applyCHAPTERS:(00:00:00) - Coming to America broke (and building anyway)(00:04:27) - Early Replit proof points kept the mission alive(00:07:02) - Cloud vs. local: why security tips the scales(00:10:09) - Execute daily, predict quarterly(00:11:35) - The 2023 roadmap Replit just finished executing(00:16:01) - Agent 4 and the end of context amnesia(00:22:01) - The death of the ICP(00:24:47) - What actually changed in AI models December 2024(00:28:52) - "Seek Pain"—Replit's most counterintuitive cultural value(00:34:55) - Why consultants are the most mispriced AI-era hire(00:38:00) - Co-founding with your partner—the honest answer(00:43:25) - Make micro-predictions or get left behind by AI(00:45:19) - Raising kids in a world you can't predict

SaaStr 851: The Agents, Episode 002. Managing 20+ AI Agents: Lazy Agents, Stealth Churn & the Death of 60% Solutions In Episode 2 of The Agents, Amelia Lerutte, Chief AI Officer at SaaStr, and Jason Lemkin, Founder and CEO of SaaStr, share the trials, tribulations, victories, and minor defeats of managing 20+ AI agents in production. With three humans and 20+ AI agents now driving more revenue and output than SaaStr did with 20+ FTEs in 2020, this weekly series goes deep on what's actually working, breaking, and changing in the agentic era. This week's episode covers: 00:00 Welcome to The Agents Episode 2 01:00 Lazy Agents: How an AI agent silently deleted Amelia's session from the SaaStr Annual top 10 06:30 When agents blame the API: agentic accountability and the need for daily QA 09:00 The 60% Solution Problem: Why HubSpot's new AEO tool failed and got vibe coded better in 10 minutes 14:00 Figma Make vs. Replit, Lovable, and v0: Why no one will pay for "good enough" AI products 17:30 Classic Figma is now Grandpa Software: Production breakdowns and why Illustrator's agent is winning 21:00 Stealth Churn in Canva, ChatGPT, and beyond: The hidden metric every leader needs to watch 27:00 Why Claude Cowork created lock-in and killed ChatGPT usage for Amelia 30:00 Forward Deployed Engineers vs. Self-Serve: Why FDE light is the answer for SMB AI deployments 36:00 Vector breaks the agent freeze: How a 15-minute CEO-led deployment won SaaStr's business 40:00 The Agent API Test: Which APIs work best with AI agents (Salesforce wins, Marketo fails) 46:00 Resend, 11 Labs, and OpenRouter: The new gold standard for agent-friendly APIs 50:00 The Marketo collapse: When your marketing automation platform can't honor unsubscribes 55:00 Building an AI VP of Finance: Why collections is the next agent frontier at SaaStr 1:00:00 SaaStr Annual 2026 is three weeks away: May 12-14 in the SF Bay Area Topics covered: AI agents, agent management, lazy agents, stealth churn, vibe coding, Replit, Lovable, v0, Figma Make, HubSpot AEO, Claude Cowork, forward deployed engineers, FDE, self-serve AI, Vector, Salesforce, Marketo, Resend, 11 Labs, agent APIs, AI VP of Finance, collections automation, SaaStr Annual 2026 SaaStr Annual 2026 | May 12-14 | Come learn how to build, deploy, and manage AI agents from the leaders at Salesforce, Replit, Vercel, Cloudflare, and more. Register at saastr.ai Subscribe for weekly episodes of The Agents and the SaaStr Podcast. #AIAgents #SaaS #SaaStr #AgenticAI #VibeCoding

Learn how to leverage AI agents to build faster, smarter, and more profitable businesses without burning yourself out. AI agents are no longer something you plan for in the future they are working right now for the founders who are paying attention, and in this episode, I sit down with one of my favorite people on the planet to show you exactly what that looks like in practice. We go deep into how AI agents are being used today to automate creative work, produce films, manage workflows, and even build the software that builds the software. If you have ever felt like you are falling behind with AI, this conversation is going to light a fire under you, because the scoreboard has reset and the window to get ahead is open right now. Mike Koenigs is a 5x serial entrepreneur with five exits, a 19x bestselling author, a stage 3a colorectal cancer survivor, and the guy founders call when they are ready to build their next act. Peter Diamandis calls him an arsonist of the mind, and Tony Robbins has said he is an extraordinary man who brings insights so valuable that you need to take advantage of what he has to offer. Mike has taken the stage at MIT, NASA, the United Nations, Abundance 360, Tony Robbins events, Strategic Coach, and Genius Network, demonstrating hands-on AI systems in real time. He is the founder of The Superpower Accelerator and AI Accelerator, co-host of two top 1% podcasts, and has spent four decades working with companies like Sony, BMW, and 20th Century Fox, as well as hundreds of entrepreneurs building high-net, low-overhead businesses they actually love. KEY TAKEAWAYS: AI agents can fully automate creative workflows including scriptwriting, mood boards, video production, and voiceover without needing a large team behind you. Your competitive edge is not the tools you use but the context, experience, and creative thinking you bring to the way you use them. Mike's go-to AI stack includes NotebookLM, Claude, Manus, Replit, and Gemini, and with those tools alone he can build almost anything he needs. We are moving from an output-based economy to an outcome-based economy, and the founders who will win are the ones who lead with creative thinking and critical problem-solving. AI does not have taste and does not know what finished looks like, which means your vision, judgment, and experience remain your greatest assets. The biggest trap in AI is not falling behind but getting pulled into infinite possibility without clear constraints, which costs you focus and precious time. Building agents that handle repetitive background work frees you up to operate as the architect, the creative force driving outcomes rather than the one doing the manual lifting. The founders who come out ahead are the ones who wake up curious every single day, embrace discomfort, and keep showing up to learn and apply before anyone else does. Connect with Mike Koenigs: aiaccelerator.com/free Growing your business is hard, but it doesn't have to be. In this podcast, we will be discussing top level strategies for both growing and expanding your business beyond seven figures. The show will feature a mix of pure content and expert interviews to present key concepts and fundamental topics in a variety of different formats. We believe that this format will enable our listeners to learn the most from the show, implement more in their businesses, and get real value out of the podcast. Enjoy the show. Please remember to rate, review and subscribe to the podcast so you don't miss any future episodes. Your support and reviews are important and help us to grow and improve the show. Follow Charles Gaudet and Predictable Profits on Social Media: Facebook: facebook.com/PredictableProfits Instagram: instagram.com/predictableprofits Twitter: twitter.com/charlesgaudet LinkedIn: linkedin.com/in/charlesgaudet Visit Charles Gaudet's Wesbites:  www.PredictableProfits.com www.predictableprofits.com/community https://start.predictableprofits.com/community  

Jack Neel speaks with Amjad Masad, CEO at Replit, about how AI is making it easier than ever to build and ship software without a technical background. They discuss Replit's rise from a browser-based coding tool to a platform generating $250 million in annual revenue, why Masad turned down a $1 billion acquisition offer, and his case for why AI represents empowerment rather than existential risk. This episode originally aired on The Jack Neel Podcast. Follow Amjad Masad on X: https://twitter.com/amasad  Follow Jack Neel on X: https://twitter.com/jackhneel Listen to Jack Neel: https://www.youtube.com/jackneel   Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

"If you don't take care of yourself, you can't help other people."In this high-energy conversation, Jon Cheney returns to the podcast to discuss his latest project, Moon Command, a game built entirely through the power of Generative AI. Jon challenges the traditional business dogma of "always charge for your time," arguing instead that in the modern economy, attention is the ultimate currency. Key highlights include:AI as an Equalizer: How Jon used tools like Replit to build apps and games without writing a single line of manual code.The GaryVee Strategy: A deep dive into Day Trading Attention and why "giving it all away" leads to massive inbound success.The "5 Priorities" Hierarchy: A radical approach to management where the job is ranked 5th, behind self, family, beliefs, and hobbies.The Flow State: How music and kayaking fuel professional "sprints" and why you don't have to do everything all at once.AI Game Development, Moon Command, Jon Cheney, Replit, Generative AI, Gary Vaynerchuk, Business Philosophy, Personal Productivity, Flow State, Time Management, Entrepreneurship, Giving Value, SEO Strategy, Software Development, Work-Life Balance, Digital Marketing.

Piyush Jain, Founder and CEO of Simpalm and co-founder of Ducknowl, is on a mission to solve real-world challenges by combining technology and entrepreneurship. With over 15 years of experience building custom software solutions, Piyush helps businesses turn complex ideas into practical applications by blending technical depth, business acumen, and a strong problem-solving mindset. We explore Piyush's AI Ideation Framework—Validate idea, Proof of concept, Design, Competitor analysis, and Feature selection—a practical approach to building software in the post-AI era. Piyush explains how AI can help teams better understand user personas, validate product assumptions, and rapidly prototype ideas, while human expertise remains essential in design, architecture, and production-grade development. He also shares how prompt engineering, peer-reviewed prompting, and a right-shoring delivery model can help businesses build smarter, faster, and more cost-effectively. — 3D Print Your Software with Piyush Jain Good day, dear listeners. Steve Preda here with the Management Blueprint, and my guest today is Piyush Jain, the Founder and CEO of Simpalm, a custom software development company, and the co-founder of Ducknowl, a candidate screening and assessment application business for high-volume recruiting. Piyush, welcome to the show.  Thank you, Steve. Thanks for inviting me.  Well, I’m very curious about the stuff that you have to share with us, and I’d like to ask first about your personal purpose. What is your “why,” and how are you manifesting it in your business?  Yeah, so that’s a very interesting question. And I think for every entrepreneur or tech founder, really, that's the motivation—why you want to do certain things. So for me, if I look at it, my personal “why” is: why are we not solving challenges? Or why are we not solving them the right way? Why are we not transforming our lives? I grew up in India and then came to the US, so I've seen many different parts of the world—from Asia to North America. I see people face different challenges, but then we are not focusing on solving those problems. A lot of it I see is there’s a lot of challenges in the world because I believe there are not enough entrepreneurs. Because entrepreneurs are the ones who really take risks, combine everything, and create solutions. That was like me, right? That’s what I learned growing up, that I think I can do that, right? I can combine the technical knowledge and the business acumen and create solutions that people like, solve their challenges. Growing up, like I'm more on the technical side.Share on X I was inclined more toward science and technology, but then as I got into my undergrad and grad school, I realized that I have that entrepreneurship aspect, but it's still around science and technology. That’s when I realized that, you know what, I cannot be a pure scientist or maybe a pure entrepreneur, but I can be someone who can combine these two, because my main driving factor is problem-solving. I can combine these two and then live my life, be very happy with what I do. That has been my motivation. I like it. So solving challenges and being an entrepreneur, and kind of combining the two—being the technical expert and the entrepreneur in one. Now, one of the things that we always talk about on this podcast is frameworks. And you have developed a really good one for AI ideation, which I think is something that everyone needs to do these days or use these days, and it helps you create business apps and other business applications. Can you share with me how that framework works, and what are the steps in it?  Sure, yeah, definitely. So just to give you a brief background, we've been building software for the last 15 years. Some companies have used different frameworks, whether it's Agile or Waterfall in SDLC, in building the software, right? There are different methodology that companies have used, and they've been good, successful—they've played their role. But now, with the advent of AI, things have changed. We had to figure out, in our organization, how to use AI, and that's how this framework was built. My team helped me building this framework as well.Share on X But we realized that we were losing business—we were losing clients—since we didn't have an AI framework that would fit our clients. Again, for me, it's a challenge. So anytime I see a challenge, it create brain juice in me, right? So I said, okay, let's figure out how we create this framework. How did you do it?  So really, we built this framework—very interesting. A lot of the steps are similar, but then a lot of things are different.Share on X Whenever client comes to us and says, “Hey, we want to solve this challenge,” what we do is we do enough research. And now we use a lot of AI tools to really understand the problem better and understand the user persona. When you build any software application, there is a person who's going to use that. Sometimes we used to do user research or focus studies to understand that. Now, with the help of AI, we can get a lot of ideas about the user persona. For example, maybe we are building a healthcare application for an anesthesiologist. I don’t know much about that. I know, I mean, because I have been through some medical surgery and all that, but I can't fully understand their user persona or their requirements with respect to the application we're building. But now, with AI, I can actually ask different AI models, “Hey, we are building this app for anesthesiologists. What are their pain points? How would they see it?” So all that deeper mindset and psychology we can get using AI.  You are validating the idea by interrogating AI applications.  What users are going to like and all that. So I will always use this term earlier. In software engineering, now we have this pre-AI and post-AI, right? If you read history, we talk about before Christ and after Christ, right? Yeah. So it's a similar thing now. Yeah, exactly. Or before Covid, after Covid. Before AI, after we did all the user research and everything and created a requirements document, we would usually do design, create like a visual design of the software. But now, with the AI framework, we don't do that. That's not the next step. What we do instead is create a quick prototype using AI platforms.Share on X  So there are a lot of AI platforms—like Lovable, Claude. Now ChatGPT launched Codex for coding, and Replit. Depending on what kind of application you're building—for example, maybe if you're building a web-based application—then I recommend using Lovable or Replit. They're very good at creating that. Whatever software you want to build, whatever user personas that you’re addressing, you can feed into that and it’ll create like a prototype application. Okay.  So what that does is actually, then this prototype, clients can just take it to their customers or internal users and get feedback. A picture is better than a thousand words. Organizations discussing an idea is very different from when they actually see something. Then everybody starts chipping in—“Oh yeah, I see this in the prototype, but I don't want this,” or “I want to move things around,” or “This is what I want.” Basically, building a prototype on AI platforms is much faster than building wireframes and design prototypes like we used to do earlier. So that has changed. So you're 3D printing your software, right?  Yes, exactly. There you go. Well, that’s a very good way you put it together. Yeah. So, yeah, exactly. You’re just 3D printing the software, right? So you can see it, visualize it, and then once you go through that, it creates a lot of better ideas about the software in faster time. So once you have that, then you go into UI/UX design. So in that also, there are two steps. One is wireframing. Wireframing is like creating the flow in black and white. It's like creating a skeleton of your software. It does not have the color, the font, or the branding, but you just create all the different user journeys, the screens, the flow, and the fields that will be there on the screen. So we have integrated AI into that step as well. Earlier, it used to be created by a designer or a business analyst. Now we are using software like Uizard or UX Pilot, where we define what we want—what kind of user journey, flows, and screens—and it creates that. It spins out those wireframes in minutes. So really that has reduced now. The time it used to take to create wire frames is faster now.  So you're designing the wireframes with AI?  Yes, but it's just the wireframe part of it, and it's still guided by our expert VA or designer—someone who knows how to really visualize things and has done a lot of wireframes and sketches. So they know what to tell the AI. Prompting is very important. It's very important that you know how to prompt—what to ask for—so that you can get variations and differentiation in the wireframes. You don't want a standard AI-created wireframe. Everybody can recognize AI-generated images now, right? If I show you one, you'd say, “Oh yeah, it's AI-generated.” I know that, right? Yeah. So again, we keep the human intelligence. We're not asking AI to create the full software end-to-end. It never works—it'll never work. It just doesn't. I know that's a strong statement, but I'm saying that based on experience and an understanding of human behavior and psychology. So AI agents will not be able to code software, in your opinion?  No, they can do the coding, but they cannot build the whole software end-to-end—a production-deployed software. Because these software are being used by humans. You have to have human intelligence to understand and define what you need and how it works.Share on X You can maybe create some software, but it doesn't work very well. Even if you use all these platforms, you can cut down your production time and cost by 30%, 40%, 50%, right? That's the number we are seeing—30 to 50% reduction, depending on the software you're building and the objectives. So just to recap—you validate the idea by interrogating Claude and ChatGPT, asking about the needs of that customer, the psychology of the customer—that's step number one. Step number two is 3D printing the software with Lovable or Replit—so proof of concept. And then you design the wireframes. And then what's next after you design the wireframes? What's the next step?  So that’s a good thing. That’s it. Now I'm going to talk about the human element—some people listening to this podcast will be surprised. Now it comes to visual design, right? So you've created the skeleton, and now you have to add the skin, the tone, the color, the emotion to the design, to the workflow. Now, we have tried AI, but it doesn't work. It's very monotonous. So we use an experienced visual designer, a UX designer, for that step—to give it emotion. When you use AI—I wish I could show you some examples—it creates very similar kinds of designs for apps and software. So what we did is we gave it three different apps with very different objectives and everything, and the designs it came up with were very similar—blocks, buttons—very monotonous. So there's no differentiation. And design is the main thing that becomes the differentiator, right?  Yeah.  So that's what we learned from our experience. And I say that very categorically in all of my talks—that visual design, final UX, has to be human, not AI.Share on X Because you are communicating emotions, right? And AI is still not there to communicate emotions.  Yeah. It doesn’t have emotions.  Well, some people will argue with you and say, “No, it can understand if you're sad or unhappy.” But my response to that is—it's because we've programmed it that way. But things change based on situation, context, ethnicity, culture, fear—how people express nervousness, fear, and all that—it's very different. So there was this AI video interviewing company five or six years ago. They were sued by the Department of Justice because they were trying to detect emotions of people like anxious, nervous, when the interview was happening.  It turned out their model was trained only on one race—they didn't account for other races or ethnicities. So their model failed, and they were sued by Department of Justice for that. So yeah, emotions is something—maybe they have unlimited dimensions, we don't know. So it's hard to program that. So basically: ideation, prototype, wireframe, and then final visual design—that's the discovery and design framework. Now, when it comes to development framework, this is where AI has been a game changer—the coding part. But again, you have to be very careful about how you use AI in your coding pattern with your coding team. It depends on the application, it depends on the tech stack, right? Every platform has its own strengths and weaknesses. For example, if you want to build a web-based application in the React JS framework, then Lovable is great. That's very good—very efficient and cost-effective. Then Claude is there. Claude has been really good in software engineering. I would say it has been built and designed mostly for coding, right? Anthropic—their idea, their starting point—was coding, how to make coding and software engineering better.  So they've been a front runner in the race. ChatGPT is trying to catch up using Codex, and Copilot is great. Copilot is mostly used by enterprises who are on the Microsoft stack. They use Copilot a lot for coding in .NET and enterprise-level applications. They’re used to co-pilot. It’s because they feel comfortable with Microsoft security policies and all that. That’s fine. But in general, we see Claude to be at the top—from our perspective. We've also built a framework for software coding. In software development, there's a popular process called peer review. So when you create source code, you get it reviewed by your peer—your colleague.Share on X  Is this what happens on GitHub?  Yeah, yes. So basically anywhere—any source code repository—you can do that. So your team members can help you make your code better and more efficient.  Yeah, I understand. But now we have a step called prompt peer review. When you're using prompts to build software, those prompts get reviewed by team members. Because if your prompts are not very specific or good enough all the way through the SDLC, you can run into a lot of challenges trying to fix the code. Because now you have a situation where you have code that you have not written fully, and when you ask AI to change something in the code, sometimes it ends up changing a lot of things that you don't want it to change. Yeah.  That's what we've seen, and that's why we evolved. Before we build any software, we create maybe a 10-, 20-, 30-page prompt document, where we go through each screen and function and write it out. It's very sophisticated—it has evolved really well. But the thing is, it takes a few days to do that within the team, because we know if we do it right, the next step is faster and more accurate. So really, the prompt document—think of it more like an architecture document. Earlier, we used to create a solution architecture document, defining all the tools, the design, everything.  But now it's more like an AI-driven solution architecture document with prompts, which get reviewed by team members. So we do that, and then we run that, and we get the code and everything. So I have a CTO club—I run a CTO Club in Maryland—and I was talking to CTOs. They're all using this, but some of them are so advanced that they actually define the test cases in the beginning. They define, “Okay, this is what I want, this is the function I want, and these are the test cases I want it to pass.” That's even more advanced. If you can do that, you can have very efficient code.  Yeah, I love it.  So is that the end? You have your test cases, you design the prompt, you peer-review the prompt, and you already had the prototype, so now you're coding the software—what's the last step?  Yeah. Then there’s an integration as well. So AI doesn’t do the integration so well. You can do the front-end coding, you can do the back-end coding, you can probably create the APIs. APIs require a lot more human intervention. But once you have that, then you have to connect it, right? You have to connect the front end with the backend. A lot of that is still done by the programmer. It's hard to rely on AI for doing that. And again, it depends on the application. Maybe if it's a smaller application, maybe you can have AI do that. But if it's a bigger application—we mostly build bigger applications—then integration, then final QA and testing, and deployment.  So all that is there. But in each of these steps, you can use some sort of AI tool to speed up the process. But the key is you still have to have your architecture, the process. You have to know the steps more. You have to be a good, experienced developer to use AI efficiently if you want to build a production-ready application. You can build a prototype. Anybody can build a prototype on Replit or Lovable, but it's not going to be production-ready that you can give to your customer and charge them money. So that’s the differentiator.  Yeah, I understand. So Piyush, I’d like to switch gears here. I understand the AI ideation framework—that's great. We talked about the technical part of it, the curiosity, the technical challenges. Let’s talk about the entrepreneurship part, which is also part of your profile. So what drives the growth of your business? What would you say drives it?  For us, there are multiple factors that drive the growth of our business. The first is, again, our problem-solving attitude. Any client that comes to us we communicate in that modelShare on X The problem, the challenge, the solution, the business part, the value proposition we bring. And the second factor is our location. We are here in Maryland, and we have another office in Chicago. So being here, we have a global shoring model—that's a main driving factor of our business from the entrepreneurship perspective. So what the global shoring model is: our client-facing team, the senior team, is here—solution architects, sales engineers, designers, project managers, business analysts—they are here in the US, client-facing. And our dev team and testers are in our offshore locations.  Some people call it hybrid shoring. I call it right shoring. The reason I call it right shoring is because in this model, you have the right people at the right shore, so you get the most value. Here, you have people who understand the culture, the product, the context—because products are used by people in a certain culture. And if you are not in that culture, if you haven't experienced it, it's always harder to design the right software solution. I was one of the first people to start that model here in the DMV area for mid-size and smaller companies. This model existed before, but mostly for large enterprise companies. They have used that. But I started to offer that 16 years ago to smaller companies. Either companies were just going offshore, or they were doing onshore, right? I introduced this hybrid—or right-shoring—model, and it has been well received by our customers. So that’s it.  So what is one thing that you’re trying to figure out in your business right now?  Right now, what I'm trying to figure out in my business is scaling. I mean, we have built solutions for many different industries. We have built solutions for different clients in fintech, healthcare, education, nonprofit, startups, IoT, construction. But now what we are trying to figure out is how do we create some off-the-shelf solutions for different industries? Because one challenge we see is that, from the client's perspective, getting custom software built takes time and money. But in certain use cases, we can have off-the-shelf, industry-specific solutions, and then customize those based on the client's needs.  So that's what we are trying to figure out—across different industries, what those solutions can be—so we can scale and also make it easier. And these are more like AI-driven, off-the-shelf solutions that are customizable. So think of it like Salesforce—its core is off-the-shelf, but then you can customize the front end and a lot of other things. Not exactly like Salesforce, but more like industry-specific solutions for different use cases—nonprofit, construction, right? With those, overall, we can build solutions faster.  That’s fascinating. So how has the offshoring—or right shoring, as you call it—model evolved over the past 10 years? Is it different now than it was 10 or 20 years ago?  Yeah, I think that's a great question. It has evolved and changed. Earlier—maybe 10, 12 years ago—when we were talking about hybrid shoring, we were mostly talking about the US and Asia. But now we have different players. We have the nearshore model, which has become quite popular as well—like South America. We have team members in nearshore locations as well, in South America, because we want to leverage different time zones, resources, and culture. And we've seen very positive results. Then you have Eastern Europe. We have competition from countries like Ukraine, Belarus, Romania, Poland. I think it’s the part of the globalized world, right? It's like energy flowing in different spaces—it's not limited to one place, which is great. That's one way it has evolved.  I also know some companies working in Kenya—there are developers there. Some companies are setting up in East Africa, West Africa. So different places are playing roles now. That’s one thing I see. And now, with the help of AI, what's going to happen is it will play two roles. One— in many situations, with AI, you can do more things onshore. That’s one aspect of it. And second—with AI, someone sitting offshore who knows how to use AI can become very competitive as well. We don't have enough data yet to fully see how this will evolve, but maybe in a year or so, we'll see how it plays out.  But I also find that with these simultaneous translation tools—like Apple, I think an iPhone can now translate in all languages. Essentially, another barrier falls that if the language and knowledge of your offshore contractor is not perfect, they can understand things much more clearly because of simultaneous translation. Even on Zoom, you can now flip a switch and they can read what's being said in their own language during a conversation. So that's amazing, I think.  Yeah. That’s amazing. That’s amazing. They can understand more about the culture and mindset. So that's something have to see. Again, I think it depends on the use case, the application, the problem we're solving. But in some cases, it might be great news for onshore—we can keep more dollars here. But keeping dollars here with AI also means a lot of that spend is going to AI, right? So that's one thing—we have to be very careful. Yesterday, in our tech breakfast, our presentation was about how to optimize your AI tokens. There are some companies spending $150,000 per year per employee on tokens. Wow.  That's like the salary of one employee.  Yeah.  A mid-level developer—$150K—they're spending that much. And then they’re trying to figure out how to optimize it. And on top of that, they have cloud costs, right? AWS, Azure—those costs are still there—and then you add AI. So it's a lot of money. You really have to be very smart about understanding and optimizing it. That’s why the prompting is so important, right? It's not just about getting the right software—it's also about getting the cost down.  Yeah. Again, you need expert people who can prompt well, because it's about being able to communicate well. Prompting is about communication—it's about clarity, brevity, security, all that stuff. So, Piyush, we're coming close to the end of the recording. If someone would like to learn more about the applications you develop, how you're using AI, and how you can help their business develop technology, where can they find you? What's the best way to get in touch with you? Sure, there are many ways people can reach out to me. They can go to my website, www.simpalm.com—we have a contact form there. They can submit the form, or they can reach out to me via email directly at contact@simpalm.com. They can also connect with me on LinkedIn. I'm on LinkedIn—message me there if somebody needs anything. I always like discussing problems and what the solutions can be. If anybody reaches out to me, I'm always very quick to respond.  That's awesome. So Piyush Jain, the CEO of Simpalm—and we didn't even talk about your other business, Ducknowl—thank you for coming, and thank you for sharing your insights and your framework on how to build an ideation framework for AI. So thanks for sharing that. And if you're listening and you enjoyed this conversation, then stay tuned, because every week we have another entrepreneur sharing their insights and frameworks with you. So make sure you follow us on YouTube, subscribe, and give us a review on Apple Podcasts. So thanks for coming. Thank you, Steve. It was a pleasure talking to you. Important Links: Piyush's LinkedIn Piyush's website

In this episode of The Selling Podcast, Jon Cheney, CEO and Chief AI Officer of GenAIPI, explains how he leveraged AI and veteran sales skills to build a million-dollar business in just six months with a $400 startup cost. The conversation explores the revolutionary concept of "vibe coding"—using natural language to develop production-ready software via platforms like Replit without writing a single line of code. Cheney breaks down how AI is leveling the playing field for entrepreneurs by closing traditional knowledge gaps in law, medicine, and engineering. Key highlights include the importance of selling a product before it's built, using AI as an "interviewer" to sharpen business conviction, and why the "post-work world" narrative is a threat to human agency. This episode is a masterclass in compressing the entrepreneurial timeline and using AI as a force multiplier for sales-driven growth.

AI is projected to disrupt over 300 million jobs globally.The question isn't whether change is coming.The question is: Will you build with it… or get replaced by it?In this episode of Inside the Vault, Ash Cash sits down with Justin Burns to break down:What Agentic AI really meansWhy simplicity wins in this new economyHow to find the “Opportunity Gap”The 1% App StrategyHow to build and launch an AI-powered appTools like Claude, Lovable, Replit, Stripe, Superbase & moreWhy most people will get left behindThe mindset shift required to survive the AI eraThey even build an “Abundance Calculator” app LIVE on the episode — showing how fast you can move from idea to execution.If you've been watching AI from the sidelines… this is your wake-up call.

Get our free AI Ad Prompt Kit: https://clickhubspot.com/skw Ep. 414 How do you create killer ads when you don't have a marketing team or big budget?  Kipp dives into testing out Replit's latest AI ad generation tool live, showing how anyone can validate ideas and iterate ad creative faster than ever. Learn more on crafting powerful prompts that drive results, iterating your way to great creative without breaking the bank, and navigating the pitfalls and possibilities of AI-powered ad creation on platforms like LinkedIn, Google, and Instagram. Mentions Replit https://replit.com/ Claude Opus 4.6 https://www.anthropic.com/news/claude-opus-4-6 Willow Voice https://willowvoice.com/ Base44 https://base44.com/ Lovable https://lovable.dev/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

Send us Fan MailShownotes can be found at https://www.profitwithlaw.com/527.AI isn't just coming for the legal industry—it's here, changing how law firms attract clients, streamline operations, and deliver superior client experiences. The real risk? Waiting on the sidelines while competitors leap ahead.On this episode of Profit with Law, host Moshe Amsel sits down with serial tech entrepreneur Alex Mehr, PhD, creator of Famous.ai and co-founder of Zoosk, to demystify how law firm owners can unlock outsized results by integrating artificial intelligence—without technical know-how or a massive budget.Alex shares the mindset, strategies, and tactical playbook you need to leverage AI for practical wins in your firm—from marketing and lead capture to automating client intake and creating custom tools unique to your workflow.Chapters:[00:00] Discover how AI supercharges law firm efficiency and client experience[01:53] Learn the founder story behind Famous.ai and its impact for entrepreneurs[03:37] See how breakthrough technology turns ideas into solutions for your practice[05:55] Why playing with new law firm tech keeps you ahead of competitors[07:44] Find legal tech apps that streamline discovery and client communications[10:35] How client-facing apps educate and convert leads for estate planning[12:23] What makes Famous.ai unique for law firms versus other software builders[13:48] Assess if you can build your practice management system without a development team[16:03] Transition your legal team from coding to strategic digital project management[18:55] Spot automation opportunities in your law practice's daily workflow[21:50] Differentiate app builders: Famous.ai vs. Replit for non-technical legal professionals[24:42] Measure the true cost savings of AI tools for your firm's operations[28:51] Rekindle your curiosity—experiment with AI to grow your law practiceResources mentioned:

Heading to Vegas this May? Join Josh at Pulse 2026 and come say hi—your oversized fluorescent daiquiri is on him. No catch.Grab your ticket at gainsightpulse.com and use code UNCHURNED for a special rate.What does the future of CS actually look like? Ghazi Masood, CRO of Replit, has some strong opinions and the growth numbers to back them up.In one year, Replit went from $2M to $150M in revenue. Now they're targeting $1B. And while most companies are still debating AI strategy, Ghazi is already rebuilding his entire GTM org around it — scaling from 40 to 230 people, scrapping the traditional CSM model, and betting that the next billion software creators won't write a single line of code.In this episode, Ghazi breaks down how he's building post-sales for the AI era, why he replaced CSMs with "product advocates," and what it looks like when your entire team builds their own tools — including their own version of Clari and a customer health dashboard, both built on Replit itself.He also shares his take on the future of SaaS, how enterprises are quietly wrapping AI layers on top of Salesforce and Workday, and why Cursor, Claude, and OpenAI aren't keeping him up at night.If you're in Customer Success, Revenue, or CS Ops, this episode will challenge how you think about your role.---Timestamps0:00 - Preview & Introduction1:25 - Meet Ghazi Masood & Overview of Replit4:40 - Building the GTM infrastructure7:30 - How anyone at Replit can build internal tools9:00 - Managing chaos when everyone becomes a creator12:40 - Enterprise security & governance guardrails15:47 - Are Cursor, Claude & OpenAI real competitors?18:10 - Usage-based pricing explained19:17 - Post-sales strategy for non-technical users23:53 - Hiring 200 people in 12 months24:40 - The future of SaaS26:56 - SMBs replacing Workday and Tableau with Replit30:40 - Lessons from Auth0 and Retool32:32 - What Ghazi looks for when hiring---What You'll Learn- What enterprise customers are quietly building on top of their existing SaaS tools — and what it means for vendors- How SMBs are replacing Workday, Tableau, and traditional CRMs entirely by building on Replit- How to handle churn when 90% of your users have zero technical background- The governance and security guardrails that got Replit into financial services and government accounts- How to structure a GTM catalog library so your team stops duplicating each other's work- Ghazi's take on whether SaaS is dying — and why the answer is completely different depending on company size- Why product passion matters more to Ghazi than years of sales experience when hiring- How Replit thinks about competitive threats from Cursor, Claude Code and OpenAI — and why they're not losing sleep over any of them---Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.---Where to Find Ghazi MasoodLinkedIn: https://www.linkedin.com/in/ghazi-masood-09195a2/---Where to Find Josh:LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/

CNBC's Deirdre Bosa delivers news regarding Apple blocking AI vibe-coding apps, including Replit, from updating in the App Store over safety concerns. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Run the Numbers, CJ sits down with Kunal Agarwal, CFO of Gorgias, to unpack how AI is reshaping pricing and operations. They discuss outcome-based pricing, how to forecast LLM-driven costs, and why order-to-cash isn't just back-office plumbing—it can be a true growth lever when designed correctly.—SPONSORS:Tabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/agarwalk/Company: https://www.gorgias.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and Intro2:14 PE to venture incubator4:39 Operational empathy from zero-to-one4:45 First paying user feeling5:40 Jet ski customer support story8:49 Hanging with IC sales reps12:24 Sponsors — Tabs | Abacum | Brex15:44 Finance as the decision engine17:36 Gorgias overview20:20 Pricing structure and iteration22:11 Outcome based / resolution pricing24:48 AI success rate as key metric25:33 Sponsors — Metronome | RightRev | Rillet28:57 Pricing value split — $1 per resolution31:17 Vertical specificity as AI moat33:27 Managing LLM costs35:41 Falling token costs and model mix39:28 Order to cash as growth engine43:31 Auditing order to cash at 25M ARR44:09 Manual choke points46:32 Learning density over titles48:30 SurveyMonkey as the most formative period50:07 Lightning round50:17 Listening to respond vs. listening to learn51:04 Advice to younger self52:02 Finance software stack52:45 Cortex — internal AI decision tool54:21 Craziest expense story54:52 Credits

Marketing has never had more tools. And yet, it's never been harder to stand out.AI is making it easier than ever to create — from content to code. But as more people gain access to these tools, a new question is emerging: what happens when everyone can build?In this episode of Talking Too Loud, Chris Savage sits down with Ron Dawson — filmmaker, brand strategist, and Content & Marketing Lead at Replit — to explore how AI is reshaping who gets to create, build, and participate.They dig into why every generation resists new technology at first, what it means for creators to start building software, and whether AI is lowering the bar or raising the ceiling.What you'll learn:Why new tools always trigger backlash and what that revealsHow AI is expanding who gets to build and what gets builtWhy making things easier doesn't necessarily make them betterWhat this moment means for creators, marketers, and buildersIf you've ever felt both excited and uncertain about AI, this episode will help you make sense of the shift — and what it means for your work.Links to Learn More: Follow Ron on LinkedInFollow Savage on LinkedInSubscribe to Talking Too Loud on WistiaWatch on YouTubeFollow Talking Too Loud on InstagramFollow Talking Too Loud on TikTokLove what you heard? Leave us a review!On AppleOn Spotify

Google, Lovable, Replit, and OpenAI all announced what look like the same product in the last two weeks. Critics say it's desperation and strategic dilution — but what if coding capability naturally unlocks everything else in knowledge work, and convergence is the inevitable result? In the headlines: Jensen Huang urges AI leaders to stop scaring people, Bezos eyes a $100B manufacturing AI fund, and Apple's App Store clashes with vibe coding platforms.For all the links referenced in the show, sign up for the newsletter: ⁠https://aidailybrief.beehiiv.com/⁠Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AIUC-1 - Get your agents certified to communicate trust to enterprise buyers - ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.aiuc-1.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

New products from Perplexity and Replit show vibe coding evolving beyond “AI helps you code” into systems that plan goals, spin up teams of agents, and execute entire workflows across apps and files. The emerging pattern combines persistent agents, collaborative canvases, and multi-agent orchestration—turning vibe coding into a broader interface for building and operating digital work. In the headlines: agents get credit cards, Anthropic surges in Ramp adoption data, OpenAI folds Sora into ChatGPT, Musk outlines an xAI–Tesla computer-use system, Netflix may buy Ben Affleck's AI startup, and Lovable adds $100M in ARR.Learn more about AGENT MADNESS: Our 64-Bracket tournament to find the coolest Agent of 2026 ⁠https://www.agentmadness.ai/⁠Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AIUC-1 - Get your agents certified to communicate trust to enterprise buyers - ⁠⁠https://www.aiuc-1.com/⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Vibe Coding is about to change the world… and I recommend you be at the forefront of that!Try Replit for Free:  https://replit.com/refer/derekvidell-----Hosted by Derek VidellLearn How to Run Profitable Facebook Ads Yourself: socialbamboo.com/30 (free call) social bamboo.com/5roas (free course) socialbamboo.com/dwy (paid program) I have DWY and DFY Meta Ads services available. Book a free call to start. Build a Perfectly Trained AI Chatbot: https://pro-bots.ai/trial (free course + 14 day software trial)Instagram | YouTube | SocialBamboo.com