Podcasts about Cursor

David George is a General Partner at Andreessen Horowitz, where he leads the firm's Growth investing team. His team has backed many of the defining companies of this era, including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI, and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. AGENDA: 03:05 – Why Everyone is Wrong: Mega Funds Does Not Reduce Returns 10:40 – Is Public Market Capital Actually Cheaper Than Private Capital? 18:55 – The Biggest Advantage of Staying Private for Longer 23:30 – The #1 Investing Rule for a16z: Always Invest in the Founder's Strength of Strengths 31:20 – Why Fear of Theoretical Competition Makes Investors Miss Great Companies 35:10 – Does Revenue Matter as Much in a World of AI? 44:10 – Does Kingmaking Still Exist in Venture Capital Today? 49:20 – Do Margins Matter Less Than Ever in an AI-First World? 53:50 – My Biggest Miss: Anthropic and What I Learn From it?  56:30 – Has OpenAI Won Consumer AI? Will Anthropic Win Enterprise? 59:45 – The Most Controversial Decision in Andreessen Horowitz History 1:01:30 – Why Did You Invest $300M into Adam Neumann and Flow?    

What if understanding your codebase was no longer a blocker for great testing? Most testers were trained to work around the code — clicking through UIs, guessing selectors, and relying on outdated docs or developer explanations. In this episode, Playwright expert Ben Fellows flip that model on its head. Using AI tools like Cursor, testers can now explore the codebase directly — asking questions, uncovering APIs, understanding data relationships, and spotting risk before a single test is written. This isn't about becoming a developer. It's about using AI to finally see how the system really works — and using that insight to test smarter, earlier, and with far more confidence. If you've ever joined a new team, inherited a legacy app, or struggled to understand what really changed in a release, this episode is for you. Registration for Automation Guild 2026 Now: https://testguild.me/podag26

Corey Quinn reconnects with Keith Townsend, founder of The CTO Advisor, for a candid conversation about the massive gap between AI hype and enterprise reality. Keith shares why a biopharma company gave Microsoft Copilot a hard no, and why AI has genuinely 10x'd his personal productivity while Fortune 500 companies treat it like radioactive material. From building apps with Cursor to watching enterprises freeze in fear of being the next AI disaster in the news, Keith and Corey dig into why the tools transforming solo founders and small teams are dead on arrival in the enterprise, and what it'll actually take to bridge that gap.About Keith TownsendKeith Townsend is an enterprise technologist and founder of The Advisor Bench LLC, where he helps major IT vendors refine their go-to-market strategies through practitioner-driven insights from CIOs, CTOs, and enterprise architects. Known as “The CTO Advisor,” Keith blends deep expertise in IT infrastructure, AI, and cloud with a talent for translating complex technology into clear business strategy.With more than 20 years of experience, including roles as a systems engineer, enterprise architect, and PwC consultant, Keith has advised clients such as HPE, Google Cloud, Adobe, Intel, and AWS. His content series, 100 Days of AI and CloudEveryday.dev, provide practical, plainspoken guidance for IT leaders. A frequent speaker at VMware Explore, Interop, and Tech Field Day, Keith is a trusted voice on cloud and infrastructure transformation.Show Highlights(01:25) Life After the Futurum Group Acquisition(03:56) Building Apps You're Not Qualified to Build with Cursor(05:45)Creating an AI-Powered RSS Reader(09:01) Why AI is Great at Language But Not Intelligence(11:39) Are You Looking for Advice or Just Validation?(13:49) Why Startups Can Risk AI Disasters and AWS Can't(17:28) You Can't Outsource Responsibility(19:52) Business Users Are Scared of AI Too(23:00) LinkedIn's AI Writing Tool Misses the Point(26:42) Private AI is Starting to Look Appealing(29:00) Never Going Back to Pre-AI Development(34:27) AI for Jobs You'd Never Hire Someone to Do(39:09) Where to Find Keith and Closing ThoughtsLinksThe CTO Advisor:  https://thectoadvisor.comSponsor: https://www.sumologic.com/solutions/dojo-aihttps://wiz.io/crying-out-cloud

Eytan Seidman, VP of product at Shopify, joins the podcast to unpack Shopify's Winter '26 Edition and how AI is emerging into the market for developers and merchants. They discuss the new Dev MCP server, showing how tools like Cursor and Claude Desktop can rapidly scaffold Shopify apps, wire up Shopify functions, and ship payment customization and checkout UI extension experiences that lean on Shopify primitives like meta fields and meta objects across online stores and point of sale. Eytan also breaks down how Sidekick connects with apps, why the new analytics API and ShopifyQL open fresh analytics use cases, and more. Links Shopify Winter '26 Edition: https://www.shopify.com/editions/winter2026 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey (https://t.co/oKVAEXipxu)! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com (mailto:elizabeth.becz@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Check out our newsletter (https://blog.logrocket.com/the-replay-newsletter/)! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. (https://logrocket.com/signup/?pdr) Chapters 01:00 — AI as the Focus of Winter '26 02:00 — MCP Server as the Ideal Dev Workflow 03:00 — Best Clients for MCP (Cursor, Claude Desktop) 04:00 — Hallucinations & Code Validation in MCP 06:00 — Developer Judgment & Platform Primitives 07:00 — Storage Choices: Meta Fields vs External Storage 09:00 — Learning UI Patterns Through MCP 10:00 — Sidekick Overview & Merchant Automation 11:00 — Apps Inside Sidekick: Data & UI Integration 13:00 — Scopes, Data Access & Developer Responsibility 14:00 — AI-Ready Platform & Explosion of New Apps 16:00 — New Developer Demographics Entering Shopify 17:00 — Where Indie Devs Should Focus (POS, Analytics) 18:00 — New Analytics API & Opportunities 19:00 — Full Platform Coverage via MCP Tools 20:00 — Building Complete Apps in Minutes 21:00 — Large Stores, Token Limits & MCP Scaling 22:00 — Reducing Errors with UI & Function Testing 23:00 — Lessons from Building the MCP Server 25:00 — Lowering Barriers for Non-Experts 26:00 — High-Quality Rust Functions via MCP 27:00 — MCP Spec Adoption: Tools Over Resources 28:00 — Future: Speed, Quality & UI Precision 29:00 — Model Evolution, Evals & Reliability 31:00 — Core Shopify Primitives to Build On 33:00 — Docs, Community & Learning Resources

Transitioning a mature organization from an API-first model to an AI-first model is no small feat. In this episode, Yash Kosaraju, CISO of Sendbird, shares the story of how they pivoted from a traditional chat API platform to an AI agent platform and how security had to evolve to keep up.Yash spoke about the industry's obsession with "Zero Trust," arguing instead for a practical "Multi-Layer Trust" approach that assumes controls will fail . We dive deep into the specific architecture of securing AI agents, including the concept of a "Trust OS," dealing with new incident response definitions (is a wrong AI answer an incident?), and the critical need to secure the bridge between AI agents and customer environments .This episode is packed with actionable advice for AppSec engineers feeling overwhelmed by the speed of AI. Yash shares how his team embeds security engineers into sprint teams for real-time feedback, the importance of "AI CTFs" for security awareness, and why enabling employees with enterprise-grade AI tools is better than blocking them entirely .Questions asked:Guest Socials - Yash's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:20) Who is Yash Kosaraju? (CISO at Sendbird)(03:30) Sendbird's Pivot: From Chat API to AI Agent Platform(05:00) Balancing Speed and Security in an AI Transition(06:50) Embedding Security Engineers into AI Sprint Teams(08:20) Threats in the AI Agent World (Data & Vendor Risks)(10:50) Blind Spots: "It's Microsoft, so it must be secure"(12:00) Securing AI Agents vs. AI-Embedded Applications(13:15) The Risk of Agents Making Changes in Customer Environments(14:30) Multi-Layer Trust vs. Zero Trust (Marketing vs. Reality) (17:30) Practical Multi-Layer Security: Device, Browser, Identity, MFA(18:25) What is "Trust OS"? A Foundation for Responsible AI(20:45) Balancing Agent Security vs. Endpoint Security(24:15) AI Incident Response: When an AI Gives a Wrong Answer(29:20) Security for Platform Engineers: Enabling vs. Blocking(30:45) Providing Enterprise AI Tools (Gemini, ChatGPT, Cursor) to Employees(32:45) Building a "Security as Enabler" Culture(36:15) What Questions to Ask AI Vendors (Paying with Data?)(39:20) Personal Use of Corporate AI Accounts(43:30) Using AI to Learn AI (Gemini Conversations)(45:00) The Stress on AppSec Engineers: "I Don't Know What I'm Doing"(48:20) The AI CTF: Gamifying Security Training(50:10) Fun Questions: Outdoors, Team Building, and Indian/Korean Food

In the "Amazon Age," customers expect custom products yesterday. Eric Turney explains why businesses are now "forced to automate" or get left behind. In this interview, he reveals how Chinese manufacturers are using robotics to reduce factory lines from 50 workers to just 5 and why speed is now the most critical metric for survival. The conversation delves into the practical side of automation: using Monday.com to manage thousands of orders , implementing AI image editors to stay competitive , and the controversial rise of "Vibe Coding." Eric shares how he bypassed expensive developers to fix a critical website bug in 30 minutes using Claude AI , while Sean details how he built a functional SaaS tool using Cursor without writing the code himself. Check out the company: https://montereycompany.com

Tomer Cohen is the longtime chief product officer at LinkedIn, where he's pioneering the Full Stack Builder program, a radical new approach to product development that fully embraces what AI makes possible. Under his leadership, LinkedIn has scrapped its traditional Associate Product Manager program and replaced it with an Associate Product Builder program that teaches coding, design, and PM skills together. He's also introduced a formal “Full Stack Builder” title and career ladder, enabling anyone from any function to take products from idea to launch. In this conversation, Tomer explains why product development has become too complex at most companies and how LinkedIn is building an AI-powered product team that can move faster, adapt more quickly, and do more with less.We discuss:1. How 70% of the skills needed for jobs will change by 20302. The broken traditional model: organizational bloat slows features to a six-month cycle3. The Full Stack Builder model4. Three pillars of making FSB work: platform, agents, and culture (culture matters most)5. Building specialized agents that critique ideas and find vulnerabilities6. Why off-the-shelf AI tools never work on enterprise code without customization7. Top performers adopt AI tools fastest, contrary to expectations about leveling effects8. Change management tactics: celebrating wins, making tools exclusive, updating performance reviews—Brought to you by:Vanta—Automate compliance. Simplify security: https://vanta.com/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Miro—The AI Innovation Workspace where teams discover, plan, and ship breakthrough products: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/why-linkedin-is-replacing-pms—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180042347/my-takeaways-from-this-conversation—Where to find Tomer Cohen:• LinkedIn: https://www.linkedin.com/in/tomercohen• Podcast: https://podcasts.apple.com/us/podcast/building-one-with-tomer-cohen/id1726672498—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Tomer Cohen(04:42) The need for change in product development(11:52) The full-stack builder model explained(16:03) Implementing AI and automation in product development(19:17) Building and customizing AI tools(27:51) The timeline to launch(31:46) Pilot program and early results(37:04) Feedback from top talent(39:48) Change management and adoption(46:53) Encouraging people to play with AI tools(41:21) Performance reviews and full-stack builders(48:00) Challenges and specialization(50:05) Finding talent(52:46) Tips for implementing in your own company(56:43) Lightning round and final thoughts—Referenced:• How LinkedIn became interesting: The inside story | Tomer Cohen (CPO at LinkedIn): https://www.lennysnewsletter.com/p/how-linkedin-became-interesting-tomer-cohen• LinkedIn: https://www.linkedin.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Devin: https://devin.ai• Figma: https://www.figma.com• Microsoft Copilot: https://copilot.microsoft.com• Windsurf: https://windsurf.com• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• APB program at LinkedIn: https://careers.linkedin.com/pathways-programs/entry-level/apb• Naval Ravikant on X: https://x.com/naval• One Song podcast: https://podcasts.apple.com/us/podcast/%D7%A9%D7%99%D7%A8-%D7%90%D7%97%D7%93-one-song/id1201883177• Song Exploder podcast: https://songexploder.net• Grok on Tesla: https://www.tesla.com/support/grok• Reid Hoffman on X: https://x.com/reidhoffman—Recommended books:• Why Nations Fail: The Origins of Power, Prosperity, and Poverty: https://www.amazon.com/Why-Nations-Fail-Origins-Prosperity/dp/0307719227• Outlive: The Science and Art of Longevity: https://www.amazon.com/Outlive-Longevity-Peter-Attia-MD/dp/0593236599• The Beginning of Infinity: Explanations That Transform the World: https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Bryan Power, who recently left Nextdoor after seven years, discusses his viral article on quitting properly, why exits define careers, manager failures, and the Growth by Design-Cursor acquisition's implications.Support our Sponsor:Metaview is the AI platform built for recruiting. Check it out: https://www.metaview.ai/heretics* Our suite of AI agents work across your hiring process to save time, boost decision quality, and elevate the candidate experience.* Learn why team builders at 3,000+ cutting-edge companies like Brex, Deel, and Quora can't live without Metaview.* It only takes minutes to get up and running.KEEP UP WITH BRYAN, NOLAN + KELLI ON LINKEDINBryan: https://www.linkedin.com/in/bryanpower/Nolan: https://www.linkedin.com/in/nolan-church/Kelli: https://www.linkedin.com/in/kellidragovich/__LINKS:For coaching and advising inquire at https://kellidragovich.com/—TIMESTAMPS:(00:00) Introduction & Power Hour Returns(00:55) Bryan's Viral “How to Quit” Article(04:00) Why Your Exit Becomes Your Entire Story(06:00) Why Companies Don't Teach Employees How to Leave(09:00) The Loyalty Expectation Problem(12:27) Sponsor: Metaview(14:38) How Managers Screw Up Exits(21:57) No Long Goodbyes: The Best Timing Advice(24:13) What to Do When Someone Resigns(27:30) Maintaining Relationships After You Leave(29:26) Growth by Design Acquired by Cursor(36:33) The State of the Recruiting Market(40:00) AI Native Skills & The Future of Entry Level Hiring(46:49) Cringey Corporate Lingo Game(50:55) Wrap This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit hrheretics.substack.com

¿Por qué los agentes de IA pierden el contexto en conversaciones largas? ¿Por qué buscan archivos por palabras y no por funcionalidad? Apple ha publicado CLaRa, una investigación que ataca los problemas fundamentales del RAG. Hoy explicamos qué es un RAG, por qué falla, y cómo esta nueva aproximación podría cambiar la forma en que programamos con IA. ------------ Escucha este podcast o ve el vídeo en Be Native: https://benative.dev Conviértete en el Maestro que las empresas necesitan con el Swift Mastery Program 26: https://acoding.academy/smp26 ------------ Si usas Claude Code, Cursor, Copilot o cualquier agente de IA para programar, seguro que has experimentado esto: llevas una hora de sesión, el modelo entiende perfectamente tu proyecto, y de repente... parece que se le va la cabeza. Te propone cosas que ya habíais descartado. Ignora archivos que le pasaste hace veinte minutos. ¿Por qué pasa esto? La respuesta está en cómo funcionan los sistemas RAG (Retrieval-Augmented Generation) y en sus limitaciones fundamentales. En este episodio explicamos: → Qué es un RAG y por qué es esencial para los agentes de código→ Por qué el buscador de archivos no entiende tu código, solo palabras→ Cómo la compresión en conversaciones largas destruye el contexto que has construido→ Qué es CLaRa, la nueva investigación de Apple que unifica búsqueda y generación→ Por qué comprimir inteligentemente puede dar mejores resultados que usar el texto completo Apple sigue apostando por la eficiencia sobre la fuerza bruta. No buscan el modelo más grande, buscan el más inteligente. Y CLaRa es un ejemplo perfecto de esa filosofía. Investigación disponible en abierto:Paper: arxiv.org/abs/2511.18659GitHub: github.com/apple/ml-claraModelos: huggingface.co/apple/CLaRa-7B-Instruct El desarrollo ha cambiado para siempre con la llegada de los agentes de IA, y para poder sacarle el mayor provecho y ser un desarrollador de los que buscan las empresas por su ultra-productividad, tienes que ser un Maestro: consígue la Maestría con el Swift Mastery Program 2026. Descárgala ya desde el App Store: Be Native y escúchanos desde ahí. Suscríbete a nuestro canal de Youtube: Apple Coding en YouTube Descubre nuestro canal de Twitch: Apple Coding en Twitch. Descubre nuestras ofertas para oyentes: - Cursos en Udemy (con código de oferta) - Apple Coding Academy - Suscríbete a Apple Coding en nuestro Patreon. - Canal de Telegram de Swift. Acceso al canal. --------------- Consigue las camisetas oficiales de Apple Coding con los logos de Swift y Apple Coding así como todo tipo de merchadising como tazas o fundas. - Tienda de merchandising de Apple Coding.

This Week In Startups is made possible by:LinkedIn Ads - http://linkedin.com/thisweekinstartupsVanta - https://www.vanta.com/twistPilot - https://pilot.com/twistToday's show: Did you know there's actually a shortage of US bricklayers? It's TRUE! So feel free to marvel at Monumental's brick-laying robots. They're not putting anyone out of work, but filling a much-needed gap.Join Alex and Monumental founder/CEO Salar al Khafaji for a deep-dive on how the startup is making construction robots play nice together by maintaining separate “zones” of operation, why Salar thinks startups need to focus on truly complex, real-world problems to truly blossom, and the secrets of fundraising in Europe.PLUS Alex chats with Seasats CEO Mike Flanigan about designing the next generation of autonomous marine crafts. (That is to say, ocean drones.) From their home base in San Diego, the company is trying to get completely independent of all Chinese parts. Find out how it's going, how they're overcoming the “wildly negative” ROI on maritime tech, and why we have so few defenses against tiny, agile drones.All that AND Jason takes some of YOUR Founder Questions.Timestamps:(03:23) How Monumental determined what kinds of robots construction sites need the most(06:49) How maintaining “zones” ensure that the robots all play nice with one another(07:52) There's a shortage of bricklayers, so Monumental's NOT taking anyone's job(9:16) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(13:21) Why startups need to tackle large-scale, complex, real-world problems to really grow(15:44) Why Monumental is building in The Netherlands, and running pilots in the UK(19:07) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(20:44) Why construction is unique among applications for automation and robots(26:01) Salar argues that fundraising in Europe is not as hard as you may have heard(27:55) We don't just need housing, we need BEAUTIFUL housing(31:11) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year. (33:25) How the Scout autonomous boat challenge inspired Seasats(35:28) Trying to make drones into an “iPhone Style” project(37:39) Why Seasats is focused on endurance and staying power more than launches(39:15) The complexities of working with fuel cells(42:27) The importance of beautiful design even when working on government technology(45:51) Why they're building Seasats in beautiful San Diego, CA(47:29) The challenge of getting entirely free from Chinese components(53:52) “The Power of Small Things Has Changed”(55:18) The “wildly negative” ROI on most humanoid robotics companies also applies to maritime tech(59:09) Why there are so few defense nets against people with tiny but agile drones(01:02:32) FOUNDER Q's: Is a founder working 24/7 a red flag?(01:10:11) How bad is it to use VC money to pay off credit cards?(01:12:49) A look at Cursor's unique recruitment strategy.(01:19:57) Should young VCs go to startup conferences?Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com/Check out the TWIST500: https://twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelm/*Thank you to our partners:(9:16) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(19:07) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(31:11) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.

My guest today is David George. David is a General Partner at Andreessen Horowitz, where he leads the firm's growth investing business. His team has backed many of the defining companies of this era – including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI – and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. This conversation is a detailed look at how David built and runs the a16z growth practice. He shares how he recruits and builds his team a “Yankees-level” culture, how his team makes investment decisions without traditional committees, and how they work with founders years before investing to win the most competitive deals. Much of our conversation centers on AI and how his team is investing across the stack, from foundational models to applications. David draws parallels to past platform shifts – from SaaS to mobile – and explains why he believes this period will produce some of the largest companies ever built. David also outlines the models that guide his approach – why markets often misprice consistent growth, what makes “pull” businesses so powerful, and why most great tech markets end up winner-take-all. David reflects on what he's learned from studying exceptional founders and why he's drawn to a particular type, the “technical terminator.” Please enjoy my conversation with David George. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. ----- This episode is brought to you by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like The Best (00:04:00) Meet David George (00:03:04) Understanding the Impact of AI on Consumers and Enterprises (00:05:56) Monetizing AI: What is AI's Business Model (00:11:04) Investing in Robotics and American Dynamism (00:13:31) Lessons from Investing in Waymo (00:15:55) Investment Philosophy and Strategy (00:17:15) Investing in Technical Terminators (00:20:18) Market Leaders Capture All of the Value Creation (00:24:56) The Maturation of VC and Competitive Landscape (00:28:18) What a16z Does to Win Deals (00:33:06) David's Daily Routine: Meetings Structure and Blocking Time to Think (00:36:34) Why David Invests: Curiosity and Competition (00:40:12) The Unique Culture at Andreessen Horowitz (00:42:46) The Perfect Conditions for Growth Investing (00:47:04) Push v. Pull Businesses (00:49:19) The Three Metrics a16z Uses to Evaluate AI Companies (00:52:15) Unique Products and Unique Distribution (00:54:55) Tradeoffs of the a16z Firm Structure (00:59:04) a16z's Semi-Algorithmic Approach to Selling (01:00:54) Three Ways Startups can Beat Incumbents in AI (01:03:44) The Kindest Thing

The web development world never stops moving - frameworks push new versions, browsers release new features, dependabot keeps chiming in, and AI tools like Cursor and the latest LLMs drop at a dizzying pace. In this episode, Mike breaks down why everything updates so fast, how he personally decides what's worth upgrading, and how he stays sane with the nonstop stream of patches, releases, and AI model announcements. From security fixes to real productivity gains, Mike shares practical strategies for keeping your workflow stable without falling behind. Show Notes: https://www.htmlallthethings.com/podcast/never-ending-updates-ai-models-cursor-frameworks Powered by CodeRabbit - AI Code Reviews: https://coderabbit.link/htmlallthethings Use our Scrimba affiliate link (https://scrimba.com/?via=htmlallthethings) for a 20% discount!! Full details in show notes.

В цьому епізоді ми обговорюємо, як найнадійніші системи можуть давати збій. Головна тема — нещодавнє падіння Cloudflare, яке паралізувало частину інтернету. Розбираємо технічні деталі інциденту, пов'язані зі статичним виділенням пам'яті, та аналізуємо, чому підхід, запозичений у NASA, цього разу не спрацював.Крім того, ми не оминули курйозну історію про Міжнародну асоціацію криптографічних досліджень, яка втратила доступ до власних зашифрованих даних через втрату ключів.Ми провели порівняльний аналіз нових інструментів для кодингу, зокрема Antigravity від Google та оновленого Cursor 2.0, обговоривши їхні підходи до управління AI-агентами.На завершення, розглянули цікавий тренд, коли іноземні розробники видають себе за українців, що свідчить про силу нашого національного ІТ-бренду. Також ми торкнулися складних умов праці та етичних дилем, з якими стикаються спеціалісти в Північній Кореї.00:26 — падіння Cloudflare06:42 — втрата доступу власних зашифрованих даних10:20 — мультипрогон задачі в AI17:49 — технології супутникового зв'язку20:45 — боротьба з ботами та тролями26:28 — огляд нових інструментів для кодування29:56 — використання AI-додатків у повсякденному житті36:30 — останні новини у світі AI та технологій40:00 — якість контенту та додатків46:07 — групові чати в ChatGPT49:38 — контроль та безпека в Північній Кореї

Jak může architekt dnes využívat AI tak, že mu to ušetří celé týdny práce? A proč je budoucnost v kombinaci odbornosti a umělé inteligence?Do dalšího dílu podcastu Budoucnost nepráce jsem si pozval Martina Jana Rosu – architekta, který nádherně propojuje svou doménovou expertízu s digitálními nástroji a AI. Tohle je další z velmi praktických rozhovorů, které jsem v poslední době vedl.Martina jsem se ptal na konkrétní scénáře, jak využívá AI v architektuře i při práci s daty. V podcastu zazní:Jak AI změnila Martinovu práci za poslední rok [03:25]Kdy má smysl používat skripty a Python v architektuře [07:13]Co je BIM / IFC a proč jsou zásadní [08:56]Automatizace rutinní práce pomocí AI [11:35]Nástroje Cursor, Replit a Cloud Code v praxi [13:59]Jak AI přemýšlí: reasoning a samoopravné skripty [17:12]Budoucnost profesí: doménová znalost + AI [28:39]„Druhý mozek“ a organizace informací [40:54]Proč je pořádek v datech klíčový pro práci s AI [48:13]Tahle epizoda je plná inspirace, konkrétních use cases a praktických tipů, které můžete začít používat hned. Co by se stalo, kdybyste se naučili využívat AI stejně efektivně jako Martin — a kolik práce by vám to ušetřilo?

On this episode I sit down with indie app builder and designer Chris ****Raroque to walk through his real AI coding workflow. Chris explains how he ships a portfolio of productivity apps doing thousands in MRR by pairing Claude Code and Cursor instead of picking just one tool. He live-demos “vibe coding” an iOS animation, then compares how Claude Code and Cursor's plan mode tackle the same task. The episode closes with concrete tips on plan mode, MCP servers, AI code review, dictation, and deep research so solo devs can build bigger apps than they could alone. Timestamps 00:00 – Intro 03:04 – Which Tools & Models to Use 09:16 – Thoughts on the Vibe Coding Mobile App Landscape 11:14 – Live demo: prompting Claude Code to build an iOS “AI searching” animation 18:07 – Live demo: prompting Cursor with same task 21:02 – Chris's Best Tips for Vibe Coders Key Points You don't have to pick one IDE copilot: Chris actively switches between Claude Code and Cursor because they have different strengths. For very complex bug-hunting, he prefers Cursor with plan mode; for big-picture app architecture, he leans on Claude Code with Opus. Non-developers should start on higher-level “vibe coding” platforms like Create Anything for mobile apps before graduating to Claude/Cursor. Plan mode plus detailed, spoken prompts dramatically improves code quality, especially for UI and animation work. MCP servers and AI code review bots let solo developers safely set up infra, enforce security, and catch bugs they'd otherwise miss. Claude's deep research is a powerful way to choose the right patterns and libraries before handing implementation back to Claude Code or Cursor. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: thevibemarketer.com Startup Empire - get your free builders toolkit to build cashflowing business - https://startup-ideas-pod.link/startup-empire-toolkit Become a member - https://startup-ideas-pod.link/startup-empire FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND CHRIS ON SOCIAL Youtube: https://www.youtube.com/@raroque X/Twitter: https://x.com/raroque Instagram: https://www.instagram.com/chris.raroque/

What happens when AI adoption surges inside companies faster than anyone can track, and the data that fuels those systems quietly slips out of sight? That question sat at the front of my mind as I spoke with Cyberhaven CEO Nishant Doshi, fresh from publishing one of the most detailed looks at real-world AI usage I have seen. This wasn't a report built on opinions or surveys. It was built on billions of actual data flows across live enterprise environments, which made our conversation feel urgent from the very first moment. Nishant explained how AI has moved out of the experimental phase and into everyday workflows at a speed few anticipated. Employees across every department are turning to AI tools not as a novelty but as a core part of how they work. That shift has delivered huge productivity gains, yet it has also created a new breed of hidden risk. Sensitive material isn't just being uploaded through deliberate actions. It is being blended, remixed, and moved in ways that older security models cannot understand. Hearing him describe how this happens in fragments rather than files made me rethink how data exposure works in 2025. We also dug into one of the most surprising findings in Cyberhaven's research. The biggest AI power users inside companies are not executives or early career talent. It is mid-level employees. They know where the friction is, and they are under pressure to deliver quickly, so they experiment freely. That experimentation is driving progress, but it is also widening the gap between how AI is used and how data is meant to be protected. Nishant shared how that trend is now pushing sensitive code, R&D material, health information, and customer data into tools that often lack proper controls. Another moment that stood out was his explanation of how developers are reshaping their work with AI coding assistants. The growth in platforms like Cursor is extraordinary, yet the risks are just as large. Code that forms the heart of an organisation's competitive strength is frequently pasted into external systems without full awareness of where it might end up. It creates a situation where innovation and exposure rise together, and older security frameworks simply cannot keep pace. Throughout the conversation, Nishant returned to the importance of visibility. Companies cannot set fair rules or safe boundaries if they cannot see what is happening at the point where data leaves the user's screen. Traditional controls were built for a world of predictable patterns. AI has broken those patterns apart. In his view, modern safeguards need to sit closer to employees, understand how fragments are created, and guide people toward safer workflows without slowing them down. By the time we reached the end of the interview, it was clear that AI governance is no longer a strategic nice-to-have. It is becoming a daily operational requirement. Nishant believes employers must create a clear path forward that balances freedom with control, and give teams the tools to do their best work without unknowingly putting their organisations at risk. His message wasn't alarmist. It was practical, grounded, and shaped by years working at the intersection of data and security. So here is the question I would love you to reflect on. If AI is quickly becoming the engine of productivity across every department, what would your organisation need to change today to keep its data safe tomorrow? And how much visibility do you honestly have over where your most sensitive information is going right now? I would love to hear your thoughts. Useful Links  Connect with Cyberhaven CEO Nishant Doshi on LinkedIn Learn more about Cyberhaven Tech Talks Daily is Sponsored by NordLayer: Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

AI Assisted Coding: Treating AI Like a Junior Engineer - Onboarding Practices for AI Collaboration In this special episode, Sergey Sergyenko, CEO of Cybergizer, shares his practical framework for AI-assisted development built on transactional models, Git workflows, and architectural conventions. He explains why treating AI like a junior engineer, keeping commits atomic, and maintaining rollback strategies creates production-ready code rather than just prototypes. Vibecoding: An Automation Design Instrument "I would define Vibecoding as an automation design instrument. It's not a tool that can deliver end-to-end solution, but it's like a perfect set of helping hands for a person who knows what they need to do."   Sergey positions vibecoding clearly: it's not magic, it's an automation design tool. The person using it must know what they need to accomplish—AI provides the helping hands to execute that vision faster. This framing sets expectations appropriately: AI speeds up development significantly, but it's not a silver bullet that works without guidance. The more you practice vibecoding, the better you understand its boundaries. Sergey's definition places vibecoding in the evolution of development tools: from scaffolding to co-pilots to agentic coding to vibecoding. Each step increases automation, but the human architect remains essential for providing direction, context, and validation. Pair Programming with the Machine "If you treat AI as a junior engineer, it's very easy to adopt it. Ah, okay, maybe we just use the old traditions, how we onboard juniors to the team, and let AI follow this step."   One of Sergey's most practical insights is treating AI like a junior engineer joining your team. This mental model immediately clarifies roles and expectations. You wouldn't let a junior architect your system or write all your tests—so why let AI? Instead, apply existing onboarding practices: pair programming, code reviews, test-driven development, architectural guidance. This approach leverages Extreme Programming practices that have worked for decades. The junior engineer analogy helps teams understand that AI needs mentorship, clear requirements, and frequent validation. Just as you'd provide a junior with frameworks and conventions to follow, you constrain AI with established architectural patterns and framework conventions like Ruby on Rails. The Transactional Model: Atomic Commits and Rollback "When you're working with AI, the more atomic commits it delivers, more easy for you to kind of guide and navigate it through the process of development."   Sergey's transactional approach transforms how developers work with AI. Instead of iterating endlessly when something goes wrong, commit frequently with atomic changes, then rollback and restart if validation fails. Each commit should be small, independent, and complete—like a feature flag you can toggle. The commit message includes the prompt sequence used to generate the code and rollback instructions.  This approach makes the Git repository the context manager, not just the AI's memory. When you need to guide AI, you can reference specific commits and their context. This mirrors trunk-based development practices where teams commit directly to master with small, verified changes. The cost of rollback stays minimal because changes are atomic, making this strategy far more efficient than trying to fix broken implementations through iteration. Context Management: The Weak Point and the Solution "Managing context and keeping context is one of the weak points of today's coding agents, therefore we need to be very mindful in how we manage that context for the agent."   Context management challenges current AI coding tools—they forget, lose thread, or misinterpret requirements over long sessions. Sergey's solution is embedding context within the commit history itself. Each commit links back to the specific reasoning behind that code: why it was accepted, what iterations it took, and how to undo it if needed. This creates a persistent context trail that survives beyond individual AI sessions. When starting new features, developers can reference previous commits and their context to guide the AI. The transactional model doesn't just provide rollback capability—it creates institutional memory that makes AI progressively more effective as the codebase grows. TDD 2.0: Humans Write Tests, AI Writes Code "I would never allow AI to write the test. I would do it by myself. Still, it can write the code."   Sergey is adamant about roles: humans write tests, AI writes implementation code. This inverts traditional TDD slightly—instead of developers writing tests then code, they write tests and AI writes the code to pass them. Tests become executable requirements and prompts. This provides essential guardrails: AI can iterate on implementation until tests pass, but it can't redefine what "passing" means. The tests represent domain knowledge, business requirements, and validation criteria that only humans should control. Sergey envisions multi-agent systems where one agent writes code while another validates with tests, but critically, humans author the original test suite. This TDD 2.0 framework (a talk Sergey gave at the Global Agile Summit) creates a verification mechanism that prevents the biggest anti-pattern: coding without proper validation. The Two Cardinal Rules: Architecture and Verification "I would never allow AI to invent architecture. Writing AI agentic coding, Vibecoding, whatever coding—without proper verification and properly setting expectations of what you want to get as a result—that's the main mistake."   Sergey identifies two non-negotiables. First, never let AI invent architecture. Use framework conventions (Rails, etc.) to constrain AI's choices. Leverage existing code generators and scaffolding. Provide explicit architectural guidelines in planning steps. Store iteration-specific instructions where AI can reference them. The framework becomes the guardrails that prevent AI from making structural decisions it's not equipped to make. Second, always verify AI output. Even if you don't want to look at code, you must validate that it meets requirements. This might be through tests, manual review, or automated checks—but skipping verification is the fundamental mistake. These two rules—human-defined architecture and mandatory verification—separate successful AI-assisted development from technical debt generation. Prototype vs. Production: Two Different Workflows "When you pair as an architect or a really senior engineer who can implement it by himself, but just wants to save time, you do the pair programming with AI, and the AI kind of ships a draft, and rapid prototype."   Sergey distinguishes clearly between prototype and production development. For MVPs and rapid prototypes, a senior architect pairs with AI to create drafts quickly—this is where speed matters most. For production code, teams add more iterative testing and polishing after AI generates initial implementation. The key is being explicit about which mode you're in. The biggest anti-pattern is treating prototype code as production-ready without the necessary validation and hardening steps. When building production systems, Sergey applies the full transactional model: atomic commits, comprehensive tests, architectural constraints, and rollback strategies. For prototypes, speed takes priority, but the architectural knowledge still comes from humans, not AI. The Future: AI Literacy as Mandatory "Being a software engineer and trying to get a new job, it's gonna be a mandatory requirement for you to understand how to use AI for coding. So it's not enough to just be a good engineer."   Sergey sees AI-assisted coding literacy becoming as fundamental as Git proficiency. Future engineering jobs will require demonstrating effective AI collaboration, not just traditional coding skills. We're reaching good performance levels with AI models—now the challenge is learning to use them efficiently. This means frameworks and standardized patterns for AI-assisted development will emerge and consolidate. Approaches like AAID, SpecKit, and others represent early attempts to create these patterns. Sergey expects architectural patterns for AI-assisted development to standardize, similar to how design patterns emerged in object-oriented programming. The human remains the bottleneck—for domain knowledge, business requirements, and architectural guidance—but the implementation mechanics shift heavily toward AI collaboration. Resources for Practitioners "We are reaching a good performance level of AI models, and now we need to guide it to make it impactful. It's a great tool, now we need to understand how to make it impactful."   Sergey recommends Obie Fernandez's work on "Patterns of Application Development Using AI," particularly valuable for Ruby and Rails developers but applicable broadly. He references Andrey Karpathy's original vibecoding post and emphasizes Extreme Programming practices as foundational. The tools he uses—Cursor and Claude Code—support custom planning steps and context management. But more important than tools is the mindset: we have powerful AI capabilities now, and the focus must shift to efficient usage patterns. This means experimenting with workflows, documenting what works, and sharing patterns with the community. Sergey himself shares case studies on LinkedIn and travels extensively speaking about these approaches, contributing to the collective learning happening in real-time.   About Sergey Sergyenko   Sergey is the CEO of Cybergizer, a dynamic software development agency with offices in Vilnius, Lithuania. Specializing in MVPs with zero cash requirements, Cybergizer offers top-tier CTO services and startup teams. Their tech stack includes Ruby, Rails, Elixir, and ReactJS.   Sergey was also a featured speaker at the Global Agile Summit, and you can find his talk available in your membership area. If you are not a member don't worry, you can get the 1-month trial and watch the whole conference. You can cancel at any time.   You can link with Sergey Sergyenko on LinkedIn.

Le sujet :L'IA et le vibe coding ont révolutionné la création de side business : plus besoin d'être un expert de la tech pour lancer un site, une application ou un média. L'essentiel est ailleurs : savoir identifier les bonnes opportunités business et utiliser les meilleurs outils.L'invité du jour :Esther Moisy-Kirschbaum est responsable du développement de Magma, une newsletter d'identification de tendances et d'opportunités de business.Aux côtés de Matthieu Stefani, Esther et Christofer Ciminelli nous expliquent comment créer un side business rentable grâce au vibe coding et aux outils d'IA les plus accessibles.Découvrez : Pourquoi l'entrepreneuriat est un pilier de l'investissementComment identifier les opportunités de side businessQu'est-ce que le vibe coding et comment se lancerLes opportunités du faceless et du live shoppingComment combiner vibe coding, IA et APIAvantages :Bonne nouvelle ! Nous avons négocié pour vous un avantage exceptionnel. Avec le code BFLAMARTINGALE, obtenez 50% de réduction sur l'abonnement annuel à la newsletter Magma. Offre valable jusqu'au 31/12/2025 (au-delà, le code vous offre tout de même 50€ de réduction

Ricky Doar is the VP of Solutions at Cursor, where he leads forward-deployed engineers. A seasoned product and technical leader with over a decade of experience in developer tools and data platforms, Ricky previously served as VP of Field Engineering at Vercel, where he led global technical solutions for the company's next-generation frontend platform.Prior to Vercel, Ricky held multiple leadership roles at Segment (acquired by Twilio), including Director of Product Management for Twilio Engage, Group Product Manager for Personas, and RVP of Solutions Engineering for the West and APAC regions. He also worked as a Product Engineer and Senior Sales Engineer at Mixpanel, bringing deep technical expertise to customer-facing roles.Thanks to  Prosus Group for collaborating on the Agents in Production Virtual Conference 2025.In this session, Ricky Doar, VP of Solutions at Cursor, shares actionable insights from leading large-scale AI developer tool implementations at the world's top enterprises. Drawing on field experience with organizations at the forefront of transformation, Ricky highlights key best practices, observed power-user patterns, and deployment strategies that maximize value and ensure smooth rollout. Learn what distinguishes high-performing teams, how tailored onboarding accelerates adoption, and which support resources matter most for driving enterprise-wide success.A Prosus | MLOps Community Production

YouTubeとSpotifyでビデオポッドキャスト公開中Off Topic 祝300EP 2025 公開収録 · Lumahttps://luma.com/kkxdk8r8＜目次＞(0:00) サンクスギビング(3:28)Gemini3 のリリース(9:15) ビデオポッドキャスト参入ぞくぞく(18:00) Apple ティム・クック退任か(28:20) 大型調達RampとCursor(30:45) アメリカ政府シャットダウンが終わった(33:00) ティモシー・シャラメ主演『Marty Supreme』マーケ戦略＜About Off Topic＞Podcast:Apple - https://apple.co/2UZCQwzSpotify - https://spoti.fi/2JakzKmOff Topic Clubhttps://note.com/offtopic/membershipX - https://twitter.com/OffTopicJP草野ミキ:https://twitter.com/mikikusanohttps://www.instagram.com/mikikusano宮武テツロー: https://twitter.com/tmiyatake1

AI Assisted Coding: Swimming in AI - Managing Tech Debt in the Age of AI-Assisted Coding In this special episode, Lou Franco, veteran software engineer and author of "Swimming in Tech Debt," shares his practical approach to AI-assisted coding that produces the same amount of tech debt as traditional development—by reading every line of code. He explains the critical difference between vibecoding and AI-assisted coding, why commit-by-commit thinking matters, and how to reinvest productivity gains into code quality. Vibecoding vs. AI-Assisted Coding: Reading Code Matters "I read all the code that it outputs, so I need smaller steps of changes."   Lou draws a clear distinction between vibecoding and his approach to AI-assisted coding. Vibecoding, in his definition, means not reading the code at all—just prompting, checking outputs, and prompting again. His method is fundamentally different: he reads every line of generated code before committing it. This isn't just about catching bugs; it's about maintaining architectural control and accountability. As Lou emphasizes, "A computer can't be held accountable, so a computer can never make decisions. A human always has to make decisions." This philosophy shapes his entire workflow—AI generates code quickly, but humans make the final call on what enters the repository. The distinction matters because it determines whether you're managing tech debt proactively or discovering it later when changes become difficult. The Moment of Shift: Staying in the Zone "It kept me in the zone. It saved so much time! Never having to look up what a function's arguments were... it just saved so much time."   Lou's AI coding journey began in late 2022 with GitHub Copilot's free trial. He bought a subscription immediately after the trial ended because of one transformative benefit: staying in the flow state. The autocomplete functionality eliminated constant context switching to documentation, Stack Overflow searches, and function signature lookups. This wasn't about replacing thinking—it was about removing friction from implementation. Lou could maintain focus on the problem he was solving rather than getting derailed by syntax details. This experience shaped his understanding that AI's value lies in removing obstacles to productivity, not in replacing the developer's judgment about architecture and design. Thinking in Commits: The Right Size for AI Work "I think of prompts commit-by-commit. That's the size of the work I'm trying to do in a prompt."   Lou's workflow centers on a simple principle: size your prompts to match what should be a single commit. This constraint provides multiple benefits. First, it keeps changes small enough to review thoroughly—if a commit is too big to review properly, the prompt was too ambitious. Second, it creates a clear commit history that tells a story about how the code evolved. Third, it enables easy rollback if something goes wrong. This commit-sized thinking mirrors good development practices that existed long before AI—small, focused changes that each accomplish one clear purpose. Lou uses inline prompting in Cursor (Command-K) for these localized changes because it keeps context tight: "Right here, don't go look at the rest of my files... Everything you need is right here. The context is right here... And it's fast." The Tech Debt Question: Same Code, Same Debt "Based on the way I've defined how I did it, it's exactly the same amount of tech debt that I would have done on my own... I'm faster and can make more code, but I invest some of that savings back into cleaning things up."   As the author of "Swimming in Tech Debt," Lou brings unique perspective to whether AI coding creates more technical debt. His answer: not if you're reading and reviewing everything. When you maintain the same quality standards—code review, architectural oversight, refactoring—you generate the same amount of debt as manual coding. The difference is speed. Lou gets productivity gains from AI, and he consciously reinvests a portion of those gains back into code quality through refactoring. This creates a virtuous cycle: faster development enables more time for cleanup, which maintains a codebase that's easier for both humans and AI to work with. The key insight is that tech debt isn't caused by AI—it's caused by skipping quality practices regardless of how code is generated. When Vibecoding Creates Debt: AI Resistance as a Symptom "When you start asking the AI to do things, and it can't do them, or it undoes other things while it's doing them... you're experiencing the tech debt a different way. You're trying to make changes that are on your roadmap, and you're getting resistance from making those changes."   Lou identifies a fascinating pattern: tech debt from vibecoding (without code review) manifests as "AI resistance"—difficulty getting AI to make the changes you want. Instead of compile errors or brittle tests signaling problems, you experience AI struggling to understand your codebase, undoing changes while making new ones, or producing code with repetition and tight coupling. These are classic tech debt symptoms, just detected differently. The debt accumulates through architecture violations, lack of separation of concerns, and code that's hard to modify. Lou's point is profound: whether you notice debt through test failures or through AI confusion, the underlying problem is the same—code that's difficult to change. The solution remains consistent: maintain quality practices including code review, even when AI makes generation fast. Can AI Fix Tech Debt? Yes, With Guidance "You should have some acceptance criteria on the code... guide the LLM as to the level of code quality you want."   Lou is optimistic but realistic about AI's ability to address existing tech debt. AI can definitely help with refactoring and adding tests—but only with human guidance on quality standards. You must specify what "good code" looks like: acceptance criteria, architectural patterns, quality thresholds. Sometimes copy/paste is faster than having AI regenerate code. Very convoluted codebases challenge both humans and AI, so some remediation should happen before bringing AI into the picture. The key is recognizing that AI amplifies your approach—if you have strong quality standards and communicate them clearly, AI accelerates improvement. If you lack quality standards, AI will generate code just as problematic as what already exists. Reinvesting Productivity Gains in Quality "I'm getting so much productivity out of it, that investing a little bit of that productivity back into refactoring is extremely good for another kind of productivity."   Lou describes a critical strategy: don't consume all productivity gains as increased feature velocity. Reinvest some acceleration back into code quality through refactoring. This mirrors the refactor step in test-driven development—after getting code working, clean it up before moving on. AI makes this more attractive because the productivity gains are substantial. If AI makes you 30% faster at implementation, using 10% of that gain on refactoring still leaves you 20% ahead while maintaining quality. Lou explicitly budgets this reinvestment, treating quality maintenance as a first-class activity rather than something that happens "when there's time." This discipline prevents the debt accumulation that makes future work progressively harder. The 100x Code Concern: Accountability Remains Human "Directionally, I think you're probably right... this thing is moving fast, we don't know. But I'm gonna always want to read it and approve it."   When discussing concerns about AI generating 100x more code (and potentially 100x more tech debt), Lou acknowledges the risk while maintaining his position: he'll always read and approve code before it enters the repository. This isn't about slowing down unnecessarily—it's about maintaining accountability. Humans must make the decisions because only humans can be held accountable for those decisions. Lou sees potential for AI to improve by training on repository evolution rather than just end-state code, learning from commit history how codebases develop. But regardless of AI improvements, the human review step remains essential. The goal isn't to eliminate human involvement; it's to shift human focus from typing to thinking, reviewing, and making architectural decisions. Practical Workflow: Inline Prompting and Small Changes "Right here, don't go look at the rest of my files... Everything you need is right here. The context is right here... And it's fast."   Lou's preferred tool is Cursor with inline prompting (Command-K), which allows him to work on specific code sections with tight context. This approach is fast because it limits what AI considers, reducing both latency and irrelevant changes. The workflow resembles pair programming: Lou knows what he wants, points AI at the specific location, AI generates the implementation, and Lou reviews before accepting. He also uses Claude Code for full codebase awareness when needed, but the inline approach dominates his daily work. The key principle is matching tool choice to context needs—use inline prompting for localized changes, full codebase tools when you need broader understanding. This thoughtful tool selection keeps development efficient while maintaining control. Resources and Community Lou recommends Steve Yegge's upcoming book on vibecoding. His website, LouFranco.com, provides additional resources.    About Lou Franco   Lou Franco is a veteran software engineer and author of Swimming in Tech Debt. With decades of experience at startups, as well as Trello, and Atlassian, he's seen both sides of debt—as coder and leader. Today, he advises teams on engineering practices, helping them turn messy codebases into momentum.   You can link with Lou Franco on LinkedIn and visit his website at LouFranco.com.

Maor Shlomo is the Founder and CEO of Base44, the AI building platform that Maor built from idea to $80M acquisition by Wix, in just 8 months. Today the company serves millions of users and will hit $50M ARR by the end of the year. Before Base44, Maor was the Co-Founder and CTO of Explorium. AGENDA: 00:05 – 00:10: How Vibe Coding is Going to Kill Salesforce and SaaS 00:13 – 00:15: Do Vibe Coding platforms have any defensibility? 00:22 – 00:24: I am not worried about Replit and Lovable, I am worried about Google… 00:28 – 00:29: Margins do not matter, the price of the models will go to zero 00:31 – 00:32: Speed to copy has never been lower; has the technical moat been eroded? 00:47 – 00:48: How does Base44 beat Cursor? 00:56 – 00:57: Do not pay attention to competition: focus on your business 00:57 – 00:58: How Base44 is helped, not hurt by not being in Silicon Valley? 00:58 – 00:59: What percent of code will be written by AI in 12 months? 01:01 – 01:02: OpenAI or Anthropic: Why Maor is Long Anthropic? 01:03 – 01:04: If I could have any board member in the world it would be Jack Dorsey      

AI Assisted Coding: From Designer to Solo Developer - Building Production Apps with AI In this special episode, Elina Patjas shares her remarkable journey from designer to solo developer, building LexieLearn—an AI-powered study tool with 1,500+ users and paying customers—entirely through AI-assisted coding. She reveals the practical workflow, anti-patterns to avoid, and why the future of software might not need permanent apps at all. The Two-Week Transformation: From Idea to App Store "I did that, and I launched it to App Store, and I was like, okay, so… If I can do THIS! So, what else can I do? And this all happened within 2 weeks."   Elina's transformation happened fast. As a designer frustrated with traditional software development where maybe 10% of your original vision gets executed, she discovered Cursor and everything changed. Within two weeks, she went from her first AI-assisted experiment to launching a complete app in the App Store. The moment that shifted everything was realizing that AI had fundamentally changed the paradigm from "writing code" to "building the product." This wasn't about learning to code—it was about finally being able to execute her vision 100% the way she wanted it, with immediate feedback through testing. Building LexieLearn: Solving Real Problems for Real Users "I got this request from a girl who was studying, and she said she would really appreciate to be able to iterate the study set... and I thought: "That's a brilliant idea! And I can execute that!" And the next morning, it was 9.15, I sent her a screen capture."   Lexie emerged from Elina's frustration with ineffective study routines and gamified edtech that didn't actually help kids learn. She built an AI-powered study tool for kids aged 10-15 that turns handwritten notes into adaptive quizzes revealing knowledge gaps—private, ad-free, and subscription-based. What makes Lexie remarkable isn't just the technology, but the speed of iteration. When a user requested a feature, Elina designed and implemented it overnight, sending a screen capture by 9:15 AM the next morning. This kind of responsiveness—from customer feedback to working feature in hours—represents a fundamental shift in how software can be built. Today, Lexie has over 1,500 users with paying customers, proving that AI-assisted development isn't just for prototypes anymore. The Workflow: It's Not Just "Vibing" "I spend 30 minutes designing the whole workflow inside my head... all the UX interactions, the data flow, and the overall architectural decisions... so I spent a lot of time writing a really, really good spec. And then I gave that to Claude Code."   Elina has mixed feelings about the term "vibecoding" because it suggests carelessness. Her actual workflow is highly disciplined. She spends significant time designing the complete workflow mentally—all UX interactions, data flow, and architectural decisions—then writes detailed specifications. She often collaborates with Claude to write these specs, treating the AI as a thinking partner. Once the spec is clear, she gives it to Claude Code and enters a dialogue mode: splitting work into smaller tasks, maintaining constant checkpoints, and validating every suggestion. She reads all the code Claude generates (32,000 lines client-side, 8,000 server-side) but doesn't write code herself anymore. This isn't lazy—it's a new kind of discipline focused on design, architecture, and clear communication rather than syntax. Reading Code vs. Writing Code: A New Skill Set "AI is able to write really good code, if you just know how to read it... But I do not write any code. I haven't written a single line of code in a long time."   Elina's approach reveals an important insight: the skill shifts from writing code to reading and validating it. She treats Claude Code as a highly skilled companion that she needs to communicate with extremely well. This requires knowing "what good looks like"—her 15 years of experience as a designer gives her the judgment to evaluate what the AI produces. She maintains dialogue throughout development, using checkpoints to verify direction and clarify requirements. The fast feedback loop means when she fails to explain something clearly, she gets immediate feedback and can course-correct instantly. This is fundamentally different from traditional development where miscommunication might not surface until weeks later. The Anti-Pattern: Letting AI Run Rampant "You need to be really specific about what you want to do, and how you want to do it, and treat the AI as this highly skilled companion that you need to be able with."   The biggest mistake Elina sees is treating AI like magic—giving vague instructions and expecting it to "just figure it out." This leads to chaos. Instead, developers need to be incredibly specific about requirements and approach, treating AI as a skilled partner who needs clear communication. The advantage is that the iteration loop is so fast that when you fail to explain something properly, you get feedback immediately and can clarify. This makes the learning curve steep but short. The key is understanding that AI amplifies your skills—if you don't know what good architecture looks like, AI won't magically create it for you. Breaking the Gatekeeping: One Person, Ten Jobs "I think that I can say that I am a walking example of what you can do, if you have the proper background, and you know what good looks like. You can do several things at a time. What used to require 10 people, at least, to build before."   Elina sees herself as living proof that the gatekeeping around software development is breaking down. Someone with the right background and judgment can now do what previously required a team of ten people. She's passionate about others experiencing this same freedom—the ability to execute their vision without compromise, to respond to user feedback overnight, to build production-quality software solo. This isn't about replacing developers; it's about expanding who can build software and what's possible for small teams. For Elina, working with a traditional team would actually slow her down now—she'd spend more time explaining her vision than the team would save through parallel work. The Future: Intent-Based Software That Emerges and Disappears "The software gets built in an instance... it's going to this intent-based mode when we actually don't even need apps or software as we know them."   Elina's vision for the future is radical: software that emerges when you need it and disappears when you don't. Instead of permanent apps, you'd have intent-based systems that generate solutions in the moment. This shifts software from a product you download and learn to a service that materializes around your needs. We're not there yet, but Elina sees the trajectory clearly. The speed at which she can now build and modify Lexie—overnight feature implementations, instant bug fixes, continuous evolution—hints at a future where software becomes fluid rather than fixed. Getting Started: Just Do It "I think that the best resource is just your own frustration with some existing tools... Just open whatever tool you're using, is it Claude or ChatGPT and start interacting and discussing, getting into this mindset that you're exploring what you can do, and then just start doing."   When asked about resources, Elina's advice is refreshingly direct: don't look for tutorials, just start. Let your frustration with existing tools drive you. Open Claude or ChatGPT and start exploring, treating it as a dialogue partner. Start building something you actually need. The learning happens through doing, not through courses. Her own journey proves this—she went from experimenting with Cursor to shipping Lexie to the App Store in two weeks, not because she found the perfect tutorial, but because she just started building. The tools are good enough now that the biggest barrier isn't technical knowledge—it's having the courage to start and the judgment to evaluate what you're building.   About Elina Patjas   Elina is building Lexie, an AI-powered study tool for kids aged 10–15. Frustrated by ineffective "read for exams" routines and gamified edtech fluff, she designed Lexie to turn handwritten notes into adaptive quizzes that reveal knowledge gaps—private, ad-free, and subscription-based. Lexie is learning, simplified.   You can link with Elina Patjas on LinkedIn.

244 | "Qualität ist das beste Rezept" - auch für AI Startups, die den nächsten Hype jagen? Mental Load von Eltern als Audio-Pitch bei "Roast my Geschäftsidee" und frische Geschäftsideen von Sam & AlexPerfekte HiFi Systeme auf www.sonoro.com - mit Code "digitaleoptimisten" gibt es 1 Jahr mehr Garantie auf German Audio Design Systeme wie den neuen AVATON.Finde eine Geschäftsidee, die perfekt zu dir passt: ⁠digitaleoptimisten.de/quiz⁠Kapitel:(00:00) Intro(02:52) AI Blase in Sartp-Land: Qualität ist das beste Rezept?(23:10) Roast my Geschäftsidee: Audio Pitch Daddy Do(37:11) ca .36:00 Samuels Weisheiten für's Leben(46:56) Post von Optimisten → Retten LLMs am Ende den Journalismus?(55:00) Geschäftsidee von Alex: Event Buddy(1:00:32) Geschäftsidee von Samuel: Brocky HunterMehr Kontext:In dieser Unicorn Ideas Folge sprechen Alex und Samuel über die Wahrheit hinter dem aktuellen AI-Hype, warum viele AI-Startups gerade kollabieren, und was Gründer wirklich aus dem „TikTok-Startups“-Trend lernen müssen. Samuel teilt exklusive Insights zu Cluely, AI Coding Tools, dem Einbruch der Nutzerzahlen und warum Viralität kein Geschäftsmodell ersetzt.Alex bringt eigene Erfahrungen aus dem Bau von PodAgent ein – und erklärt, warum viele AI-Produkte in der Praxis nicht halten, was der Pitch verspricht.Außerdem in dieser Folge:Warum viele Startups heute nur für Viralität bauenWie gefährlich „Vibe Coding“ und massiver Technical Debt wirklich sindWelche Rolle VC-Hypes spielen und warum Gründer oft nur Reaktionen auf Investoren zeigenDie Frage: Sind wir mitten im ersten echten AI-Crash?Dazu gibt's zwei neue Geschäftsideen: Alex pitcht seinen „EventBuddy“ – einen ROI-Tracker für Networking Events. Samuel stellt „Brocki Hunter“ vor – die AI, die verborgene Schätze in Flohmärkten und Second-Hand-Läden findet und weiterverkauft.Und: Eine spannende Hörer-Einsendung zum Thema Mental Load, Kindern, Beziehung und unfairer Aufgabenverteilung. Eine Idee, die viele Eltern betrifft – und die Alex & Samuel gnadenlos auseinandernehmen.Diese Folge ist perfekt für alle, die sich für Business-Ideen, AI-Trends, Startups, Growth, Produktentwicklung und Entrepreneurship interessieren – und Lust auf ehrliche Hot Takes haben.Keywords:AI Hype, AI Startups, AI Crash, Startup Ideen, Business Ideen, Unicorn Ideas, Digitale Optimisten, Cluely, AI Coding Tools, Technical Debt, Vibe Coding, VC Hype, Entrepreneurship, Networking ROI, EventBuddy, Brocki Hunter, Mental Load, Eltern Startup, Business Podcast Deutsch, AI Trends, Gründer Podcast

Youtube Channel: https://www.youtube.com/@GenerativeAIMeetup Mark's Travel Vlog: https://www.youtube.com/@kumajourney11 Mark's Personal Youtube Channel: https://www.youtube.com/@markkuczmarski896 Attend a live event: https://genaimeetup.com/ Shashank Linked In: https://www.linkedin.com/in/shashu10/    In this episode of the Generative AI Meetup Podcast, Mark (in Ohio) and Shashank (in India) finally sit down after a month of travel to unpack a very eventful stretch in AI. They dive into Google's new Gemini 3 Pro, its standout scores on Humanity's Last Exam and ARC-AGI, and why these reasoning benchmarks matter more than yet another near-perfect standardized test score. Mark also makes a public feature request to DeepMind: please increase Gemini's max output tokens. From there they get hands-on with the developer experience: Google's new Anti-Gravity coding IDE (and how it compares to Cursor) Using GPT-5.1 Codex High in Cursor's autonomous “plan mode” Why long context and long output windows are critical for deep research and book-length projects The conversation then shifts to the bigger picture: LLMs as therapists, sycophancy, safety, and the danger of AI always agreeing with you Mark's rant on robotics, humanoid robots, and a coming age of extreme abundance where robots handle most physical and intellectual work Why learning to code may become the mental equivalent of going to the gym—a “brain gym” in a world where AI can do most practical tasks They also cover the latest AI industry drama and milestones: Yann LeCun leaving Meta, what that might signal about Big Tech AI labs, and how godfathers like Hinton, LeCun, and Bengio see the road to AGI DeepMind's new game-playing agent and why world models in 3D environments matter for real-world robotics Genspark hitting unicorn status and what it means for “ChatGPT wrapper” startups Co-inventing a new term on air: a “narwhal” = a trillion-dollar private company If you're curious about where frontier models, coding agents, robotics, and AGI trajectories all intersect—plus some philosophical musing on jobs, meaning, and abundance—this episode is for you.

Our 225th episode with a summary and discussion of last week's big AI news!Recorded on 11/16/2025Hosted by Andrey Kurenkov and co-hosted by Michelle LeeFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:New AI model releases include GPT-5.1 from OpenAI and Ernie 5.0 from Baidu, each with updated features and capabilities.Self-driving technology advancements from Baidu's Apollo Go and Pony AI's IPO highlight significant progress in the automotive sector.Startup funding updates include Incept taking $50M for diffusion models, while Cursor and Gamma secure significant valuations for coding and presentation tools respectively.AI-generated content is gaining traction with songs topping charts and new marketplaces for AI-generated voices, indicating evolving trends in synthetic media.Timestamps:(00:01:19) News PreviewTools & Apps(00:02:13) OpenAI says the brand-new GPT-5.1 is ‘warmer' and has more ‘personality' options | The Verge(00:04:51) Baidu Unveils ERNIE 5.0 and a Series of AI Applications at Baidu World 2025, Ramps Up Global Push(00:07:00) ByteDance's Volcano Engine debuts coding agent at $1.3 promo price(00:08:04) Google will let users call stores, browse products, and check out using AI | The Verge(00:10:41) Fei-Fei Li's World Labs speeds up the world model race with Marble, its first commercial product | TechCrunch(00:13:30) OpenAI says it's fixed ChatGPT's em dash problem | TechCrunchApplications & Business(00:16:01) Anthropic announces $50 billion data center plan | TechCrunch(00:18:06) Baidu teases next-gen AI training, inference accelerators • The Register(00:20:50) Meta chief AI scientist Yann LeCun plans to exit and launch own start-up(00:24:41) Amazon Demands Perplexity Stop AI Tool From Making Purchases - Bloomberg(00:27:32) AI PowerPoint-killer Gamma hits $2.1B valuation, $100M ARR, founder says | TechCrunch(00:29:33) Inception raises $50 million to build diffusion models for code and text | TechCrunch(00:31:14) Coding assistant Cursor raises $2.3B 5 months after its previous round | TechCrunch(00:33:56) China's Baidu says it's running 250,000 robotaxi rides a week — same as Alphabet's Waymo(00:35:26) Driverless Tech Firm Pony AI Raises $863 Million in HK ListingProjects & Open Source(00:36:30) Moonshot's Kimi K2 Thinking emerges as leading open source AIResearch & Advancements(00:39:22) [2510.26787] Remote Labor Index: Measuring AI Automation of Remote Work(00:45:21) OpenAI Researchers Train Weight Sparse Transformers to Expose Interpretable Circuits - MarkTechPost(00:49:34) Kimi Linear: An Expressive, Efficient Attention Architecture(00:53:33) Watch Google DeepMind's new AI agent learn to play video games | The Verge(00:57:34) arXiv Changes Rules After Getting Spammed With AI-Generated 'Research' PapersPolicy & Safety(00:59:35) Stability AI largely wins UK court battle against Getty Images over copyright and trademark | AP News(01:01:48) Court rules that OpenAI violated German copyright law; orders it to pay damages | TechCrunch(01:03:48) Microsoft's $15.2B UAE investment turns Gulf State into test case for US AI diplomacy | TechCrunchSynthetic Media & Art(01:06:39) An AI-Generated Country Song Is Topping A Billboard Chart, And That Should Infuriate Us All | Whiskey Riff(01:10:59) Xania Monet is the first AI-powered artist to debut on a Billboard airplay chart, but she likely won't be the last | CNN(01:13:34) ElevenLabs' new AI marketplace lets brands use famous voices for ads | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Wendell joins the show with a literal fire background (the “this is fine” meme), which he admits he can’t use anymore because of company backgrounds. But it’s an accurate representation of daily developer life, and we can all relate. Teaching PHP Six Months After Learning It At 16 years old, working in a small-town Brazilian school teaching Word and Excel, Wendell took a PHP course. Five or six months later, the teacher left and they asked Wendell to take over—teaching PHP to 13 and 14-year-olds when he was barely older himself. Students would ask questions he didn’t know the answer to, forcing him to say “give me a minute” while frantically searching the documentation. But that pressure? It taught him the most valuable developer skill: knowing how to find answers to things you don’t know. No Computer at Home Here’s the kicker: Wendell didn’t even have a computer at home during all this. He could only use the computers at work, so he’d finish lunch in 15 minutes just to get back to his desk and keep learning PHP. The obsession was real, and it paid off. PHP Documentation: The Unsung Hero Everyone agrees—PHP’s documentation is insanely good. You can find almost anything without even hitting Stack Overflow. Comments from 15-20 years ago still work today because PHP maintains backwards compatibility like no other language. Those old comments aren’t just relics; they’re still valid, working code that new developers can learn from. Try that in JavaScript land. Rector: The Migration Miracle Moving legacy code to modern PHP used to be a nightmare. Now? Install Rector and watch it automatically migrate your codebase to use new features. Wendell highlights this as one of PHP’s secret weapons—the community builds tools that make everyone’s life easier. When AI Becomes Part of Your Workflow some literally can’t work without Claude, Cursor, and PHPStorm anymore. Not because he needs AI for everything, but because the anxiety of “what if I need to ask something?” kicks in if it’s not there. It’s wild how quickly we adapt to new tools—especially considering 25 years ago we barely had IDEs. We had Notepad. If we were lucky. The Imposter Syndrome Reality Check Everyone Googles stuff. Every. Single. Person. It doesn’t matter how experienced you are or how many packages you’ve written—at some point, you’re searching for answers. The skill isn’t memorizing everything; it’s knowing where to look and how to find the right answer. Mike and Chris both admit they struggle with imposter syndrome constantly. You’re not alone. PHP Can Do Everything Now CLI apps? Easy. Web apps? Obviously. Desktop applications? Yep. Mobile applications with PHP? Absolutely—and Wendell admits he never thought that would be possible. With AI advancements and tools like the new official MCP SDK for PHP, the possibilities keep expanding. JavaScript might get there first, but PHP always catches up. New Security Challenges: Prompt Injection Frameworks already protect us from SQL injection and script injection. But now with MCP (Model Context Protocol) and AI integration, we have a new threat: prompt injection. How will PHP frameworks adapt? How do we secure AI-powered applications? These are the new challenges keeping the community on its toes. Teaser: Laravel Service Container Deep Dive Wendell drops a teaser—he’s publishing his longest blog post yet about how Laravel’s service container works. By the time this episode goes live, it’ll probably already be out. Worth the read. Listen to hear why the PHP community attracts experts from other languages, and why everyone keeps confusing their show schedule with the video game Fortnite. Links From The Show: Wendell’s blog: https://wendelladriel.com/blog Inside The Service Container: https://wendelladriel.com/blog/inside-the-laravel-service-container Laravel Queues Under The Hood: https://wendelladriel.com/blog/laravel-queues-under-the-hood Laravel Actions As A Service: https://wendelladriel.com/blog/laravel-aaas-actions-as-a-service Best Practices For Laravel Applications: https://wendelladriel.com/best-practices-for-laravel-enterprise-applications PHP Architect Social Media: X: https://x.com/phparch Mastodon: https://phparch.social/@phparch Bluesky: https://bsky.app/profile/phparch.com Discord: https://discord.phparch.com Subscribe to our magazine: https://www.phparch.com/subscribe/ Streams: Youtube Channel Twitch Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore Honeybadger.io Honeybadger helps you deploy with confidence and be your team's DevOps hero by combining error, uptime, and performance monitoring in one simple platform. Check it out at honeybadger.io Music Provided by Epidemic Sound https://www.epidemicsound.com/ The post PHP Alive And Kicking – Episode 16 – Wendell Adriel appeared first on PHP Architect.

Ryo Lu spent years watching his designs die in meetings. Then he discovered the tool that lets designers ship code at the speed of thought: Cursor, the company where Ryo is now Head of Design. In this episode, we discuss why "taste" is the wrong framework for understanding the future, why purposeful apps are "selfish," how System 7 holds secrets about AI interfaces, and the radical bet that one codebase can serve everyone if you design the concepts right instead of the buttons.Follow Ryo Lu on X: https://x.com/ryolu_Check Out Ryo's Website: https://os.ryo.lu/Follow Jennifer Li on X: https://x.com/JenniferHliFollow Erik Torenberg on X: https://x.com/eriktorenberg Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. 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.

AGENDA: 04:47 Cursor Raises $2.3BN at $29BN Valuation 11:36 What Gemini 3 Means for Lovable, Cursor and Replit 30:54 Peter Thiel and Softbank Sell NVIDIA: The Bubble Bursting? 48:54 Oracle Credit Default Swaps: The Risk is Increasing 01:07:22 Stripe Does Tender at All-Time High: Why the Best Companies Will Never IPO 01:19:18 Why Retail WIll Cause a Surge of Capital into VC Funds  

There's a whole narrative right now that “vibe coding is a bubble” and all the MRR from AI-built apps isn't real.In this episode, we chat with Jacob Klug, founder of the agency Creme, which specializes in building lovable MVPs on top of tools like Lovable and AI coding assistants. Jacob makes the case that most of the “AI apps are trash” discourse is really a skill issue, not a tool issue—and he breaks down the exact process his team uses to ship full platform-level apps in two-week sprints.We dig into how to scope and design software that doesn't look AI-generated, how to think about personal operating systems vs. SaaS, why ideas are getting worse even as tools get better, and how creators and agencies can turn niche domain expertise into real products.If you're an operator, marketer, or founder trying to figure out how to actually use AI coding tools (instead of just tweeting about them), this one's for you.GuestJacob Klug — founder of Creme, an agency building “lovable MVPs” and full-stack products with Lovable + AI tools; helps founders, startups & enterprises ship production apps in weeks without sacrificing UX.Guest LinksWebsite: https://www.creme.digital/LinkedIn: https://www.linkedin.com/in/jacob-klug-37b254156/X (Twitter): https://x.com/JacobsklugWhat You'll LearnWhy the “vibe coding is a bubble” take is mostly a skill and discipline problemHow Jacob's agency ships full startup-grade products using Lovable and AIThe PRD-first formula they use before ever opening a builderHow to decide when to build vs. when to buy software in 2025Why we're entering a wave of personal OSes and custom internal toolsHow to avoid shipping janky AI UI and make your app look intentionally designedThe mindset shift from “I could build anything” → “I will build this one specific thing”Why specializing in one AI tool (Lovable, Cursor, n8n, etc.) beats being “the AI guy”Tactical content and lead-gen plays for agencies on LinkedIn and YouTubeHow to learn AI tooling without getting paralyzed by the infinite possibilitiesTimestamps00:00 — Vibe coding: bubble or breakthrough?02:23 — Effective use of no-code tools05:23 — Stack and scoping for MVP development07:08 — Trends in personal software development10:33 — Personal projects: blood work analysis tool13:00 — Steps to start building custom software17:49 — Successful and unsuccessful product categories21:01 — Learning and adopting AI tools27:45 — Creator collaboration in software development32:14 — Lead generation strategies for AI-powered agenciesKey Topics & Ideas1. Bubble or Skill Issue?Why early no-code/AI apps looked jankyHow tools like Lovable increased automation from ~50% → ~85%The remaining 10–15% where real engineering still mattersMany failures come from non-devs skipping fundamentals2. How Creme Builds Lovable MVPsEvery project starts with a clear PRD (often drafted with ChatGPT)AI is used to tighten scope before buildingWhen Creme stays fully in Lovable vs. moving code to CursorUsing Lovable Cloud for hosting, database, and analytics3. Personal Operating Systems & Internal ToolsPeople replacing SaaS subscriptions with their own custom toolsIn a 20-person cohort, nearly everyone built workflow appsRise of the Personal OS: one system for life + workExample builds:Bloodwork tracker from PDF uploadsUnified messaging CRM (WhatsApp, Telegram, SMS, email)Automated 30-second sales briefings4. How to Learn AI Coding ToolsHalf the cohort hadn't built anything before startingMain blocker: overwhelm, not skillLearn core concepts: frontend vs. backend, auth, roles, securityBuild daily reps, focus on the next thing you need—not “all of AI”5. Designing Apps That Don't Look AI-GeneratedGood design is still the hardest and biggest edgeCreme process: build a /components library, define buttons/cards/inputs, assign stable IDsTools: Mobbin, Figma Community kits, 21st.devBest prompt: “Here's a screenshot → copy this.”6. What Works in Product IdeasMost of Creme's builds are full startup platforms, not micro-toolsAI makes shipping easier, but ideas are getting worse without depthReal advantage = domain expertise + niche problem + AI speed7. Creators x SoftwareCreators can now ship products without capitalJacob prefers retainers over equityAnalogy: Like creator brands—most fail, a few go huge8. Career Strategy: SpecializeFuture = verticalized expertise, not “AI generalists”Specialist lanes: Lovable, Cursor, n8n, automationBe the person for one tool + one market9. Content & Lead GenJacob's two rules for content: people are selfish and people are boredBuild content that teaches, sparks emotion, and creates curiosityPost ~5x/week, prioritize visual postsLong-term: YouTube deep dives for high-intent inboundSponsorToday's episode is brought to you by Graphed – an AI data analyst & BI platform.With Graphed you can:Connect data like GA4, Facebook Ads, HubSpot, Google Ads, Search Console, AmplitudeBuild interactive dashboards just by chatting (no Looker Studio/Tableau learning curve)Use it as your ETL + data warehouse + BI layer in one placeAsk:“Build me a stacked bar chart of new users vs. all users over time from GA4”…and Graphed just builds it for you.

Join Du'An Lightfoot, AI Developer at AWS, as he dives deep into building AI agents with the strands framework. In this technical walkthrough, Du'An demonstrates how to create custom AI coding assistants and multi-agent systems in just a few lines of code. Learn how agentic AI frameworks have evolved from basic function calling to sophisticated systems that can rival tools like Cursor and Cloud Code. Du'An shares practical examples, including building content pipelines, preprocessing systems, and even generating a book outline from his own YouTube content. Whether you're looking to automate workflows or build your own AI-powered tools, this session covers the frameworks and techniques you need to get started with AI agents. Perfect for developers, DevOps engineers, and anyone interested in leveraging AI to enhance their development workflow. Subscribe to vBrownBag for more community-driven tech education! ⸻ Timestamps 0:00 - Introduction & Welcome 6:43 - AI Tools Discussion & Current Usage 9:33 - Technical Background & Getting Started with Agents 15:00 - Introduction to Strands Framework 25:00 - Building Custom AI Agents Demo 40:00 - Multi-Agent Systems & Workflows 55:00 - Content Pipeline & Preprocessing Examples 1:05:00 - Book Generation Demo 1:10:00 - Q&A & Wrap Up How to find Du'An: https://www.linkedin.com/in/duanlightfoot/ Links from the show: https://s12d.com/vbrownbag-2025 https://github.com/strands-agents/samples https://modelcontextprotocol.io/docs/getting-started/intro https://www.anthropic.com/engineering/building-effective-agents https://github.com/awslabs/amazon-bedrock-agentcore-samples https://github.com/modelcontextprotocol/python-sdk https://modelcontextprotocol.io/llms-full.txt https://openai.com/index/whisper/ https://github.com/openai/whisper

Where is OpenAI going, and what does it mean for the broader AI and tech ecosystem? Madrona Partner Vivek Ramaswami sits down with Jason Kwon, Chief Strategy Officer at OpenAI, for a rare behind-the-scenes look at the decisions shaping one of the most influential AI companies in the world. In this live conversation from the 2025 IA Summit, Jason shares what OpenAI will build, what it won't, and how founders can use that as a roadmap to go big without going head-to-head. They unpack OpenAI's ecosystem-first mindset, what "full-stack AI" really means, and how the rise of agentic AI is reshaping what gets built — and by whom.  They also unpack: The real reason OpenAI is investing so heavily in compute infrastructure How to interpret and work alongside OpenAI's product moves as a founder, rather than fear them Why the most compelling startups bet on model progress, not workarounds Where OpenAI wants partners, and where it's staying hands-off What reasoning + agentic AI unlock for next-gen products How OpenAI is navigating its AGI mission while staying product-relevant This episode is essential listening for anyone building in AI and wondering: Where should I build — and how will OpenAI operate in the space?  Full Transcript:  https://www.madrona.com/how-what-to-build-in-the-age-of-openai Chapters: (00:00) – Introduction (01:17) – Jason Kwon's background and role at OpenAI (02:43) – What is the "full stack of AI"? (Jason's breakdown) (04:07) – Where founders should build: Opportunities in the AI ecosystem (05:43) – OpenAI's partnerships and why compute matters (06:57) – The "reasoning revolution" and agent capabilities (07:57) – Agentic commerce: Stripe partnership and agent protocols (09:15) – OpenAI's philosophy: Platform vs. product, and the value of partnerships (10:47) – What does AGI mean inside OpenAI? Research focus and company culture (11:44) – How OpenAI decides what to build (and what not to) (14:42) – Where OpenAI won't build: Advice/opportunity for founders (17:31) – Q&A: Profitability, business models, and compute margins (20:00) – How ChatGPT changed OpenAI: Growth, culture, and leadership (21:20) – Sam Altman's ruthless prioritization and company focus (23:20) – Q&A: OpenAI's role in commerce and monetization (24:55) – Q&A: Application vs. model layer, and the Cursor partnership (26:48) – Looking ahead: What Jason hopes OpenAI will accomplish next year

If you're using AI to just write code, you're missing out.Two engineers at Every shipped six features, five bug fixes, and three infrastructure updates in one week—and they did it by designing workflows with AI agents, where each task makes the next one easier, faster, and more reliable.In this episode of AI & I, Dan Shipper interviewed the pair—Kieran Klaassen, general manager of Cora, our inbox management tool, and Cora engineer Nityesh Agarwal—about how they're compounding their engineering with AI. They walk Dan through their workflow in Anthropic's agentic coding tool, Claude Code, and the mental models they've developed for making AI agents truly useful. Kieran, our resident AI-agent aficionado, also ranked all the AI coding assistants he's used.If you found this episode interesting, please like, subscribe, comment, and share!Want even more?Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It's usually only for paying subscribers, but you can get it here for free.To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperHead to ai.studio/build to create your first app.Pitch is the AI presentation platform that helps professionals collaborate on, create, and deliver winning slide decks — all while staying on brand: https://pitch.com/use-cases/ai-presentation-maker/?utm_medium=paid-influencer&utm_campaign=every Timestamps:Episode start: 00:00:00Introduction: 00:01:16Why Kieran believes agents are turning a corner: 00:03:18Why Claude Code stands out from other agents: 00:06:36What makes agentic coding different from using tools like Cursor: 00:11:58The Cora team's workflow to turn tasks into momentum: 00:15:20How to build a prompt that turns ideas into plans: 00:23:07The new mental models for this age of software engineering: 00:34:00Why traditional tests and evals still matter: 00:39:13Kieran ranks all the AI coding agents he's used: 00:42:00Links to resources mentioned in the episode:Try Cora, our AI email assistant: https://cora.computer/Kieran Klaassen: @kieranklaassenNityesh Agarwal: @nityeshagaThe book that helps Nityesh form mental models to work with AI agents: High Output Management

Don't miss out on this game-changing Cursor technique designed specifically for AI entrepreneurs. While most use Cursor AI just for coding, there's a revolutionary way to manage your entire business, documents, emails, projects, and sales, directly within its interface, treating everything like code in a GitHub repository. In this demo, Seva Ustinov unveils this powerful workflow. Forget scattered AI tools; see how centralizing your company's context (strategy docs, meeting transcripts, competitor analysis) allows AI agents like Claude inside Cursor to automate huge portions of your non-coding work. Learn how this technique saves hours, increases accuracy, and could be the key to building a leaner, faster AI-driven business. Check out the company: https://ellyanalytics.com

У свіжому дайджесті DOU News обговорюємо викрадення даних співробітників GlobalLogic, свіжий GPT-5.1 від OpenAI та новий VR-шолом і міні-ПК від Valve. А ще — неймовірний раунд фінансування для Cursor, дивний в'язаний чохол для iPhone та інші теми українського ІТ та світового тек-сектору. Таймкоди 00:00 Інтро 00:23 Дані 10 тисяч співробітників GlobalLogic викрали хакери 02:32 EPAM обмежить бенефіти для ФОПів 04:28 United Tech створює майбутнє сошуал нетворкінгу і стрімінгу 05:13 Google запускає перевірку Android-розробників але дозволить «досвідченим користувачам» завантажувати неперевірені програми 08:38 OpenAI представила GPT-5.1 — теплішу й «людянішу» модель 13:50 Valve анонсувала серію Steam Hardware: VR-шолом і міні-ПК 20:03 Стартап Cursor залучив $2,3 млрд інвестицій 22:38 Apple і Issey Miyake створили в'язаний чохол для iPhone 24:04 Apple відклала реліз iPhone Air 2 24:59 Blue Origin повернула ракету New Glenn після польоту 28:28 Chad: The Brainrot IDE — новий продукт від Y Combinator 32:00 Пісня, створена ШІ, очолила кантрі-чарт 33:00 Для українців тимчасово відкрили безплатний доступ до профкурсів 34:32 Що цього тижня рекомендує Женя: Space DJ та статтю про роботу в Cursor

AI is accelerating software delivery, but it's also introducing new security risks that most developers and automation engineers never see coming. In this episode, we explore how AI-generated code can embed vulnerabilities by default, how "vibe coding" is reshaping developer workflows, and what teams must do to secure their pipelines before bad code reaches production. You'll learn how to prompt more securely, how guardrails can stop vulnerabilities at generation time, how to prioritize real risks instead of false positives, and how AI can be used to protect your applications just as effectively as attackers use it to exploit them. Whether you're using Cursor, Copilot, Playwright MCP, or any AI tool in your automation workflow, this conversation gives you a clear roadmap for staying ahead of AI-driven vulnerabilities — without slowing down delivery. Featuring Sarit Tager, VP of Product for Application Security at Palo Alto Networks, who reveals real-world insights on securing AI-generated code, understanding modern attack surfaces, and creating a future-proof DevSecOps strategy.

In this special live Web Summit edition from Lisbon, I sit down with Tom Haworth, founder of D13 AI, to talk about why “good enough” AI might actually be one of the most dangerous places we can get stuck.And you'll hear Tom say it's time for the leaders of vibe coding platforms (e.g. Lovable, Replit, Cursor) to acknowledge that they're great when you need to “demo not memo”, but not great (today and maybe ever) at delivering production-grade, secure code. We also make a few detours as we detail a ridiculous week in Lisbon, including:How (shocker!) 90% of the conference was about AIWhy “good enough” AI is not a good place to beWhether we'll graduate to great AIAI's ROI now and in the futureWhy it's still iffy whether AI agents they can be trusted to accomplish complex jobsRobots wander Web Summit, do the Macarena, fall downHow tennis great Maria Sharapova uses (IBM's) AI How the presumptuous Web Summit's app prominently suggests we all message Maria… (as if!) Visa wants to help creators monetize (yay! it me!), using Web3 technologies (yes, they said “Web3”; no, I was not expecting to hear a non-ironic use of that phrase)Why self-driving cars are the best robots — and coming soon to more of EuropeHow much Web Summit pampers (and corrupts) the media: I was like a stuffed goose. Hurray for Portuguese custard and other delicacies!How even the beer at Web Summit was high tech---Featured voices:Tom Haworth: Founder of D13 AI, a UK-based consultancy that “builds intelligent tools that help businesses make sense of messy data.”Me (Dan Blumberg) — I'm the host of CRAFTED. and the founder of Modern Product Minds. HMU if you want to build something great. I love building from zero to one.---And if you please…Share with a friend! Word of mouth is by far the most powerful way for podcasts to growSubscribe to the CRAFTED. newsletter at crafted.fmShare your feedback! I'm experimenting with new episode formats and would love your honest feedback on this and other episodes. Email me: dan@modernproductminds.com or DM me on LinkedInSponsor the show? I'm actively speaking to potential sponsors for 2026 episodes. Drop me a line and let's talk.Get psyched!… There are some big updates to this show coming soon!

In this episode, we break down Cursor's massive $2.3B raise and what it signals about the exploding demand for AI coding assistants. We also explore how this rapid follow-on round could reshape competition in developer tooling.Get the top 40+ AI Models for $20 at AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustleSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

OpenAI is testing out group chats as a sort of collaborative prompting experience. The hyperscalers are lining up against Nvidia in one specific arena. The Sam Altman Elon Musk feud isn't over. Google knows who sent you that fake UPS shipment alert text. And, of course, the Weekend Longreads Suggestions. ChatGPT launches pilot group chats across Japan, New Zealand, South Korea, and Taiwan (TechCrunch) Amazon and Microsoft Back Effort That Would Restrict Nvidia's Exports to China (WSJ) OpenAI, Apple Lose Bid to Toss Musk xAI Suit Over Competition (Bloomberg) AI startup Cursor raises $2.3 billion funding round at $29.3 billion valuation (CNBC) You know those fake USPS texts? Google says it's found who's behind them (Fast Company) Weekend Longreads Suggestions: Sundar Pichai Is Google's AI ‘Wartime CEO' After All (Bloomberg) CRYPTO: Realm of the Coin (Vanity Fair) I'm Going to Be a Dad. Here's Why I'm Not Posting About My Kid Online (CNET) Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's episode examines the core debate shaping the AI industry: whether application-layer companies can survive the pace and instability of the model layer. The discussion covers the arguments that apps can't outrun rapid model shifts, the counter-case for deep vertical products, and what Cursor's momentum reveals about where durable value might emerge. The episode also includes a fast headlines sweep on agentic cyber-espionage, major infrastructure investments, breakthrough agents, and the latest updates to GPT-5.1.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://rovo.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & 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/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

Plus: Harbinger Motors raises $160 million and secures an order from FedEx. And AI startup Cursor raises an additional $2.3 billion. Zoe Kuhlkin hosts. Learn more about your ad choices. Visit megaphone.fm/adchoices

Valve is taking on gaming consoles and creating a new type of VR headset, all at the same time. GPT-5 gets “warmer.” Cursor's new raise means it has 10x'd its valuation in the span of a year. And we're one step closer to your phone completely replacing your wallet. Valve brings back Steam Machine and Steam Controller — hands-on with Valve's new AMD-based living room gaming hardware (Tom's Hardware) The Steam Frame is a surprising new twist on VR (The Verge) Microsoft to Use OpenAI's Custom Chip Work to Help In-House Effort (Bloomberg) OpenAI says the brand-new GPT-5.1 is ‘warmer' and has more ‘personality' options (The Verge) The AI Coding Startup Favored by Tech CEOs Is Now Worth $29.3 Billion (WSJ) Apple launches Digital ID, a way to carry your passport on your phone for use at TSA checkpoints (TechCrunch) Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus: AI startup Cursor raises $2.3 billion in its third funding round this year. And the IRS shares new contribution limits for 401(k)s and IRAs for 2026. Zoe Kuhlkin hosts. Sign up for WSJ's free What's News newsletter. An artificial-intelligence tool assisted in the making of this episode by creating summaries that were based on Wall Street Journal reporting and reviewed and adapted by an editor. Learn more about your ad choices. Visit megaphone.fm/adchoices

Stocks sold off today, especially in tech. More fears around the valuations in the AI trade hitting some high-momentum names. One bright spot: health care. Bernstein analyst Lance Wilkes breaks down his top picks. Cursor CEO Michael Truell on the company's latest fundraise and valuation, nearly tripling in just five months. Plus, our Melissa Lee on some popular insurance schemes that offer huge savings on medications – but could put patient lives in danger.  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, Sasha Orloff talks with Nick Abouzeid, Co-founder and CEO of Rivet, about raising venture capital from XYZ, Haystack, and other angels to build a modern tax and accounting firm that serves 600+ category-defining companies like Cursor by combining world-class tax practitioners with custom-built software infrastructure, delivering premium responsive service while challenging the traditional accounting firm model of unresponsive service and outdated technology. -- SPONSORS: Notion Boost your startup with Notion—the ultimate connected workspace trusted by thousands worldwide! From engineering specs to onboarding and fundraising, Notion keeps your team organized and efficient. For a limited time, get 6 months of Notion AI FREE to supercharge your workflow. Claim your offer now at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://notion.com/startups/puzzle⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Puzzle

When four MIT grads decided to build a code editor while everyone else was building AI agents, they created the fastest-growing developer tool ever built. Cursor CEO Michael Truell joins a16z's Martin Casado to discuss the deliberate constraints that led to breakthroughs: why they rejected the "democratization" narrative to focus on power users, how their 2-day work trials test for agency over credentials, and the strategic decision to own the editor when conventional wisdom said it was impossible. Resources:Follow Michael on X: https://x.com/mntruell Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast 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.

Uber moves more than 36 million trips a day, a scale that would overwhelm most systems. But as AI reshapes every corner of business, even a tech giant like Uber must evolve faster than ever. The real question is, how do you lead an organization this massive through an AI revolution without losing reliability, human connection, or trust? In this episode, I sit down with Praveen Neppalli Naga, Uber's Chief Technology Officer for Mobility and Delivery, to explore the leadership blueprint driving Uber's AI-powered transformation. He shares how Uber is transforming its software engineering systems using tools like Cursor and agentic AI workflows, integrating machine learning into real-time marketplace technology, and balancing automation with human oversight to avoid what he calls "AI slop." We also dive into how his teams are preparing for autonomous vehicles, managing global scale across 36 million daily trips, and rethinking the engineering culture to adopt AI responsibly and sustainably. For CHROs, this episode reveals how to lead large-scale transformation by aligning people, technology, and purpose, and how to build a culture where AI doesn't replace human capability, but amplifies it.   ________________ Start your day with the world's top leaders by joining thousands of others at Great Leadership on Substack. Just enter your email: ⁠⁠https://greatleadership.substack.com/ Future-ready organizations are built, not hoped for. My latest book, -The 8 Laws of Employee Experience shows how. Preorder here: 8EXLaws.com

Live from GitHub Universe, Wes, Scott, and CJ talk about the latest AI and developer tools from GitHub, including Agent HQ, Copilot integrations, and the new mission control for agents. They also share stories from the Syntax meetup, hack their conference badges, and debate AI's role in coding. Show Notes 00:00 Welcome to Syntax! 03:39 This year's GitHub Universe badges were next-level 07:35 Keynote recap: GitHub Agents, Copilot, and Mission Control 18:21 Brought to you by Sentry.io 20:33 Plan Mode and the future of collaborative coding 23:40 Cursor's new trick: firing off agents straight from Slack 25:32 Copilot Metrics Dashboard and agent analytics 27:53 Effortless MCP integration and custom agent workflows 31:35 Wrapping up GitHub Universe 2025 Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

This week's episode started with the usual existential sigh before tumbling straight into the corporate bloodbath. Amazon chopped 14,000 jobs under the noble banner of “embracing AI,” which CEO Andy Jassy insists isn't about money—despite swimming Scrooge McDuck–style in profit. GM's cutting 1,700 workers, YouTube's dangling “voluntary” buyouts, and economists can't decide if AI is killing jobs or if the economy's just trash. Microsoft's winning either way, sitting pretty on OpenAI's planned $1 trillion IPO, while Meta stock cratered because Zuckerberg's still shoveling billions into the AI bonfire instead of quietly burying the metaverse. Meanwhile, Elon managed to cram a week's worth of disasters into a single news cycle: Tesla's being probed for its idiotic “Mad Max” mode, recalling thousands more Cybertrucks because they can't figure out glue, launching Grokipedia (Wikipedia's evil twin), and turning Truth Social into a crypto casino. Somewhere between the chaos, more people tuned into a fake NVIDIA livestream than the real one, and the only vaguely uplifting story was a grieving family using an AI chatbot to hack a $195K hospital bill down to $33K.In media misery, we soothed our nuclear anxiety with A House of Dynamite, tolerated Welcome to Derry, rolled our eyes at Stranger Things 5, and confirmed Slow Horses still rules. Music listeners, please stop streaming fascism—cancel Spotify. On the tech toy front, Grammarly's having an identity crisis as “Superhuman,” Affinity caved to the subscription gods, and Apple's prepping to inject ads into Maps because the world wasn't already annoying enough. The chaos didn't stop there: a rogue Goodreads librarian rewrote Trump's book listings to protest censorship, Cursor 2.0 actually impressed us with a working currency converter, and Enshittification: Why Everything Suddenly Got Worse and What to Do About It turned out to be the perfect title for the entire digital era.Sponsors:Private Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordMasterClass - Get an additional 15% off any annual membership at MASTERCLASS.com/GRUMPYOLDGEEKSCleanMyMac - clnmy.com/GrumpyOldGeeks - Use code OLDGEEKS for 20% off.Show notes at https://gog.show/720FOLLOW UPWhat both sides of America's polarized divide share: Deep anxieties about the meaning of life and existence itself720° © 1986 Atari Games.IN THE NEWSAmazon cuts its workforce by 14,000 in further embrace of AIIs AI Leading to Layoffs or Does the Economy Just Suck?Amazon CEO Now Says AI Is Not Responsible for Recent LayoffsAmazon Accused of Trapping Drivers in AI PanopticonGM lays off 1,700 workers making EVs and batteries in Michigan, TennesseeTesla Recalls Thousands More Cybertrucks, Is Bad at Gluing ThingsYouTube is offering employees buyouts as part of an AI-focused reorganizationEveryone Is Laying People Off This Week. Researchers Say They're Going to Regret ItOpenAI completes restructure, solidifying Microsoft as a major shareholderOpenAI lays groundwork for juggernaut IPO at up to $1 trillion valuationMeta Stock Plummets as Investors Horrified at How Much Zuckerberg Is Spending on Misfired AIFederal investigators are looking into Tesla's Mad Max mode, which reportedly defies speed limitsGrokipedia Is the Antithesis of Everything That Makes Wikipedia Good, Useful, and HumanMore people watched a fake NVIDIA livestream than the real thingTrump's Media Company Set To Roll Out Polymarket-Like Prediction Market on Truth SocialSurprising no one, researchers confirm that AI chatbots are incredibly sycophanticGrieving family uses AI chatbot to cut hospital bill from $195,000 to $33,000 — family says Claude highlighted duplicative charges, improper coding, and other violationsMEDIA CANDYA House of DynamiteWelcome to DerryStranger Things 5 | Official Trailer | NetflixSlow HorsesDon't Stream Fascism: Cancel SpotifyAPPS & DOODADSGrammarly has rebranded to SuperhumanAffinity's image-editing apps go “freemium” in first major post-Canva updateApple is reportedly getting ready to introduce ads to its Maps appRogue Goodreads Librarian Edits Site to Expose 'Censorship in Favor of Trump Fascism'Introducing Cursor 2.0 and ComposerEnshittification: Why Everything Suddenly Got Worse and What to Do About It by Cory DoctorowThe Disenshittify ProjectCurrency ConverterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.