Podcasts about lovable

This bonus AI Operators episode experiments with a skills-focused format inside the AI Daily Brief community, using the New Year's AI resolution program as a live case study. The episode walks through week two's “model mapping” challenge and why building a personal mental map of which models and tools excel at which tasks can be one of the biggest sources of practical AI leverage. It then goes hands-on with a newly vibe-coded Model Map Builder app, covering its use case library, testing workflow, scoring system, and model history views, alongside a candid look at how fast, low-stakes software gets built using tools like Lovable, Claude, and WhisperFlow. The broader takeaway is a shift from thinking about AI usage to continuously translating opportunities into small, living pieces of software, and how that mindset is becoming central to being an effective AI operator in 2026.Link to the model map: https://aidbmodelmap.com/Insanely cheesy theme music: created with Suno

Nine years in. 500 episodes later. Hundreds of CMOs on the mic. A deep well of marketing wisdom for anyone brave enough to draw from it. This milestone episode is a celebration of the bold B2B ideas, experiments, and hard-earned lessons that have filled the show from day one. Thank-you to every marketer who has listened, shared, and dared to try something new because of what they heard here.    Recorded live at the 2025 Super Huddle, Drew's conversations with Udi Ledergor, Denise Persson and Chris Degnan, and Carilu Dietrich anchor this milestone episode.  In this episode:  Udi shares how Gong pulled off a Super Bowl spot on a regional budget, aimed it at VPs of Sales, and tracked impact in traffic, conversations, and pipeline.  Denise and Chris explain how a CMO and CRO stayed aligned through four CEOs at Snowflake and evolved the story from "cloud data warehouse" to "data cloud," all in lockstep.  Carilu shows how Lovable is building a movement with real users as influencers, a CEO who lives on social, and a speed-first mindset tuned to the pace of AI and customer buzz.  Plus:  Why a "crazy ideas" budget creates room for standout plays that still satisfy the CFO  How empathy for sales and shared ownership of the number strengthen CRO-CMO alignment  How CEO-led social, customer stories, and edutainment power modern B2B brands  What it takes to move at AI speed while keeping product value and customer love at the center If you want a concentrated hit of CMO-level courage, alignment, and playmaking, this milestone episode is your highlight reel.  For full show notes and transcripts, visit https://renegademarketing.com/podcasts/ To learn more about CMO Huddles, visit https://cmohuddles.com/

- Ready to churn less and win more?

In today's episode, Elizabeth sits down with Amber Rae, bestselling author and speaker, to explore what happens when we choose safety over truth and the cost of staying in relationships that no longer align. Amber opens up about her experience in a sexless marriage, the fear of disappointing others, and the moment she realized love and safety are not the same thing. She shares how meeting her now husband became a catalyst for radical honesty and forced her to confront long buried wounds around intimacy, self worth, and abandonment.They dive into why so many women stay in “good enough” lives, how contrast reveals what is truly out of alignment, and what it means to take a trust fall toward growth instead of comfort. Amber also reflects on nervous system regulation, learning how to feel rather than fix emotions, becoming a mother, and how writing her memoir Lovable became an act of self liberation.Follow Amber RaeWebsite: https://amberrae.com/Instagram: https://www.instagram.com/heyamberrae/Follow usInstagram: https://www.instagram.com/thewellnessprocesspodTikTok: https://www.tiktok.com/@thewellnessprocessYouTube: https://www.youtube.com/@TheWellnessProcessProduced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Apresentado por Bruno Natal.--Loja RESUMIDO (camisetas, canecas, casacos, sacolas): https://www.studiogeek.com.br/resumido/--Faça sua assinatura!https://resumido.cc/assinatura--Alexandre Messina é embaixador e líder de Enterprise GTM da Lovable no Brasil, plataforma de inteligência artificial que permite a qualquer pessoa criar sites e aplicativos web funcionais. Ele também é MIT Innovator Under 35 em IA e professor convidado na Singularity e na Exame, onde ensina aplicações práticas de Vibe Coding para construir negócios dez vezes mais rápido. No RESUMIDO #346: Alexandre Messina co-fundou a Zaia, primeira plataforma de Agentes de IA da América Latina, com mais de 500 mil usuários em 6 meses. Fundou a Pedala, vendida para Americanas em 2019, onde depois liderou Corporate Venture Capital, avaliando centenas de startups e viabilizando dezenas de parcerias estratégicas.--Ouça e confira todos os links comentados no episódio: https://resumido.cc/

In this episode, Anderson Business Advisors host Clint Coons, Esq., sits down with Brian Hanson, co-founder of Real Advisors and AI for Business, to explore how artificial intelligence is revolutionizing real estate investing. Brian, who has been teaching business owners and investors about AI and marketing for several years, shares how investors can use AI to crunch massive amounts of data in seconds to identify the most predictable houses likely to sell — something that used to cost $20,000+ from data scientists. They discuss using humanized chatbots and voice bots that can have thousands of personalized conversations simultaneously without sounding robotic, automating follow-up sequences that never miss opportunities, and building custom apps in under five minutes without any coding knowledge. Brian reveals specific tools like Rest Bag for analyzing repair costs at 10 cents per photo, Yellow Pages Scraper for building 20,000-person cash buyer lists for just $80, and browser-use.com for creating custom APIs by simply showing the system what you do manually. As Brian explains, "I just don't think that most people really realize what's possible out there." The conversation covers everything from data mining and lead generation to creating high-converting marketing campaigns using competitive intelligence, virtual staging, and automation tools like Lovable, Google's AI Studio, Air DNA, House Canary, and Semrush. Tune in to discover how AI is the ultimate force multiplier for real estate investors looking to scale their businesses efficiently! Brian Hanson is the co-founder of Real Advisors and AI for Business. He got his start in real estate in his early 20s working with renowned real estate educator Ron LeGrand, where he developed a passion for marketing. Over the years, Brian has become obsessed with finding smarter, faster ways to grow businesses, and when AI emerged, he immediately recognized its transformative potential. Brian now teaches business owners and investors how to leverage AI to dramatically scale their operations, reduce costs, and increase output. He hosts the AI for Business podcast and regularly conducts three-day intensive training events where he shares cutting-edge AI strategies and tools. Brian's approach focuses on practical implementation—helping entrepreneurs automate processes, eliminate roadblocks, and achieve results they never thought possible. Highlights/Topics: (00:00) - Brian Hanson and the AI Opportunity (05:23) - Finding Off-Market Deals: Data Crunching and Lead Generation (11:35) - Automating Follow-Up and Conversations with AI (17:24) - Property Analysis, Contracts, and What AI Can't Replace (25:19) - Building Custom Apps in Minutes Without Coding (30:13) - AI-Powered Marketing and Competitive Intelligence (33:17) - Where to Learn More and Final Thoughts   Resources: https://podcasts.apple.com/ke/podcast/ai-for-business-podcast/id1821570230 https://www.linkedin.com/in/brian-hanson-1548797 https://www.facebook.com/brian.hanson1?mibextid=LQQJ4d https://events.aiforbusiness.com/ Schedule Your FREE Consultation https://andersonadvisors.com/strategy-session/?utm_source=ai-for-real-estate-investing&utm_medium=podcast Tax and Asset Protection Events https://andersonadvisors.com/real-estate-asset-protection-workshop-training/?utm_source=ai-for-real-estate-investing&utm_medium=podcast  Anderson Advisors https://andersonadvisors.com/ Anderson Advisors Podcast https://andersonadvisors.com/podcast/ Clint Coons YouTube https://www.youtube.com/channel/UC5GX-U6VbvMkhSM1ONBiW8w Anderson Advisors Tax Planning Appointment https://andersonadvisors.com/ss/  

Jason Lemkin is the founder of SaaStr, the world's largest community for software founders, and a veteran SaaS investor who has deployed over $200 million into B2B startups. After his last salesperson quit, Jason made a radical decision: replace his entire go-to-market team with AI agents. What started as an experiment has transformed into a new operating model, where 20 AI agents managed by just 1.2 humans now do the work previously handled by a team of 10 SDRs and AEs. In this conversation, Jason shares his hands-on experience implementing AI to run his sales org, including what works, what doesn't, and how the GTM landscape is quickly being transformed.We discuss:1. How AI is fundamentally changing the sales function2. Why most SDRs and BDRs will be “extinct” within a year3. What Jason is observing across his portfolio about AI adoption in GTM4. How to become “hyper-employable” in the age of AI5. The specific AI tools and tactics he's using that have been working best6. Practical frameworks for integrating AI into your sales motion without losing what works7. Jason's 2026 predictions on where SaaS and GTM are heading next—Brought to you by:DX—The developer intelligence platform designed by leading researchersVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Transcript: https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/182902716/my-biggest-takeaways-from-this-conversation—Where to find Jason Lemkin:• X: https://x.com/jasonlk• LinkedIn: https://www.linkedin.com/in/jasonmlemkin• Website: https://www.saastr.com• Substack: https://substack.com/@cloud—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 Jason Lemkin(04:36) What SaaStr does(07:13) AI's impact on sales teams(10:11) How SaaStr's AI agents work and their performance(14:18) How go-to-market is changing in the AI era(19:19) The future of SDRs, BDRs, and AEs in sales(22:03) Why leadership roles are safe(23:43) How to be in the 20% who thrive in the AI sales future(28:40) Why you shouldn't build your own AI tools(30:10) Specific AI agents and their applications(36:40) Challenges and learnings in AI deployment(42:11) Making AI-generated emails good (not just acceptable)(47:31) When humans still beat AI in sales(52:39) An overview of SaaStr's org(53:50) The role of human oversight in AI operations(58:37) Advice for salespeople and founders in the AI era(01:05:40) Forward-deployed engineers(01:08:08) What's changing and what's staying the same in sales(01:16:21) Why AI is creating more work, not less(01:19:32) Why Jason says these are magical times(01:25:25) The "incognito mode test" for finding AI opportunities(01:27:19) The impact of AI on jobs(01:30:18) Lightning round and final thoughts—Referenced:• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org• SaaStr Annual: https://www.saastrannual.com• Delphi: https://www.delphi.ai/saastr/talk• Amelia Lerutte on LinkedIn: https://www.linkedin.com/in/amelialerutte/• Vercel: https://vercel.com• What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google): https://www.lennysnewsletter.com/p/what-the-best-gtm-teams-do-differently• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• ElevenLabs: https://elevenlabs.io• The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack• Bolt: https://bolt.new• Lovable: https://lovable.dev• Harvey: https://www.harvey.ai• Samsara: https://www.samsara.com/products/platform/ai-samsara-intelligence• UiPath: https://www.uipath.com• Denise Dresser on LinkedIn: https://www.linkedin.com/in/denisedresser• Agentforce: https://www.salesforce.com/form/agentforce• SaaStr's AI Agent Playbook: https://saastr.ai/agents• Brian Halligan on LinkedIn: https://www.linkedin.com/in/brianhalligan• Brian Halligan's AI: https://www.delphi.ai/minds/bhalligan• Sierra: https://sierra.ai• Fin: https://fin.ai• Deccan: https://www.deccan.ai• Artisan: https://www.artisan.co• Qualified: https://www.qualified.com• Claude: https://claude.ai• HubSpot: https://www.hubspot.com• Gamma: https://gamma.app• Sam Blond on LinkedIn: https://www.linkedin.com/in/sam-blond-791026b• Brex: https://www.brex.com• Outreach: https://www.outreach.io• Gong: https://www.gong.io• Salesloft: https://www.salesloft.com• Mixmax: https://www.mixmax.com• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Clay: https://www.clay.com• Owner: https://www.owner.com• Momentum: https://www.momentum.io• Attention: https://www.attention.com• Granola: https://www.granola.ai• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Palantir: https://www.palantir.com• Databricks: https://www.databricks.com• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan• Rippling: https://www.rippling.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• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Pluribus on AppleTV+: https://tv.apple.com/us/show/pluribus/umc.cmc.37axgovs2yozlyh3c2cmwzlza• Sora: https://openai.com/sora• Reve: https://app.reve.com• Everything That Breaks on the Way to $1B ARR, with Mailchimp Co-Founder Ben Chestnut: https://www.saastr.com/everything-that-breaks-on-the-way-to-1b-arr-with-mailchimp-co-founder-ben-chestnut/• The Revenue Playbook: Rippling's Top 3 Growth Tactics at Scale, with Rippling CRO Matt Plank: https://www.youtube.com/watch?v=h3eYtzBpjRw• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths—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

En 2025, une nouvelle expression s'est imposée dans le vocabulaire de la tech : le « vibe coding ». Derrière ce terme intrigant se cache une pratique qui transforme en profondeur la manière de développer des logiciels.Le vibe coding, que l'on peut traduire par « programmation intuitive », désigne une approche où le développeur ne code plus ligne par ligne, mais décrit simplement ce qu'il souhaite obtenir à une intelligence artificielle. Popularisé par Andrei Karpathy, ancien responsable de l'IA chez Tesla et cofondateur d'OpenAI, ce concept est né dans les communautés de développeurs avant de se diffuser largement dans l'écosystème numérique.Concrètement, il suffit désormais de formuler une demande en langage naturel : créer un script Python, concevoir une page web avec un formulaire, modifier l'interface d'une application ou même développer un jeu ou une application mobile complète. Cette méthode permet un gain de temps spectaculaire et ouvre la création logicielle à des non-développeurs, capables de produire des outils fonctionnels pour le web, le mobile ou des usages métiers comme des CMS ou des ERP.De nombreux outils incarnent cette tendance, à commencer par GitHub Copilot, mais aussi Cursor, Windsurf ou des assistants généralistes comme ChatGPT, Claude ou Gemini, qui génèrent du code à intégrer ensuite de manière classique. D'autres solutions vont plus loin encore, en produisant directement des applications prêtes à l'emploi, comme le propose la startup suédoise Lovable.Dans cet épisode, Sébastien Stormacq, responsable des relations développeurs chez AWS, partage une expérience concrète : la création, en une heure et sans écrire une seule ligne de code, d'un jeu inspiré de Pac-Man grâce au vibe coding. Un exemple révélateur de la puissance, mais aussi des limites de cette approche.Le phénomène soulève des questions cruciales : qualité et sécurité du code généré, risques de bugs majeurs, mais aussi impact sur l'emploi. Si le vibe coding accélère le travail des équipes et augmente la productivité des développeurs expérimentés, il fragilise davantage les profils juniors. Une chose est sûre : plus qu'un simple outil, le vibe coding redéfinit en profondeur le métier de développeur.-----------♥️ Soutien : https://mondenumerique.info/don

In this expert interview, Sarah Doody is joined by Patrick Neeman, Director of UX & AI Experiences at Workday, to pull back the curtain on how UX hiring actually works today—and where candidates are getting tripped up.Patrick brings a rare perspective: he's led UX teams, taught UX at General Assembly, worked inside applicant tracking systems, and now hires designers in an AI-driven product environment. Together, Sarah and Patrick unpack the biggest misconceptions about ATS systems, why portfolios often fail the six-second test, how soft skills influence hiring decisions, and what senior designers really need to focus on to stand out in today's market.This episode is especially valuable if you're making it to interviews but not offers, feeling unsure how AI fits into your skillset, or questioning whether your resume and portfolio are helping—or hurting—you.What You'll Learn in This Episode:✔️ Why companies are often bad at hiring—and how that impacts candidates✔️ The truth about ATS filters, knockout questions, and resume formatting✔️ Why two-column resumes fail ATS systems (and what to do instead)✔️ What hiring managers notice in the first 6 seconds of reviewing a resume✔️ How soft skills like alignment, collaboration, and communication influence hiring✔️ Why decks often outperform portfolio websites in UX interviews✔️ How AI tools like Lovable are changing expectations for prototyping✔️ The role of “weak ties” in landing jobs—and why relationships matter more than applications✔️ Red flags candidates should avoid during interviews and outreach✔️ Why being “nice to work with” is a real career advantageLinks From This Episode:Patrick's Book: uxGPT: Mastering AI Assistants for User Experience Designers and Product Management ProfessionalsPatrick's Article: What's makes an effective UX professionalPatrick's Article: What's your Ideal Designer Profile?The Strength of Weak Ties: A Network Theory RevisitedThe ADP Checklist: Resources about Resumes, Portfolios and Interviews for UX ProfessionalsTimestamps:00:00 Introduction to Sarah Doody and Career Strategy Lab00:38 Welcoming Patrick Neiman: Insights into UX Hiring01:19 Patrick's Background and Experience04:19 The State of the UX Job Market07:21 The Importance of Writing Skills in UX08:49 Applicant Tracking Systems and AI in Hiring13:28 Contract Roles in UX: Myths and Realities14:42 Standing Out as a UX Candidate17:48 Soft Skills: The Superpower of UX Professionals22:05 Tips for Early Career UX Designers24:15 Prototyping vs. Figma: The Future of Design24:28 The Value of Personal Projects in Portfolios24:57 Challenges in Redesigning Complex Systems26:10 Misconceptions About Hiring Software27:23 The Six-Second Resume Test29:16 Networking and the Power of Weak Ties33:10 Tips for Advancing in Your UX Career41:46 Balancing Figma and AI-Assisted Design Tools43:21 Final Thoughts and Advice for Job Seekers

Tomek wrócił z nart, a w świecie tech... lawina newsów! ⛷️ Wojtek, Tomek i Sebastian biorą na warsztat tydzień pełen miliardowych transakcji, nowych unicornów i agentów, którzy sami naprawiają kod.O czym rozmawiamy?

Lovable CEO Anton Osika joins the AI Daily Brief to unpack how AI-assisted coding evolved from early GitHub experiments into load-bearing infrastructure inside companies, why 2025 marked the inflection point for vibe coding, and why 2026 will belong to builders who can think, plan, and ship with AI end to end. The conversation covers the shift from prototypes to production, how enterprises are rethinking workflows and SaaS, the rise of personal and ephemeral software, and what skills will actually matter as AI takes on more of the mechanics of building. 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/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.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

Has design work been made obsolete by tools like lovable?Lovable - https://lovable.dev/ Free Email Course - https://bootstrappersparadise.com/courseOnline Community - https://bootstrappersparadise.com/communityBootstrapper's Paradise - https://bootstrappersparadise.com/

Here's what I'm building with as of Dec 2025.Lovable - https://lovable.dev/Cursor - https://www.cursor.comClaude Code - https://claude.ai/ (Sonnet/Ops 4.5)Vercel - https://vercel.com/Railway - https://railway.com/Supabase - https://supabase.com/ Free Email Course - https://bootstrappersparadise.com/courseOnline Community - https://bootstrappersparadise.com/communityBootstrapper's Paradise - https://bootstrappersparadise.com/

Nvidia übernimmt die Assets des Chip-Startups Groq für 20 Milliarden Dollar. KI hat 2025 über 50 neue Milliardäre geschaffen, darunter die Gründer von Cursor, Lovable und 11 Labs. OpenAI veröffentlicht Nutzungsdaten: 90 Prozent der User machen weniger als fünf Anfragen pro Tag, nur 5 Prozent zahlen für den Service. Die New York Times vergleicht Tesla und Waymo: Tesla hat 30 Robotaxis in Austin, Waymo 2500 insgesamt. Das Manager Magazin deckt den Closed-Skandal auf: Der CFO der Hamburger Modemarke hat sich mutmaßlich 20 Millionen Euro von der Firma geliehen. Die USA sanktionieren fünf europäische Bürger, darunter Ex-EU-Kommissar Thierry Breton und die Geschäftsführerinnen von HateAid. Elon Musk wird zum unbeliebtesten Tech-Leader 2025 gewählt. Apple muss durch den Digital Markets Act Proximity Pairing und Notifications für Drittanbieter öffnen. Und 61 Prozent der US-Pastoren nutzen inzwischen KI für ihre Predigten. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:01:15) Nvidia kauft Groq für 20 Mrd. (00:17:16) 50 neue KI-Milliardäre 2025 (00:27:29) OpenAI Nutzungsdaten: 90% unter 5 Anfragen/Tag (00:30:45) OpenAI Prompt Packs für Berufsgruppen (00:37:43) Tesla vs Waymo: 30 vs 2500 Robotaxis (00:44:52) Closed-Insolvenz: CFO leiht sich 20 Mio. (00:54:42) USA sanktionieren HateAid & Thierry Breton (01:01:04) Elon Musk unbeliebtester Tech-Leader (01:05:15) Epstein-Akten: Adobe-Schwärzung versagt (01:06:15) Apple öffnet AirPods-Kopplung (EU DMA) (01:09:21) 61% der Pastoren nutzen KI für Predigten Shownotes Nvidia wirbt Ingenieure von KI-Startup Groq ab - manager-magazin.de KI schuf über 50 neue Milliardäre 2025 - forbes.com Benedick Evans- linkedin.com OpenAI Prompt Packs - academy.openai.com Tesla Robotaxis in Austin: Konkurrenz für Waymo - nytimes.com Insolvenz der Modemarke: So ruinierten die Chefs alles - manager-magazin.de Breton plant Tech-Verbot - apnews.com Marco Rubio - patreon.com Elon Musk mochte Tech nicht - cybernews.com Musk Weihnachts Tweet - x.com Epstein-Akten: DOJ-Streichungen und Links - theverge.com will the robot shoot the human? - youtu.be iOS 26.3: AirPods-Kopplung verbessern - macrumors.com Pastors KI-Predigt - cybernews.com Glöcki KI Weihnachtsvideo - youtube.com

Our 229th episode with a summary and discussion of last week's big AI news!Recorded on 12/19/2025Hosted by Andrey Kurenkov and Jeremie HarrisFeel 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:Notable releases include OpenAI's GPT-5.2 Codex for advanced coding and Google's Gemini Free Flash for competitive AI application performance. Nvidia's new open-source Trion-3 models also showcase impressive benchmarks.Funding updates highlight Lovable's $330M Series B, valuing the AI coding startup at $6.6B, and Faya's $140M Series D for AI model hosting, valued at $4.5B.China makes significant strides in semiconductor technology with advances in EUV lithography machines, led by Huawei and SMIC, potentially disrupting global chip manufacturing dominance.Key safety and policy updates include OpenAI's GPT-5.2 system card focusing on biosecurity and cybersecurity risks, while Google partners with the US military to power a new AI platform with Gemini models.Timestamps:(00:00:10) Intro / Banter(00:02:09) News PreviewTools & Apps(00:02:56) Google launches Gemini 3 Flash, makes it the default model in the Gemini app | TechCrunch(00:10:13) ChatGPT launches an app store, lets developers know it's open for business | TechCrunch(00:13:35) Introducing GPT-5.2-Codex | OpenAI(00:19:23) Story about OpenAI release - GPT image 1.5(00:22:27) Meta partners with ElevenLabs to power AI audio across Instagram, Horizon - The Economic TimesApplications & Business(00:23:16) OpenAI to End Equity Vesting Period for Employees, WSJ Says(00:28:20) How China built its ‘Manhattan Project' to rival the West in AI chips(00:36:47) China's Huawei, SMIC Make Progress With Chips, Report Finds(00:41:03) OpenAI in Talks to Raise At Least $10 Billion From Amazon and Use Its AI Chips(00:43:32) Amazon has a new leader for its ‘AGI' group as it plays catch-up on AI | The Verge(00:47:27) Broadcom reveals its mystery $10 billion customer is Anthropic(00:49:12) Vibe-coding startup Lovable raises $330M at a $6.6B valuation | TechCrunch(00:50:38) Fal nabs $140M in fresh funding led by Sequoia, tripling valuation to $4.5B | TechCrunchProjects & Open Source(00:51:10) Nvidia Becomes a Major Model Maker With Nemotron 3 | WIRED(00:59:24) Meta introduces new SAM AI able to isolate and edit audio • The Register(00:59:54) [2512.14856] T5Gemma 2: Seeing, Reading, and Understanding Longer(01:03:10) Anthropic makes agent Skills an open standard - SiliconANGLEResearch & Advancements(01:03:47) Budget-Aware Tool-Use Enables Effective Agent Scaling(01:08:21) Rethinking Thinking Tokens: LLMs as Improvement Operators(01:10:50) What if AI capabilities suddenly accelerated in 2027? How would the world know?Policy & Safety(01:12:58) Update to GPdfT-5 System Card: GPT-5.2(01:18:04) Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors(01:20:47) Async Control: Stress-testing Asynchronous Control Measures for LLM Agents(01:24:37) Google is powering a new US military AI platform | The VergeSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We're excited to bring on Elena Levi, Director of Product Management at Payoneer, data analytics veteran, and passionate advocate for product-driven teams, for a special episode exploring what it's really like to use Lovable and other AI-powered vibe coding tools in product development.Elena shares insights from 15 years in data analytics and product, with the journey from data analyst to product leadership fueling her curiosity about how AI can reshape prototyping, design, and collaboration. Drawing from hands-on experience building predictive analytics solutions, Elena reveals why she chose Lovable for fast prototyping, user testing, product sense interviews, and collaborating with both developers and designers.Join Matt, Moshe, and Elena as they explore:The strengths and limitations of Lovable for prototyping: rapid iteration, easy sharing, changing flows on the fly, user testing, and developer handoffWhen vibe coding works, and where you still need engineering and design expertiseThe realities of code generation, versioning, Supabase integration, and why Lovable stood out from the competition at the time she chose itUsing Lovable for product sense interviewsPractical tips: breaking tasks into smaller prompts, saving tokens with up-front documents, and why the first prompt is the most importantThe trade-offs of using AI tools for MVPs, B2B vs. B2C products, and where privacy and maintainability concerns come inResponses from engineers and designers, what these tools mean for their work, learning curves, and whether they help or hinder junior team membersExpectations vs. reality: how close AI tools get you to the finish line, and why “the last mile” is the toughestConundrums, gotchas, frustrations, and how to keep flexibility in your workflowWhy do PMs must always ask “Why?”, and why AI alone can't replace a critical data mindsetAnd much more!Want to connect with Elena or learn more?LinkedIn: https://www.linkedin.com/in/elena-levi-dataYou can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproductMoshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

$330M powers Lovable $6.6B destiny in no-code AI. Magic.link auths. Stars align.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.

$6.6B ignited by $330M for Lovable's dev dream. Buildship automations flow. Growth galactic.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.

У свіжому дайджесті DOU News говоримо про стан ІТ-ринку у 2025 році, стрімке зростання української мови в ШІ та податкові зміни для ФОПів. А ще — про нові релізи Google й OpenAI, великі інвестиції в ШІ-стартапи, слово року та інші теми українського ІТ та світового тек-сектору. Таймкоди 00:00 Інтро 00:23 Хто почувається краще на ІТ-ринку у 2025 році 06:29 Українська мова — найшвидше зростає в open-source ШІ 07:49 ПДВ для ФОПів: що пропонує Мінфін 11:15 Скільки користувачів у нового застосунку «Нової пошти» 13:05 Direct to Cell від «Київстар» став доступним для iPhone 13:56 Зміни цін на GitHub Actions 19:43 Google представила Gemini 3 Flash 22:52 OpenAI запустила ChatGPT Images 24:50 OpenAI шукає фінансування до $100 млрд 26:53 Vibe-coding стартап Lovable залучив $330 млн 29:10 Слово року 2025 — slop 31:10 Google припиняє dark web-сповіщення 32:58 Чергове дивне рішення росії 35:56 Starlink втратив супутник через аномалію 38:06 Що цього тижня рекомендує Женя: Стаття: Cloudflare Radar Year Review Книга: «Хроніки Буресвітла», книга 5 — «Вітер і істина» Серіал: «Андор» Фільм: «Ти — космос» Музичний альбом: Arcane S2 OST (honorable mention — «Поле каніфолі») Музичне відкриття: Клер (на вінілі)

$330M catapults Lovable to $6.6B unicorn status in AI dev. Figma import accelerates design-to-code. Metrics scream scalability.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.

Lovable leaps to $6.6B on $330M wings for no-expertise app building. Supabase backend pairs perfectly. Enterprise ARR ramps.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.

Lovable achieves $6.6B valuation boosted by $330M for generative dev. Voice prompts expand accessibility. Rapid onboarding converts free users fast.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.

Lovable leaps to $6.6B valuation on $330M raise, redefining software development paradigms. Iterative refinement via chat makes complex apps accessible. Backers eye billion-user potential.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.

As one of the fastest growing companies in the world, Lovable is scaling their design team by tapping into the [Dive Talent Network](https://www.dive.club/talent-network).So I interviewed their Head of Design, [Nad Chishtie](https://x.com/nadonomy), to learn everything I can about how to get hired as a designer at Lovable.Some highlights:- A breakdown of a recent design hire's portfolio- How to avoid getting your portfolio screened out- How to crush the later stages of the hiring process- How you can win Nad over with side projects alone- What to do if you're not confident in your visual skills- The 2 things Nad cares most about when hiring designers- Spotify's cross-functional squad model (https://medium.com/found-ation/agile-team-organization-a-deep-dive-on-the-spotify-model-f5b32dfc37dd)- Matt's portfolio (designer hired through Dive Talent Network) (https://www.lfs.gd/)

Die Woche der Mega-Funding-Runden: Databricks wird mit 134 Milliarden bewertet, Waymo peilt 100 Milliarden an, und OpenAI soll bald 830 Milliarden wert sein. Amazon will 10 Milliarden in OpenAI investieren. Coinbase launcht Prediction Markets und tokenisierte Aktien. Revolut plant 3,5 Milliarden Profit bei 40% Marge – profitabler als die meisten Banken. Lovable aus Schweden rast auf 6,6 Milliarden. Das US-Handelsministerium droht der EU mit Vergeltung wegen Tech-Regulierung und nennt explizit SAP, Siemens und DHL. Der TikTok-Deal soll im Januar kommen – Oracle, Silver Lake und Abu Dhabi übernehmen 45%. Ein Andreessen-Startup baut synthetische KI-Influencer mit Phone Farms. Instacart steht wegen KI-Preismanipulation unter FTC-Beschuss. Yann LeCun gründet Ami Labs mit 500 Millionen Seed. Trade Republic wird mit 12,5 Milliarden bewertet. Trump Media fusioniert mit einer Fusionsenergie-Firma. Wann kommt es zur großen Doppelgänger Cola Blindverkostung? Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:00:49) Coinbase: Aktien & Prediction Markets (00:08:14) Databricks 134 Mrd. Bewertung (00:13:39) Revolut: 40% Profitmarge (00:19:26) Waymo 100 Mrd. Bewertung (00:36:15) Oscars wechseln zu YouTube (00:37:22) Sam Altman: KI wie Zahnpasta? (00:46:16) Lovable 6,6 Mrd. Bewertung (00:49:53) Amazon investiert 10 Mrd. in OpenAI (00:53:48) OpenAI 830 Mrd. Bewertung (00:56:55) Trump bedroht EU wegen Tech-Regulierung (01:00:56) Andreessen-Startup baut Fake-Influencer (01:08:13) Instacart KI-Preismanipulation (01:12:25) Yann LeCun gründet Ami Labs (01:14:50) TikTok-Deal im Januar (01:18:47) Trump Media fusioniert mit Fusionsfirma (01:24:14) Trade Republic 12,5 Mrd. Shownotes Coinbase Prognosemärkte Aktienhandel Stablecoins - cnbc.com Databricks sammelt Kapital bei 134-Milliarden-Bewertung - wsj.com Revolut strebt 2026 $9B Umsatz und $3.5B Gewinn an. - connectingthedotsinfin.tech Waymo plant Finanzierung bei 100-Milliarden-Bewertung - bloomberg.com Oscars wechseln 2029 von ABC zu YouTube - hollywoodreporter.com OpenAI ChatGPT verbessert Bilderstellung - bloomberg.com OpenAI-Gespräche: 10 Milliarden von Amazon für KI-Chips - theinformation.com OpenAI neue Finanzierungsrunde könnte Startup mit bis zu 83 Milliarden bewerten - wsj.com Start-up Lovable sammelt 330 Millionen ein - nytimes.com EU-Strafen für US-Tech-Unternehmen - nytimes.com Hack enthüllt a16z-unterstützte Telefonfarm, die TikTok mit KI-Influencern flutet - 404media.co FTC untersucht Instacarts KI-Preistool - reuters.com Instacart FTC Vergleich Täuschende Abrechnung - cnbc.com Seb Johnson auf X: "Metas ehemaliger Chief AI Officer sammelt €500 Mio. ein. - x.com TikTok schließt Verkauf seiner US-Einheit nach jahrelanger Saga ab. - axios.com Trump Media - ft.com Es wird kein Armut geben, universelles hohes Einkommen. - x.com Trade Republic: Zwei reiche europäische Familien beteiligen sich am Milliardendeal - manager-magazin.de Phishing-Versuch bei Outfittery: Datenleck beim Bekleidungshändler? - heise.de

Welcome to AI Unraveled (December 19, 2025): Your daily strategic briefing on the business impact of artificial intelligence.Listen at https://podcasts.apple.com/us/podcast/ai-daily-news-rundown-and-what-it-means-for-your/id1684415169?i=1000742031008Key Topics:

Elena Verna is the head of growth at Lovable, the leading AI-powered app builder that hit $200 million in annual recurring revenue in under a year with just 100 employees. In this record fourth appearance on the podcast, Elena shares how the traditional growth playbook has been completely rewritten for AI companies. She explains why Lovable focuses on innovation over optimization, how they've shifted from activation to building new features, and why giving away their product for free has become their most powerful growth strategy.We discuss:1. Why 60% to 70% of traditional growth tactics no longer apply in AI2. Why you have to re-find product-market fit every 3 months3. The specific growth tactics driving Lovable's unprecedented growth4. Why giving away product is a growth strategy that beats paid ads5. “Minimum lovable product” as the new standard (not minimum viable product)6. Why activation now belongs to product teams, not growth teams7. Whether you should join an AI startup (honest tradeoffs)—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webPersona—A global leader in digital identity verification—Transcript: ⁠https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna⁠—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/181207556/my-biggest-takeaways-from-this-conversation⁠—Where to find Elena Verna:• X: https://x.com/elenaverna• LinkedIn: https://www.linkedin.com/in/elenaverna• Newsletter: https://www.elenaverna.com—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 Elena Verna(05:19) The scale and growth of Lovable(08:55) Confidence in Lovable as a business(12:17) Retention at Lovable(15:02) Lovable's unique growth levers(28:13) The role of marketing in Lovable's success(38:09) Launching new features(40:59) Hiring and team dynamics(43:17) The value of vibe coding(49:46) The importance of community(51:47) Giving away your product for free(56:26) Tripling their company size(01:00:23) Product-market-fit challenges(01:08:50) Advice for joining AI companies(01:12:00) Work-life balance(01:15:20) What it's like to work at Lovable(01:19:45) Women in tech(01:25:29) Final thoughts and lightning round—Referenced:• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna• 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• Stripe: https://stripe.com• What differentiates the highest-performing product teams | John Cutler (Amplitude, The Beautiful Mess): https://www.lennysnewsletter.com/p/what-differentiates-the-highest-performing• How to win in the AI era: Ship a feature every week, embrace technical debt, ruthlessly cut scope, and create magic your competitors can't copy | Gaurav Misra (CEO and co-founder of Captions): https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra• “Dumbest idea I've heard” to $100M ARR: Inside the rise of Gamma | Grant Lee (CEO): https://www.lennysnewsletter.com/p/how-50-people-built-a-profitable-ai-unicorn• Eric Ries on LinkedIn: https://www.linkedin.com/in/eries• Elena's post on LinkedIn about Lovable Missions: https://www.linkedin.com/posts/elenaverna_everythingispossible-lovableway-activity-7401627519646474242-hn6e• SheBuilds: https://shebuilds.lovable.app• Shopify + Lovable: https://lovable.dev/shopify• The Product-Market Fit Treadmill: Why every AI company is sprinting just to stay in place: https://www.elenaverna.com/p/the-product-market-fit-treadmill• 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• Unorthodox frameworks for growing your product, career, and impact | Bangaly Kaba (YouTube, Instagram, Facebook, Instacart): https://www.lennysnewsletter.com/p/frameworks-for-growing-your-career-bangaly-kaba• The adjacent user: https://brianbalfour.com/quick-takes/the-adjacent-user• Granola: https://www.granola.ai• Wispr Flow: https://wisprflow.ai• I'm worried about women in tech: https://www.elenaverna.com/p/im-worried-about-women-in-tech• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield—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

In this episode, we break down how vibe-coding startup Lovable secured a massive $330M funding round at a $6.6B valuation and what that says about investor appetite for developer-first AI tools. In this episode, we explore what “vibe-coding” actually means, why Lovable is scaling so fast, and how this raise could shape the future of software creation.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.

Lovable has seen its valuation more than triple just five months after the company's last funding round in July. Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, I'm taking you behind the scenes of a live app build where I create a fully functional mental health therapy app from scratch - in under an hour. No coding experience required. What makes this episode special is that it's completely unscripted and live. You'll watch as I prompt Lovable.dev with a single request and see it generate production-ready code, databases, and integrations in real-time. I also show how to create custom graphics using Google's Gemini Pro for a more personalized touch. Whether you're a coach, consultant, entrepreneur, or just someone with an app idea, this episode will show you exactly how to transform your vision into a working product that can take payments tomorrow. I walk through every step, share the exact prompts I use, and explain how these tools are democratizing software development.

TLDR: It was Claude :-)When I set out to compare ChatGPT, Claude, Gemini, Grok, and ChatPRD for writing Product Requirement Documents, I figured they'd all be roughly equivalent. Maybe some subtle variations in tone or structure, but nothing earth-shattering. They're all built on similar transformer architectures, trained on massive datasets, and marketed as capable of handling complex business writing.What I discovered over 45 minutes of hands-on testing revealed not just which tools are better for PRD creation, but why they're better, and more importantly, how you should actually be using AI to accelerate your product work without sacrificing quality or strategic thinking.If you're an early or mid-career PM in Silicon Valley, this matters to you. Because here's the uncomfortable truth: your peers are already using AI to write PRDs, analyze features, and generate documentation. The question isn't whether to use these tools. The question is whether you're using the right ones most effectively.So let me walk you through exactly what I did, what I learned, and what you should do differently.The Setup: A Real-World Test CaseHere's how I structured the experiment. As I said at the beginning of my recording, “We are back in the Fireside PM podcast and I did that review of the ChatGPT browser and people seemed to like it and then I asked, uh, in a poll, I think it was a LinkedIn poll maybe, what should my next PM product review be? And, people asked for ChatPRD.”So I had my marching orders from the audience. But I wanted to make this more comprehensive than just testing ChatPRD in isolation. I opened up five tabs: ChatGPT, Claude, Gemini, Grok, and ChatPRD.For the test case, I chose something realistic and relevant: an AI-powered tutor for high school students. Think KhanAmigo or similar edtech platforms. This gave me a concrete product scenario that's complex enough to stress-test these tools but straightforward enough that I could iterate quickly.But here's the critical part that too many PMs get wrong when they start using AI for product work: I didn't just throw a single sentence at these tools and expect magic.The “Back of the Napkin” Approach: Why You Still Need to Think“I presume everybody agrees that you should have some formulated thinking before you dump it into the chatbot for your PRD,” I noted early in my experiment. “I suppose in the future maybe you could just do, like, a one-sentence prompt and come out with the perfect PRD because it would just know everything about you and your company in the context, but for now we're gonna do this more, a little old-school AI approach where we're gonna do some original human thinking.”This is crucial. I see so many PMs, especially those newer to the field, treat AI like a magic oracle. They type in “Write me a PRD for a social feature” and then wonder why the output is generic, unfocused, and useless.Your job as a PM isn't to become obsolete. It's to become more effective. And that means doing the strategic thinking work that AI cannot do for you.So I started in Google Docs with what I call a “back of the napkin” PRD structure. Here's what I included:Why: The strategic rationale. In this case: “Want to complement our existing edtech business with a personalized AI tutor, uh, want to maintain position industry, and grow through innovation. on mission for learners.”Target User: Who are we building for? “High school students interested in improving their grades and fundamentals. Fundamental knowledge topics. Specifically science and math. Students who are not in the top ten percent, nor in the bottom ten percent.”This is key—I got specific. Not just “students,” but students in the middle 80%. Not just “any subject,” but science and math. This specificity is what separates useful AI output from garbage.Problem to Solve: What's broken? “Students want better grades. Students are impatient. Students currently use AI just for finding the answers and less to, uh, understand concepts and practice using them.”Key Elements: The feature set and approach.Success Metrics: How we'd measure success.Now, was this a perfectly polished PRD outline? Hell no. As you can see from my transcript, I was literally thinking out loud, making typos, restructuring on the fly. But that's exactly the point. I put in maybe 10-15 minutes of human strategic thinking. That's all it took to create a foundation that would dramatically improve what came out of the AI tools.Round One: Generating the Full PRDWith my back-of-the-napkin outline ready, I copied it into each tool with a simple prompt asking them to expand it into a more complete PRD.ChatGPT: The Reliable GeneralistChatGPT gave me something that was... fine. Competent. Professional. But also deeply uninspiring.The document it produced checked all the boxes. It had the sections you'd expect. The writing was clear. But when I read it, I couldn't shake the feeling that I was reading something that could have been written for literally any product in any company. It felt like “an average of everything out there,” as I noted in my evaluation.Here's what ChatGPT did well: It understood the basic structure of a PRD. It generated appropriate sections. The grammar and formatting were clean. If you needed to hand something in by EOD and had literally no time for refinement, ChatGPT would save you from complete embarrassment.But here's what it lacked: Depth. Nuance. Strategic thinking that felt connected to real product decisions. When it described the target user, it used phrases that could apply to any edtech product. When it outlined success metrics, they were the obvious ones (engagement, retention, test scores) without any interesting thinking about leading indicators or proxy metrics.The problem with generic output isn't that it's wrong, it's that it's invisible. When you're trying to get buy-in from leadership or alignment from engineering, you need your PRD to feel specific, considered, and connected to your company's actual strategy. ChatGPT's output felt like it was written by someone who'd read a lot of PRDs but never actually shipped a product.One specific example: When I asked for success metrics, ChatGPT gave me “Student engagement rate, Time spent on platform, Test score improvement.” These aren't wrong, but they're lazy. They don't show any thinking about what specifically matters for an AI tutor versus any other educational product. Compare that to Claude's output, which got more specific about things like “concept mastery rate” and “question-to-understanding ratio.”Actionable Insight: Use ChatGPT when you need fast, serviceable documentation that doesn't need to be exceptional. Think: internal updates, status reports, routine communications. Don't rely on it for strategic documents where differentiation matters. If you do use ChatGPT for important documents, treat its output as a starting point that needs significant human refinement to add strategic depth and company-specific context.Gemini: Better Than ExpectedGoogle's Gemini actually impressed me more than I anticipated. The structure was solid, and it had a nice balance of detail without being overwhelming.What Gemini got right: The writing had a nice flow to it. The document felt organized and logical. It did a better job than ChatGPT at providing specific examples and thinking through edge cases. For instance, when describing the target user, it went beyond demographics to consider behavioral characteristics and motivations.Gemini also showed some interesting strategic thinking. It considered competitive positioning more thoughtfully than ChatGPT and proposed some differentiation angles that weren't in my original outline. Good AI tools should add insight, not just regurgitate your input with better formatting.But here's where it fell short: the visual elements. When I asked for mockups, Gemini produced images that looked more like stock photos than actual product designs. They weren't terrible, but they weren't compelling either. They had that AI-generated sheen that makes it obvious they came from an image model rather than a designer's brain.For a PRD that you're going to use internally with a team that already understands the context, Gemini's output would work well. The text quality is strong enough, and if you're in the Google ecosystem (Docs, Sheets, Meet, etc.), the integration is seamless. You can paste Gemini's output directly into Google Docs and continue iterating there.But if you need to create something compelling enough to win over skeptics or secure budget, Gemini falls just short. It's good, but not great. It's the solid B+ student: reliably competent but rarely exceptional.Actionable Insight: Gemini is a strong choice if you're working in the Google ecosystem and need good integration with Docs, Sheets, and other Google Workspace tools. The quality is sufficient for most internal documentation needs. It's particularly good if you're working with cross-functional partners who are already in Google Workspace. You can share and collaborate on AI-generated drafts without friction. But don't expect visual mockups that will wow anyone, and plan to add your own strategic polish for high-stakes documents.Grok: Not Ready for Prime TimeLet's just say my expectations were low, and Grok still managed to underdeliver. The PRD felt thin, generic, and lacked the depth you need for real product work.“I don't have high expectations for grok, unfortunately,” I said before testing it. Spoiler alert: my low expectations were validated.Actionable Insight: Skip Grok for product documentation work right now. Maybe it'll improve, but as of my testing, it's simply not competitive with the other options. It felt like 1-2 years behind the others.ChatPRD: The Specialized ToolNow this was interesting. ChatPRD is purpose-built for PRDs, using foundational models underneath but with specific tuning and structure for product documentation.The result? The structure was logical, the depth was appropriate, and it included elements that showed understanding of what actually matters in a PRD. As I reflected: “Cause this one feels like, A human wrote this PRD.”The interface guides you through the process more deliberately than just dumping text into a general chat interface. It asks clarifying questions. It structures the output more thoughtfully.Actionable Insight: If you're a technical lead without a dedicated PM, or you're a PM who wants a more structured approach to using AI for PRDs, ChatPRD is worth the specialized focus. It's particularly good when you need something that feels authentic enough to share with stakeholders without heavy editing.Claude: The Clear WinnerBut the standout performer, and I'm ranking these, was Claude.“I think we know that for now, I'm gonna say Claude did the best job,” I concluded after all the testing. Claude produced the most comprehensive, thoughtful, and strategically sound PRD. But what really set it apart were the concept mocks.When I asked each tool to generate visual mockups of the product, Claude produced HTML prototypes that, while not fully functional, looked genuinely compelling. They had thoughtful UI design, clear information architecture, and felt like something that could actually guide development.“They were, like, closer to, like, what a Lovable would produce or something like that,” I noted, referring to the quality of low-fidelity prototypes that good designers create.The text quality was also superior: more nuanced, better structured, and with more strategic depth. It felt like Claude understood not just what a PRD should contain, but why it should contain those elements.Actionable Insight: For any PRD that matters, meaning anything you'll share with leadership, use to get buy-in, or guide actual product development, you might as well start with Claude. The quality difference is significant enough that it's worth using Claude even if you primarily use another tool for other tasks.Final Rankings: The Definitive HierarchyAfter testing all five tools on multiple dimensions: initial PRD generation, visual mockups, and even crafting a pitch paragraph for a skeptical VP of Engineering, here's my final ranking:* Claude - Best overall quality, most compelling mockups, strongest strategic thinking* ChatPRD - Best for structured PRD creation, feels most “human”* Gemini - Solid all-around performance, good Google integration* ChatGPT - Reliable but generic, lacks differentiation* Grok - Not competitive for this use case“I'd probably say Claude, then chat PRD, then Gemini, then chat GPT, and then Grock,” I concluded.The Deeper Lesson: Garbage In, Garbage Out (Still Applies)But here's what matters more than which tool wins: the realization that hit me partway through this experiment.“I think it really does come down to, like, you know, the quality of the prompt,” I observed. “So if our prompt were a little more detailed, all that were more thought-through, then I'm sure the output would have been better. But as you can see we didn't really put in brain trust prompting here. Just a little bit of, kind of hand-wavy prompting, but a little better than just one or two sentences.”And we still got pretty good results.This is the meta-insight that should change how you approach AI tools in your product work: The quality of your input determines the quality of your output, but the baseline quality of the tool determines the ceiling of what's possible.No amount of great prompting will make Grok produce Claude-level output. But even mediocre prompting with Claude will beat great prompting with lesser tools.So the dual strategy is:* Use the best tool available (currently Claude for PRDs)* Invest in improving your prompting skills ideally with as much original and insightful human, company aware, and context aware thinking as possible.Real-World Workflows: How to Actually Use This in Your Day-to-Day PM WorkTheory is great. Here's how to incorporate these insights into your actual product management workflows.The Weekly Sprint Planning WorkflowEvery PM I know spends hours each week preparing for sprint planning. You need to refine user stories, clarify acceptance criteria, anticipate engineering questions, and align with design and data science. AI can compress this work significantly.Here's an example workflow:Monday morning (30 minutes):* Review upcoming priorities and open your rough notes/outline in Google Docs* Open Claude and paste your outline with this prompt:“I'm preparing for sprint planning. Based on these priorities [paste notes], generate detailed user stories with acceptance criteria. Format each as: User story, Business context, Technical considerations, Acceptance criteria, Dependencies, Open questions.”Monday afternoon (20 minutes):* Review Claude's output critically* Identify gaps, unclear requirements, or missing context* Follow up with targeted prompts:“The user story about authentication is too vague. Break it down into separate stories for: social login, email/password, session management, and password reset. For each, specify security requirements and edge cases.”Tuesday morning (15 minutes):* Generate mockups for any UI-heavy stories:“Create an HTML mockup for the login flow showing: landing page, social login options, email/password form, error states, and success redirect.”* Even if the HTML doesn't work perfectly, it gives your designers a starting pointBefore sprint planning (10 minutes):* Ask Claude to anticipate engineering questions:“Review these user stories as if you're a senior engineer. What questions would you ask? What concerns would you raise about technical feasibility, dependencies, or edge cases?”* This preparation makes you look thoughtful and helps the meeting run smoothlyTotal time investment: ~75 minutes. Typical time saved: 3-4 hours compared to doing this manually.The Stakeholder Alignment WorkflowGetting alignment from multiple stakeholders (product leadership, engineering, design, data science, legal, marketing) is one of the hardest parts of PM work. AI can help you think through different stakeholder perspectives and craft compelling communications for each.Here's how:Step 1: Map your stakeholders (10 minutes)Create a quick table in a doc:Stakeholder | Primary Concern | Decision Criteria | Likely Objections VP Product | Strategic fit, ROI | Company OKRs, market opportunity | Resource allocation vs other priorities VP Eng | Technical risk, capacity | Engineering capacity, tech debt | Complexity, unclear requirements Design Lead | User experience | User research, design principles | Timeline doesn't allow proper design process Legal | Compliance, risk | Regulatory requirements | Data privacy, user consent flowsStep 2: Generate stakeholder-specific communications (20 minutes)For each key stakeholder, ask Claude:“I need to pitch this product idea to [Stakeholder]. Based on this PRD, create a 1-page brief addressing their primary concern of [concern from your table]. Open with the specific value for them, address their likely objection of [objection], and close with a clear ask. Tone should be [professional/technical/strategic] based on their role.”Then you'll have customized one-pagers for your pre-meetings with each stakeholder, dramatically increasing your alignment rate.Step 3: Synthesize feedback (15 minutes)After gathering stakeholder input, ask Claude to help you synthesize:“I got the following feedback from stakeholders: [paste feedback]. Identify: (1) Common themes, (2) Conflicting requirements, (3) Legitimate concerns vs organizational politics, (4) Recommended compromises that might satisfy multiple parties.”This pattern-matching across stakeholder feedback is something AI does really well and saves you hours of mental processing.The Quarterly Planning WorkflowQuarterly or annual planning is where product strategy gets real. You need to synthesize market trends, customer feedback, technical capabilities, and business objectives into a coherent roadmap. AI can accelerate this dramatically.Six weeks before planning:* Start collecting input (customer interviews, market research, competitive analysis, engineering feedback)* Don't wait until the last minuteFour weeks before planning:Dump everything into Claude with this structure:“I'm creating our Q2 roadmap. Context:* Business objectives: [paste from leadership]* Customer feedback themes: [paste synthesis]* Technical capabilities/constraints: [paste from engineering]* Competitive landscape: [paste analysis]* Current product gaps: [paste from your analysis]Generate 5 strategic themes that could anchor our Q2 roadmap. For each theme:* Strategic rationale (how it connects to business objectives)* Key initiatives (2-3 major features/projects)* Success metrics* Resource requirements (rough estimate)* Risks and mitigations* Customer segments addressed”This gives you a strategic framework to react to rather than starting from a blank page.Three weeks before planning:Iterate on the most promising themes:“Deep dive on Theme 3. Generate:* Detailed initiative breakdown* Dependencies on platform/infrastructure* Phasing options (MVP vs full build)* Go-to-market considerations* Data requirements* Open questions requiring research”Two weeks before planning:Pressure-test your thinking:“Play devil's advocate on this roadmap. What are the strongest arguments against each initiative? What am I likely missing? What failure modes should I plan for?”This adversarial prompting forces you to strengthen weak points before your leadership reviews it.One week before planning:Generate your presentation:“Create an executive presentation for this roadmap. Structure: (1) Market context and strategic imperative, (2) Q2 themes and initiatives, (3) Expected outcomes and metrics, (4) Resource requirements, (5) Key risks and mitigations, (6) Success criteria for decision. Make it compelling but data-driven. Tone: confident but not overselling.”Then add your company-specific context, visual brand, and personal voice.The Customer Research WorkflowAI can't replace talking to customers, but it can help you prepare better questions, analyze feedback more systematically, and identify patterns faster.Before customer interviews:“I'm interviewing customers about [topic]. Generate:* 10 open-ended questions that avoid leading the witness* 5 follow-up questions for each main question* Common cognitive biases I should watch for* A framework for categorizing responses”This prep work helps you conduct better interviews.After interviews:“I conducted 15 customer interviews. Here are the key quotes: [paste anonymized quotes]. Identify:* Recurring themes and patterns* Surprising insights that contradict our assumptions* Segments with different needs* Implied needs customers didn't articulate directly* Recommended next steps for validation”AI is excellent at pattern-matching across qualitative data at scale.The Crisis Management WorkflowSomething broke. The site is down. Data was lost. A feature shipped with a critical bug. You need to move fast.Immediate response (5 minutes):“Critical incident. Details: [brief description]. Generate:* Incident classification (Sev 1-4)* Immediate stakeholders to notify* Draft customer communication (honest, apologetic, specific about what happened and what we're doing)* Draft internal communication for leadership* Key questions to ask engineering during investigation”Having these drafted in 5 minutes lets you focus on coordination and decision-making rather than wordsmithing.Post-incident (30 minutes):“Write a post-mortem based on this incident timeline: [paste timeline]. Include:* What happened (technical details)* Root cause analysis* Impact quantification (users affected, revenue impact, time to resolution)* What went well in our response* What could have been better* Specific action items with owners and deadlines* Process changes to prevent recurrence Tone: Blameless, focused on learning and improvement.”This gives you a strong first draft to refine with your team.Common Pitfalls: What Not to Do with AI in Product ManagementNow let's talk about the mistakes I see PMs making with AI tools. Pitfall #1: Treating AI Output as FinalThe biggest mistake is copy-pasting AI output directly into your PRD, roadmap presentation, or stakeholder email without critical review.The result? Documents that are grammatically perfect but strategically shallow. Presentations that sound impressive but don't hold up under questioning. Emails that are professionally worded but miss the subtext of organizational politics.The fix: Always ask yourself:* Does this reflect my actual strategic thinking, or generic best practices?* Would my CEO/engineering lead/biggest customer find this compelling and specific?* Are there company-specific details, customer insights, or technical constraints that only I know?* Does this sound like me, or like a robot?Add those elements. That's where your value as a PM comes through.Pitfall #2: Using AI as a Crutch Instead of a ToolSome PMs use AI because they don't want to think deeply about the product. They're looking for AI to do the hard work of strategy, prioritization, and trade-off analysis.This never works. AI can help you think more systematically, but it can't replace thinking.If you find yourself using AI to avoid wrestling with hard questions (”Should we build X or Y?” “What's our actual competitive advantage?” “Why would customers switch from the incumbent?”), you're using it wrong.The fix: Use AI to explore options, not to make decisions. Generate three alternatives, pressure-test each one, then use your judgment to decide. The AI can help you think through implications, but you're still the one choosing.Pitfall #3: Not IteratingGetting mediocre AI output and just accepting it is a waste of the technology's potential.The PMs who get exceptional results from AI are the ones who iterate. They generate an initial response, identify what's weak or missing, and ask follow-up questions. They might go through 5-10 iterations on a key section of a PRD.Each iteration is quick (30 seconds to type a follow-up prompt, 30 seconds to read the response), but the cumulative effect is dramatically better output.The fix: Budget time for iteration. Don't try to generate a complete, polished PRD in one prompt. Instead, generate a rough draft, then spend 30 minutes iterating on specific sections that matter most.Pitfall #4: Ignoring the Political and Human ContextAI tools have no understanding of organizational politics, interpersonal relationships, or the specific humans you're working with.They don't know that your VP of Engineering is burned out and skeptical of any new initiatives. They don't know that your CEO has a personal obsession with a specific competitor. They don't know that your lead designer is sensitive about not being included early enough in the process.If you use AI-generated communications without layering in this human context, you'll create perfectly worded documents that land badly because they miss the subtext.The fix: After generating AI content, explicitly ask yourself: “What human context am I missing? What relationships do I need to consider? What political dynamics are in play?” Then modify the AI output accordingly.Pitfall #5: Over-Relying on a Single ToolDifferent AI tools have different strengths. Claude is great for strategic depth, ChatPRD is great for structure, Gemini integrates well with Google Workspace.If you only ever use one tool, you're missing opportunities to leverage different strengths for different tasks.The fix: Keep 2-3 tools in your toolkit. Use Claude for important PRDs and strategic documents. Use Gemini for quick internal documentation that needs to integrate with Google Docs. Use ChatPRD when you want more guided structure. Match the tool to the task.Pitfall #6: Not Fact-Checking AI OutputAI tools hallucinate. They make up statistics, misrepresent competitors, and confidently state things that aren't true. If you include those hallucinations in a PRD that goes to leadership, you look incompetent.The fix: Fact-check everything, especially:* Statistics and market data* Competitive feature claims* Technical capabilities and limitations* Regulatory and compliance requirementsIf the AI cites a number or makes a factual claim, verify it independently before including it in your document.The Meta-Skill: Prompt Engineering for PMsLet's zoom out and talk about the underlying skill that makes all of this work: prompt engineering.This is a real skill. The difference between a mediocre prompt and a great prompt can be 10x difference in output quality. And unlike coding or design, where there's a steep learning curve, prompt engineering is something you can get good at quickly.Principle 1: Provide Context Before InstructionsBad prompt:“Write a PRD for an AI tutor”Good prompt:“I'm a PM at an edtech company with 2M users, primarily high school students. We're exploring an AI tutor feature to complement our existing video content library and practice problems. Our main competitors are Khan Academy and Course Hero. Our differentiation is personalized learning paths based on student performance data.Write a PRD for an AI tutor feature targeting students in the middle 80% academically who struggle with science and math.”The second prompt gives Claude the context it needs to generate something specific and strategic rather than generic.Principle 2: Specify Format and ConstraintsBad prompt:“Generate success metrics”Good prompt:“Generate 5-7 success metrics for this feature. Include a mix of:* Leading indicators (early signals of success)* Lagging indicators (definitive success measures)* User behavior metrics* Business impact metricsFor each metric, specify: name, definition, target value, measurement method, and why it matters.”The structure you provide shapes the structure you get back.Principle 3: Ask for Multiple OptionsBad prompt:“What should our Q2 priorities be?”Good prompt:“Generate 3 different strategic approaches for Q2:* Option A: Focus on user acquisition* Option B: Focus on engagement and retention* Option C: Focus on monetizationFor each option, detail: key initiatives, expected outcomes, resource requirements, risks, and recommendation for or against.”Asking for multiple options forces the AI (and forces you) to think through trade-offs systematically.Principle 4: Specify Audience and ToneBad prompt:“Summarize this PRD”Good prompt:“Create a 1-paragraph summary of this PRD for our skeptical VP of Engineering. Tone: Technical, concise, addresses engineering concerns upfront. Focus on: technical architecture, resource requirements, risks, and expected engineering effort. Avoid marketing language.”The audience and tone specification ensures the output will actually work for your intended use.Principle 5: Use Iterative RefinementDon't try to get perfect output in one prompt. Instead:First prompt: Generate rough draft Second prompt: “This is too generic. Add specific examples from [our company context].” Third prompt: “The technical section is weak. Expand with architecture details and dependencies.” Fourth prompt: “Good. Now make it 30% more concise while keeping the key details.”Each iteration improves the output incrementally.Let me break down the prompting approach that worked in this experiment, because this is immediately actionable for your work tomorrow.Strategy 1: The Structured Outline ApproachDon't go from zero to full PRD in one prompt. Instead:* Start with strategic thinking - Spend 10-15 minutes outlining why you're building this, who it's for, and what problem it solves* Get specific - Don't say “users,” say “high school students in the middle 80% of academic performance”* Include constraints - Budget, timeline, technical limitations, competitive landscape* Dump your outline into the AI - Now ask it to expand into a full PRD* Iterate section by section - Don't try to perfect everything at onceThis is exactly what I did in my experiment, and even with my somewhat sloppy outline, the results were dramatically better than they would have been with a single-sentence prompt.Strategy 2: The Comparative Analysis PatternOne technique I used that worked particularly well: asking each tool to do the same specific task and comparing results.For example, I asked all five tools: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This forced each tool to synthesize the entire PRD into a compelling pitch while accounting for a specific, challenging audience. The variation in quality was revealing—and it gave me multiple options to choose from or blend together.Actionable tip: When you need something critical (a pitch, an executive summary, a key decision framework), generate it with 2-3 different AI tools and take the best elements from each. This “ensemble approach” often produces better results than any single tool.Strategy 3: The Iterative Refinement LoopDon't treat the AI output as final. Use it as a first draft that you then refine through conversation with the AI.After getting the initial PRD, I could have asked follow-up questions like:* “What's missing from this PRD?”* “How would you strengthen the success metrics section?”* “Generate 3 alternative approaches to the core feature set”Each iteration improves the output and, more importantly, forces me to think more deeply about the product.What This Means for Your CareerIf you're an early or mid-career PM reading this, you might be thinking: “Great, so AI can write PRDs now. Am I becoming obsolete?”Absolutely not. But your role is evolving, and understanding that evolution is critical.The PMs who will thrive in the AI era are those who:* Excel at strategic thinking - AI can generate options, but you need to know which options align with company strategy, customer needs, and technical feasibility* Master the art of prompting - This is a genuine skill that separates mediocre AI users from exceptional ones* Know when to use AI and when not to - Some aspects of product work benefit enormously from AI. Others (user interviews, stakeholder negotiation, cross-functional relationship building) require human judgment and empathy* Can evaluate AI output critically - You need to spot the hallucinations, the generic fluff, and the strategic misalignments that AI inevitably producesThink of AI tools as incredibly capable interns. They can produce impressive work quickly, but they need direction, oversight, and strategic guidance. Your job is to provide that guidance while leveraging their speed and breadth.The Real-World Application: What to Do Monday MorningLet's get tactical. Here's exactly how to apply these insights to your actual product work:For Your Next PRD:* Block 30 minutes for strategic thinking - Write your back-of-the-napkin outline in Google Docs or your tool of choice* Open Claude (or ChatPRD if you want more structure)* Copy your outline with this prompt:“I'm a product manager at [company] working on [product area]. I need to create a comprehensive PRD based on this outline. Please expand this into a complete PRD with the following sections: [list your preferred sections]. Make it detailed enough for engineering to start breaking down into user stories, but concise enough for leadership to read in 15 minutes. [Paste your outline]”* Review the output critically - Look for generic statements, missing details, or strategic misalignments* Iterate on specific sections:“The success metrics section is too vague. Please provide 3-5 specific, measurable KPIs with target values and explanation of why these metrics matter.”* Generate supporting materials:“Create a visual mockup of the core user flow showing the key interaction points.”* Synthesize the best elements - Don't just copy-paste the AI output. Use it as raw material that you shape into your final documentFor Stakeholder Communication:When you need to pitch something to leadership or engineering:* Generate 3 versions of your pitch using different tools (Claude, ChatPRD, and one other)* Compare them for:* Clarity and conciseness* Strategic framing* Compelling value proposition* Addressing likely objections* Blend the best elements into your final version* Add your personal voice - This is crucial. AI output often lacks personality and specific company context. Add that yourself.For Feature Prioritization:AI tools can help you think through trade-offs more systematically:“I'm deciding between three features for our next release: [Feature A], [Feature B], and [Feature C]. For each feature, analyze: (1) Estimated engineering effort, (2) Expected user impact, (3) Strategic alignment with making our platform the go-to solution for [your market], (4) Risk factors. Then recommend a prioritization with rationale.”This doesn't replace your judgment, but it forces you to think through each dimension systematically and often surfaces considerations you hadn't thought of.The Uncomfortable Truth About AI and Product ManagementLet me be direct about something that makes many PMs uncomfortable: AI will make some PM skills less valuable while making others more valuable.Less valuable:* Writing boilerplate documentation* Creating standard frameworks and templates* Generating routine status updates* Synthesizing information from existing sourcesMore valuable:* Strategic product vision and roadmapping* Deep customer empathy and insight generation* Cross-functional leadership and influence* Critical evaluation of options and trade-offs* Creative problem-solving for novel situationsIf your PM role primarily involves the first category of tasks, you should be concerned. But if you're focused on the second category while leveraging AI for the first, you're going to be exponentially more effective than your peers who resist these tools.The PMs I see succeeding aren't those who can write the best PRD manually. They're those who can write the best PRD with AI assistance in one-tenth the time, then use the saved time to talk to more customers, think more deeply about strategy, and build stronger cross-functional relationships.Advanced Techniques: Beyond Basic PRD GenerationOnce you've mastered the basics, here are some advanced applications I've found valuable:Competitive Analysis at Scale“Research our top 5 competitors in [market]. For each one, analyze: their core value proposition, key features, pricing strategy, target customer, and likely product roadmap based on recent releases and job postings. Create a comparison matrix showing where we have advantages and gaps.”Then use web search tools in Claude or Perplexity to fact-check and expand the analysis.Scenario Planning“We're considering three strategic directions for our product: [Direction A], [Direction B], [Direction C]. For each direction, map out: likely customer adoption curve, required technical investments, competitive positioning in 12 months, and potential pivots if the hypothesis proves wrong. Then identify the highest-risk assumptions we should test first for each direction.”This kind of structured scenario thinking is exactly what AI excels at—generating multiple well-reasoned perspectives quickly.User Story GenerationAfter your PRD is solid:“Based on this PRD, generate a complete set of user stories following the format ‘As a [user type], I want to [action] so that [benefit].' Include acceptance criteria for each story. Organize them into epics by functional area.”This can save your engineering team hours of grooming meetings.The Tools Will Keep Evolving. Your Process Shouldn'tHere's something important to remember: by the time you read this, the specific rankings might have shifted. Maybe ChatGPT-5 has leapfrogged Claude. Maybe a new specialized tool has emerged.But the core principles won't change:* Do strategic thinking before touching AI* Use the best tool available for your specific task* Iterate and refine rather than accepting first outputs* Blend AI capabilities with human judgment* Focus your time on the uniquely human aspects of product managementThe specific tools matter less than your process for using them effectively.A Final Experiment: The Skeptical VP TestI want to share one more insight from my testing that I think is particularly relevant for early and mid-career PMs.Toward the end of my experiment, I gave each tool this prompt: “Please compose a one paragraph exact summary I can share over DM with a highly influential VP of engineering who is generally a skeptic but super smart.”This is such a realistic scenario. How many times have you needed to pitch an idea to a skeptical technical leader via Slack or email? Someone who's brilliant, who's seen a thousand product ideas fail, and who can spot b******t from a mile away?The quality variation in the responses was fascinating. ChatGPT gave me something that felt generic and safe. Gemini was better but still a bit too enthusiastic. Grok was... well, Grok.But Claude and ChatPRD both produced messages that felt authentic, technically credible, and appropriately confident without being overselling. They acknowledged the engineering challenges while framing the opportunity compellingly.The lesson: When the stakes are high and the audience is sophisticated, the quality of your AI tool matters even more. That skeptical VP can tell the difference between a carefully crafted message and AI-generated fluff. So can your CEO. So can your biggest customers.Use the best tools available, but more importantly, always add your own strategic thinking and authentic voice on top.Questions to Consider: A Framework for Your Own ExperimentsAs I wrapped up my Loom, I posed some questions to the audience that I'll pose to you:“Let me know in the comments, if you do your PRDs using AI differently, do you start with back of the envelope? Do you say, oh no, I just start with one sentence, and then I let the chatbot refine it with me? Or do you go way more detailed and then use the chatbot to kind of pressure test it?”These aren't rhetorical questions. Your answer reveals your approach to AI-augmented product work, and different approaches work for different people and contexts.For early-career PMs: I'd recommend starting with more detailed outlines. The discipline of thinking through your product strategy before touching AI will make you a stronger PM. You can always compress that process later as you get more experienced.For mid-career PMs: Experiment with different approaches for different types of documents. Maybe you do detailed outlines for major feature PRDs but use more iterative AI-assisted refinement for smaller features or updates. Find what optimizes your personal productivity while maintaining quality.For senior PMs and product leaders: Consider how AI changes what you should expect from your PM team. Should you be reviewing more AI-generated first drafts and spending more time on strategic guidance? Should you be training your team on effective AI usage? These are leadership questions worth grappling with.The Path Forward: Continuous ExperimentationMy experiment with these five AI tools took 45 minutes. But I'm not done experimenting.The field of AI-assisted product management is evolving rapidly. New tools launch monthly. Existing tools get smarter weekly. Prompting techniques that work today might be obsolete in three months.Your job, if you want to stay at the forefront of product management, is to continuously experiment. Try new tools. Share what works with your peers. Build a personal knowledge base of effective prompts and workflows. And be generous with what you learn. The PM community gets stronger when we share insights rather than hoarding them.That's why I created this Loom and why I'm writing this post. Not because I have all the answers, but because I'm figuring it out in real-time and want to share the journey.A Personal Note on Coaching and ConsultingIf this kind of practical advice resonates with you, I'm happy to work with you directly.Through my pm coaching practice, I offer 1:1 executive, career, and product coaching for PMs and product leaders. We can dig into your specific challenges: whether that's leveling up your AI workflows, navigating a career transition, or developing your strategic product thinking.I also work with companies (usually startups or incubation teams) on product strategy, helping teams figure out PMF for new explorations and improving their product management function.The format is flexible. Some clients want ongoing coaching, others prefer project-based consulting, and some just want a strategic sounding board for a specific decision. Whatever works for you.Reach out through tomleungcoaching.com if you're interested in working together.OK. Enough pontificating. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Conversión en CRMsTítulo, CTA email o llamada, mostrar demo caso pluto.tvhttps://analizador.top/https://instaradar.top/Tutorial Lovable: https://www.youtube.com/watch?v=boMDBTedfbQConsigue 10 créditos extra con Lovable desde https://borjagiron.com/lovableComprar dominio con Hostinger con descuento: https://borjagiron.com/hostingerConviértete en un seguidor de este podcast: https://www.spreaker.com/podcast/seo-para-google--1693061/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

The "vibe coding will kill SaaS" narrative is everywhere right now, and I think it's completely wrong. Yes, anyone can spin up a Lovable or Bolt.new project in an afternoon. But there's a fundamental confusion happening: people are mistaking software products for software businesses. SaaS was never really about the software — it was always about the service, the operations, the years of edge cases and integrations and customer conversations that make a product actually work. In this episode, I break down why vibe-coded solutions fall apart the moment real customers show up, why "comprehension debt" is the hidden killer of AI-built projects, and how we might need to shift our messaging to make the invisible 20% of our work visible to buyers who now think they could build everything themselves.This episode of The Bootstraped Founder is sponsored by Paddle.comYou'll find the Black Friday Guide here: https://www.paddle.com/learn/grow-beyond-black-fridayThe blog post: https://thebootstrappedfounder.com/vibe-coding-wont-kill-saas/The podcast episode: https://tbf.fm/episodes/427-vibe-coding-wont-kill-saas Check out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

Guest: Derek Baird, CEO & Co-founder, Switchboard HealthResources:Switchboard Health: https://switchboardhealth.com/Conduce Health: https://www.conducehealth.com/Connect with Derek: https://www.linkedin.com/in/debaird/Connect with Nick: https://www.linkedin.com/in/nick-crabbs-5674a233/ Product in Healthtech is community for healthtech product leaders, by product leaders. For more information, and to sign up for our free webinars, visit www.productinhealthtech.com.

In this episode of Future of UX, Patricia breaks down the most important tech and AI shifts of 2025 — the trends that fundamentally changed how we design, build, research, and work. If you missed anything this year or simply want the essential takeaways, this episode is your shortcut.From AI Agents and deepfake-proof UX to vibe-coding, AI-native browsers, research automation, and the rise of general-purpose robots — here are the big transformations shaping the future of design.Why 2025 was the year AI Agents became real — not as chatbots, but as autonomous coworkers running full workflows.• How MCP unlocked the agent ecosystem• Vibe coding and intent-driven development• The shift from execution to oversight in human rolesVisual trust collapsed — and UX became responsible for rebuilding it.• Why humans can't detect deepfakes anymore• What actually worked: C2PA, identity checks, and UI “micro-literacy”• Designing interfaces that communicate authenticity and uncertaintyHow tools like Lovable, Replit, and AI builders changed who gets to create.• From pixel pushing to strategic direction• Conversational creation flows• What this means for designers and innovatorsAI automated more than ever — but made human oversight more important.• Where full autonomy worked• Where humans stayed essential• Why the future depends on intentional human-in-the-loop designResearch and analysis were transformed by automated synthesis.• Superagency: managing research instead of doing it manually• The new trust problem in fast research• Data provenance, model transparency, and expert validationBrowsers became intelligence layers instead of navigation tools.• Context-aware, predictive UX• Browsers that act, not just display• How this changes product and interaction designWhy general-purpose robots finally left the lab in 2025.• Embodied AI• Real-world perception• Language-driven task executionAI for Designers: 5-week Bootcamp

In this episode, Prerna Singh, CPTO at Avaaz, walks us through how AI is reshaping the way we prototype, learn and build digital products. Rather than replacing teams or skipping straight to production, she argues that AI shines when used as a “thought partner” to accelerate early‑stage experimentation. Through her own journey building a community platform on weekends, she demonstrates how tools like ChatGPT, Lovable (and later Claude / Replet) and Figma AI enabled her to move from blank page to clickable prototype in hours — while retaining the human insight, iteration and context that underpin good product work. The conversation reframes common assumptions about “fast‑AI = bypass human work,” and instead proposes a balanced adoption path: start in “sandbox mode,” learn and play — before graduating to “architect mode” where the real value to business begins.Chapters00:00 – Introduction & AI's impact on product cycles01:43 – Meet Prerna Singh: her background in product and community building03:50 – The community problem: logistics over connection05:11 – Turning to AI to solve her own problem06:50 – What AI can't do: user insight and human judgment08:08 – From waterfall to short-cycle prototyping10:54 – Using ChatGPT as a Socratic thought partner13:07 – Working solo vs team: where AI fits17:17 – From prompt to prototype: using Lovable19:06 – Iterating with Figma AI and other tools23:00 – Real feedback from real users25:02 – Creating a feedback knowledge base with AI26:16 – AI vs design sprints: same principles, new toolsOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath. Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

MY NEWSLETTER - https://nikolas-newsletter-241a64.beehiiv.com/subscribeJoin me, Nik (https://x.com/CoFoundersNik), as I interview Minh Nguyen (@minhnxn). In this episode, we dive deep into the exciting world of AI and how it's revolutionizing the way we build businesses and gather information online.Minh, coming straight from the heart of tech in the Bay Area, shares his practical experiences using various Large Language Models like Claude, ChatGPT, and the often-overlooked Gemini for tasks ranging from coding to deep research.We explore his journey building Cash On, a powerful Chrome extension for real estate investors, and uncover the surprising potential of simple browser tools. Get ready to learn about AI-powered web scraping, the rise of directory websites and programmatic SEO, and how no-code platforms like Lovable and Bolt.new are empowering non-technical founders to bring their app ideas to life.Questions This Episode Answers:• What AI tools (like LLMs) do you use and for what specific purposes?• Why do you think people are "sleeping on" Gemini?• How can AI be utilized for web scraping and data acquisition?• What are some good types of app ideas to start with when using no-code or low-code tools?• How can I leverage my existing content (like podcast episodes) using AI?Enjoy the conversation!__________________________Love it or hate it, I'd love your feedback.Please fill out this brief survey with your opinion or email me at nik@cofounders.com with your thoughts.__________________________MY NEWSLETTER: https://nikolas-newsletter-241a64.beehiiv.com/subscribeSpotify: https://tinyurl.com/5avyu98yApple: https://tinyurl.com/bdxbr284YouTube: https://tinyurl.com/nikonomicsYT__________________________This week we covered:00:00 Automating Data Collection for Real Estate02:59 Exploring AI Tools and Their Applications05:48 Building AI-Powered Web Scrapers09:10 The Future of Programmatic SEO12:07 Leveraging AI for Business Ideas14:54 Creating Chatbots for Business Analysis17:45 Building Without Coding: New Possibilities21:08 Frameworks for Identifying Business Opportunities

Dans cet épisode de Connected Mate, PPC et Alexandre explorent leurs meilleurs outils d'intelligence artificielle du moment, classés en trois catégories : les assistants généralistes, les outils de code, et les générateurs visuels.Ils commencent par ChatGPT, Manus, Perplexity et Notebook LM, en comparant leurs points forts et faiblesses. Alexandre dévoile comment il utilise ces IA au quotidien pour automatiser ses recherches, reformuler ses textes ou synthétiser des documents complexes.Puis direction le monde du code avec Cursor et Lovable. L'un est un véritable copilote pour développeurs, l'autre un outil plus “sexy” mais aux capacités plus limitées. Alexandre raconte comment il développe des apps iPhone sans être développeur, grâce à un système d'agents IA bien rôdé.Enfin, PPC et Alexandre s'attaquent aux IA visuelles, de Seelab à Sora 2, en passant par Higgsfield, et dressent un panorama puissant mais nuancé de ce que ces technologies permettent aujourd'hui… et de ce qu'elles ne permettent pas encore.Un épisode riche, concret et sans langue de bois.Pour suivre les actualités de ce podcast, abonnez-vous gratuitement à la newsletter écrite avec amour et garantie sans spam https://bonjourppc.substack.com Et pour découvrir l'ouvrage de PPC Réinventez votre entreprise à l'ère de l'IA, préfacé par Serge Papin, rdv ici https://amzn.to/4gTLwxSHébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

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

AGENDA: 04:20 Thrive and OpenAI Partnership  07:14 Databricks Raising $5BN at $134BN Valuation: Cheap or Not? 17:39 Eventbrite Acquired by Bending Spoons for $500M 21:39 Pagerduty's $1BN Market Cap, Just 2x Revenue 26:59 The TAM Trap: Why SaaS Is Like Japan 37:42 Lessons from Companies Hitting $100M ARR 44:57 The Future of Labour Markets is F****** 52:10 The Importance of Compounding in Investments 56:45 The Relevance Game in Venture Capital 01:05:01 Supabase at $5BN or Lovable at $6BN: Which One?  

Welcome back to another episode of Upside at the EUVC Podcast, where ⁠Dan Bowyer⁠,⁠ Mads Jensen⁠ of ⁠SuperSeed⁠, ⁠Lomax Ward⁠ of ⁠Outsized Ventures⁠⁠⁠, and this week's special guest Robin Haak break down the real stories behind the headlines shaping European tech and venture.Robin joins us as the founder of Robin Capital, an early employee at SmartRecruiters, angel in 100+ companies, including eight unicorns, and one of the most active emerging GPs in Europe. He brings deep operator insight, especially into the German ecosystem, politics, and economy, which this episode leans heavily into.We cover everything from UK policy signals to German recession warnings, AI dominance to Europe's bureaucratic drag, the rise of solo GPs, and why the next decade of tech will be won or lost on energy availability more than anything else.What's covered:04:00 EU wants to restrict social media for minorsThe team debates the proposals to ban or limit social media for children under 16, the mental health case, and the tension between safety and overreach.06:00 Surveillance creep & messaging regulationRobin explains concerning drafts that would've allowed governments to read private messages. The group breaks down the slippery slope of “protect the children” legislation.10:00 UK Budget: surprisingly startup-friendlyDan and Lomax unpack EMI reforms, EIS/VCT clarity, and why the market reacted calmly. Signals of a more innovation-forward UK emerge.12:45 Lovable.ai's VAT scandal & Europe's compliance mazeA Swedish engineer's viral post on LinkedIn sparks a discussion on Europe's inconsistent VAT rules, compliance complexity, and whether hypergrowth and European regulation can co-exist.17:00 N26's long struggle with German regulatorsRobin, an early angel, offers an insider's view on the fintech's challenges—BaFin restrictions, governance issues, and the counterfactual: “Would N26 be worth €20B if it were French?”20:00 Germany's big macro problem: stagnation + overloadA brutally honest breakdown of the German economy: energy scarcity, migration overload, rising welfare costs, labor shortages, and political paralysis.28:00 Education, welfare, pensions & the cost structure crisisRobin explains why Germany's systems are buckling: the collapse of PISA scores, overloaded municipalities, and an economic model no longer supported by productivity.33:00 Nuclear shutdowns & Europe's AI energy deficitWhy Germany shut down its safest reactors, how it backfired, and why France and the Nordics will become the new AI infrastructure hubs.40:00 Startup ecosystem: the good, the bad, the bureaucraticFrom Munich's deep tech boom to notary nightmares, ESOP fixes, GmbH limitations, and how founders are learning to hack the system.55:00 The rise of Solo GPsThe team discusses the American roots, European trajectory, operator funds, fund-of-funds appetite, and why founders increasingly prefer solo GPs.01:00:00 AI CornerOpenAI's trillion-dollar capex future, Google's TPU resurgence, Anthropic momentum, Michael Burry shorting AI (and why it's misguided), and the geopolitics of compute.

AGENDA: 04:06 Anthropic's $30BN Investment from Microsoft and NVIDIA 07:01 Google vs. OpenAI: Sam Altman's "War Mode" Memo 15:27 NVIDIA's Customer Concentration: Bull or Bear 22:12 Is "War Mode" BS: Does Hyper-Aggressive Ever Work? 36:12 Sierra Hits $100M ARR: Justify $10BN Price? 46:14 Implementation is the Biggest Barrier to Enterprise AI Growth 01:04:04 Is LLM Search Optimisation (GEO) Selling Snake Oil? What AI is a Fraud vs Real? 01:14:27 Figma Market Cap: Is the IPO Market F****** for 2026    

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

In this special “build with me” episode, Aydin and Manuela walk through how Aydin used Lovable to build a unicorn-themed multiplication and division game with his nine-year-old twin daughters. They show how to go from a spoken idea to a working web app in minutes, then keep iterating to add playful design, timers, division mode, mix mode, and a leaderboard—using it as a fun way to teach kids both math and how to think and communicate clearly with AI.The episode closes with a push for parents, aunts, uncles, and anyone with kids in their lives to start doing, not just watching: use AI builders like Lovable as a playful way to get kids hands-on with AI, programming, and creative problem solving.Timestamps00:00 - Welcome to the episode01:07 – Why Aydin wants parents to teach kids AI through projects01:40 – Twin nine-year-olds and the idea for a multiplication game03:33 – Screen share: introducing Lovable and Super Whisper05:44 – Dictating the first prompt for the multiplication quiz08:13 – First working version of the game and scoring demo11:25 – Adding unicorn theme, confetti, poop emoji, and multiple choice13:49 – Using Lovable's free plan and email accounts for kids16:11 – Publishing the game and sharing it via a public link17:22 – Adding division mode, mix mode, and a timer22:12 – Demoing division mode and brainstorming a leaderboard24:38 – Explaining why the app now needs a database27:41 – Registration, login, and live leaderboards in action29:50 – “Now is the time to build” with tools like Lovable30:51 – Parting advice for parents, aunts, and uncles: start doing, not just watchingTools & Technologies Mentioned:Lovable (lovable.dev)Super WhisperLovable's built-in voice-to-textCloud database (via Lovable)Bolt.newClaudeChatGPTGoogle/Gmail family accounts for kidsFellow.aiSubscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

In this episode, Carlos Gonzalez de Villaumbrosia interviews Elena Verna, Head of Growth at Lovable—the fastest-growing AI startup to ever surpass $100M in ARR, hitting the milestone in just eight months. With a proven track record leading growth at Miro, Amplitude, Superhuman, and Dropbox, Elena brings unparalleled expertise in driving sustainable, product-led growth across both hyper-growth and turnaround environments.Elena shares how building in the fast-moving “vibe coding” category requires a radical shift in how we define product-market fit, structure growth teams, and measure success. From product-led monetization loops to redefining brand as a product responsibility, Elena outlines a bold vision for what growth looks like in the age of AI-native products.What you'll learn:How Lovable ships at record speed, with daily product updates and a 3-tier launch model.How AI-native products redefine activation, retention, and monetization.Why product teams must now own brand experience—not just featuresHow Elena designs feedback, education, and referral loops that turn users into growth engines.The evolving role of activation, retention, and monetization in AI-native PLG.Key Takeaways

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      

Pete Syme talks with Drew Falkman about vibe coding, a way for tour operators to build custom software tools using plain English prompts instead of traditional programming. Drew explains how AI tools like ChatGPT and Claude have been trained on code repositories, allowing them to generate working applications from simple descriptions. The conversation covers why this matters for small operators, what you can build, the learning curve, costs, security considerations, and how this technology could shift the relationship between tour operators and the software they depend on. Pete emphasizes that operators already have the same AI access as hundred million dollar companies and encourages spending at least an hour daily experimenting with these tools.Top 10 TakeawaysYou can build tools without coding knowledge. AI tools trained on code repositories can generate working applications from plain English descriptions, making app building accessible to anyone.Most SaaS tools don't fit your exact workflow. You end up paying for applications where 80% of features you're not using because they're designed for other industries, but the things you do use aren't quite refined enough.Start with internal workflows, not customer-facing apps. Build tools for internal processes first. Don't go public with what you build until you have experience, as you can get 80 to 90% correct quickly, but that last bit is more challenging.Map your processes before building. Write down all your processes on paper, rank what's most important, and list what you really don't like doing. This helps identify where custom tools can have the biggest impact.The learning curve has three main steps. First, learn to plan what you want to build (20 to 30 hours). Second, design the workflow and user interface (a few hours). Third, understand data and databases (a couple days). Total time to get comfortable is roughly a few weeks of focused learning.Tools like Lovable cost around $20 per month. There are small monthly fees for vibe coding platforms, plus hosting costs if your tool is public-facing. Tools like Lovable, Bolt, Replit, Magic Patterns, and N8n each serve different purposes.Keep data storage minimal for security. Don't store sensitive information like credit card numbers or social security numbers. Use third-party authentication (Google, Microsoft, Apple) and payment processors like Stripe to handle sensitive data.You can build custom booking flows and optimize conversions. Create your own booking engine where you control every step, then use analytics tools to see where people drop off and experiment with improvements to increase completion rates.This threatens the traditional SaaS industry. Large companies spending millions monthly on SaaS are already exploring vibe coding to reduce costs. What happens at that level will cascade down through the industry to the tools small operators use today.Just try it to understand the possibilities. Go to lovable.dev, run a prompt, and build something. You won't fully understand what you can do until you experiment. You have nothing to lose with free versions, and no one else will see your experiments.Want to learn vibe coding yourself? Drew teaches courses on building apps without code. Visit drewfalkman.com to explore free resources and paid courses that walk you through the process step by step.

Get our free Google Gemini bundle with our favorite prompts + workflows: https://clickhubspot.com/sce Ep. 380 Is Gemini 3 better than GPT-5? Kipp and Kieran dive into the seismic release of Gemini 3 and what it means for the future of marketing, search, and AI-driven business tools. Learn more on how Gemini's state-of-the-art multimodal reasoning unlocks custom interactive apps in search, why dynamic AI-generated UIs signal the end of the blue links era, and how marketers can leverage Gemini to automate research, customer insights, and content more effortlessly than ever before. Mentions Gemini 3 https://gemini.google.com/ ChatGPT 5 https://chat.chatbot.app/gpt5 Claude https://claude.ai/ Replit https://replit.com/ Lovable https://lovable.dev/ Fiverr https://www.fiverr.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

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  

If you've ever caught yourself wondering, "Am I good enough? Lovable enough? Worthy enough?"... this episode is for you. Today, I'm diving into the powerful difference between external and internal validation, and how learning to validate yourself is the key to breaking free from codependency, people-pleasing, and enmeshment. When we rely on others to tell us who we are, we lose touch with our true selves. But when we start building that validation from within, everything changes: our confidence, our sense of worth, and the way we move through the world. In this episode, I share: How codependency develops when we seek our worth outside of ourselves Simple ways to practice self-validation every day Why internal validation becomes even more essential as we age How celebrating your small wins rewires your brain and boosts self-worth This episode is all about bringing it home. Finding your value from within and learning to celebrate the beautifully unique human you already are. ✨ Ready to deepen your connection with yourself? Join my free 30-Day Journaling Challenge at michellechalfant.com/journal and explore the power of daily reflection to build confidence, clarity, and self-trust.   Resources from this Episode: The Adult Chair book is NOW AVAILABLE!