2025-12-02 00:03:01
Hey friends 👋,
Here’s a weekly recap of new podcast episodes across Lenny’s Podcast Network:
Every Monday, host Claire Vo shares a 30- to 45-minute episode with a new guest demoing a practical, impactful way they’ve learned to use AI in their work or life. No pontificating—just specific and actionable advice.
Brought to you by:
Google AI Product Lead Marily Nika breaks down the exact end-to-end AI workflow she uses to dramatically speed up her product development—from mining Reddit debates for user insights with Perplexity, to generating PRDs with custom GPTs, to building interactive prototypes with v0, and even creating stakeholder-ready product videos using Flow and Sora. Using a smart-fridge concept as the thread through it all, Marily shows how “tool hopping” across specialized AI apps creates a compounding effect that turns weeks of PM work into about 20 minutes.
Biggest takeaways:
Create “pro” and “against” AI agents to debate your product idea. Instead of getting biased, agreeable responses from AI, prompt it to create two agents with opposing viewpoints on your product. Have them debate for 20 rounds, then extract the minimum feature set needed to convince the skeptical agent. This technique uncovers potential objections and helps you find product-market fit faster.
“Tool hopping” an end-to-end AI product workflow. Marily’s process moves from Perplexity (market research) to custom GPTs (PRD generation) to v0 (prototyping) to Flow/Sora (video creation). Each tool’s output becomes the input for the next, creating a seamless workflow that transforms an idea into a complete product vision.
“PMs who use AI will replace those who don’t.” AI isn’t replacing product managers, but PMs who leverage AI tools will outperform those who don’t. The productivity gap between AI power-users and non-users is growing rapidly, making AI literacy essential for career advancement in product management.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
More shows coming soon. . . 👀
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.
2025-12-01 20:03:59
WorkOS—Make your app enterprise-ready today
Miro—A collaborative visual platform where your best work comes to life
Marily Nika, AI Product Lead at Google and founder of the AI Product Academy, demonstrates how product managers can leverage AI tools to dramatically accelerate their workflow. Using a smart-fridge concept as an example, Marily walks us through the exact workflow she uses to build products faster: doing user research with Reddit debates, generating PRDs with custom GPTs, prototyping with v0, and even creating stakeholder-ready video mockups using VEO and Sora. She shows how “tool hopping” between specialized AI applications creates a powerful workflow that transforms traditional PM processes and enables more compelling product storytelling.
What you’ll learn:
How to use Perplexity’s “discussions and opinions” filter to mine Reddit for user insights and create pro/con agent debates that reveal product-market fit requirements
A workflow for transforming market research into comprehensive PRDs using custom GPTs that maintain your personal voice and style
Techniques for turning PRDs into interactive prototypes using v0.dev that make your product vision tangible for stakeholders
How to create persuasive product videos using Flow and Sora that communicate your vision more effectively than traditional presentations
Why “tool hopping” between specialized AI applications creates a more powerful workflow than using a single tool
How to use NotebookLM as an interactive judge for product demos and pitch competitions
LinkedIn: https://www.linkedin.com/in/marilynika/
Website: https://www.marilynika.me/
Substack: https://marily.substack.com/
AI Product Management Bootcamp & Certification by AI Product Academy: https://bit.ly/4p8tn2r
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
(00:00) Introduction to Marily Nika
(02:54) Smart-fridge use case inspiration
(06:15) Using Perplexity to mine Reddit for user research
(11:19) Creating a comprehensive PRD with ChatGPT
(13:40) Building an interactive prototype with v0
(16:20) Using prototypes as stakeholder influence tools in product reviews
(21:30) Generating product videos with Flow and Sora
(30:17) The complete 20-minute product workflow, from research to video
(32:06) Using NotebookLM as an AI judge for product demo days
(37:38) What to do when AI tools aren’t giving you what you want
• Perplexity: https://www.perplexity.ai/
• ChatGPT: https://chat.openai.com/
• v0: https://v0.dev/
• Flow (Google Labs): https://labs.google/flow/about
• Sora: https://openai.com/sora
• NotebookLM: https://notebooklm.google/
• AI Product Management Bootcamp: https://maven.com/lenny/ai-product-management
• Lenny’s List on Maven: https://maven.com/lenny
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2025-12-01 01:32:37
👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.
2025-11-30 20:03:26
Jeanne DeWitt Grosser built world-class GTM teams at Stripe, Google, and, most recently, Vercel, where she serves as COO and oversees marketing, sales, customer success, revenue operations, and field engineering. She transformed Stripe’s early sales organization from the ground up and advises founders on GTM strategy.
We discuss:
Why GTM is becoming more strategically important in the AI era
The rise of the GTM engineer
A primer on segmentation
How to build a sales org that engineers and product teams respect
The changing calculus of build vs. buy for go-to-market tools in the AI era
Why most customers buy to avoid pain rather than to gain upside
Also on Spotify and Apple Podcasts
Datadog—Now home to Eppo, the leading experimentation and feature flagging platform
Lovable—Build apps by simply chatting with AI
Stripe—Helping companies of all sizes grow revenue
• LinkedIn: https://www.linkedin.com/in/jeannedewitt
• Vercel: https://vercel.com
• Stripe: https://stripe.com
• Rosalind Franklin: https://en.wikipedia.org/wiki/Rosalind_Franklin
• Ben Salzman on LinkedIn: https://www.linkedin.com/in/bensalzman
• SDK: https://ai-sdk.dev/docs/introduction
• Gong: https://www.gong.io
• Lyft: https://www.lyft.com
• Instacart: https://www.instacart.com
• DoorDash: https://www.instacart.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
• A step-by-step guide to crafting a sales pitch that wins | April Dunford (author of Obviously Awesome and Sales Pitch): https://www.lennysnewsletter.com/p/a-step-by-step-guide-to-crafting
• Kate Jensen on LinkedIn: https://www.linkedin.com/in/kateearle
• Lessons from scaling Stripe | Claire Hughes Johnson (former COO of Stripe): https://www.lennysnewsletter.com/p/lessons-from-scaling-stripe-tactics
• Atlassian: atlassian.com
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Lenny may be an investor in the companies discussed.
2025-11-25 00:02:50
Hey friends 👋,
Here’s a weekly recap of new podcast episodes across Lenny’s Podcast Network:
Every Monday, host Claire Vo shares a 30- to 45-minute episode with a new guest demoing a practical, impactful way they’ve learned to use AI in their work or life. No pontificating—just specific and actionable advice.
Brought to you by:
WorkOS—Make your app enterprise-ready today
Google Gemini—Your everyday AI assistant
In this episode, Lucas Werthein, the COO and co-founder of Cactus, breaks down how he built a personal AI health coach that pulls together everything—MRIs, blood tests, wearables, nutrition plans, even journal entries—to help him train smarter, recover faster, and avoid re-injury. After years of surgeries and competing demands on his body, he realized the missing piece wasn’t more data but better synthesis. His AI coach now connects dots across dozens of sources, sets clear performance boundaries, adapts on the fly to real-world changes, and helps him “feel 25 in a 40-year-old body.”
Biggest takeaways:
Data synthesis—not data collection—is the missing link in health optimization. Lucas had access to blood tests, nutritionists, physical therapists, wearable data, and InBody scans, but the challenge was synthesizing this information into actionable insights. His AI coach solved this by integrating all data sources to provide clear, personalized recommendations.
The “last mile” of optimization often comes from unexpected sources. Despite having access to top medical professionals and performance experts, Lucas found that his AI coach unlocked breakthroughs that had eluded him. The AI’s ability to connect dots across siloed data sources provided insights that specialists working in isolation couldn’t deliver.
Effective AI coaches need clear boundaries and anti-prompts. Lucas explicitly instructs his GPT not to push past volume and intensity when metrics show under-recovery, to avoid unproven supplements, and to stick to evidence-based recommendations. These guardrails prevent the AI from giving potentially harmful advice.
AI health coaches will transform the doctor-patient relationship. Within five years, Lucas believes everyone will have access to a personal AI health coach that helps them live healthier between doctor visits and show up more informed. Eventually, a patient’s AI will communicate directly with the doctor’s AI before appointments.
Health-care leaders will eventually sell their knowledge as trained AI models. Lucas predicts that institutions like Mayo Clinic will package decades of patient data into AI models that provide personalized guidance grounded in the best medical science, making expert-level health coaching accessible to everyone.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
Brought to you by: WorkOS—Make your app enterprise-ready today
In this episode, Claire walks through how she built a warm, handcrafted “Thanksgiving party hub” using Lovable—and along the way shares a bunch of practical tricks for making AI-generated apps feel intentional instead of generic. She breaks down how she upleveled the typography, visuals, and structure using Google Fonts, Midjourney style references, and a few simple ChatGPT prompts, plus her go-to recipe-cleaning workflow that actually works when you’re cooking with kids.
Biggest takeaways:
“Farm-to-table software” is the new standard for personal projects. Claire’s approach combines multiple AI tools (Lovable for structure, Google Fonts for typography, Midjourney for imagery, ChatGPT for content) to create something that feels artisanally crafted rather than generically generated.
Sometimes the fastest solution is to restart. When ChatGPT 5.1 got stuck processing her recipe request, Claire simply restarted with the “instant” setting rather than waiting—a practical reminder that AI tools sometimes need a reset.
Free Google font combinations are the easiest way to instantly uplevel any design. Instead of accepting default typography, Claire demonstrates how searching “Google font combinations” leads to curated pairings like “Homemade Apple” and “Railway” that immediately transform the look and feel of an application.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
More shows coming soon. . . 👀
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.
2025-11-24 20:03:47
WorkOS—Make your app enterprise-ready today
Google Gemini—Your everyday AI assistant
Lucas Werthein, the COO and co-founder of Cactus, shares how he built a personalized AI wellness coach using ChatGPT to optimize his athletic performance while managing past injuries. After multiple surgeries on his knees, shoulder, and foot, Lucas created a system that synthesizes data from medical imaging, blood tests, wearable devices, and nutrition plans to provide personalized recommendations. His AI coach helps him balance competitive tennis, weightlifting, and running a company while maintaining his goal of “feeling 25 in a 40-year-old body.” Lucas demonstrates how this approach transforms siloed health information into actionable insights that protect joints, optimize recovery, and extend peak performance.
What you’ll learn:
How to configure a ChatGPT with multiple data types, including MRIs, x-rays, blood tests, and wearable metrics, to create a comprehensive health profile
A framework for setting clear performance boundaries that prioritize joint protection, energy optimization, and injury prevention
Techniques for using AI to balance nutrition around special events like social dinners while maintaining performance goals
How to use images and videos to get AI feedback on physical symptoms and injury recovery timelines
A method for validating and contextualizing medical advice by having AI synthesize information from multiple health-care providers
Why creating clear rules and anti-prompts helps AI deliver practical, evidence-based recommendations instead of trendy supplements or extreme protocols
Website: https://cactus.is/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
(00:00) Introduction to Lucas’s athletic background and injury history
(04:55) The challenge of synthesizing siloed health data
(06:11) Building a GPT to optimize performance and recovery
(09:57) Demonstrating the data types integrated into the AI coach
(13:54) Configuring the GPT with clear performance goals and boundaries
(16:31) Setting realistic expectations for the AI coach
(17:50) Creating nutrition, training, and recovery frameworks
(21:47) Establishing hard boundaries and anti-prompts
(24:25) Example: Managing nutrition around special events
(27:30) Accessibility and affordability of on-demand coaching
(28:24) Practical examples and real-life scenarios
(29:31) Using AI for injury management and recovery planning
(34:19) Validating expert opinions and translating medical advice
(37:25) Vision for the future of AI in personal health coaching
(43:27) Other AI workflows: synthetic clients and AI co-founders
(48:48) Final thoughts on AI reliability and evolution
• ChatGPT: https://chat.openai.com/
• InBody scan: https://inbodyusa.com/
• Whoop: https://www.whoop.com/
Act as my performance strategist and health optimization coach.
You know my physiology, labs, imaging, and wearables. You coach me like a high-performance operator: I’m balancing tennis, lifting, recovery, and running a company. Your job is to safeguard my joints, amplify my output, and extend my peak. I want to perform like a 25-year-old in the body of a 40-year-old!
When I share a prompt, update, or idea, you should:
• Interrogate it through my context (bloods, scans, Whoop, workload, soreness, sleep, and mood)
• Flag red/yellow zones (early signs of overtraining, underfueling, inflammation, or regression)
• Prescribe only high-ROI actions that compound performance (no fluff, no hacks, just results)
• Keep me inside the zone where I wake up rested, move pain-free, and play high-output tennis with no breakdown.
⸻
Optimize across 4 critical axes:
1. Fueling & Inflammation
Stick to my nutrition plan unless there’s a data-driven reason to adapt. Prioritize energy availability, stable glucose, low systemic inflammation, and muscle retention.
2. Training & Load Management
Balance strength, endurance, and mobility—while protecting knees, shoulder, and joints under current imaging conditions. Don’t overload when HRV, CPK, or readiness scores say pull back.
3. Recovery & Regeneration
Sleep drives readiness. PT, mobility, sauna/cold, massage, and mindfulness are not optional—they’re part of the training cycle. Map recovery inputs to readiness trends.
4. Tracking & Feedback Loops
Integrate data across Whoop, InBody, labs, diet, and journal entries. Decisions must be cross-validated: don’t recommend something if it’s not aligned across inputs.
⸻
Hard boundaries:
• Never push volume/intensity when metrics show under-recovery.
• No stacking supplements unless there’s measurable ROI.
• No novelty chasing—stick to what compounds.
• Always act on red flags: recurring soreness, low HRV, decreased sleep quality, creeping inflammation.
⸻
Align every decision on values:
• Precision — is this grounded in my data?
• Energy — does it improve tomorrow’s energy, not just today’s output?
• Adaptation — does this stressor drive growth or wear me down?
• Kinetics — is my movement getting cleaner, faster, more fluid?
⸻
Tone & Response Style:
• Write like a coach who’s been in the trenches with me.
• Fast, clear, tactical—no fluff, no lectures.
• Connect dots across labs, sleep, training, and diet.
• Prioritize what matters this week, not vague long-term theory.
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].