Good podcast

In this episode, we cover:

(00:00) Introduction to the AI design challenge

(01:25) The question: Which model is the better designer?

(03:08) The prompt used for all three models

(04:10) Gemini 3 Pro?s approach and results

(06:00) Opus 4.5?s approach and results

(10:54) Codex 5.1?s approach and disappointing results

(14:51) Comparing the three designs side by side

(16:03) Analyzing the change logs and SEO improvements from each model

(22:43) Final verdict

(23:00) Conclusion and next steps

Tools referenced:

? Gemini 3 Pro: https://deepmind.google/models/gemini/pro/

? Anthropic Opus 4.5: https://www.anthropic.com/news/claude-opus-4-5

? OpenAI Codex 5.1: https://platform.openai.com/docs/models/gpt-5.1-codex

? Cursor: https://cursor.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-12-03
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?PMs who use AI will replace those who don?t?: Google?s AI product lead on the new PM toolkit | Marily Nika

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 requirementsA workflow for transforming market research into comprehensive PRDs using custom GPTs that maintain your personal voice and styleTechniques for turning PRDs into interactive prototypes using v0.dev that make your product vision tangible for stakeholdersHow to create persuasive product videos using Flow and Sora that communicate your vision more effectively than traditional presentationsWhy ?tool hopping? between specialized AI applications creates a more powerful workflow than using a single toolHow to use NotebookLM as an interactive judge for product demos and pitch competitions

Brought to you by:

WorkOS?Make your app enterprise-ready today

Miro?The AI Innovation Workspace where teams discover, plan, and ship breakthrough products

Where to find Marily Nika:

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

Where to find Claire Vo:

In this episode, we cover:

(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

Tools referenced:

? Perplexity: https://www.perplexity.ai/

? ChatGPT: https://chat.openai.com/

? v0.dev: https://v0.dev/

? Flow (Google Labs): https://labs.google/flow/about

? Sora: https://openai.com/sora

? NotebookLM: https://notebooklm.google/

Other references:

? 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
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How to create your own AI performance coach: Optimizing your unique nutrition, recovery, and injury management needs | Lucas Werthein (Cactus)

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 profileA framework for setting clear performance boundaries that prioritize joint protection, energy optimization, and injury preventionTechniques for using AI to balance nutrition around special events like social dinners while maintaining performance goalsHow to use images and videos to get AI feedback on physical symptoms and injury recovery timelinesA method for validating and contextualizing medical advice by having AI synthesize information from multiple health-care providersWhy creating clear rules and anti-prompts helps AI deliver practical, evidence-based recommendations instead of trendy supplements or extreme protocols

Copy Lucas?s Health Coach Prompt: https://www.lennysnewsletter.com/p/how-to-create-your-own-ai-performance-coach

Brought to you by:

WorkOS?Make your app enterprise-ready today

Google Gemini?Your everyday AI assistant

Where to find Lucas Werthein:

Website: https://cactus.is/

Where to find Claire Vo:

In this episode, we cover:

(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

Tool referenced:

? ChatGPT: https://chat.openai.com/

Other references:

? InBody scan: https://inbodyusa.com/

? Whoop: https://www.whoop.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-24
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?Farm-to-table software?: How I built a Thanksgiving party hub using Lovable for managing invites, dishes, shared recipes, and photos

In today?s pre-Thanksgiving episode, I walk you through how I vibe coded my very own ?Thanksgiving party hub? using Lovable?and how I transformed it from AI-generated slop into something warm, personal, and genuinely useful. I show you exactly how I upleveled the typography, visuals, and structure using Google Fonts and Midjourney style references, and then I share one of my favorite real-life AI hacks: how to turn any messy online recipe into a clean, step-by-step, kid-friendly version that?s actually usable while you?re cooking. This is a cozy, practical walkthrough of my real design process?the little tricks I use to make AI-built apps feel handcrafted instead of generic.

What you?ll learn:

How to build a fully functional Thanksgiving party hub in Lovable?guests, dishes, recipes, and photosHow I uplevel AI-generated designs using Google Fonts and TailwindHow to use Midjourney style references to create custom images that match your aestheticHow to add custom features to vibe-coded apps, like dietary preferences and allergen tagsHow to iterate on layouts inside Lovable using screenshots and small, targeted promptsHow I use ChatGPT to restructure recipes so the measurements are embedded directly in each stepHow to make recipes kid-friendly and easier to follow using a simple formatting prompt

Brought to you by:

WorkOS?Make your app enterprise-ready today

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to the Thanksgiving party hub concept

(02:20) Starting a project in Lovable and initial design assessment

(04:59) Upleveling typography with Google Font combinations

(08:36) Creating custom header images with Midjourney

(11:39) Adjusting aspect ratios for Midjourney images

(14:22) Fixing design issues incrementally

(18:52) Adding dietary-restriction functionality

(23:36) AI recipe reformatting for easier cooking

(26:02) Thoughts on ChatGPT 5.1

(30:51) Final implementation and recipe sharing

Tools referenced:

? Lovable: https://lovable.dev/

? Midjourney: https://www.midjourney.com/

? Google Fonts: https://fonts.google.com/

? ChatGPT: https://chat.openai.com/

? Canva Font Combinations: https://www.canva.com/font-combinations/

Other references:

? Polenta and Sausage Stuffing Recipe: https://www.epicurious.com/recipes/food/views/polenta-and-sausage-stuffing-233030

? Runaway Pancakes (kid-friendly recipe site): https://runawaypancakes.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-19
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?Nobody wanted to do this work?: How Emmy Award?winning filmmakers use AI to automate the tedious parts of documentaries

Tim McAleer is a producer at Ken Burns?s Florentine Films who is responsible for the technology and processes that power their documentary production. Rather than using AI to generate creative content, Tim has built custom AI-powered tools that automate the most tedious parts of documentary filmmaking: organizing and extracting metadata from tens of thousands of archival images, videos, and audio files. In this episode, Tim demonstrates how he?s transformed post-production workflows using AI to make vast archives of historical material actually usable and searchable.

What you?ll learn:

How Tim built an AI system that automatically extracts and embeds metadata into archival images and footageThe custom iOS app he created that transforms chaotic archival research into structured, searchable dataHow AI-powered OCR is making previously illegible historical documents accessibleWhy Tim uses different AI models for different tasks (Claude for coding, OpenAI for images, Whisper for audio)How vector embeddings enable semantic search across massive documentary archivesA practical approach to building custom AI tools that solve specific workflow problemsWhy AI is most valuable for automating tedious tasks rather than replacing creative work

Brought to you by:

Brex?The intelligent finance platform built for founders

Where to find Tim McAleer:

Website: https://timmcaleer.com/

LinkedIn: https://www.linkedin.com/in/timmcaleer/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Tim McAleer

(02:23) The scale of media management in documentary filmmaking

(04:16) Building a database system for archival assets

(06:02) Early experiments with AI image description

(08:59) Adding metadata extraction to improve accuracy

(12:54) Scaling from single scripts to a complete REST API

(15:16) Processing video with frame sampling and audio transcription

(19:10) Implementing vector embeddings for semantic search

(21:22) How AI frees up researchers to focus on content discovery

(24:21) Demo of ?Flip Flop? iOS app for field research

(29:33) How structured file naming improves workflow efficiency

(32:20) ?OCR Party? app for processing historical documents

(34:56) The versatility of different app form factors for specific workflows

(40:34) Learning approach and parallels with creative software

(42:00) Perspectives on AI in the film industry

(44:05) Prompting techniques and troubleshooting AI workflows

Tools referenced:

? Claude: https://claude.ai/

? ChatGPT: https://chat.openai.com/

? OpenAI Vision API: https://platform.openai.com/docs/guides/vision

? Whisper: https://github.com/openai/whisper

? Cursor: https://cursor.sh/

? Superwhisper: https://superwhisper.com/

? CLIP: https://github.com/openai/CLIP

? Gemini: https://deepmind.google/technologies/gemini/

Other references:

? Florentine Films: https://www.florentinefilms.com/

? Ken Burns: https://www.pbs.org/kenburns/

? Muhammad Ali documentary: https://www.pbs.org/kenburns/muhammad-ali/

? The American Revolution series: https://www.pbs.org/kenburns/the-american-revolution/

? Archival Producers Alliance: https://www.archivalproducersalliance.com/genai-guidelines

? Exif metadata standard: https://en.wikipedia.org/wiki/Exif

? Library of Congress: https://www.loc.gov/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-17
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How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy)

Matt Britton is the founder and CEO of Suzy, a consumer insights platform that has raised over $100 million in venture capital and works with top brands like Coca-Cola, Google, Procter & Gamble, and Nike. Matt is also the bestselling author of YouthNation, a blueprint for understanding the seismic shifts shaping our future economy, and Generation AI, which explores how Gen Alpha and artificial intelligence will transform business, culture, and society. In this episode, Matt demonstrates how he built a comprehensive AI workflow using Zapier that transforms customer call transcripts into a wealth of actionable intelligence. Despite not being a coder, Matt created a system that automatically generates call summaries, sentiment analysis, coaching feedback, follow-up emails, SEO-optimized blog posts, and more?all from a single customer conversation.

What you?ll learn:

How to build a trigger-based workflow that automatically scrapes and processes customer call transcripts from platforms like GongA systematic approach to quantifying customer sentiment on a 1-10 scale that has proven highly predictive of churn and upsell opportunitiesHow to create an automated coaching system that provides personalized feedback to sales reps after every customer interactionA workflow for extracting keywords from customer conversations to inform Google ad campaigns without manual interventionTechniques for automatically generating privacy-compliant blog content from customer calls that drives organic traffic and paid search performanceWhy CEOs and executives need to build AI skills firsthand rather than delegating implementation to engineering teamsHow to use Google Sheets as structured databases for AI lookups and enrichment within automated workflows

Brought to you by:

Brex?The intelligent finance platform built for founders

Zapier?The most connected AI orchestration platform

Where to find Matt Britton:

LinkedIn: linkedin.com/in/mattbbritton

Instagram: https://www.instagram.com/mattbrittonnyc/

Company: https://www.suzy.com/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Matt Britton

(02:36) Why Zapier became the backbone of Matt?s AI automations

(04:17) Identifying your core business problem

(09:02) How Matt built the initial trigger automation with Browse AI

(13:42) The value of CEOs getting hands-on with building

(14:00) Scraping and processing call transcripts

(20:14) Using LLMs to generate call summaries and sentiment scores

(23:25) Creating a Slack channel for real-time call insights

(26:17) Extracting keywords for Google Ads campaigns

(28:35) Building an AI coach for sales and customer success teams

(29:48) Creating a follow-up email writer for post-call communication

(35:25) Generating redacted blog content from customer conversations

(37:51) How this approach changes team building and hiring priorities

(40:19) Matt?s prompting techniques and final thoughts

Tools referenced:

? Zapier: https://zapier.com/

? Gong: https://www.gong.io/

? Browse AI: https://www.browse.ai/

? ChatGPT: https://chat.openai.com/

Other references:

? Qualtrics: https://www.qualtrics.com/

? SurveyMonkey: https://www.surveymonkey.com/

? Slack: https://slack.com/

? Google Sheets: https://www.google.com/sheets/about/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-10
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The complete beginner?s guide to coding with AI: from PRD to generating your very first lines of code

This episode is for complete beginners. I walk you through how to build your very first coding project using AI tools?even if you?ve never written a line of code. Together, we?ll create a personal project hub that automatically generates documentation and lets you build interactive prototypes. I?ll show you the process step by step?from setting up a repository, to creating AI agents that help with specific tasks, to deploying a functional web app locally.

What you?ll learn:

How to set up a simple Next.js application from scratch using Cursor?s AI agent capabilities

My workflow for creating AI agents that generate consistent documentation (like PRDs in Markdown format)

How to build and display clickable prototypes without worrying about complex backend functionality

The basics of using GitHub to track changes and manage your code repository as a non-technical person

Why starting with a personal project hub is the best way to ease into AI-assisted coding

My favorite practical tips for iterating on designs and functionality using AI tools?without needing deep technical expertise

Brought to you by:

ChatPRD?An AI copilot for PMs and their teams

In this episode, we cover:

(00:00) Introduction

(05:11) Starting with a requirements document in ChatPRD

(08:22) Attempting to use v0 for initial prototyping

(15:02) Pivoting to Cursor for initial prototyping

(20:20) Running the app locally and reviewing the initial version

(24:07) Setting up GitHub for version control

(27:09) Creating an AI agent for writing PRDs

(31:04) Using the agent to create a sample PRD

(35:00) Building a prototype based on the PRD

(37:00) Testing and improving the prototype

(40:00) Adding documentation and improving the design

(43:20) Recap of the complete workflow

Tools referenced:

? Cursor: https://cursor.com/

? ChatPRD: https://www.chatprd.ai/

? GitHub Desktop: https://desktop.github.com/

? Next.js: https://nextjs.org/

? Tailwind CSS: https://tailwindcss.com/

Other references:

? Lovable: https://lovable.ai/

? Bolt: https://bolt.new/

? Claude Code: https://www.claude.com/product/claude-code

? Markdown: https://www.markdownguide.org/

? GitHub: https://github.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-05
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?Vibe analysis?: How Faire?s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights

Tim Trueman and Alexa Cerf from Faire?s data team demonstrate how AI tools are revolutionizing data analysis workflows. They show how data teams, product managers, and engineers can use tools like Cursor, ChatGPT, and custom agents to investigate business metrics, analyze experiment results, and extract insights from user surveys?all while dramatically reducing the time and technical expertise required.

What you?ll learn:

1. How to use AI to investigate sudden drops in business metrics by searching documentation and codebases

2. Techniques for creating a semantic layer that helps AI understand your business data

3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs)

4. Ways to automate experiment analysis and create standardized reports

5. How AI can help design and analyze customer surveys

6. Strategies for creating executive-ready documents from raw data analysis

7. Why every team member should have access to code repositories?not just engineers

Brought to you by:

Zapier?The most connected AI orchestration platform

Brex?The intelligent finance platform built for founders

Where to find Tim Trueman:

LinkedIn: https://www.linkedin.com/in/tim-trueman-99788592/

Where to find Alexa Cerf:

LinkedIn: https://www.linkedin.com/in/alexandra-cerf/

Where to find Claire Vo:

X: https://x.com/amrcn_werewolf?lang=en

In this episode, we cover:

(00:00) Introduction to Tim and Alexa from Faire

(02:53) The challenge of analyzing product quality and usage

(04:14) Breaking down what analytics actually involves beyond data manipulation

(05:46) Demo: Investigating a conversion rate drop using enterprise AI search

(09:05) Using ChatGPT Deep Research to analyze code changes

(12:40) Leveraging Cursor as the ultimate context engine for code analysis

(18:55) Analyzing a new product feature?s performance with Cursor

(26:27) How semantic layers make AI tools more effective for data analysis

(30:00) Using Model Context Protocols (MCPs) to connect AI with data tools

(34:17) Creating visualizations and dashboards with Mode integration

(37:04) Generating structured analysis documents with Notion integration

(44:39) Building custom agents to automate experiment result documentation

(53:10) Designing and analyzing customer surveys

(59:40) Lightning round and final thoughts

Tools referenced:

? Cursor: https://cursor.com/

? ChatGPT: https://chat.openai.com/

? Notion: https://www.notion.so/

? Snowflake: https://www.snowflake.com/

? Mode: https://mode.com

? Qualtrics: https://www.qualtrics.com/

? GitHub: https://github.com/

Other references:

? Model Context Protocol (MCP): https://www.anthropic.com/news/model-context-protocol

? Faire Careers: https://www.faire.com/careers

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-11-03
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Vibe-coding a kid-friendly AI fortune teller for your Halloween festivities | Marco Casalaina (Microsoft VP)

In this impromptu Halloween special, Marco Casalaina (VP of Products for Core AI at Microsoft) demonstrates how he uses GitHub Spark to quickly build a mobile app that generates kid-friendly fortunes for trick-or-treaters.

Where to find Marco Casalaina:

LinkedIn: https://www.linkedin.com/in/marcocasalaina/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Intro

(00:40) Marco?s Halloween fortune teller tradition

(02:54) Using GitHub Spark to create a fortune teller app

(04:32) Using Spec Kit for scoping out complex feature specs

(06:53) Making fortunes more concrete and kid-friendly

(10:20) Closing thoughts

Tools referenced:

? GitHub Spark: https://github.com/features/spark

? SpecKit: https://github.com/github/spec-kit

? GitHub Copilot: https://github.com/features/copilot

? Cursor: https://cursor.com/

? Claude Code: https://www.claude.com/product/claude-code

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-10-31
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?Cursor is a much better product manager than I ever was?: How this PM uses AI for PRDs, Jira tickets, and replying to coworkers | Dennis Yang (Chime)

Dennis Yang is the Principal Product Manager for Generative AI at Chime, where he?s pioneered AI workflows that meaningfully increase productivity. While most people use Cursor as a coding tool, Dennis has turned it into a comprehensive product-management system that automates PRD creation, documentation management, ticket creation, status reporting, and even comment responses?without writing code. In this episode, he shares his end-to-end workflow and how non-technical professionals can leverage AI-powered IDEs.

What you?ll learn:

Why Cursor is the perfect hub for product management (even if you don?t code)How to use MCPs (Model Context Protocols) to push content between Cursor, Confluence, and NotionThe workflow for creating PRDs in Cursor and automatically responding to commentsHow to automate Jira ticket creation directly from your PRDsA system for generating comprehensive status reports without manual workHow to prototype AI products in minutes using Cursor as a ?super MVP? environmentWhy source-controlled markdown files might replace traditional SaaS tools

Brought to you by:

Zapier?The most connected AI orchestration platform

Brex?The intelligent finance platform built for founders

Where to find Dennis Yang:

Twitter/X: https://twitter.com/sinned

LinkedIn: https://www.linkedin.com/in/dennisyang/

Chime: https://www.chime.com/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Dennis Yang

(03:00) Why Cursor is ideal for product management workflows

(04:53) Setting up Cursor for non-coding use cases with markdown preview

(09:35) Creating PRDs in Cursor and using source control for documentation

(10:33) Using MCPs to publish content to Confluence and Notion

(11:38) Bridging the gap between engineering and product

(17:00) Reading and responding to document comments with AI assistance

(21:37) Creating comprehensive Jira tickets directly from PRDs

(25:51) Generating automated status reports from Jira data

(30:23) Building a morning briefing system with ChatGPT

(35:03) Generating personal morning briefings using ChatGPT

(40:04) The ?super MVP? approach to AI product development

(46:37) Lightning round and final thoughts

Tools referenced:

? Cursor: https://cursor.com/

? Confluence: https://www.atlassian.com/software/confluence

? Notion: https://www.notion.so/

? Jira: https://www.atlassian.com/software/jira

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Git: https://git-scm.com/

Other references:

? News API: https://newsapi.org/

? Semrush: https://www.semrush.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-10-27
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Claude Skills explained: How to create reusable AI workflows

Today I dive into Anthropic?s latest feature that lets anyone create reusable workflows for Claude?no coding required. I break down exactly what Claude Skills are, how to build them from scratch, and how to use them inside Claude Code and Cursor to automate recurring AI tasks like generating PRDs, writing changelog summaries, and turning demo notes into follow-up emails.

What you?ll learn:

What Claude Skills are and how they differ from Claude Projects and custom GPTsHow to structure a Skill (metadata, instructions, and linked files)Why defining workflows in natural language beats rigid automation toolsHow to create Claude Skills using Claude Code and CursorHow to validate your skills with Python scripts and folder referencesHow to upload and use Claude Skills inside Claude?s web or desktop appPractical examples: turning changelogs into newsletters, demo notes into emails, and more

Brought to you by:

ChatPRD?An AI copilot for PMs and their teams

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction

(01:39) What are Claude Skills and how do they work?

(08:30) The structure of Claude Skills files

(11:00) Demo: Creating Skills using Claude?s built-in skill creator

(16:08) A more efficient workflow: Creating Skills with Cursor

(17:42) Using Python validation scripts

(18:37) Testing Skills with Claude Code

(20:52) Creating a changelog-to-newsletter Skill

(22:16) Creating a demo-to-follow-up-email Skill

(23:45) Uploading Skills to the Claude web interface

(26:04) Conclusion and summary

Tools referenced:

? Claude: https://claude.ai/

? Claude Code: https://claude.ai/code

? Cursor: https://cursor.sh/

Other references:

? Equipping agents for the real world with Agent Skills: https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills

? Anthropic Skills Documentation: https://docs.claude.com/en/docs/claude-code/skills?utm_source=chatgpt.com

? Claude Projects:https://claude.ai/projects

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-10-22
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How this Yelp AI PM works backward from ?golden conversations? to create high-quality prototypes using Claude Artifacts and Magic Patterns | Priya Badger

Priya Badger, a product manager at Yelp, shares her innovative approach to designing AI-powered products by starting with example conversations rather than traditional wireframes or PRDs. In this episode, she demonstrates how she uses Claude and Magic Patterns to prototype Yelp?s AI assistant features?from exploring conversation flows to designing user interfaces.

What you?ll learn:

1. How to use example conversations as your first ?wireframe? when designing conversational AI products

2. A step-by-step workflow for using Claude to generate and refine sample conversations that guide your AI product development

3. Techniques for creating interactive prototypes with Claude Artifacts that use real LLM responses without complex API integrations

4. How to use Magic Patterns? Inspiration mode to rapidly explore multiple UI variations for your AI features

5. Why starting with conversations and working backward to system prompts creates more natural AI interactions

6. How to apply these AI prototyping techniques to personal projects to build your AI product management skills

Brought to you by:

GoFundMe Giving Funds?One account. Zero hassle.

Persona?Trusted identity verification for any use case

Where to find Priya Badger:

LinkedIn: https://www.linkedin.com/in/priyamathewprofile/

Substack: https://almostmagic.substack.com/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Priya

(02:54) The unique challenges of managing AI-powered products

(04:33) Using example conversations as a starting point for design

(05:53) Demo: Prompting Claude to generate sample conversations

(09:10) Prototyping advice

(09:53) Testing with multiple example images and scenarios

(15:03) Refining conversations based on qualitative assessment

(15:59) Demo: Creating interactive prototypes with Claude Artifacts

(21:22) Using Magic Patterns to design the user interface

(25:30) Exploring multiple design variations with Inspiration mode

(31:02) Quick summary

(33:35) How to apply these AI prototyping techniques to personal projects

(38:57) Final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Magic Patterns: https://magicpatterns.com/

? Lovable: https://lovable.ai/

? ChatGPT: https://chat.openai.com/

Other references:

? How to build prototypes that actually look like your product | Colin Matthews (product leader, AI prototyping instructor at Maven): https://www.lennysnewsletter.com/p/how-to-build-prototypes-that-actually

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-10-20
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Evals, error analysis, and better prompts: A systematic approach to improving your AI products | Hamel Husain (ML engineer)

Hamel Husain, an AI consultant and educator, shares his systematic approach to improving AI product quality through error analysis, evaluation frameworks, and prompt engineering. In this episode, he demonstrates how product teams can move beyond ?vibe checking? their AI systems to implement data-driven quality improvement processes that identify and fix the most common errors. Using real examples from client work with Nurture Boss (an AI assistant for property managers), Hamel walks through practical techniques that product managers can implement immediately to dramatically improve their AI products.

What you?ll learn:

1. A step-by-step error analysis framework that helps identify and categorize the most common AI failures in your product

2. How to create custom annotation systems that make reviewing AI conversations faster and more insightful

3. Why binary evaluations (pass/fail) are more useful than arbitrary quality scores for measuring AI performance

4. Techniques for validating your LLM judges to ensure they align with human quality expectations

5. A practical approach to prioritizing fixes based on frequency counting rather than intuition

6. Why looking at real user conversations (not just ideal test cases) is critical for understanding AI product failures

7. How to build a comprehensive quality system that spans from manual review to automated evaluation

Brought to you by:

GoFundMe Giving Funds?One account. Zero hassle: https://gofundme.com/howiai

Persona?Trusted identity verification for any use case: https://withpersona.com/lp/howiai

Where to find Hamel Husain:

Website: https://hamel.dev/

Twitter: https://twitter.com/HamelHusain

Course: https://maven.com/parlance-labs/evals

GitHub: https://github.com/hamelsmu

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Hamel Husain

(03:05) The fundamentals: why data analysis is critical for AI products

(06:58) Understanding traces and examining real user interactions

(13:35) Error analysis: a systematic approach to finding AI failures

(17:40) Creating custom annotation systems for faster review

(22:23) The impact of this process

(25:15) Different types of evaluations

(29:30) LLM-as-a-Judge

(33:58) Improving prompts and system instructions

(38:15) Analyzing agent workflows

(40:38) Hamel?s personal AI tools and workflows

(48:02) Lighting round and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Braintrust: https://www.braintrust.dev/docs/start

? Phoenix: https://phoenix.arize.com/

? AI Studio: https://aistudio.google.com/

? ChatGPT: https://chat.openai.com/

? Gemini: https://gemini.google.com/

Other references:

? Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences: https://dl.acm.org/doi/10.1145/3654777.3676450

? Nurture Boss: https://nurtureboss.io

? Rechat: https://rechat.com/

? Your AI Product Needs Evals: https://hamel.dev/blog/posts/evals/

? A Field Guide to Rapidly Improving AI Products: https://hamel.dev/blog/posts/field-guide/

? Creating a LLM-as-a-Judge That Drives Business Results: https://hamel.dev/blog/posts/llm-judge/

? 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].

2025-10-13
Link to episode

?I?m incapable of doing my job without AI?: How this top PM uses Claude + ChatGPT as his second brain

Amir Klein is a product manager at Monday.com, leading their AI agents initiative. Despite taking two months of paternity leave, he ranked #4 out of 90 PMs in AI tool usage at his company. In this episode, Amir reveals how he?s become ?highly dependent and maybe incapable? of doing his job without AI, showing his custom GPT workflows that help him manage context switching, analyze customer feedback, improve his writing, and prepare for product interviews.

What you?ll learn:

How to create project-specific ?second brains? in Claude and ChatGPT that hold context for you across multiple workstreamsA step-by-step process for using Claude to build a Reddit scraper that gathers thousands of customer conversations, without coding expertiseHow to analyze large datasets of customer feedback using AI to identify patterns, priorities, and key discussion pointsA workflow for creating custom GPTs that help you improve specific skills based on manager feedbackTechniques for using GPT voice mode to conduct realistic mock interviews that provide candid feedback on your responsesWhy ?everything is text? should be your mindset when feeding information into AI tools, from PDFs to slide decksHow to use AI to respond quickly to stakeholder requests even when you?re context switching between multiple projects

Brought to you by:

GoFundMe Giving Funds?One account. Zero hassle.

Miro?A collaborative visual platform where your best work comes to life

Where to find Amir Klein:

LinkedIn: https://www.linkedin.com/in/amir-klein-9b8444189/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Amir

(03:11) Using custom GPT project folders as ?second brains?

(06:24) Building a Reddit scraper with Claude?s help

(11:02) Analyzing 34,000 rows of Reddit conversations

(14:06) How to build effective custom GPT knowledge bases

(18:04) Creating a custom writing coach from Lenny?s Newsletter

(21:53) Using AI for professional development and feedback

(24:08) Preparing for product interviews with GPT voice mode

(31:49) Additional use cases for voice mode

(33:04) Recap of Amir?s AI workflows

(35:43) Lightning round and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? ChatGPT: https://chat.openai.com/

? Reddit API: https://www.reddit.com/dev/api/

? Python: https://www.python.org/

? Slack: https://slack.com/

Other references:

? Wes Kao: https://weskao.com/

? Become a better communicator: Specific frameworks to improve your clarity, influence, and impact | Wes Kao (coach, entrepreneur, advisor): https://www.lennysnewsletter.com/p/become-a-better-communicator-specific

? On Writing Well by William Zinsser: https://www.amazon.com/Writing-Well-Classic-Guide-Nonfiction/dp/0060891548

? The Elements of Style by Strunk and White: https://www.amazon.com/Elements-Style-Fourth-William-Strunk/dp/020530902X

? Exponent YouTube channel: https://www.youtube.com/c/ExponentTV

? monday.com: https://monday.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-10-06
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The secret to better AI prototypes: Why Tinder?s CPO starts with JSON, not design | Ravi Mehta (product advisor, previously EIR at Reforge)

Ravi Mehta, now a product advisor, has built and scaled products used by millions. His past roles include Chief Product Officer at Tinder, Entrepreneur in Residence at Reforge, and senior product leadership positions at Facebook, TripAdvisor, and Xbox. In this episode, Ravi demonstrates his data-driven approach to AI prototyping that produces dramatically better results than traditional "vibe prototyping." He also shares his structured framework for generating professional-quality images in Midjourney that look like they were shot by a professional photographer.

What you?ll learn:

Why most product managers and designers are ?vibe prototyping? with AI and getting mediocre resultsHow to use JSON data models instead of design systems as the foundation for better AI prototypesA simple three-part framework for structuring Midjourney prompts to get professional-quality photosHow to use Claude and Unsplash?s MCP server to generate realistic data and images for your prototypesWhy real data (not Lorem Ipsum) is critical for getting meaningful feedback from stakeholdersThe film stock ?cheat code? that instantly elevates your AI-generated photos

Brought to you by:

Google Gemini?Your everyday AI assistant

Persona?Trusted identity verification for any use case

Where to find Ravi Mehta:

Website: https://www.ravi-mehta.com/

Reforge: https://www.reforge.com/profiles/ravi-mehta

LinkedIn: https://www.linkedin.com/in/ravimehta/

X: https://x.com/ravi_mehta

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Ravi and data-driven prototyping

(02:31) The problem with ?vibe prototyping? in product development

(04:18) Spec-driven prototyping vs. data-driven prototyping

(05:27) Demo: Spec-driven approach to prototyping

(08:26) Limitations of the basic AI prototype approach

(11:24) The data-driven prototyping approach explained

(12:08) Demo: Data-driven prototyping

(17:45) Creating a prototype with the generated JSON data

(23:33) Comparing the quality difference between approaches

(26:44) Modifying the prototype

(28:53) Benefits of this approach

(34:40) Structured Midjourney prompting

(36:20) The subject-setting-style framework for better image prompts

(44:27) Using camera metadata to refine your results

(48:54) Lightning round and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Reforge Build: https://www.reforge.com/build

? Midjourney: https://www.midjourney.com/

? Unsplash MCP: https://github.com/okooo5km/unsplash-mcp-server-go?utm_source=chatgpt.com

Other references:

? Reforge AI Strategy Course: https://www.reforge.com/courses/ai-strategy

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-09-29
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The beginner's guide to coding with Cursor | Lee Robinson (Head of AI education)

Lee Robinson is the head of AI education at Cursor, where he teaches people how to build software with AI. Previously, he helped build Vercel and Next.js as an early employee. In this episode, he demonstrates how Cursor's AI-powered code editor bridges the gap between beginners and experienced developers through automated error fixing, parallel task execution, and writing assistance. Lee walks through practical examples of using Cursor's agent to improve code quality, manage technical debt, and even enhance your writing by eliminating common AI patterns and clichés.

What you'll learn:

1. How to use Cursor's AI agent to automatically detect and fix linting errors without needing to understand complex terminal commands

2. A workflow for running parallel coding tasks by focusing on your main work while the agent handles secondary features in the background

3. Why setting up typed languages, linters, formatters, and tests creates guardrails that help AI tools generate better code

4. How to create custom commands for code reviews that automatically check for security issues, test coverage, and other quality concerns

5. A technique for improving your writing by creating a custom prompt with banned words and phrases that eliminates AI-generated patterns

6. Strategies for managing context in AI conversations to maintain high-quality responses and avoid degradation

7. Why looking at code?even when you don't fully understand it?is one of the best ways to learn programming

Brought to you by:

Google Gemini?Your everyday AI assistant

Persona?Trusted identity verification for any use case

Where to find Lee Robinson:

Twitter/X: https://twitter.com/leeerob

Website: https://leerob.com

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Lee

(02:04) Understanding Cursor's three-panel interface

(06:27) The importance of typed languages, linters, and tests

(11:28) Demo: Using the agent to automatically fix lint errors

(15:17) Running parallel coding tasks with the agent

(18:50) Setting up custom rules

(23:24) Understanding the different AI models

(24:48) Micro-slicing agent chats for better success

(27:22) Tips for effective agent usage

(29:00) Using AI to improve your writing

(35:47) Lightning round and final thoughts

Tools referenced:

? Cursor: https://cursor.com/

? ChatGPT: https://chat.openai.com/

? JavaScript: https://developer.mozilla.org/en-US/docs/Web/JavaScript

? Python: https://www.python.org/

? TypeScript: https://www.typescriptlang.org/

? Git: https://git-scm.com/

Other references:

? Linting: https://en.wikipedia.org/wiki/Lint_(software)

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-09-22
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How I built an Apple Watch workout app using Cursor and Xcode (with zero mobile-app experience)

Terry Lin is a product manager and developer who built Cooper?s Corner, an AI-powered fitness tracking app that works across iPhone and Apple Watch. Frustrated with traditional fitness apps that require extensive setup and manual logging, Terry created a solution that lets users simply speak their exercises, weights, and reps. The app automatically structures this data and provides analytics on workout consistency and progress. In this episode, Terry shares his vibe-coding process using Cursor and Xcode and explains how he optimizes his codebase for AI collaboration.

What you?ll learn:

1. How Terry built a voice-powered fitness tracker that works across iPhone and Apple Watch

2. His ?dual-wielding? workflow, using Cursor for coding and Xcode for building and debugging

3. Terry?s three-step process for working with AI: create, review, and execute

4. Why optimizing your codebase for AI collaboration can dramatically improve productivity

5. How to use index cards and GPT-4 to rapidly prototype mobile interfaces

6. A technique for ?vibe refactoring? that keeps code organized and optimized for both human and AI readability

7. His ?rubber duck? technique to better understand generated code and improve your learning process

Brought to you by:

Paragon?Ship every SaaS integration your customers want

Miro?A collaborative visual platform where your best work comes to life

Where to find Terry Lin:

LinkedIn: https://www.linkedin.com/in/itsmeterrylin/

GitHub: https://github.com/itsmeterrylin

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Terry and his fitness tracker app

(02:30) Demo of the voice-powered workout tracking across devices

(06:40) Analytics and history views for tracking consistency

(07:20) Dual-wielding Cursor and Xcode for mobile development

(09:05) Building a v1 using AI tools

(11:19) A three-step AI workflow: create, review, execute

(19:38) Token conservation and vibe refactoring explained

(23:25) Optimizing file sizes for better AI performance

(25:28) Using ?rubber duck? rules to learn from AI-generated code

(28:13) Prototyping with index cards and GPT-4

(31:20) Human creativity and the last 10%

(32:29) Lightning round and final thoughts

Tools referenced:

? Cursor: https://cursor.sh/

? Xcode: https://developer.apple.com/xcode/

? GPT-4: https://openai.com/gpt-4

? UX Pilot: https://uxpilot.ai/

? Linear: https://linear.app/

Other references:

? Apple UI Kit: https://developer.apple.com/design/human-interface-guidelines/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-09-15
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How Devin replaces your junior engineers with infinite AI interns that never sleep | Scott Wu (Cognition CEO)

Scott Wu is the co-founder and CEO of Cognition Labs, the creators of Devin, an AI agent designed to function as a junior engineer on software development teams. In this conversation, Scott demonstrates how his team uses their own product to accelerate development workflows, reduce engineering toil, and handle routine tasks asynchronously. Scott walks us through real examples of how Devin integrates into Cognition?s daily operations?from researching and implementing new features to responding to crashes and handling frontend fixes. He explains how Devin differs from traditional AI coding assistants by functioning more like a team member than a tool, allowing engineers to delegate well-scoped tasks while focusing on higher-level problems.

What you?ll learn:

1. How to use DeepWiki to research your codebase and generate better prompts for AI engineering tasks

2. A workflow for treating AI agents as asynchronous junior engineers who can handle multiple tasks while you attend meetings

3. Why public channels create better learning environments for both humans and AI when implementing engineering solutions

4. The top five engineering tasks AI excels at: frontend fixes, version upgrades, documentation, incident response, and testing

5. How to implement a ?first line of defense? system where AI agents analyze crashes before humans need to intervene

6. A technique for bringing voice AI into meetings as an additional participant to answer questions without disrupting flow

Brought to you by:

Google Gemini?Your everyday AI assistant

Vanta?Automate compliance. Simplify security.

Where to find Scott Wu:

X: https://x.com/ScottWu46

LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Scott Wu and Devin

(03:53) Where Devin excels

(06:08) Using DeepWiki to research codebases and create better prompts

(10:27) Prompting tips

(11:24) The asynchronous nature of working with Devin

(13:38) Multithreading tasks

(14:43) Using Devin to implement an MCP server integration

(18:38) Setting up workflows in Slack for first-line responses

(23:22) Encouraging AI adoption in public Slack channels

(25:50) Top five engineering tasks for Devin

(32:17) Using ChatGPT voice as a meeting participant

(35:57) Lightning round

Tools referenced:

? Devin: https://devin.ai/

? DeepWiki: https://deepwiki.org/

? ChatGPT: https://chat.openai.com/

? Windsurf: https://windsurf.ai/

? Slack: https://slack.com/

? Linear: https://linear.app/

? GitHub: https://github.com/

Other references:

? MCP (model context protocol): https://www.anthropic.com/news/model-context-protocol

? TanStack Router: https://tanstack.com/router/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-09-08
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How to turn meeting notes into prototypes that your sales team can immediately demo to customers | Anjan Panneer Selvam (Acolyte Health)

Anjan Panneer Selvam is the Chief Product and Technology Officer at Acolyte Health, where he?s pioneering the use of AI across the entire product development lifecycle. In this episode, he demonstrates how AI tools can dramatically accelerate alignment between stakeholders, reduce development time from months to minutes, and enable teams to validate ideas with customers before committing engineering resources.

What you?ll learn:

1. How to transform meeting transcripts into interactive prototypes in under 30 minutes using ChatGPT, Lovable, and other AI tools

2. A step-by-step workflow for creating market analyses and competitive research in minutes instead of days

3. How to build a ?living product library? that allows sales and customer success teams to demo prototypes to customers before engineering begins

4. Techniques for using AI to break deadlocks with engineering by demonstrating what?s possible without requiring technical expertise

5. Why AI enables faster stakeholder alignment by converting abstract ideas into tangible, interactive experiences

6. How to use ChatPRD to validate product requirements and ensure you?ve considered all critical aspects before engaging engineering

Brought to you by:

Notion?The best AI tools for work: https://www.notion.com/howiai

Lovable?Build apps by simply chatting with AI: https://lovable.dev/

Where to find Anjan Panneer Selvam:

LinkedIn: https://www.linkedin.com/in/anjanps/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Anjan

(02:36) How AI changes the relationship between product and engineering

(04:08) Workflow for converting stakeholder ideas into prototypes

(08:50) Using the Limitless pendant to capture meeting transcripts

(12:45) Creating interactive prototypes with Lovable

(15:57) Benefits of using prototypes instead of documentation

(19:07) Conducting market research with Perplexity

(21:45) Creating presentation decks with Gamma

(23:08) AI doesn?t replace PMs; it elevates them

(25:05) Using ChatPRD to validate product requirements

(29:10) Building a living product library for sales and customer success

(35:50) Breaking deadlocks with engineering using Rork for mobile prototypes

(39:00) Takeaways for building with AI

(42:34) Cultural implications of AI in product development

(45:20) Strategies for when AI doesn?t give you what you want

Tools referenced:

? ChatGPT: https://chat.openai.com/

? Lovable: https://lovable.dev/

? Limitless: https://www.limitless.ai/

? Perplexity: https://www.perplexity.ai/

? Gamma: https://gamma.app/

? ChatPRD: https://www.chatprd.ai/

? Rork: https://rork.com/

? Magic Patterns: https://www.magicpatterns.com/

Other references:

? React Flow: https://reactflow.dev/

? Acolyte Health: https://acolytehealth.com/

? Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-ai-glasses

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-09-01
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How to digest 36 weekly podcasts without spending 36 hours listening | Tomasz Tunguz (Theory Ventures)

Tomasz Tunguz is the founder of Theory Ventures, which invests in early-stage enterprise AI, data, and blockchain companies. In this episode, Tomasz reveals his custom-built ?Parakeet Podcast Processor,? which helps him extract value from 36 podcasts weekly without spending 36 hours listening. He walks through his terminal-based workflow that downloads, transcribes, and summarizes podcast content, extracting key insights, investment theses, and even generating blog post drafts. We explore how AI enables hyper-personalized software experiences that weren?t feasible before recent advances in language models.

What you?ll learn:

1. How to build a terminal-based podcast processing system that downloads, transcribes, and extracts key insights from multiple podcasts daily

2. A workflow for using Nvidia?s Parakeet and other AI tools to clean transcripts and generate structured summaries of podcast content

3. How to extract actionable investment theses and company mentions from podcast transcripts using AI prompting techniques

4. A systematic approach to generating blog post drafts with AI that maintains your personal writing style through iterative feedback

5. Why using an ?AP English teacher? grading system can help improve AI-generated content through multiple revision cycles

6. How to leverage Claude Code for maintaining and updating personal productivity tools with minimal friction

Brought to you by:

Notion?The best AI tools for work

Miro?A collaborative visual platform where your best work comes to life

25k giveaway:

?To celebrate 25,000 YouTube followers, we?re doing a giveaway. Win a free year of my favorite AI products, including v0, Replit, Lovable, Bolt, Cursor, and, of course, ChatPRD, by leaving a rating and review on your favorite podcast app and subscribing to the podcast on YouTube. To enter: https://www.howiaipod.com/giveaway

Where to find Tomasz Tunguz:

Blog: https://tomtunguz.com/

Theory Ventures: https://theory.ventures/

LinkedIn: https://www.linkedin.com/in/tomasztunguz/

X: https://x.com/ttunguz

In this episode, we cover:

(00:00) Introduction to Tomasz Tunguz

(03:32) Overview of the podcast ripper system and its components

(05:06) Demonstration of the transcript cleaning process

(06:59) Extracting quotes, investment theses, and company mentions

(10:20) Why Tomasz prefers terminal-based tools

(12:38) The benefits of personalized software versus off-the-shelf solutions

(15:31) A workflow for generating blog posts from podcast insights

(17:34) Using the ?AP English teacher? grading system for blog posts

(18:25) Challenges with matching personal writing style using AI

(22:00) Tomasz?s three-iteration process for improving blog posts

(26:13) The grading prompt and evaluation criteria

(28:16) AI?s role in writing education

(30:28) Final thoughts

Tools referenced:

? Whisper (OpenAI): https://openai.com/research/whisper

? Parakeet: https://build.nvidia.com/nvidia/parakeet-ctc-0_6b-asr

? Ollama: https://ollama.com/

? Gemma 3: https://deepmind.google/models/gemma/gemma-3/

? Claude: https://claude.ai/

? Claude Code: https://claude.ai/code

? Gemini: https://gemini.google.com/

? FFmpeg: https://ffmpeg.org/

? DuckDB: https://duckdb.org/

? LanceDB: https://lancedb.com/

Other references:

? 35 years of product design wisdom from Apple, Disney, Pinterest, and beyond | Bob Baxley: https://www.lennysnewsletter.com/p/35-years-of-product-design-wisdom-bob-baxley

? Dan Luu?s blog post on latency: https://danluu.com/input-lag/

? GitHub CEO: The AI Coding Gold Rush, Vibe Coding & Cursor: https://www.readtobuild.com/p/github-ceo-the-ai-coding-gold-rush

? Stanford Named Entity Recognition library: https://nlp.stanford.edu/software/CRF-NER.html

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-08-25
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Using Veo 3 to create AI-generated music videos, like a Tiny Desk Concert with Notorious B.I.G. and Kurt Cobain

Anish Acharya is an entrepreneur and general partner at Andreessen Horowitz, focusing on consumer investing and AI-native products. In this episode, he demonstrates how AI can be used for creative and personal projects beyond typical work applications. He walks through creating an AI-generated Tiny Desk Concert for Notorious B.I.G. and Kurt Cobain, building a book cataloging app using video analysis, and using browser automation for personal finance insights. Anish shares how these technologies allow anyone to bring creative ideas to life with minimal technical expertise, transforming what would have been impossible projects just a few years ago into accessible weekend activities.

What you?ll learn:

1. A step-by-step workflow for creating AI-generated music videos featuring artists like Kurt Cobain and Notorious B.I.G.

2. How to extract vocals from existing tracks to create unique audio combinations for your AI-generated videos

3. A simple method for cataloging your book or record collection using video analysis and Gemini Flash

4. How to use Comet to analyze personal finances and get investment recommendations without manual data analysis

5. Ways AI is transforming childhood learning and play by enabling interactive storytelling and creative exploration

Brought to you by:

Notion?The best AI tools for work

Lenny?s List on Maven?Hands-on AI education curated by Lenny and Claire

Where to find Anish Acharya:

? Andreessen Horowitz: https://a16z.com/author/anish-acharya/

? LinkedIn: https://www.linkedin.com/in/anishacharya/

? X: https://x.com/illscience

Where to find Claire Vo:

In this episode, we cover:

(?00:00?) Introduction to Anish Acharya

(?03:05?) How AI transforms creative constraints in music and video

(?06:00?) Creating an AI-generated Notorious B.I.G. Tiny Desk Concert

(?07:36?) Using GPT-4o to generate still images

(?09:27?) Using Hedra to animate still frame images

(?10:40?) Adding custom audio to video

(?11:30?) Using Adobe Audition to clip and sync audio

(?15:42?) How to use Demucs to extract vocals from any song

(?16:36?) Using Hedra to generate a Tiny Desk Concert featuring Kurt Cobain

(?19:40?) Creating a ?90s-style Nirvana music video with Veo 3

(?27:40?) Building a book collection cataloging tool with Gemini Flash

(?35:35?) Using the Comet browser for personal finance analysis

(?37:20?) How AI is transforming childhood learning and play

(?41:23?) Tips for getting better results from AI tools

Tools referenced:

? GPT-4o: https://openai.com/index/hello-gpt-4o/

? Hedra: https://www.hedra.com/

? Adobe Audition: https://www.adobe.com/products/audition.html

? Demucs: https://github.com/facebookresearch/demucs

? Perplexity: https://www.perplexity.ai/

? Veo 3: https://deepmind.google/models/veo/

? Kapwing: https://www.kapwing.com/

? Cursor: https://cursor.com/

? Google AI Studio: https://makersuite.google.com/

? Gemini Flash: https://ai.google.dev/gemini-api

? Comet: https://www.perplexity.ai/comet

Other references:

? Anish?s Notorious B.I.G. AI-generated Tiny Desk Concert: https://x.com/illscience/status/1935721063876550939

? NPR Tiny Desk Concerts: https://www.npr.org/series/tiny-desk-concerts/

? Notorious B.I.G.: https://en.wikipedia.org/wiki/The_Notorious_B.I.G.

? Kurt Cobain: https://www.kurtcobain.com/

? Robinhood: https://robinhood.com

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-08-18
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How Amplitude built an internal AI tool that the whole company?s obsessed with (and how you can too) | Wade Chambers

Wade Chambers, Chief Engineering Officer at Amplitude, shares how his team built Moda?an internal AI tool that gives employees access to enterprise data across multiple systems, enabling faster product development and decision-making while fostering cross-functional collaboration.

What you?ll learn:

1. How Amplitude built a powerful internal AI tool in just 3 to 4 weeks of engineers? spare time

2. A social engineering approach that made their AI tool go viral company-wide in just one week

3. How product managers use AI to analyze customer feedback across multiple data sources and identify key themes

4. A streamlined workflow that compresses research, PRD creation, and prototyping into a single meeting

5. Why role-swapping exercises with AI tools build empathy and cross-functional fluency across product, design, and engineering teams

6. How AI tools are helping engineering teams tackle persistent tech debt challenges more effectively

Brought to you by:

CodeRabbit?Cut code review time and bugs in half. Instantly.

Vanta?Automate compliance and simplify security

25k giveaway:

Where to find Wade Chambers:

LinkedIn: https://www.linkedin.com/in/wadechambers/

Amplitude: https://amplitude.com/blog/meet-the-team-wade-chambers

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Wade Chambers

(02:53) The build vs. buy decision for internal AI tools

(04:55) What Moda is and how it works

(07:19) The social engineering approach to adoption

(09:17) Demo of Moda in Slack

(10:58) Data sources Moda has access to

(12:43) Analyzing customer feedback themes with Moda

(17:41) Behind the scenes: how Moda works technically

(23:24) Creating a PRD from a single customer insight

(27:30) How teams actually use AI-generated PRDs

(29:09) Impact on product development velocity

(32:37) Engineers, designers, and PMs swapping roles

(34:38) Recap of creating Moda

(36:00) Lightning round and final thoughts

Tools referenced:

? Glean: https://www.glean.com/

? ChatGPT: https://chat.openai.com/

? Cursor: https://cursor.com/

? Bolt: https://bolt.new/

? Lovable: https://lovable.dev/

Other references:

? Amplitude: https://amplitude.com/

? Slack: https://slack.com/

? Confluence: https://www.atlassian.com/software/confluence

? Jira: https://www.atlassian.com/software/jira

? Salesforce: https://www.salesforce.com/

? Zendesk: https://www.zendesk.com/

? Google Drive: https://drive.google.com/

? Productboard: https://www.productboard.com/

? Zoom: https://zoom.us/

? Asana: https://asana.com/

? Dropbox: https://www.dropbox.com/

? GitHub: https://github.com/

? HubSpot: https://www.hubspot.com/

? Abnormal Security: https://abnormalsecurity.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-08-11
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An exclusive inside look at GPT-5

In this episode, I share my hands-on experience with OpenAI?s GPT-5, the company?s new frontier model. As one of the first users outside of OpenAI to test the model, I put GPT-5 head-to-head with GPT-4.1 across real-world product use cases?from writing PRDs to generating code to assisting with visual design work. This is my unfiltered look at what GPT-5 can (and can?t) do?and how it changes the game for builders.

What you?ll learn:

1. How GPT-5 differs from previous models with its engineering-focused approach to problem-solving and tendency to prioritize technical details over business context

2. A comparative analysis of how GPT-5 and GPT-4.1 generate different types of product requirement documents and prototypes for the same prompt

3. Why GPT-5 excels at technical writing, functional requirements, and code generation while potentially skipping important business discovery questions

4. The model?s impressive spatial awareness capabilities when generating images for interior design and other visual tasks

5. Practical considerations for choosing the right model based on your specific use case and audience

6. How GPT-5?s extensive tool-calling behavior and bullet-point communication style reflect its engineering-oriented design

Brought to you by ChatPRD?an AI copilot for PMs and their teams: https://www.chatprd.ai/howiai

25k giveaway:

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to GPT-5

(04:34) Testing GPT-5 in ChatPRD for document generation

(07:10) Comparing GPT-5 and GPT-4.1 on business vs. technical orientation

(11:22) Side-by-side comparison of PRDs generated by both models

(15:23) Where GPT-5 excels: Technical considerations and documentation quality

(17:35) Comparing prototypes generated from different model outputs

(19:57) Testing homepage critique capabilities between models

(23:14) OpenAI?s strengths in API design and developer support

(25:37) GPT-5?s performance as a coding assistant

(27:26) Examining GPT-5 in ChatGPT?s interface

(28:50) Testing GPT-5?s front-end design capabilities

(31:17) Personal use case: bathroom remodel planning

(33:45) Comparing GPT-5 vs. GPT-4 for interior design visualization

(38:10) Summary of key findings and recommendations

Tools referenced:

? OpenAI: https://openai.com/

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Gemini: https://gemini.google.com/

? Cursor: https://cursor.sh/

? Lovable: https://lovable.dev/

? Bolt: https://bolt.com/

? LaunchDarkly AI Configs: https://launchdarkly.com/docs/home/ai-configs

Other reference:

? Benjamin Moore paints: https://www.benjaminmoore.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-08-07
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How a VC and tech founder used AI to launch a brick-and-mortar business in their spare time | Andrew Mason (CEO of Descript) & Nabeel Hyatt (Partner at Spark Capital)

Andrew Mason (founder of Groupon, now CEO of Descript) and Nabeel Hyatt (General Partner at Spark Capital) teamed up to open a physical board-game social club in Berkeley, with AI as their business partner. In this episode, they break down how they used Claude to generate a full business plan, model financials, plan the space layout, navigate Berkeley permitting, categorize hundreds of games using a custom Dewey Decimal?style system, and build an AI concierge that matches players with games via text. They also share how working on this side project helped rewire how they use AI in their day jobs?and why more people should use AI to build real-world things.

What you?ll learn:

1. How to use Claude Projects as your business copilot to create comprehensive business plans, financial projections, and space layouts

2. A workflow for categorizing hundreds of board games using an AI-generated ?Dewey Decimal System? that makes game discovery intuitive

3. How they built an AI concierge service that matches players with games and coordinates group play sessions via text message

4. Why AI enables side projects that would otherwise be impossible due to time constraints and specialized knowledge requirements

5. A simple system for creating customer personas that inform your business model and event programming

6. How to use model context protocols (MCPs) to connect AI assistants to business tools like Airtable without complex coding

Brought to you by:

Lovable?Build apps by simply chatting with AI

Persona?Trusted identity verification for any use case

Where to find Andrew Mason:

LinkedIn: https://www.linkedin.com/in/andrewmason/

X: https://x.com/andrewmason

Where to find Nabeel Hyatt:

LinkedIn: https://www.linkedin.com/in/nabeelhyatt/

X: https://x.com/nabeel

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to the board-game social club concept

(02:44) How AI made a challenging side project possible

(06:14) Using Claude as a business copilot for planning

(12:53) Developing customer personas with AI

(15:45) Using AI to determine business viability

(21:02) Navigating Berkeley real estate and permitting

(25:18) Building an AI concierge for game matchmaking

(28:10) Database design with Airtable for non-technical founders

(32:04) Creating a custom board-game categorization system

(36:20) Demo of the text-based AI concierge service

(40:38) Enabling experiences that wouldn?t exist without AI

(43:42) Lightning round and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Airtable: https://airtable.com/

? n8n: https://n8n.io/

? Twilio: https://www.twilio.com/

? Cursor: https://cursor.sh/

? Windsurf: https://www.windsurf.io/

? Python: https://www.python.org/

Other references:

? Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol

? Tabletop Library: https://tabletoplibrary.com/

? Descript: https://www.descript.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-08-04
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How Block?s custom AI agent supercharges every team, from sales to data to engineering | Jackie Brosamer & Brad Axen

VP of engineering Jackie Brosamer and principal engineer Brad Axen join me to demo Goose, Block?s open-source AI agent that runs locally, plugs into your existing tools through model context protocol (MCP) servers, and peels away the rote parts of work so people can focus on insight and impact.

This episode is packed with in-depth demos: starting with a messy farm-stand sales CSV, Goose analyzes the data, builds visualizations, and generates a shareable HTML report. We then spin up an MCP that lets Goose talk to Square?s dashboard for inventory management, vibe code an email MCP that can send payment links automatically, and unpack how environment setup, debugging, and tool orchestration get handled behind the scenes.

What you?ll learn:

A practical, repeatable workflow for turning any working script or function into a custom MCP?and exposing it to natural-language controlHow to transform messy CSVs into visualizations, HTML reports, and actionable business insights without needing a data science backgroundWays to hook Goose into live business systems (e.g. Square inventory, payments) so analysis flows directly into operational actionThe thinking behind Block?s decision to open-source GooseLessons from Block?s bottom-up meets top-down adoption modelWhy organizational transformation, not just picking the right LLM, will separate AI winners from laggards over the next few yearsHow to scale an internal MCP catalogThe organizational transformation required to fully leverage AI capabilities

Brought to you by:

CodeRabbit?Cut code review time and bugs in half. Instantly.

Lenny?s List?Hands-on AI education curated by Lenny and Claire

Where to find Jackie Brosamer:

LinkedIn: https://www.linkedin.com/in/jbrosamer/

Where to find Brad Axen:

LinkedIn: https://www.linkedin.com/in/bradleyaxen/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Goose and its data analysis capabilities

(02:27) How Block embraced AI across the organization

(04:48) What Goose is and why Block open-sourced it

(07:45) Demo: Analyzing farm-stand sales data with Goose

(12:18) Creating shareable HTML reports from data analysis

(14:15) Model context protocols (MCPs) that Goose uses

(18:56) Demo: Using Square MCP to create a product catalog

(23:35) Creating payment links from analyzed data

(26:30) Demo: Building a custom email MCP

(31:18) Testing the new email MCP with Goose

(36:09) Debugging and fixing MCP code errors

(38:44) Connecting workflows: sending payment links via email

(41:30) Lightning round and final thoughts

Tools referenced:

? Goose: https://block.github.io/goose/

? Pandas: https://pandas.pydata.org/

? Plotly: https://plotly.com/

? Python: https://www.python.org/

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Cursor: https://www.cursor.com/

? Mailgun: https://www.mailgun.com/

Other references:

? Block: https://block.com/

? Model context protocol (MCP): https://www.anthropic.com/news/model-context-protocol

? GitHub: https://github.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-07-28
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Successfully coding with AI in large enterprises: Centralized rules, workflows for tech debt, and training your team | Zach Davis (Director of Engineering at LaunchDarkly)

Zach Davis is a product-minded engineering leader and builder at heart, with over 12 years of experience building high?performing teams and crafting developer tools at companies like Atlassian and LaunchDarkly. In this episode, he shares how he?s helping his 100-plus-person engineering team successfully adopt AI tools by creating centralized documentation, using agents to tackle technical debt, and improving hiring processes?all while maintaining high quality standards in a mature codebase.

What you?ll learn:

1. How to create a centralized rules system that works across multiple AI tools instead of duplicating documentation

2. A systematic approach to using AI agents like Devin and Cursor to analyze and reduce test noise in large codebases

3. How to leverage AI tools to document your codebase more effectively by extracting knowledge from existing sources

4. Why ?what?s good for humans is also good for LLMs? should guide your documentation strategy

5. A custom GPT workflow for improving interview feedback quality and coaching interviewers

6. How to approach tech debt reduction with AI by creating prioritized task lists that both humans and AI agents can work from

Brought to you by:

WorkOS?Make your app enterprise-ready today

Lenny?s List on Maven?Hands-on AI education curated by Lenny and Claire

Where to find Zach Davis:

LaunchDarkly: https://www.launchdarkly.com

LinkedIn: https://www.linkedin.com/in/zach-davis-28207195/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Zach Davis

(02:44) Overview of AI tools used at LaunchDarkly

(04:00) The importance of having someone responsible for driving AI adoption

(05:44) Why vibe coding isn?t acceptable for enterprise development

(06:42) Making engineers successful with AI on their first attempt

(07:55) Creating centralized documentation for both humans and AI agents

(10:19) Using feature flagging rules to improve AI outputs

(12:33) Advice for getting started with rules

(14:28) Demo: Setting up Devin?s environment in a large codebase

(24:33) Devin?s plan overview

(27:55) Demo: Creating a prioritized tech debt reduction plan

(36:40) Demo: Using AI to improve hiring processes and interview feedback

(40:34) Summary of key approaches for integrating AI into engineering workflows

(42:08) Lightning round and final thoughts

Tools referenced:

? Cursor: https://www.cursor.com/

? Devin: https://devin.ai/

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Windsurf: https://windsurf.com/

? Lovable: https://lovable.dev/

? ChatPRD: https://www.chatprd.ai/

? GitHub Copilot: https://github.com/features/copilot

Other references:

? Jest: https://jestjs.io/

? Vitest: https://vitest.dev/

? MCP: https://www.anthropic.com/news/model-context-protocol

? Confluence: https://www.atlassian.com/software/confluence

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-07-21
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How this PM streamlines 60k-page FDA submissions and saves millions with Claude, Streamlit, and clever AI workflows | Prerna Kaul

Prerna Kaul is a product and platform leader who has spent over 14 years turning machine-learning research into consumer and B2B products at Amazon Alexa, AGI, Moderna, and now Panasonic Well. In today?s episode, she explains how she?s using AI to slash some of the most time-consuming, expensive tasks in life sciences?from generating 60,000-page FDA submissions to crafting communication frameworks that help product managers navigate complex stakeholder dynamics. Her innovations are saving millions of dollars and helping lifesaving treatments reach the market faster.

What you?ll learn:

How Prerna built an AI system that automates the creation of 60,000-page regulatory documents for the FDA?reducing a process that took 4 to 6 months and 20 specialists to just minutesA step-by-step system for detecting and redacting PHI (protected health information) in clinical trial data using ClaudeHow to build user-friendly interfaces for non-technical colleagues using Streamlit to democratize AI toolsHow to use Claude?s prompt generator to create powerful communication frameworks that help PMs navigate complex stakeholder situationsWhy transparency about AI costs is crucial for gaining organizational buy-in and tracking ROIA practical framework for approaching AI safety and ethics in highly regulated industries

Brought to you by:

CodeRabbit?Cut code review time and bugs in half. Instantly: https://lovable.dev/

Lovable?Build apps by simply chatting with AI: https://lovable.dev/

Where to find Prerna Kaul:

LinkedIn: https://www.linkedin.com/in/prernakkaul/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Prerna

(03:01) The FDA submission challenge: 60,000 pages, months of work, millions in costs

(05:20) Getting started in Claude: from prompt to production-ready prototype

(10:13) How Claude selected the right models for medical entity recognition

(12:04) Using Streamlit to create accessible UIs for non-technical users

(16:04) Detecting and redacting PHI in unstructured clinical notes

(18:44) Generating the Common Technical Document (CTD) for FDA submission

(21:54) Tracking and displaying AI operation costs for stakeholder buy-in

(24:38) Real-world impact on vaccine development timelines and costs

(26:12) Creating an AI communication coach for product managers

(30:22) Training Claude on classic literature and persuasion techniques

(31:53) Analyzing a complex stakeholder scenario with multiple competing priorities

(34:40) Getting personalized communication strategies inspired by tech leaders

(35:40) Summarizing strategic approaches

(38:26) Conclusion and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Streamlit: https://streamlit.io/

? Anthropic Console: https://console.anthropic.com/

? Claude Sonnet 4: https://www.anthropic.com/claude/sonnet

Other references:

? Claude project chat (AI Product Management Stakeholder Challenges): https://claude.ai/share/caba4ab0-b28a-480c-8633-71920b12999e

? XML: ?https://www.w3.org/XML/?

? Python: ?https://www.python.org/?

? RegEx: ?https://regex101.com/

? Moderna: https://www.modernatx.com/

? FDA: https://www.fda.gov/

? Project Gutenberg: https://www.gutenberg.org/

? FDA Biologics License Application: https://www.fda.gov/vaccines-blood-biologics/development-approval-process-cber/biologics-license-applications-bla-process-cber

? Protected health information (PHI): https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-07-14
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Mastering ChatGPT: Advanced techniques for workplace communication and productivity | Hiten Shah

Hiten Shah is a serial founder who has started several analytics and security companies, including Crazy Egg and KISSmetrics. The latest one, Nira, was acquired by Dropbox in 2024. In this episode, he shares how he turns ChatGPT from a simple chatbot into a personal workplace coach, sales strategist, and productivity multiplier.

What you?ll learn:

How to create AI versions of your boss by loading operating manuals and personality tests into ChatGPT projectsA simple approach for turning sales frameworks into customized discovery call scripts for any productWhy context is everything?and how to load ChatGPT with the right information before asking for outputsThe ?show it what great looks like? technique that dramatically improves AI responsesHow to build a personal AI coach using your own personality assessments and communication styleWhy you should use temporary sessions for random queries to keep your main ChatGPT memory clean

Brought to you by:

Paragon?Ship every SaaS integration your customers want

Notion?The best AI tools for work

Where to find Hiten Shah:

Blog: https://hitenism.com/

X: https://twitter.com/hnshah

LinkedIn: https://www.linkedin.com/in/hnshah/

Where to find Claire Vo:

https://www.lennysnewsletter.com/p/how-to-get-your-entire-team-prototyping

In this episode, we cover:

(00:00) Introduction to Hiten

(02:55) Why Hiten primarily uses ChatGPT

(04:12) The importance of context and memory management

(07:58) Demo: Creating ?What Would Morgan Do? project

(13:30) Using personality types to improve AI coaching

(16:20) Building a personal operating system in ChatGPT

(20:55) Mixing structured frameworks and personal context

(23:20) Demo: Winning by Design sales framework implementation

(30:00) Creating discovery call scripts

(31:44) Using ChatGPT?s deep research feature to understand Claire?s leadership style

(36:30) Lightning round and final thoughts

Tools referenced:

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

Other references:

? Hiten's Google Doc: https://docs.google.com/document/d/1j15hoR3qZLQMJuW-mtfYFyhXM0CpYHQkZJuUgqHBsZs/edit?tab=t.0

? Winning by Design: https://winningbydesign.com/

? Enneagram: https://www.enneagraminstitute.com/

? Human Design: https://humandesign.tools/

? Myers-Briggs: https://www.myersbriggs.org/

? DISC: https://www.discprofile.com/

? Lex: https://lex.page/

? The Lean Startup: https://theleanstartup.com/

? Sean Ellis score: https://pmfsurvey.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-07-07
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How to build prototypes that actually look like your product | Colin Matthews (Product leader, AI prototyping instructor at Maven)

Colin Matthews is a product manager, founder, and hobbyist engineer. After spending the past eight years in healthtech, he recently left his role as a PM at Datavant to go full-time on building his own products. He is currently a top Maven instructor, helping PMs build their first AI prototype. In this episode, he shares a step-by-step workflow for creating component libraries from screenshots that stay true to your brand and reveals a clever Chrome extension trick for extracting code from any website to build reusable components.

What you?ll learn:

1. How to create component libraries from screenshots that match your brand?s design system

2. A Chrome extension that can extract components directly from any website with a single click

3. Why forking prototypes is the key to efficient iteration without breaking your baseline

4. The structured prompting technique that makes AI tools actually listen to your instructions

5. How to introduce AI prototyping to your team without stepping on designers? toes

6. The debugging approach that solves 90% of AI prototyping errors

Brought to you by:

WorkOS?Make your app enterprise-ready today

Notion?The best AI tools for work

Go deeper with Colin?s in-depth post in Lenny?s Newsletter:

Where to find Colin Matthews:

LinkedIn: https://www.linkedin.com/in/colinmatthews-pm/

Tech For Product newsletter: https://colinmatthews.substack.com/

Tech For Product one-day team workshop: https://teams.techforproduct.com/

Maven course: AI Prototyping for PMs: https://bit.ly/3FQgZmw

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Colin Matthews

(02:46) Creating component libraries from screenshots in v0

(05:50) Using prompts to extract components from existing products

(06:31) Building an Airbnb prototype from component libraries

(11:36) Using the Magic Patterns Chrome extension to extract components directly from websites

(18:38) The importance of improving components rather than the composed application

(20:15) Using forks and versions for iterative prototyping

(25:05) Managing team dynamics when introducing AI prototyping

(26:54) Final thoughts

Tools referenced:

? Magic Patterns: https://magicpatterns.com/

? Magic Patterns Chrome Extension: https://chromewebstore.google.com/detail/html-to-react-figma-by-ma/chgehghmhgihgmpmdjpolhkcnhkokdfp?hl=en

? Cursor: https://cursor.sh/

? ChatGPT: https://chat.openai.com/

? Bolt: https://bolt.new/

Other references:

? Colin?s AI prototyping prompt library: https://technical-foundations.notion.site/16c8fafdb669800ea6eeca11f40d046c?v=16c8fafdb6698069a6e4000c84a9ff2c

? Airbnb: https://www.airbnb.com/

? Notion: https://www.notion.so/

? Amplitude: https://amplitude.com/

? PostHog: https://posthog.com/

? GitHub: https://github.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-06-30
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How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman

John Blackman, a 91-year-old retired electrical engineer, shares how he used Claude and Replit to build a complex application for his church?s community service events?with no prior software development experience and for less than $350. His app allows event organizers to create events, recruit volunteers, and manage sign-ups, with a standout feature for organizing free oil changes for participants.

What you?ll learn:

How John used Claude to create detailed product requirements and user storiesJohn?s philosophy on embracing new technology throughout his careerThe exact process for integrating third-party APIs (like VIN lookup for oil changes) with minimal technical knowledgeHow he automated report generation for volunteer management and resource planningHow the software generates personalized Impact Passports for event participantsWhy letting AI build without preconceived notions of ?correct? implementation can lead to faster, more functional resultsHow to troubleshoot common development-to-production issues when working with AI coding tools

Brought to you by:

WorkOS?Make your app enterprise-ready today

Orkes?The enterprise platform for reliable applications and agentic workflows

Where to find John Blackman:

Website: http://johnbeng.com/

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to John Blackman and his background

(02:55) John?s impressive career

(03:59) How the church project started

(05:06) Using Claude to create a development roadmap and requirements document

(07:29) The concept of the Impact Passport for event participants

(08:57) Generating user stories and requirements with Claude

(10:32) The multi-tenant architecture with system and local church administrators

(12:54) Building the application with Replit

(13:32) Demo of the administrator interface and event management features

(17:56) Specialized reports for different services (food pantry, vision center, oil changes)

(20:30) The participant registration flow with QR code scanning

(21:55) Adding new features like volunteer name tag generation

(24:40) Troubleshooting AI ?rabbit trails? during development

(26:09) Challenges moving from development to production

(27:13) John?s lack of coding experience

(29:42) The advantage of having no preconceived notions about implementation

(30:25) Total development costs and timeline

(31:31) Impact and reception from the church community

(32:42) Lightning round and final thoughts

Tools referenced:

? Claude: https://claude.ai/

? Replit: https://replit.com/

? SendGrid: https://sendgrid.com/

? AutoCAD: https://www.autodesk.com/products/autocad/

Other references:

? OpenAI API: https://openai.com/api/

? VIN (vehicle identification number): https://en.wikipedia.org/wiki/Vehicle_identification_number

? Multi-tenant architecture: https://en.wikipedia.org/wiki/Multitenancy

? Role-based access control: https://en.wikipedia.org/wiki/Role-based_access_control

? Excel: https://www.microsoft.com/en-us/microsoft-365/excel

? Docusign: https://www.docusign.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-06-23
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A designer's guide to Cursor: How to build interactive prototypes with sound, explore visual styles, and transform data visualizations | Elizabeth Lin

Elizabeth Lin is an independent design educator who has crafted learning experiences for Khan Academy, Primer, and Lambda School. She currently runs design is a party, an alternative online design school where she teaches courses like The Art of Visual Design and Prototyping with Cursor. In this episode, she shares how designers can leverage Cursor to create interactive prototypes with sound, explore different visual aesthetics, and transform basic designs into polished interfaces?all without deep coding knowledge.

What you'll learn:

How to use Cursor to explore different design aesthetics?from brutalist to Y2K to cyberpunkA simple workflow for creating interactive sound elements in prototypes that would be difficult with traditional design toolsA step-by-step process for transforming an ugly dashboard into a polished design using strategic promptingWhy broadening your inspiration sources helps Cursor generate more unique and creative designTechniques for teaching AI tools to understand your design preferences and tasteA practical approach to creating data-driven prototypes by connecting Cursor with Notion databasesHow to use Cursor Rules to streamline your prototyping workflow and avoid repetitive setup tasks

Brought to you by:

Lovable?Build apps by simply chatting with AI

Retool?AI that's designed for developers, and built for the enterprise

Where to find Elizabeth Lin:

Website: https://www.lalizlabeth.com/

LinkedIn: https://www.linkedin.com/in/elizabethylin/

X: https://x.com/lalizlabeth

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Elizabeth

(02:20) Demo: Exploring different visual styles with Cursor

(08:20) Comparing different design iterations from the same prompt

(12:35) Building a working piano prototype with one prompt

(16:30) Understanding what?s happening behind the scenes

(18:28) Practical design team scenarios using Cursor

(21:00) Step-by-step walkthrough of transforming an ugly finance dashboard

(27:29) Using targeted prompts to improve layout and visual design

(29:22) Building data-driven prototypes powered by Notion databases

(31:12) Lightning round and final thoughts

Tools referenced:

? Cursor: https://cursor.sh/

? Notion: https://www.notion.so/

? ChatGPT: https://chat.openai.com/

Other references:

? Edward Tufte: https://www.edwardtufte.com/

? Robinhood: https://robinhood.com/

? Cash App: https://cash.app/

? Stripe: https://stripe.com/

? Neopets: https://www.neopets.com/

? Goodreads: https://www.goodreads.com/

? Shad CN: https://ui.shadcn.com/

? Sketch: https://www.sketch.com/

? Goodreads: https://www.goodreads.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-06-16
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Gamma?s head of design shares how his small team uses AI to synthesize feedback, generate on-brand imagery, and maintain design quality while serving users in 60+ countries | Zach Leach

Zach Leach, head of design at Gamma, reveals how his small team uses AI to analyze global feedback, create on-brand imagery, and maintain design quality while serving users in more than 60 countries.

What you?ll learn:

How Gamma analyzes feedback from their 60% international user base using ChatGPT?s deep research capabilitiesHow to transform hundreds of multilingual feedback items into actionable design insightsA simple workflow for creating on-brand imagery using Midjourney-style referencesHow to use AI to maintain brand consistency across a globally distributed productThe secret to removing image backgrounds instantly using ReplicateHow to create consistent, high-quality job descriptions in minutes using AI templates

Brought to you by:

WorkOS?Make your app enterprise-ready today

Retool?AI that?s designed for developers and built for the enterprise

Where to find Zach Leach:

LinkedIn: https://www.linkedin.com/in/zleach

X: https://x.com/thisiszach

Where to find Claire Vo:

In this episode, we cover:

(00:00) Intro

(02:42) Building the Gamma AI image editing feature

(05:25) Using ChatGPT?s deep research for feedback analysis

(09:10) How feedback was analyzed before AI tools

(10:10) Benefits of deep research vs. basic scripting

(12:40) Insights from ChatGPT's deep research

(16:41) Demo of Midjourney workflow for creating on-brand art

(23:54) Using Replicate for background removal

(25:40) Style references (SREF) and brand consistency in Midjourney

(29:19) An AI workflow for creating consistent job descriptions

(32:27) Conclusion and final thoughts

ChatGPT feedback prompt

?This is some feedback we?ve received about our AI image editing feature. I want you to analyze the feedback and find where we are doing poorly and where we are doing well. Break down for our product team what kinds of things we are doing well and why, and what kinds of things we are doing poorly and why. What do people love? What do people hate? Where can we improve??

Tools referenced:

? Gamma: https://gamma.app/

? ChatGPT: https://chat.openai.com/

? Midjourney: https://www.midjourney.com/

? Midjourney Style Reference (SREF): https://docs.midjourney.com/hc/en-us/articles/32180011136653-Style-Reference

? Replicate: https://replicate.com/

? Claude Projects: https://claude.ai/projects

? GPT 4o image model https://openai.com/index/introducing-4o-image-generation/

Other reference:

? LaunchDarkly: https://launchdarkly.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-06-09
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The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth)

Luke Harries, Head of Growth at ElevenLabs, the leading AI voice technology company, shares how he?s automating marketing workflows with AI?from case studies to translations to WhatsApp integrations?saving his company over $140,000 while making everything a launch.

What you?ll learn:

1. How to create polished case studies in minutes using AI transcription and a custom GPT

2. How ElevenLabs built a custom AI translation system that saved them $140,000 annually and eliminated agency headaches

3. How to use Model Context Protocols (MCPs) to connect AI assistants to your WhatsApp messages

4. The ?everything is a launch? philosophy that helps ElevenLabs maintain consistent marketing momentum

5. Why marketers should learn to code with AI tools like Cursor

6. How to create effective custom GPTs by focusing on prompt engineering rather than output editing

Brought to you by:

Orkes?The enterprise platform for reliable applications and agentic workflows

Retool?AI that?s designed for developers, and built for the enterprise

Where to find Luke Harries:

Website: https://harries.co/

LinkedIn: https://www.linkedin.com/in/luke-harries/

GitHub: https://github.com/lharries

X: https://x.com/lukeharries

Where to find Claire Vo:

In this episode, we cover:

(00:00) Intro

(02:41) The future of AI in marketing

(04:22) Using Granola and custom GPTs to write case studies

(12:10) Generating tweet threads using ChatGPT

(13:58) Building case studies into a systematic workflow

(15:14) Best practices for prompt engineering

(19:39) Building a custom translation system that saved $140k

(25:10) Open sourcing the translation solution

(29:47) Building a WhatsApp MCP

(38:07) Creating specialized AI agents on demand

(41:08) Lightning round and final thoughts

Tools referenced:

? Granola: https://www.granola.ai/

? ChatGPT: https://chat.openai.com/

? Cursor: https://www.cursor.com/

? Claude: https://claude.ai/

? ElevenLabs: https://elevenlabs.io/

? WhatsApp: https://www.whatsapp.com/

? GitHub: https://github.com/

? Zapier: https://zapier.com/

? Calendly: https://calendly.com/

? Salesforce: https://www.salesforce.com/

Other references:

? MCP (Model Context Protocol): https://www.anthropic.com/news/model-context-protocol

? WhatsApp MCP repo: https://github.com/lharries/whatsapp-mcp

? Whatsmeow library: https://github.com/tulir/whatsmeow

? LaunchDarkly: https://launchdarkly.com/

? Introducing ElevenLabs MCP: https://elevenlabs.io/blog/introducing-elevenlabs-mcp

? Ordering a pizza using the ElevenLabs MCP server: https://x.com/elevenlabsio/status/1909300782673101265

? Chess.com: https://www.chess.com/

? Lovable: https://lovable.ai/

? Launch and launch again ? how to launch your products: https://harries.co/launch-your-product

? Your first growth hire: https://harries.co/first-growth-hire

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-06-02
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A 3-step AI coding workflow for solo founders | Ryan Carson (5x founder)

Ryan Carson is a five-time founder who has spent the past 20 years building, scaling, and selling startups. In this episode, he shares his playbook for using AI to build products, turning ?vibe coding? into a structured and scalable approach that can replace full engineering teams.

What you?ll learn:

1. A simple three-file system that transforms chaotic AI coding into a structured, reliable process

2. How to create AI-generated PRDs and task lists that actually work

3. A step-by-step workflow using Cursor to build features systematically

4. Why slowing down to provide proper context is the secret to speeding up your AI development

5. How to use model context protocols (MCPs) to extend your AI?s capabilities beyond just coding

6. Why founders can now build entire companies with minimal engineering teams and how Ryan is doing it himself

Brought to you by:

ChatPRD?An AI copilot for PMs and their teams

Notion?The best AI tools for work

Where to find Ryan Carson:

Website: https://www.ryancarson.com/about

LinkedIn: https://www.linkedin.com/in/ryancarson/

X: https://x.com/ryancarson

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction and Ryan?s recent AI projects

(03:25) Demo: Creating a PRD with Cursor

(05:00) Ryan?s open source links: https://github.com/snarktank/ai-dev-tasks

(09:23) Repo Prompt: https://repoprompt.com/

(09:53) Quick recap and common mistakes to avoid

(11:00) Demo: Generating a task list from the PRD

(15:31) The importance of context when working with LLMs

(18:07) Demo: Working through tasks systematically using Cursor

(18:56) Change management

(20:00) How task lists save time for product managers

(21:50) Demo: Using MCPs for front-end testing

(24:50) Specific MCPs and what to use them for

(26:45) Demo: Using Repo Prompt to gain precise control over context

(31:23) Music?s role in Ryan?s development stack

(32:10) Lightning round and final thoughts

Tools referenced:

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Gemini 2.5 Pro: https://deepmind.google/models/gemini/pro/.

? Repo Prompt: https://repoprompt.com/

? Taskmaster: https://github.com/nooqta/taskmaster

? Browserbase: https://browserbase.com/

? Stagehand: https://docs.stagehand.dev/integrations/mcp-server

Other references:

? PostgreSQL: https://www.postgresql.org/

? Prisma: https://www.prisma.io/

? SQL: https://www.sql.org/

? MCP: https://www.anthropic.com/news/model-context-protocol

? VS Code: https://code.visualstudio.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-05-26
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How custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop)

Hilary Gridley, Head of Core Product at Whoop, shares how she uses dozens of custom GPTs for her team that think and give feedback like her, allowing her to scale herself up and create time for higher-value work.

What you?ll learn:

1. A step-by-step process for creating GPTs that ?think like you? by reverse engineering your own decision criteria

2. How to turn your management expertise into clear evaluation rubrics that AI can consistently apply

3. Practical techniques for improving team writing and presentations with AI-powered feedback

4. Why GPTs are the perfect tool for scaling good management practices without requiring prompt engineering skills

5. How to use AI to get invited to more strategic meetings by improving your written point of view

Brought to you by:

Orkes?The enterprise platform for reliable applications and agentic workflows

Vanta?Automate compliance and simplify security

Where to find Hilary Gridley:

Newsletter: https://hils.substack.com/

LinkedIn: https://www.linkedin.com/in/hilarygridley/

X: https://x.com/yourgirlhils

Where to find Claire Vo:

In this episode, we cover:

(00:00) Intro

(02:52) Creating GPTs that think like you

(04:23) Demo: Reverse engineering a recommendation algorithm

(13:06) The value of articulating taste

(15:33) Demo: Creating a slide deck evaluator GPT

(19:19) Testing your new GPT

(21:32) Scaling GPTs across your team

(23:52) Demo: Using AI to improve your writing

(30:32) Lightning round and final thoughts

Tools referenced:

? GPTs: https://chat.openai.com/gpts

? ChatGPT: https://chat.openai.com/

? Claude: https://claude.ai/

? Bolt: https://bolt.new/

Other references:

? Whoop: https://www.whoop.com/

? Norwegian School of Economics: https://www.nhh.no/en/

? Researchers at NHH have uncovered significant gender disparities in the adoption of generative AI tools like ChatGPT: https://www.nhh.no/en/nhh-bulletin/article-archive/2024/september/study-reveals-gender-gap-in-ai-tool-usage-among-students/

? How to Become a Supermanager with AI: https://maven.com/hilary-gridley/ai-powered-people-management

? Girls in the Loop: https://grrlsintheloop.ai/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-05-19
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A designer's guide to AI: Why designing in Cursor lets you prototype 10x faster, simplifies collaboration, and accelerates getting ideas out of your head | Joel Unger

Joel Unger, design director at Atlassian, shares how AI is transforming the way he designs Trello and other products. He walks through real-world workflows using tools like Midjourney and Cursor to prototype complex interactions, re-create brand assets, and explore creative directions faster than ever. You?ll hear how AI is helping designers focus on higher-level thinking, communicate better with developers, and push creative boundaries?all without replacing the human touch.

What you?ll learn:

How to prototype complex UI interactions using AIA workflow for re-creating animated brand assets without motion design toolsHow to leverage image generation tools like Midjourney to explore design directions and create mood boardsHow to use Cursor to re-create animated SVG assetsWhy AI frees designers to operate at a higher level of creativityHow AI improves designer-developer collaboration by showcasing interactive intentWhy embracing AI is key to staying ahead in the evolving design landscapeThe limitations of current AI tools and where they still fall short

Brought to you by:

? ?Paragon??Ship every SaaS integration your customers want

?? WorkOS??Make your app enterprise-ready today

Where to find Joel Unger:

Website: https://joelunger.com/

LinkedIn: https://www.linkedin.com/in/joelunger/

Where to find Claire Vo:

X: https://x.com/Codydearkland

In this episode, we cover:

(00:00) Intro and Joel's background

(02:46) Trello's new productivity features

(04:24) Traditional design process limitations in Figma

(05:22) Using Cursor to prototype interactive panel systems

(07:39) From prototype to production: collaborating with developers

(08:52) How Joel used AI to prepare for the podcast

(10:50) How AI saves designer time for deeper thinking

(11:23) Last-minute logo animation using Cursor

(13:50) Using Midjourney for character design exploration

(14:56) Creating a mood board for Taco: the Trello husky mascot

(16:49) How AI is changing design thinking and workflows

(18:18) Lightning round and closing thoughts

Tools referenced:

? Confluence: https://www.atlassian.com/software/confluence

? Bitbucket: https://bitbucket.org/product/

? Trello: https://trello.com/home

? Figma: https://www.figma.com

? Cursor: https://www.cursor.com/

? ChatGPT: https://chatgpt.com/

? Midjourney: https://www.midjourney.com/

Other reference:

? Atlassian: https://www.atlassian.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-05-12
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Vibe coding a 3D multiplayer game in 15 minutes?with no game dev experience | Cody De Arkland (Senior Director of Developer Experience at Sentry)

Cody De Arkland is the senior director of developer experience at Sentry, leading a team that empowers developers to build and ship software with greater safety and efficiency. Watch him speed-run the creation of a 3D multiplayer flight simulator?from scratch?in just 15 minutes, demonstrating the power (and creativity) that vibe coding enables.

What you?ll learn:

? How to approach building complex applications with AI by starting broad and iterating on specific features

? The process of using multiple AI coding assistants simultaneously to build different components

? Techniques for learning new technologies and frameworks through AI-assisted exploration

? How to troubleshoot and fix issues when AI implementations don?t work as expected

? The parallels between building fun projects and enterprise software with AI assistance

? Strategies for keeping AI tools focused when they go off track or add unwanted features

? The incredible velocity and productivity gains possible with modern AI coding tools

? How anyone can now build sophisticated applications with minimal prior experience

Brought to you by:

?Enterpret??Customer superintelligence platform for product and CX teams

?WorkOS??Make your app enterprise-ready today with WorkOS

Where to find Cody De Arkland:

Website: https://codyde.io/

LinkedIn: https://www.linkedin.com/in/codydearkland/

GitHub: https://github.com/codyde

Where to find Claire Vo:

In this episode, we cover:

(00:00) Introduction to Cody

(02:45) AI tools he?s using

(04:38) How Cody vibe coded a multiplayer game: Spaceflight

(09:37) Demo: Starting a new flight simulator project from scratch

(13:49) How to learn about libraries and technologies for projects

(17:06) First run of the new flight simulator game

(19:26) Using multiple AI coding assistants simultaneously

(20:43) Unexpected features and visual improvements

(21:26) Testing the multiplayer functionality

(22:31) Reflecting on the development process and iteration

(26:47) Lightning round and final thoughts

Tools referenced:

? Cursor: https://www.cursor.com/

? Windsurf: https://windsurf.com/

? Claude: https://claude.ai/new

? Bolt: https://bolt.new/

? React: https://react.dev/

Other references:

? Sentry: https://sentry.io/

? MCP: https://www.anthropic.com/news/model-context-protocol

? Spaceflight: http://spaceflight.gg/

? Three.js: https://threejs.org/

? Socket.io: https://socket.io/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected]

2025-05-05
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How this former NYT columnist uses ChatGPT to brainstorm ideas, do research, and find the perfect metaphor | Farhad Manjoo

Farhad Manjoo, a former New York Times and Wall Street Journal columnist, reveals his AI-enhanced writing workflow, from research to finding the perfect metaphor, and how these tools have transformed his creative process without replacing his unique voice.

What you?ll learn:

? How AI evolved from a simple tool to an essential writing companion

? Using ChatGPT as a research assistant with web search capabilities

? The ?super-thesaurus? technique for finding the perfect words and idioms

? How AI helps brainstorm ideas and refine arguments

? The benefits of having an ?always-on? writing partner in a remote work world

? Using AI as a first reader to evaluate drafts in progress

? Why AI enhances rather than replaces a writer?s unique voice

? Practical tips for getting unstuck when AI doesn?t deliver

? How AI speeds up the writing process while improving quality

? The future improvements that would make AI even more valuable for writers

Brought to you by:

? Enterpret?Customer SuperIntelligence Platform for Product and CX teams

? Vanta?Automate compliance and simplify security with Vanta

Where to find Farhad Manjoo:

? LinkedIn: https://www.linkedin.com/in/farhad-manjoo-161229/

? X: https://x.com/fmanjoo

Where to find Claire Vo:

? ChatPRD: https://www.chatprd.ai/

? Website: https://clairevo.com/

? LinkedIn: https://www.linkedin.com/in/clairevo/

? X: https://x.com/clairevo

In this episode, we cover:

(00:00) Intro

(02:40) Farhad?s journey from skepticism to adoption of AI tools

(04:20) Brainstorming with ChatGPT

(06:54) Assessing the quality of AI-sourced information

(08:34) How ChatGPT helps identify new angles and perspectives

(10:52) Using ChatGPT to find alternatives to clichéd expressions

(16:44) The ?super-thesaurus? technique for finding perfect words and idioms

(20:12) Using AI as a first reader for draft evaluation

(22:15) Lightning round

Tools referenced:

? ChatGPT: https://openai.com/chatgpt/overview/

? Cursor: https://www.cursor.com

Other references:

? New York Times: https://www.nytimes.com/

? The Wall Street Journal: https://www.wsj.com/

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2025-04-28
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Gumroad CEO's playbook to 40x his team's productivity with v0, Cursor, and Devin | Sahil Lavingia

Sahil Lavingia is the founder and CEO of Gumroad, where AI agents are already writing 41% of all code commits, and he?s targeting 80% by year?s end. Sahil demonstrates how this approach allows him to transform what would typically be two-week projects into two-hour implementations?a 40x productivity increase.

What you?ll learn:

The exact AI workflow Sahil uses to build features 40x faster?from prototyping in v0 to implementation with DevinHow Gumroad incentivizes AI adoption across the organization with $33,000 bounties for engineers who outperform the CEOHow to use component libraries like shadcn/ui for effective AI developmentHow AI is shifting engineering roles toward architecture and tech-debt removal while enabling designers and PMs to ship features directlyWhy spending more time on UX iteration becomes possible (and necessary) when implementation costs drop dramaticallyWhich organizational functions will be transformed by AI next

Brought to you by:

Enterpret?Customer SuperIntelligence Platform for Product and CX teams

Vanta?Automate compliance and simplify security with Vanta

Where to find Sahil Lavingia:

Gumroad: https://gumroad.com/

Website: https://sahillavingia.com/

LinkedIn: https://www.linkedin.com/in/sahillavingia

X: https://x.com/shl

Where to find Claire Vo:

In this episode, we cover:

(?00:00?) Sahil?s background

(?02:31?) How soon will AI do most engineering?

(?04:08?) Live demo: redesigning with v0, Devin, and Cursor

(?09:30?) Using the right tools

(?11:03?) Prototyping and iteration with AI

(?19:45?) Incentivizing AI adoption in teams

(?24:50?) ?Magical? date-picker component development

(?31:47?) AI?s impact on marketing, sales, and support

(?36:50?) Deciding what to build when AI builds everything

(?40:02?) Conclusion and final thoughts

Referenced:

? Devin: https://devin.ai/

? Cursor: https://www.cursor.so/

? Tobi Lütke?s tweet on how AI usage is now a baseline expectation at Shopify: https://x.com/tobi/status/1909231499448401946

? Flexile: https://app.flexile.com/

? shadcn: https://github.com/shadcn/ui

? Gusto: https://gusto.com/

? GitHub: https://github.com/