How I AI

• 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 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 [email protected].

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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 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 [email protected].

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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 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 [email protected].

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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 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 [email protected].

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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 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 [email protected].

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