How I AI

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: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) 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 jordan@penname.co.

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 assistantPersona—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-mehtaLinkedIn: https://www.linkedin.com/in/ravimehta/X: https://x.com/ravi_mehta—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) 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 jordan@penname.co.

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 commands2. A workflow for running parallel coding tasks by focusing on your main work while the agent handles secondary features in the background3. Why setting up typed languages, linters, formatters, and tests creates guardrails that help AI tools generate better code4. How to create custom commands for code reviews that automatically check for security issues, test coverage, and other quality concerns5. A technique for improving your writing by creating a custom prompt with banned words and phrases that eliminates AI-generated patterns6. Strategies for managing context in AI conversations to maintain high-quality responses and avoid degradation7. 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 assistantPersona—Trusted identity verification for any use case—Where to find Lee Robinson:Twitter/X: https://twitter.com/leeerobWebsite: https://leerob.com—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) 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 jordan@penname.co.

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 Watch2. His “dual-wielding” workflow, using Cursor for coding and Xcode for building and debugging3. Terry’s three-step process for working with AI: create, review, and execute4. Why optimizing your codebase for AI collaboration can dramatically improve productivity5. How to use index cards and GPT-4 to rapidly prototype mobile interfaces6. A technique for “vibe refactoring” that keeps code organized and optimized for both human and AI readability7. 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 wantMiro—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: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) 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/• Figma: https://www.figma.com/• 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 jordan@penname.co.

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 tasks2. A workflow for treating AI agents as asynchronous junior engineers who can handle multiple tasks while you attend meetings3. Why public channels create better learning environments for both humans and AI when implementing engineering solutions4. The top five engineering tasks AI excels at: frontend fixes, version upgrades, documentation, incident response, and testing5. How to implement a “first line of defense” system where AI agents analyze crashes before humans need to intervene6. 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 assistantVanta—Automate compliance. Simplify security.—Where to find Scott Wu:X: https://x.com/ScottWu46LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/—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) 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 jordan@penname.co.

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 tools2. A step-by-step workflow for creating market analyses and competitive research in minutes instead of days3. How to build a “living product library” that allows sales and customer success teams to demo prototypes to customers before engineering begins4. Techniques for using AI to break deadlocks with engineering by demonstrating what’s possible without requiring technical expertise5. Why AI enables faster stakeholder alignment by converting abstract ideas into tangible, interactive experiences6. 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/howiaiLovable—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: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) 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/• v0: https://v0.dev/• Magic Patterns: https://www.magicpatterns.com/—Other references:• React Flow: https://reactflow.dev/• Figma: https://www.figma.com/• 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 jordan@penname.co.

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 daily2. A workflow for using Nvidia’s Parakeet and other AI tools to clean transcripts and generate structured summaries of podcast content3. How to extract actionable investment theses and company mentions from podcast transcripts using AI prompting techniques4. A systematic approach to generating blog post drafts with AI that maintains your personal writing style through iterative feedback5. Why using an “AP English teacher” grading system can help improve AI-generated content through multiple revision cycles6. 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 workMiro—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 jordan@penname.co.

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 videos3. A simple method for cataloging your book or record collection using video analysis and Gemini Flash4. How to use Comet to analyze personal finances and get investment recommendations without manual data analysis5. 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 workLenny’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: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) 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 jordan@penname.co.

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 time2. A social engineering approach that made their AI tool go viral company-wide in just one week3. How product managers use AI to analyze customer feedback across multiple data sources and identify key themes4. A streamlined workflow that compresses research, PRD creation, and prototyping into a single meeting5. Why role-swapping exercises with AI tools build empathy and cross-functional fluency across product, design, and engineering teams6. 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: 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 Wade Chambers:LinkedIn: https://www.linkedin.com/in/wadechambers/Amplitude: https://amplitude.com/blog/meet-the-team-wade-chambers—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) 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/• Figma: https://www.figma.com/• Lovable: https://lovable.dev/• v0: https://v0.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 jordan@penname.co.

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 context2. A comparative analysis of how GPT-5 and GPT-4.1 generate different types of product requirement documents and prototypes for the same prompt3. Why GPT-5 excels at technical writing, functional requirements, and code generation while potentially skipping important business discovery questions4. The model’s impressive spatial awareness capabilities when generating images for interior design and other visual tasks5. Practical considerations for choosing the right model based on your specific use case and audience6. 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: 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 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) 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/• v0: https://v0.dev/• 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 jordan@penname.co.

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 layouts2. A workflow for categorizing hundreds of board games using an AI-generated “Dewey Decimal System” that makes game discovery intuitive3. How they built an AI concierge service that matches players with games and coordinates group play sessions via text message4. Why AI enables side projects that would otherwise be impossible due to time constraints and specialized knowledge requirements5. A simple system for creating customer personas that inform your business model and event programming6. 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 AIPersona—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: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) 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 jordan@penname.co.

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: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) 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 jordan@penname.co.

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 documentation2. A systematic approach to using AI agents like Devin and Cursor to analyze and reduce test noise in large codebases3. How to leverage AI tools to document your codebase more effectively by extracting knowledge from existing sources4. Why “what’s good for humans is also good for LLMs” should guide your documentation strategy5. A custom GPT workflow for improving interview feedback quality and coaching interviewers6. 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 todayLenny’s List on Maven—Hands-on AI education curated by Lenny and Claire—Where to find Zach Davis:LaunchDarkly: https://www.launchdarkly.comLinkedIn: https://www.linkedin.com/in/zach-davis-28207195/—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) 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/• v0: https://v0.dev/• ChatPRD: https://www.chatprd.ai/• Figma: https://www.figma.com/• 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 jordan@penname.co.

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: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) 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 jordan@penname.co.

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 wantNotion—The best AI tools for work—Where to find Hiten Shah:Blog: https://hitenism.com/X: https://twitter.com/hnshahLinkedIn: https://www.linkedin.com/in/hnshah/—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) 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 jordan@penname.co.

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 system2. A Chrome extension that can extract components directly from any website with a single click3. Why forking prototypes is the key to efficient iteration without breaking your baseline4. The structured prompting technique that makes AI tools actually listen to your instructions5. How to introduce AI prototyping to your team without stepping on designers’ toes6. The debugging approach that solves 90% of AI prototyping errors—Brought to you by:WorkOS—Make your app enterprise-ready todayNotion—The best AI tools for work —Go deeper with Colin’s in-depth post in Lenny’s Newsletter:https://www.lennysnewsletter.com/p/how-to-get-your-entire-team-prototyping—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: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) 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:• v0: https://v0.dev/• 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/• Figma: https://www.figma.com/• GitHub: https://github.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 todayOrkes—The enterprise platform for reliable applications and agentic workflows—Where to find John Blackman:Website: http://johnbeng.com/—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) 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 jordan@penname.co.

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 AIRetool—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: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) 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/• v0: https://v0.dev/• 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/• Figma: https://www.figma.com/• Goodreads: https://www.goodreads.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 todayRetool—AI that’s designed for developers and built for the enterprise—Where to find Zach Leach:LinkedIn: https://www.linkedin.com/in/zleachX: https://x.com/thisiszach—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(02:42) 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/• Figma: https://www.figma.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 jordan@penname.co.

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 GPT2. How ElevenLabs built a custom AI translation system that saved them $140,000 annually and eliminated agency headaches3. How to use Model Context Protocols (MCPs) to connect AI assistants to your WhatsApp messages4. The “everything is a launch” philosophy that helps ElevenLabs maintain consistent marketing momentum5. Why marketers should learn to code with AI tools like Cursor6. 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 workflowsRetool—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/lharriesX: https://x.com/lukeharries—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(02:41) 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/• v0: https://v0.dev/• Figma: https://www.figma.com/• 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 jordan@penname.co.