How I AI

Guillermo Rauch, the CEO of Vercel, demonstrates how v0 has evolved from a simple prototyping tool to a complete development environment that supports the entire Git workflow. Guillermo shows how Vercel built skills.sh—a viral marketplace with over 34,000 community-submitted skills—using v0, and how the tool enables non-technical team members to contribute production-ready code changes. He walks through creating branches, implementing features, previewing changes, and submitting pull requests, all within v0.What you’ll learn:How v0’s new Git workflow integration enables anyone to contribute production-ready code changesWhy skills.sh became a viral hub for AI skills, with 500 new submissions per hourHow to implement features in v0 that consider production concerns like abuse prevention and rate limitingThe benefits of branch previews for testing changes in a production-like environment before mergingHow v0 eliminates development environment setup challenges for non-technical team membersWhy the “terminal core” design aesthetic became central to skills.sh’s interfaceHow Vercel uses v0 internally to democratize code contributions across teamsThe future of AI at Vercel, including upcoming tools for text-to-SVG and video generation—In this episode, we cover:(00:00) Introduction(01:22) Overview of skills.sh(04:40) Demonstration of v0’s GitHub integration and branch creation(06:40) Exploring the v0 development environment(09:05)  Building a rating system feature for skills.sh(11:18) Testing the new feature in the preview environment(13:20) Creating a pull request and deploying to a preview environment(15:25) How Vercel is using v0 internally for production work(17:48) Organizational adoption and cultural impact(22:04) Favorite non-coding AI use cases(25:17) AI-powered chess game built with v0(27:57) Teaching kids about coding with AI(31:44) Troubleshooting techniques when AI gets stuck(34:43) Final thoughts and audience Q&A—Tools referenced:• v0: https://v0.dev/• Skills by Vercel: https://skills.sh/vercel• Vercel: https://vercel.com/• GitHub: https://github.com/• Nano Banana: https://gemini.google/overview/image-generation/• Vestaboard: https://vestaboard.com/—Other references:• v0 Chess Match: https://v0-chess-match.app/• React Native: https://reactnative.dev/—Where to find Guillermo Rauch:LinkedIn: linkedin.com/in/rauchgX: https://twitter.com/rauchg—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Reid Robinson, Principal AI Product Strategist at Zapier, shares how he uses Model Context Protocols (MCPs) to automate tedious tasks and create powerful workflows. He demonstrates practical workflows that combine Zapier’s more than 8,000 app connections with AI tools like Claude to create systems that work while he sleeps.What you’ll learn:How to use Zapier’s MCP server to create custom collections of tools that work seamlessly with Claude, ChatGPT, and other AI assistantsA workflow for using Claude Projects to provide detailed instructions for tool usage, improving reliability and consistencyHow to automate CRM updates and meeting preparation by connecting AI to your calendar, notes, and internal knowledge basesA system for creating a virtuous cycle of customer feedback by automatically analyzing support tickets and updating knowledge basesWhy thinking about “what your AI could do while you sleep” is a powerful framework for identifying high-impact automation opportunitiesPersonal use cases, including family calendar management and creating custom songs that demonstrate AI’s ability to bring joy beyond work—Brought to you by:WorkOS—Make your app enterprise-ready todayVanta—Automate compliance and simplify security—In this episode, we cover:(00:00) Introduction to Reid Robinson and his role at Zapier(02:41) Understanding MCPs as app integrations for AI tools(04:05) How Zapier’s approach to MCPs works with over 8,000 apps(09:00) Using Claude Projects to improve tool usage instructions(12:05) Post-meeting notes management(15:25) Comparing deterministic workflows vs. agentic instructions(18:15) Reid’s idea jammer(20:04) Building a customer interview preparation workflow(23:10) Using Gemini for processing file-based data(25:05) Creating a virtuous cycle of customer feedback analysis(29:16) The “if you could run ChatGPT in your sleep” framework(31:48) Quick recap(33:03) Personal use cases(37:16) Using Notebook AI to prepare personalized interview prep—Tools referenced:• Reid’s Resources for How I AI: https://how-i-ai-reid.zapier.app/resources• Claude: https://claude.ai/• Zapier: https://zapier.com/• Zapier MCP: https://zapier.com/mcp• Granola: https://www.granola.ai/• Coda: https://coda.io/• Suno: https://suno.ai/• Notebook AI: https://www.notebook.ai/• Gemini: https://gemini.google.com/—Other references:• HubSpot: https://www.hubspot.com/• Databricks: https://www.databricks.com/—Where to find Reid Robinson:LinkedIn: https://www.linkedin.com/in/reidtrobinson/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

In this episode, I take you through my unfiltered experience with Clawdbot, the viral open-source AI agent that’s been taking over tech Twitter. (In the time since this was recorded, the tool was renamed Moltbot, but we’re calling it Clawdbot here to match the episode.) It’s an autonomous AI that can run code, spin up sub-agents, join video calls, and take real actions on your machine. I invite it onto the podcast, give it screen access, and walk through what it’s like to go from zero to one with an agentic AI that actually does things. Along the way, I share the real experience: installation headaches, dependency chaos, security warnings you shouldn’t ignore, and the very real tension of giving an AI access to your messaging apps, files, and accounts. I also break down how I thought about permissions, identity, model choice, and cost while testing Clawdbot as a personal assistant.—What you’ll learn:How to install and set up Clawdbot (and why it’s not as simple as the “one-liner” suggests)The security implications of giving an autonomous AI access to your computer and accountsHow to safely limit Clawdbot’s permissions while still making it usefulWhy Clawdbot struggles with basic time concepts but excels at research tasksThe future of AI assistants—and who might build the consumer-friendly versionHow to use voice messaging with AI agents for on-the-go productivityWhy latency is one of the biggest challenges for autonomous AI assistants—Brought to you by:Lovable—Build apps by simply chatting with AI—Detailed workflow walkthroughs from this episode:• How I AI: My 24 Hours with Clawdbot (aka Moltbot)—3 Workflows for a Powerful (and Terrifying) AI Agent: https://www.chatprd.ai/how-i-ai/24-hours-with-clawdbot-moltbot-3-workflows-for-ai-agent• How to Securely Set Up and Configure an Open-Source AI Agent like Clawdbot: https://www.chatprd.ai/how-i-ai/workflows/how-to-securely-set-up-and-configure-an-open-source-ai-agent-like-clawdbot• How to Safely Delegate Calendar Scheduling to an AI Agent: https://www.chatprd.ai/how-i-ai/workflows/how-to-safely-delegate-calendar-scheduling-to-an-ai-agent• Automate Market Research on Reddit Using an AI Agent: https://www.chatprd.ai/how-i-ai/workflows/automate-market-research-on-reddit-using-an-ai-agent—In this episode, we cover:(00:00) Introduction and getting Clawdbot to join the podcast(02:07) What Clawdbot is and how it works(03:50) Installation process and hardware requirements(07:26) Security considerations and creating separate accounts(08:03) Setting up Telegram integration(10:02) Use case: Clawdbot as an EA(13:08) Configuring the AI agent (14:31) Granting Google Calendar access(18:03) Testing Clawdbot as a personal assistant(23:16) Speed frustrations(23:54) Email mishaps and impersonation issues(26:33) Why prompting matters more than ever with autonomous agents(27:32) Quick recap and family calendar management gone wrong(32:11) Using voice messaging with Clawdbot(36:14) Product thoughts(37:06) Building a Next.js app to show chat history(42:29) Research capabilities and Reddit analysis(46:10) Final thoughts on security concerns(48:00) The future of AI assistants and who will build them—Tools referenced:• Moltbot (formerly Clawdbot): https://www.molt.bot/• Telegram: https://telegram.org/• Vercel: https://vercel.com/• Devin: https://www.devin.ai/—Other references:• 1Password: https://1password.com/• Next.js: https://nextjs.org/• Google Workspace: https://workspace.google.com/• Claude Sonnet 4.5: https://www.anthropic.com/news/claude-sonnet-4-5• OAuth: https://oauth.net/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

John Lindquist is the co-founder of egghead.io and an expert in leveraging AI tools for professional software development. In this episode, John shares advanced techniques for using AI coding tools like Claude Code and Cursor that go far beyond basic prompting. He demonstrates how senior engineers can use mermaid diagrams for context loading, create custom hooks for automated code quality checks, and build efficient command-line tools that streamline AI workflows.What you’ll learn:How to use mermaid diagrams to preload context into Claude Code for faster, more accurate coding assistanceCreating custom hooks in Claude Code to automatically check for TypeScript errors and commit working codeBuilding efficient command-line aliases and tools to streamline your AI workflowsTechniques for using AI to generate documentation that works for both humans and machinesHow to leverage AI for code investigation and orientation when tackling unfamiliar codebasesStrategies for resetting AI conversations when they go off track—Brought to you by:WorkOS—Make your app enterprise-ready todayTines—Start building intelligent workflows today—In this episode, we cover:(00:00) Introduction to John Lindquist(03:15) Using context and diagrams to provide context to AI tools(05:38) Demo: Mermaid diagrams(06:48) Preloading context with system prompts in Claude Code(10:30) The rise of specialized file formats for AI consumption(13:23) Mermaid diagram use cases(19:01) Demo: Creating aliases for common AI commands(21:05) Building custom command-line tools for AI workflows(26:39) Demo: Setting up stop hooks for automated code quality checks(35:16) Investing in quality outputs(36:40) Additional use cases for hooks beyond code quality(39:19) Quick review(41:14) Terminal UI vs. IDE(45:35) Selling AI to skeptical teams(51:57) Prompting reset tricks—Tools referenced:• Claude Code: https://claude.ai/• Cursor: https://cursor.sh/• Gemini: https://gemini.google.com/—Other references:• Zsh: https://www.zsh.org/• GitHub: https://github.com/• TypeScript: https://www.typescriptlang.org/• Bun: https://bun.sh/• Claude hooks: https://code.claude.com/docs/en/hooks—Where to find John Lindquist:Website: https://egghead.ioNewsletter: https://egghead.io/newsletters/ai-dev-essentialsLinkedIn: linkedin.com/in/john-lindquist-84230766X: https://x.com/johnlindquist—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Teresa Torres is the author of Continuous Discovery Habits and an internationally acclaimed speaker and coach. In this episode, Teresa demonstrates how she’s built a personalized productivity system using Claude Code to manage her tasks, automate research collection, and improve her writing. She shows how non-developers can leverage AI tools to create personalized workflows that match their unique needs and thinking style.What you’ll learn:How Teresa built a personalized task management system in Claude Code that matches her exact workflow needsWhy she moved from Trello to a markdown-based system that gives her complete control and searchabilityHow she automated academic research collection with daily digests of relevant papersHer strategy for organizing context files to make Claude more effective without overwhelming itWhy “pair programming” with Claude has become her approach to everything from writing to task managementHow she uses Claude as a writing partner while maintaining her authentic voiceThe power of slash commands and automation to reduce friction in daily workflows—Brought to you by:Brex—The intelligent finance platform built for foundersGraphite—The next generation of code review—In this episode, we cover:(00:00) Introduction to Teresa Torres(02:10) Why Claude Code became Teresa’s productivity tool of choice(03:00) The evolution from browser-based AI to terminal-based workflows(04:14) Demo: Creating a personalized task management system(07:52) How the task system works with markdown files and Obsidian(12:56) Quick recap(14:13) Taking notes within tasks for better searchability(15:54) Demo: Automated research digest workflow(19:32) How the research plugin searches and summarizes academic papers(24:43) Filtering overwhelming information sources(29:00) Using small, focused context files instead of one large document(32:58) Claude as a writing partner: review, research, and refinement(35:34) Recap of workflows and lightning round—Tools referenced:• Claude Code: https://claude.ai/• Obsidian: https://Obsidian.md/• VS Code: https://code.visualstudio.com/• Descript: https://www.descript.com/• ChatGPT: https://chat.openai.com/• Trello: https://trello.com/—Other references:• Continuous Discovery Habits: https://www.producttalk.org/continuous-discovery-habits/• Google Scholar: https://scholar.google.com/• Claude Code: What It Is, How It’s Different, and Why Non-Technical People Should Use It: https://www.producttalk.org/claude-code-what-it-is-and-how-its-different—Where to find Teresa Torres:Blog: https://producttalk.org/Podcast: https://justnowpossible.com/Book: https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309LinkedIn: https://www.linkedin.com/in/teresatorres/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Alexander Embiricos, the product lead for Codex at OpenAI, shares practical workflows for getting the most out of this AI coding agent. In this episode, he demonstrates how both non-technical users and experienced engineers can leverage Codex to accelerate development, from making simple code changes to building production-ready applications. Alex walks through real examples of using Codex in VS Code and terminal environments, implementing parallel workflows with Git worktrees, and creating detailed implementation plans for complex projects. He also reveals how OpenAI uses Codex internally, including how they built the Sora Android app in just 28 days, and offers insights on automated code review and the future of AI-assisted development.What you’ll learn:How to set up and use Codex in VS Code and terminal environments for both simple and complex coding tasksA practical workflow for running multiple Codex instances in parallel using Git worktrees to avoid conflictsHow to create detailed implementation plans using the Plans.md technique for complex engineering projectsWhy context is critical when prompting Codex—and how to provide the right information for better resultsHow OpenAI uses automated code review to accelerate development while maintaining high quality standardsThe key differences between vibe coding for prototypes versus building production-ready applications with AIHow the new GPT-5.2 model improves Codex’s capabilities with faster reasoning and better problem-solving—Brought to you by:Brex—The intelligent finance platform built for foundersGraphite—Your AI code review platform—Detailed workflow walkthrough from this episode:https://chatprd.ai/how-i-ai/advanced-codex-workflows-with-openai-alex-embiricos—In this episode, we cover:(00:00) Introduction to Alex and Codex(02:06) Getting started with Codex(04:54) Using Codex for parallel tasks(07:34) Understanding Git worktrees(09:51) Terminal shortcuts and command-line efficiency(12:16) How OpenAI built the Sora Android app with Codex(15:37) Using PLANS.md for problem solving(17:57) The importance of high agency(22:22) Deciding between what needs a plan and what doesn’t(26:42) How to multiply the impact of Codex(28:08) Implementing automated code review with GitHub(31:58) Delivering the benefits of AGI to all humanity(34:35) Accelerating developer productivity(36:38) Recap and final thoughts—Tools referenced:• Codex: https://openai.com/blog/openai-codex• VS Code: https://code.visualstudio.com/• Cursor: https://cursor.com/• Git: https://git-scm.com/• GitHub: https://github.com/• Atlas: https://openai.com/atlas• ChatGPT: https://chat.openai.com/• Slack: https://slack.com/• Linear: https://linear.app/—Other references:• Sora Android app: https://openai.com/blog/sora• GPT-5.2 model: https://openai.com/index/introducing-gpt-5-2/• SWE-bench: https://openai.com/index/introducing-swe-bench-verified/—Where to find Alexander Embiricos:LinkedIn: https://www.linkedin.com/in/embiricoX: https://x.com/embirico—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Wade Foster is the co-founder and CEO of Zapier. In this episode, Wade shows how he uses meeting transcripts, Zapier agents, and even Grok to analyze company culture, evaluate interview candidates, and source talent from unexpected places. He explains why CEOs need to lead by example when it comes to AI adoption and shares practical workflows that any executive can implement to make hiring more effective and efficient.What you’ll learn:How to use meeting transcripts to extract your company’s “unspoken culture” and compare it against your stated valuesA workflow for creating AI interview evaluators that assess candidates against your job descriptions and company valuesHow to use Zapier agents to provide objective feedback on candidate interviews while checking your own biasesWhy CEOs should participate in AI “hackathons” and “show and tells” rather than just delegating AI adoptionA surprising technique for using Grok to find “diamonds in the rough” talent outside traditional recruiting channelsHow AI enables companies to complete tasks that were previously not economically viable—This entire episode is brought to you by:Brex—The intelligent finance platform built for founders—In this episode, we cover:(00:00) Introduction to Wade Foster(02:32) Zapier’s AI adoption(06:50) Creating AI fluency rubrics(08:37) Using Granola to extract company culture from meeting transcripts(10:49) Practical use cases for company culture rubrics(13:38) Building an AI interview evaluation agent in Zapier(16:50) Using Copilot to improve agent prompts(18:49) Ideas for enhancing the interview agent(22:31) Mistakes people make when using agents(25:11) Using Grok to find talent on social media platforms(33:39) Recap of AI workflows for recruiting and hiring(34:40) Lightning round and final thoughts—Tools referenced:• Zapier: https://zapier.com/• Zapier Agents: https://zapier.com/agents• Granola: https://granola.ai/• Grok: https://grok.x.ai/• ChatGPT: https://chat.openai.com/• Copilot: https://copilot.microsoft.com/—Other references:• Zapier values: https://zapier.com/about• How Zapier’s EA built an army of AI interns to automate meeting prep, strengthen team culture, and scale internal alignment | Cortney Hickey: https://www.lennysnewsletter.com/p/how-zapiers-ea-built-an-army-of-ai• How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy): https://www.lennysnewsletter.com/p/how-this-ceo-turned-25000-hours-of—Where to find Wade Foster:Zapier: https://zapier.com/LinkedIn: https://www.linkedin.com/in/wadefoster/X: https://twitter.com/wadefoster—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Rachel Wolan, the chief product officer at Webflow, has embraced AI not just as a product leader but as a hands-on builder. A coder since age 16, Rachel has returned to her technical roots by creating a custom AI chief-of-staff application that helps manage her executive workload. In this episode, she demonstrates how she uses personal AI software to prep for meetings, triage her calendar, manage emails, and even get brutally honest feedback about how she’s spending her time.What you’ll learn:How Rachel built a custom AI chief-of-staff application that integrates with her calendar, email, and moreWhy building personal software can be a gateway to understanding AI’s capabilities for executivesHow her AI agents help her prep for podcasts, dinners, and meetings with just-in-time informationThe technical approach to building personal AI software using markdown files, API tokens, and multiple LLM interfacesHow Rachel organized company-wide “builder days” that dramatically increased AI tool adoption across her organizationWhy she believes executives must lead by example in AI adoption to authentically drive organizational change—Brought to you by:Graphite—Your AI code review platformAtlassian for Startups —From MVP to IPO—In this episode, we cover:(00:00) Introduction to Rachel Wolan(02:26) Why Rachel started leaning into AI(06:26) Building an AI chief of staff(08:17) Prepping for the podcast(10:00) Rachel’s morning flow with her AI chief of staff(14:14) Designing a personalized interface with custom note cards(16:34) Getting “brutal truth” feedback from your AI assistant(19:34) Email triage and management workflows(23:31) Prepping for networking dinners and events(28:18) The result of building an AI chief of staff(30:09) Organizing “builder days” to drive AI adoption(35:38) Measuring the impact of AI adoption initiatives(38:00) Lightning round and final thoughts—Tools referenced:• Claude: https://claude.ai/• Claude Code: https://claude.ai/code• Cursor: https://cursor.com/• Google Calendar API: https://developers.google.com/calendar• Gmail API: https://developers.google.com/gmail• Webflow: https://webflow.com/• Figma: https://www.figma.com/• Make: https://www.make.com/• Hex: https://hex.tech/—Where to find Rachel Wolan:LinkedIn: https://www.linkedin.com/in/rachelwolan/X: https://x.com/rachelwolanWebflow: https://webflow.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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Brian Greenbaum is a Senior Staff Product Designer at Pendo who led a company-wide AI transformation after a personal epiphany while on paternity leave. After experiencing the power of AI coding tools firsthand, he created a structured approach to help his entire product organization adopt AI. In this episode, Brian shares his complete playbook for driving AI adoption across teams, measuring success, and navigating the organizational challenges that come with new technology adoption.What you’ll learn:The exact Slack message Brian sent while on paternity leave that kickstarted his company’s AI transformationHow to structure both synchronous and asynchronous AI learning opportunities for maximum adoptionThe two-pronged approach that dramatically increased AI tool usage across teamsWhy becoming your company’s AI champion is one of the best career moves you can make right nowHow to measure AI adoption success with sentiment surveys and clear metricsThe critical role of creating a “golden path” for AI tool usage with legal, security, and finance teams—Brought to you by:Google Gemini—Your everyday AI assistantLovable—Build apps by simply chatting with AI—In this episode, we cover:(00:00) Introduction to Brian Greenbaum(01:38) Brian’s paternity leave epiphany that sparked an AI initiative(05:00) Sending the message that launched a transformation(12:25) The two-pronged approach: synchronous and asynchronous learning(17:29) Encouraging experimentation and creative exploration(18:41) How AI enables designers to move beyond MVP thinking(22:00) Quick summary of the two-pronged approach(24:43) Measuring AI adoption(33:48) Creating a centralized AI knowledge center(35:58) Building an MCP server to demonstrate AI’s potential(44:08) Why technical understanding is crucial for non-technical roles(46:01) Final thoughts—Tools referenced:• Cursor: https://cursor.com/• Bolt.new: https://bolt.new/• Claude: https://claude.ai/• ChatGPT: https://chat.openai.com/• Midjourney: https://www.midjourney.com/• Gemini: https://gemini.google.com/—Other references:• Pendo: https://www.pendo.io/• Confluence: https://www.atlassian.com/software/confluence• Slack: https://slack.com/—Where to find Brian Greenbaum:LinkedIn: https://www.linkedin.com/in/briangreenbaum/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Cortney Hickey is the executive assistant to the CEO at Zapier, where she’s leveraging AI to transform traditional EA responsibilities into scalable, organization-wide systems. In this episode, she demonstrates how she’s built AI workflows that automate meeting preparation, reinforce company culture through automated feedback, and democratize strategic knowledge across the organization. Her approach shows how EAs can use AI not to replace their roles but to elevate them—working on higher-impact initiatives while creating systems that benefit the entire company.What you’ll learn:How to build an automated meeting prep system that researches participants, checks CRM data, and delivers actionable insights before important meetingsA framework for creating AI-powered culture reinforcement through automated meeting feedback aligned with company values and operating principlesHow to develop an AI-powered document review system that helps teams align with executive expectations before formal reviewsA strategy for creating a centralized knowledge base that makes company strategy accessible and interactive for all employeesWhy “progress over perfection” is the key mindset for building effective AI workflows that evolve over timeHow EAs can use AI automation to work themselves out of repetitive tasks and into higher-impact strategic roles—Brought to you by:WorkOS—Make your app enterprise-ready todayBrex—The intelligent finance platform built for founders—In this episode, we cover:(00:00) Introduction to Cortney(02:48) Overview of meeting prep automation with Zapier Agents(04:43) How the meeting prep agent works(10:21) An example of the meeting prep agent in practice(12:16) Creating a culture reinforcement system through meeting feedback(15:45) EAs’ unique position to leverage these tools(18:12) Building an automated meeting coach(24:03) Developing an executive document review system(33:15) Creating a centralized strategy companion in NotebookLM(36:18) How AI is transforming the EA role, not replacing it(40:00) Lightning round and final thoughts—Tools referenced:• Zapier: https://zapier.com/• Zapier Agents: https://zapier.com/agents• Todoist: https://todoist.com/• Slack: https://slack.com/• HubSpot: https://www.hubspot.com/• ChatGPT: https://chat.openai.com/• Google NotebookLM: https://notebooklm.google/—Where to find Cortney Hickey:LinkedIn: https://www.linkedin.com/in/cortneyhickey/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Michal Peled is a Technical Operations Engineer at HoneyBook who specializes in building internal tools and automations that eliminate friction for teams. In this episode, Michal demonstrates three practical AI use cases: using ChatGPT’s agent mode to automate LinkedIn recruiting, transforming customer research into interactive AI personas, and creating a custom calendar solution for a very San Francisco–specific problem—avoiding expensive parking during Giants games.What you’ll learn:How to use ChatGPT agent mode to automate LinkedIn recruiting and find high-quality candidates that manual searches missedThe step-by-step process for turning static customer research into interactive AI personas that product and marketing teams can actually useWhy NotebookLM excels at creating prompts from source material with proper citationsHow to structure agent-mode prompts to create effective “little helpers” that follow your exact workflowA practical framework for improving your prompts when AI tools aren’t giving you the results you wantHow internal tools teams can drive massive impact by focusing on eliminating friction in everyday workflows—Brought to you by:Brex—The intelligent finance platform built for foundersGoogle Gemini—Your everyday AI assistant—In this episode, we cover:(00:00) Introduction to Michal and ChatGPT agent mode(02:10) Using agent mode for LinkedIn recruiting automation(05:14) Creating effective prompts for agent mode(10:50) Demo of agent mode searching LinkedIn profiles(16:29) Results and team reception of the recruiting automation(19:53) The outcome of implementing on Michal’s team(23:50) Creating custom GPT personas from customer research(28:43) Using NotebookLM to transform research into persona prompts(35:00) Adding guardrails to custom GPT personas(37:20) Demo of interacting with custom-persona GPTs(41:02) Creating a calendar automation for parking during baseball games(48:15) Lightning round and final thoughts—Tools referenced:• ChatGPT: https://chat.openai.com/• NotebookLM: https://notebooklm.google.com/• Claude: https://claude.ai/—Other references:• Google Calendar: https://calendar.google.com/• HoneyBook: https://www.honeybook.com/• LinkedIn: https://www.linkedin.com/—Where to find Michal Peled:LinkedIn: https://www.linkedin.com/in/michalpeled/—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—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

I put three cutting-edge AI models to the test in a head-to-head design competition. Using the exact same prompt, I challenged Google’s Gemini 3, Anthropic’s Opus 4.5, and OpenAI’s Codex 5.1 to redesign my blog page, evaluating them on visual design quality, user experience improvements, and SEO optimization capabilities. One model produced a beautiful, polished, production-ready redesign. One was fine. And one completely whiffed. If you’re trying to figure out where each model fits in your workflow—design, planning, back-end, or something else—this episode will save you a lot of trial and error.What you’ll learn:How each AI model approaches the same design challenge differentlyWhy planning capabilities dramatically impact design qualityThe specific visual and functional improvements each model madeWhich model excels at front-end design versus back-end functionalityHow to strategically choose the right AI model for different parts of your workflowThe importance of model-switching based on specific use cases—Blog design: https://www.chatprd.ai/blog—Brought to you by:Lovable—Build apps by simply chatting with AI—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 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 jordan@penname.co.

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 todayMiro—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: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 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 jordan@penname.co.

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 todayGoogle Gemini—Your everyday AI assistant—Where to find Lucas Werthein:Website: https://cactus.is/—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 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 jordan@penname.co.

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: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 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 jordan@penname.co.

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: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 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 jordan@penname.co.

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 foundersZapier—The most connected AI orchestration platform—Where to find Matt Britton:LinkedIn: linkedin.com/in/mattbbrittonInstagram: https://www.instagram.com/mattbrittonnyc/Company: https://www.suzy.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 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 jordan@penname.co.

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 capabilitiesMy 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 functionalityThe basics of using GitHub to track changes and manage your code repository as a non-technical personWhy starting with a personal project hub is the best way to ease into AI-assisted codingMy 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/• v0: https://v0.dev/• 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 jordan@penname.co.

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 codebases2. Techniques for creating a semantic layer that helps AI understand your business data3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs)4. Ways to automate experiment analysis and create standardized reports5. How AI can help design and analyze customer surveys6. Strategies for creating executive-ready documents from raw data analysis7. Why every team member should have access to code repositories—not just engineers—Brought to you by:Zapier—The most connected AI orchestration platformBrex—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: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 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 jordan@penname.co.

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/X: https://x.com/amrcn_werewolf?lang=en—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(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 jordan@penname.co.