How I AI

Claire breaks down the biggest announcements from Anthropic’s “Code with Claude” event and what they actually mean for builders shipping AI products today. From scheduled AI routines to outcome-based agents, multi-agent orchestration, and new memory systems, Claire walks through the features she’s most excited to use immediately—and how they could reshape the future of agentic software.What you’ll learn:How Claude Code routines let you automate recurring workflows on schedules or webhooksWhat “Outcomes” are and how rubric-based agent grading worksHow multi-agent orchestration enables specialized AI teams with different roles and toolsWhy Anthropic’s new “Dreams” memory system matters for long-term agent behaviorWhy increased Claude Code usage limits are a bigger deal than they soundHow Claire thinks about building practical agentic products today—Resources:• Code with Claude: https://claude.com/code-with-claude• Claude Code Routines Docs: https://code.claude.com/docs/en/routines• Define Outcomes Docs: https://platform.claude.com/docs/en/managed-agents/define-outcomes• Dreams Docs: https://platform.claude.com/docs/en/managed-agents/dreams• Multi-Agent Docs: https://platform.claude.com/docs/en/managed-agents/multi-agent• Managed Agent Webhooks Docs: https://platform.claude.com/docs/en/managed-agents/webhooks#supported-event-types• Codex (OpenAI): https://openai.com/codex• GitHub: https://github.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.

John Kim is the co-founder and CEO of Delight.ai, a customer experience platform that’s transforming how companies deploy AI. But what makes John’s story fascinating isn’t just his product; it’s how he’s turned his entire company into an AI-native organization. His marketing team built a fully functional e-commerce swag store with Stripe integration in days. His sales team built their own CRM tools. His recruiting team automated their entire workflow. And it’s all tracked, measured, and celebrated through an internal platform called Automators.What you’ll learn:How Sendbird’s marketing team built a fully functional swag store with Stripe integration in a day (with no engineering support)How the Automators platform works—an internal marketplace where anyone can request AI tools and engineers (or AI agents) can build themHow to create secure, compliant templates so non-technical teams can ship to production safelyHow Sendbird built a token usage dashboard with five tiers (beginner through AI God) and why tracking the smoothness of the curve matters more than the totalWhy visible leadership usage is the most powerful adoption signalWhy Sendbird rewrote job descriptions to prioritize curiosity, agency, and energy over years of experienceHow John uses AI for his own learning—Brought to you by:WorkOS—Make your app enterprise-ready todayThoughtSpot—Build AI-powered analytics into your product—In this episode, we cover:(00:00) Introduction to John Kim(02:45) The Delight.ai swag store built by marketing in two days(05:51) The before times: when fun had to earn its place on the roadmap(07:55) Demo: The Automators platform and quest system(13:47) The AI Engineer for Internal Operations role(16:06) Demo: The company-wide skills marketplace(17:19) Treating AI adoption as a product(18:43) Real wins: team-level and campaign examples(21:51) Why SaaS isn’t dead—it’s being rebuilt internally(23:46) Demo: The token tracking dashboard(26:32) Measuring without fear: setting expectations, not punishments(28:54) Quick recap(30:51) Personal AI use cases: endless knowledge at your fingertips(36:15) Lightning round and final thoughts—Tools referenced:• Claude Code: https://claude.ai/code• Codex (OpenAI): https://openai.com/codex• Obsidian: https://obsidian.md• GitHub: https://github.com• Stripe: https://stripe.com—Other references:• Jason Levin (CEO of Memelord) on How I AI: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how• Konami Code: https://en.wikipedia.org/wiki/Konami_Code• Andrew Huberman’s podcast: https://hubermanlab.com/• Y Combinator: https://www.ycombinator.com/—Where to find John Kim:X: https://x.com/doshkimInstagram: https://instagram.com/doshLinkedIn: https://www.linkedin.com/in/doshkim/Company: https://delight.aiDelight.ai Spark Conference (May 7, SF): https://delight.ai/spark—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.

Owen Williams is a design manager at Stripe who built Protodash, an internal AI-powered prototyping platform that lets designers and PMs create high-quality Stripe dashboard prototypes without writing code. What started as a bundle of Cursor rules and React components evolved into a full web-based prototyping studio that runs in dev boxes, complete with design review modes, variant testing, and AI-powered iteration. Surprisingly, PMs now use Protodash just as much as designers, fundamentally changing how Stripe approaches prototyping, design reviews, and engineering handoffs.What you’ll learn:How Stripe built an internal AI prototyping tool using Cursor rules, MCPs, and their design systemWhy “blurple slop” happens when designers use generic AI tools—and how to fix itThe architecture behind Protodash: React router, design system components, and MCP integrationsHow Stripe prototypes in dev boxes so designers never have to worry about local setupWhy “demos, not memos” transformed Stripe’s design review cultureHow Stripe built design review modes, variant testing, and AI annotation directly into your prototyping toolWhy internal tools don’t need to be production-grade to be transformative—Brought to you by:Celigo—Intelligent automation built for AICursor—The best way to code with AI—In this episode, we cover:(00:00) Welcome and intro to Owen Williams(02:19) The “blurple slop” problem with AI design tools(03:50) Protodash: an internal vibe-coding tool for Stripe prototypes(05:26) Why an engineering background helped Owen lower the bar for designers(07:55) The Cursor rules that taught the Stripe design system(09:04) Running prototypes on dev boxes vs. locally(10:30) “Demos, not memos” and rewiring design reviews at Stripe(14:50) Building Protodash Studio: a browser-based wrapper for prototyping(19:04) Live demo: variants, line charts, and remixing prototypes in browser(21:02) Self-testing prototypes that take screenshots and check their work(23:20) Multiple variant features(26:08) The annotate-for-AI button for in-canvas feedback(27:21) Design review mode: comments, summaries, and AI follow-up(29:39) Why building internal tools beats buying off-the-shelf(32:50) PMs as the surprise power users of Protodash(35:20) Live demo: a Black Friday/Cyber Monday pet store dashboard(42:03) Lo-fi modes, monospace fonts, and “Comic Sans for WIP” at Shopify(44:45) Quick recap(45:35) The Radar prototype that changed engineering handoff(49:08) Lightning round and final thoughts—Blog & detailed workflow walkthroughs from this episode:Stripe’s Owen Williams on Killing ‘Blurple Slop’ with an Internal Prototyping Studio: http://chatprd.ai/how-i-ai/stripe-owen-williams-on-buildling-internal-prototyping-studio↳ How To Connect a Design System to an AI Code Editor for High Fidelity Prototypes: https://www.chatprd.ai/how-i-ai/workflows/how-to-connect-a-design-system-to-an-ai-code-editor-for-high-fidelity-prototypes↳ Streamline Design Reviews with an AI-Powered Prototyping Studio: https://www.chatprd.ai/how-i-ai/workflows/streamline-design-reviews-with-an-ai-powered-prototyping-studio↳ Build a Personal AI App to Track Purchases and User Manuals: https://www.chatprd.ai/how-i-ai/workflows/build-a-personal-ai-app-to-track-purchases-and-user-manuals—Tools referenced:• v0: https://v0.app/• Cursor: https://cursor.com/• Claude Code: https://www.claude.com/product/claude-code• Claude Design: https://www.anthropic.com/news/claude-design-anthropic-labs• Figma: https://www.figma.com/• Stripe Radar: https://stripe.com/radar• Balsamiq: https://balsamiq.com/—Where to find Owen Williams:X: https://x.com/owWebsite: https://owenwillia.ms/LinkedIn: https://www.linkedin.com/in/owenpwilliams—Where to find Claire Vo:ChatPRD: https://www.chatprd.ai/Website: https://clairevo.com/X: https://x.com/clairevo—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

Jason Levin is the CEO and founder of Memelord, an AI-powered meme creation platform that helps brands and individuals create contextual, trending memes. He started Memelord as a $6.90-per-month newsletter sending subscribers to a Google Slides deck, grew it to $100K ARR on Bubble without hiring engineers, then raised $3M to build it into an API-first product.What you’ll learn:How Jason grew Memelord from a $6.90/month newsletter to $100K ARR without writing a single line of codeWhy “no UX is the best UX” and how agents are becoming Memelord’s primary usersThe mandatory vibe-coding rule for his marketing team and how it unlocks unprecedented creativityWhy free tools are the new PDF downloads and how they’ve generated hundreds of thousands of emailsJason’s hardware hacking projects, including a bedside keyboard that creates Linear tickets without waking his wifeWhy AI can be funny (but humans are still funnier) and which model is the funniestThe philosophy of building hyper-personalized software just for yourself—Brought to you by:WorkOS—Make your app enterprise-ready todayPersona—Trusted identity verification for any use case—In this episode, we cover:(00:00) Introduction to Jason Levin and Memelord(04:28) Demo: Agentic meme creation with OpenClaw(06:55) “No UX is the best UX”—building for an agent-first future(08:35) How Memelord started as a $6.90 newsletter with Google Slides(12:35) Building to $100K ARR on Bubble with 395 workflows(15:20) Demo: Free tools section that generates hundreds of thousands of emails(17:59) Why Cursor is perfect for non-technical founders(20:20) Let your marketers cook—or watch them leave(24:19) Commit graph that shows the vibe-coding inflection point(25:25) Tools: Claude, Gemini, Linear, PostHog(28:19) Build weird stuff in the real world(33:24) Creative AI use cases(39:56) Using OpenClaw for calendar analysis(43:37) Can AI be funny? Which model is funniest?(45:26) Memes are not slop(46:45) What Jason doesn’t use AI for(48:12) Final thoughts—Blog & detailed workflow walkthroughs from this episode:How I AI: Jason Levin’s Workflows for Agentic Memes, Vibe Coding, and Hardware Hacking: https://www.chatprd.ai/how-i-ai/jason-levins-workflows-for-agentic-memes-vibe-coding-and-hardware-hacking↳ Build a Custom Bedside Keyboard for Idea Capture with Raspberry Pi and ChatGPT: https://www.chatprd.ai/how-i-ai/workflows/build-a-custom-bedside-keyboard-for-idea-capture-with-raspberry-pi-and-chatgpt↳ Build Free Marketing Tools as Lead Magnets Using AI Code Assistants: https://www.chatprd.ai/how-i-ai/workflows/build-free-marketing-tools-as-lead-magnets-using-ai-code-assistants↳ Automate Meme Marketing with an AI Agent and OpenClaw: https://www.chatprd.ai/how-i-ai/workflows/automate-meme-marketing-with-an-ai-agent-and-openclaw—Tools referenced:• Memelord API: https://memelord.com/api• Cursor: https://cursor.com/• Bubble: https://bubble.io/• OpenClaw: https://openclaw.ai• Claude: https://claude.ai/• ChatGPT: https://chat.openai.com/• Gemini: https://gemini.google.com/• Grok: https://grok.x.ai/• Linear: https://linear.app/• PostHog: https://posthog.com/• Zapier: https://zapier.com/—Other references:• Diego Zaks—“The best UX is no UX”: https://x.com/diegozaks/status/1966526522136649980• Sam Lessin: https://wlessin.com/• “Stop giving me advice”: https://stopgivingmeadvice.com• Memelord free tools: https://memelord.com/tools—Where to find Jason Levin:Twitter: https://twitter.com/iamjasonlevinInstagram: https://instagram.com/iamjasonlevinLinkedIn: https://www.linkedin.com/in/iamjasonlevin/Memelord: https://memelord.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.

In this mini episode, I break down OpenAI’s new GPT 5.5 and GPT 5.5 Pro after weeks of early testing. I walk through three real jobs I threw at the model:  building an app for me to teach my second grader more advanced subtraction concepts, tackling a tech debt problem in the ChatPRD codebase, and hacking into a proprietary Bluetooth pixel display that every other model had failed me on. My verdict: higher intelligence, better efficiency, and genuinely autonomous long-running loops that change what I think is worth tackling.What you’ll learn:How I think about GPT 5.5 Pro’s pricing vs engineering time, and when I believe the “intelligence tax” is worth payingWhy I treat GPT 5.5 as a developer model first, and why I couldn’t find a consumer use case that justified its intelligenceThe exact prompt pattern I use to unlock a long-running autonomous subagent loopHow I got a near-six-hour autonomous run to one-shot 98% of edge cases in a migration over millions of chat threads and drop my Sentry error rate to the floorWhy I’m now throwing GPT 5.5 at tech debt, flaky tests, and security backlogs firstHow I combined a Bluetooth packet sniffer and GPT 5.5 to reverse-engineer a proprietary pixel speaker after Claude Code and GPT 5.4 both gave upHow I use the /personality command inside Codex to swap the default “baked potato” tone for something I actually enjoy working with—In this episode, I cover:(00:00) Introduction to GPT 5.5 testing(00:40) What is GPT 5.5 and how much does it cost?(03:23) Testing GPT 5.5 in ChatGPT: the intelligence overhang problem(07:12) Moving to Codex: where GPT 5.5 really shines(16:01) Hacking a Chinese Bluetooth speaker(21:47) Final thoughts on GPT 5.5’s intelligence and efficiency—Tools referenced:• GPT 5.5 and GPT 5.5 Pro: https://openai.com/index/introducing-gpt-5-5/• Codex: https://openai.com/codex/• ChatGPT: https://chat.openai.com/• Claude Code: https://claude.ai/code• Sentry: https://sentry.io/• Divoom MiniToo: https://divoom.com/products/minitoo—Other references:• OpenAI Codex Security: https://openai.com/index/codex-security-now-in-research-preview/—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 mini episode, I do a full walkthrough of the AI design tools that dropped in April 2026: Anthropic’s new Claude Design, OpenAI’s GPT Images 2.0, and Google Labs’ open-source DESIGN.md format. I import a full design system from Lenny’s Newsletter, build a landing page, turn my own article into a polished deck, generate a brand kit for ChatPRD, and run a personal color analysis from a photo.What you’ll learn:How Claude Design handles design system imports and whether it can actually replace FigmaThe three best use cases for Claude Design: marketing landing pages, slide decks, and creative redesignsWhy ChatGPT Images 2.0 is a breakthrough for brand kits and layout workGoogle’s new DESIGN.md standardThe practical limits of AI design tools (spoiler: you’ll hit credit limits fast)—Brought to you by:WorkOS—Make your app enterprise-ready todayRippling—Stop wasting time on admin tasks, build your startup faster—In this episode, we cover:(00:00) Welcome and what’s in the spring 2026 AI design drop(01:45) Claude Design overview(03:05) Importing Lenny’s Newsletter design system into Claude Design(04:06) How Claude Design structures a design system(05:42) Google Labs’ DESIGN.md standard(06:41) Building Lenny Doc, a PRD generator landing page using the Lenny design system(09:44) Why the three-variation output is Claude Design’s smartest UX choice(10:20) Hitting the Claude Design limit and paying $200 to keep going(11:05) Where Figma still wins(13:20) Reviewing Lenny Doc(16:19) Turning an Open Claude article into a branded slide deck(17:57) The ’90s GeoCities “Lenny’s Product Zone” redesign(19:44) Claude Design recap(20:15) ChatGPT Images 2.0 and what makes it the first “thinking” image model(21:25) Generating a multi-page brand kit for ChatPRD and iterating with reference images(23:43) Personal color analysis demo(26:02) Recap—Detailed workflow walkthroughs from this episode:• How I Put Claude Design and GPT Images 2.0 to the Test: Building Landing Pages, Slides, and Brand Kits: https://www.chatprd.ai/how-i-ai/claude-design-and-gpt-images-2-building-landing-pages-slides-and-brand-kits• How to Generate a Professional Brand Kit with GPT Images 2.0: https://www.chatprd.ai/how-i-ai/workflows/how-to-generate-a-professional-brand-kit-with-gpt-images-2-0• How to Convert an Article into a Polished Slide Deck with AI: https://www.chatprd.ai/how-i-ai/workflows/how-to-convert-an-article-into-a-polished-slide-deck-with-ai• How to Build a High-Fidelity Landing Page with Claude Design: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-high-fidelity-landing-page-with-claude-design—Tools referenced:• Claude Design: https://claude.ai/design• ChatGPT Images 2.0: https://openai.com/index/introducing-chatgpt-images-2-0/• Midjourney: https://www.midjourney.com/—Other references:• Google’s DESIGN.md: https://stitch.withgoogle.com/docs/design-md/overview• Lenny’s Newsletter: https://www.lennysnewsletter.com/• Jamie Gannon “How I AI” episode on reference styles: https://www.lennysnewsletter.com/p/mastering-midjourney-how-to-create• Brand prompt inspiration: https://x.com/riomadeit/status/2046682442791071787• Figma team “How I AI” episode on design systems: https://www.lennysnewsletter.com/p/from-figma-to-claude-code-and-back—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 Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code.What you’ll learn:How Intercom doubled their merged PRs per R&D employee in just nine months using Claude CodeThe telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineersWhy they built a skills repository with hooks that enforce engineering standards automaticallyHow they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIsThe permission and accountability framework that enabled rapid AI adoptionWhy backlog zero is now achievable and what that means for engineering culture—Brought to you by:Celigo—Intelligent automation built for AICursor—The best way to code with AI—In this episode, we cover:(00:00) Introduction to Brian Scanlan(02:40) Why Intercom went all-in on AI for both product and engineering(05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025(07:02) Demo: Intercom’s merged PRs per R&D head(12:50) Agent-first work as a fundamental reimagining of technical workflows(14:27) The cost tradeoff: treating AI spend as an investment(16:47) Measuring quality(21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code(24:03) Creating a custom PR skill(26:33) Building a software factory with predictable quality standards(30:15) Telemetry infrastructure: Honeycomb for skill usage tracking(32:10) Session data collection and personalized usage insights(36:08) Quick overview(39:20) Walking through Intercom’s skills repository(42:16) Deep dive: The flaky spec skill and how it reached 100x capability(46:44) The “and then” workflow for building comprehensive skills(52:31) The live website and overview of workflows(53:32) How internal AI experience informs customer product decisions(56:18) Making SaaS products agent-friendly with CLIs and helpful hints(01:03:49) Why conversion drop-off is invisible in agent-driven workflows(01:05:28) Lightning round and final thoughts—Detailed workflow walkthroughs from this episode:• How Intercom Doubled Engineering Output: Brian Scanlan's 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code• Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding• Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests• Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill—Tools referenced:• Claude Code: https://claude.ai/code• Cursor: https://cursor.com/• Honeycomb: https://www.honeycomb.io/• Snowflake: https://www.snowflake.com/• Fin AI: https://www.intercom.com/fin• Vercel: https://vercel.com/—Other references:• Intercom GitHub Repo: https://github.com/intercom• Google API Go Client Repo: https://github.com/googleapis/google-api-go-client—Where to find Brian Scanlan:X: https://x.com/brian_scanlanLinkedIn: https://www.linkedin.com/in/scanlanb/Company: https://www.intercom.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.

JJ Englert leads community enablement at Tenex. In this episode, JJ provides a complete zero-to-one tutorial on Claude Cowork, Anthropic’s desktop tool that sits between simple chat and full terminal-based coding.What you’ll learn:How to create your first Claude Cowork project by connecting a folder on your computer and building context over timeThe “brain” file strategy: how to create a preferences document that Claude reads every time to understand who you are and how you workWhy one-click connectors to Gmail, Slack, Notion, and Google Calendar unlock AI that actually does work instead of just suggesting itHow to analyze your sent emails to build a writing skill that perfectly matches your tone and styleThe sub-advisory-board technique: spinning up three AI agents with different personas to review your work from multiple perspectivesHow to set permissions for each connector so Claude only drafts (never sends) or always asks before taking actionThe scheduled-task workflow that creates a morning debrief by reading your email, Slack, and calendar every day at 7:30 a.m.Why projects with shared memory beat individual chat threads for consistent, high-quality AI outputs—Brought to you by:Tines—Start building intelligent workflows todayCursor—The best way to code with AI—In this episode, we cover:(00:00) Introduction to JJ Englert(02:48) What Cowork is and who it’s for(05:49) Getting started: Opening the Cowork tab in Claude Desktop(07:04) Understanding projects as folders on your computer(07:54) Creating your “brain” file, with working preferences and context(10:24) Demo: Building a daily operating system project from scratch(12:18) How to prompt Cowork when starting a new project(14:54) Understanding the project interface and shared memory(18:37) Setting up connectors to Gmail, Slack, Google Calendar, and other tools(21:00) Using connectors to analyze your emails and build personalized writing skills(24:21) Creating a thinking-partner skill for decision support(26:18) Cowork vs. OpenClaw(27:18) Building a sub-advisory skill with multiple AI personas for feedback(34:03) Advanced skill example: Multi-step newsletter creation with research and evaluation(36:08) Setting up scheduled tasks for morning debriefs(37:57) Going beyond one-off tasks with AI(41:00) Progressive trust and the tradeoff of information for productivity(44:08) Different use cases beyond work productivity(46:08) Lightning round—Tools referenced:• Claude Code: https://claude.ai/code• Wispr Flow: https://whisperflow.ai/• Monologue: https://www.monologue.to/• Domo: https://www.domo.com/• Pencil.dev: https://pencil.dev/• Remotion: https://www.remotion.dev/• Obsidian: https://obsidian.md/• OpenClaw: https://openclaw.com/• Notion: https://notion.so/—Other references:• Get Started with Claude Cowork: https://support.claude.com/en/articles/13345190-get-started-with-cowork—Where to find JJ Englert:YouTube: https://www.youtube.com/channel/UCv2ovDhYVtlJw4QMidLFP8QX: https://twitter.com/jjenglertLinkedIn: https://www.linkedin.com/in/jj-englert-a08836a6/—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.

Yash Tekriwal is the head of education at Clay. A self-described hyper-optimizer, Yash has built multiple custom productivity applications using Perplexity Computer and OpenClaw to manage his overwhelming daily workflow—including a Slack digest system that categorizes over 150 daily notifications into actionable priorities, and a consolidated news/email/Slack dashboard that serves as his personal command center.What you’ll learn:How Yash built a custom Slack digest that categorizes 150+ daily notifications into action-required, need-to-read, and FYI bucketsWhy Perplexity Computer beats Claude Code and Codex for building personal productivity appsHis “anti-to-do list” framework: spending an hour daily automating tasks you never want to do againHow to use AI for deterministic tasks (APIs, structured data) vs. subjective tasks (categorization, summarization)Why the SaaS apocalypse narrative is wrong—and why we’re about to see an explosion of micro-softwareHow his team uses Perplexity Computer to prototype design systems and communicate with cross-functional partners—Brought to you by:Guru—The AI layer of truthThoughtSpot—Build AI-powered analytics into your product—In this episode, we cover:(00:00) Introduction to Yash(02:38) The burden of 150 daily Slack notifications(05:45) When to use AI for tasks vs. building deterministic code(06:38) Building the Slack digest with OpenClaw(11:33) Introducing Perplexity Computer and the visual dashboard(14:28) Three reasons Perplexity Computer beats Claude Code(16:14) Using connectors to automate meeting follow-ups across Notion and Asana(18:21) The Kanban-style Slack dashboard(20:15) The long tail of customer requests and the future of micro-software(24:09) The anti-to-do list framework(26:21) Building a consolidated news, email, and Slack digest(29:48) How Perplexity Computer handles authentication and deployment(31:46) Team use case: Prototyping persona-based learning journeys for Clay University(35:49) Lightning round and final thoughts—Tools referenced:• Perplexity Computer: https://www.perplexity.ai/computer/new• OpenClaw: https://openclaw.ai/• Discord: https://discord.com/• Claude Code: https://claude.ai/code• Codex: https://openai.com/codex/• Asana: https://asana.com/• Airtable: https://airtable.com/• Figma: https://www.figma.com/• Vercel: https://vercel.com/• ChatGPT: https://chat.openai.com/—Other references:• Slack: https://slack.com/• Notion: https://www.notion.so/• Superhuman: https://superhuman.com/• Clay University: https://www.clay.com/university• Kanban boards: https://en.wikipedia.org/wiki/Kanban_board—Where to find Yash Tekriwal:LinkedIn: https://www.linkedin.com/in/yashtekriwal/X: https://x.com/yash_tekCompany: https://www.clay.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.

Al Chen is a field engineer at Galileo, an observability platform for AI applications, where he works on the front lines with enterprise customers asking highly technical questions. Despite never having held an engineering role, Al has built a system using Claude Code to query Galileo’s 15 separate repositories, combine that with Confluence documentation and customer-specific quirks, and deliver hyper-personalized answers that would otherwise require constant engineering support.What you’ll learn:How to use Claude Code to query multiple repositories simultaneously for customer supportWhy code is often a better source of truth than documentationHow to combine repository context with Confluence and Slack using MCPsThe “customer quirks” system that creates hyper-personalized deployment guidesHow to build virtuous loops that turn single customer questions into scalable knowledgeWhy information organization matters less in the AI eraA simple 16-line script (written by Claude Code) that pulls the latest main branch across all your repositories to keep your context currentHow to reduce engineering interruptions to near-zero by empowering customer-facing teams to query the codebase directly—Brought to you by:Orkes—The enterprise platform for reliable applications and agentic workflowsTines—Start building intelligent workflows today—In this episode, we cover:(00:00) Introduction to Al Chen(02:50) The problem: documentation wasn’t enough(04:23) Pulling 15 repos into VS Code(06:03) How Claude Code queries the entire codebase(08:00) Why current code beats documentation(08:31) The pull script that keeps everything updated(09:54) Opening projects at the multi-repo level(11:40) Live demo: answering deployment questions(13:25) The customer quirks system(15:00) Living in chaos: why organization matters less now(17:03) Competing on customer experience, not just product(18:20) Should customers be able to query the code directly?(20:05) Where humans still add value(25:46) Using AI for reactive Slack support(29:16) The “and then” workflow discovery(32:07) Scaling processes across the team(34:07) Lightning round and final thoughts—Tools referenced:• Claude Code: https://claude.ai/code• VS Code: https://code.visualstudio.com/• Pylon: https://usepylon.com/• Confluence: https://www.atlassian.com/software/confluence—Other references:• Slack: https://slack.com/• Kubernetes: https://kubernetes.io/• Stack Overflow: https://stackoverflow.com/• Intercom: https://www.intercom.com/—Where to find Al Chen:LinkedIn: https://www.linkedin.com/in/thealchen/Company: https://www.rungalileo.io—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.

Hilary Gridley is an entrepreneur, former product leader, and new mom who previously appeared on the podcast discussing AI for managers. She returns to share how she's transformed her approach to personal productivity using Claude Code as her primary tool for managing both professional work and life admin. Hilary demonstrates her "anti-system system"—a philosophy that prioritizes simplicity over complex setup, allowing AI to learn preferences through observation rather than upfront configuration.What you’ll learn:How to capture to-dos instantly using a simple iPhone back-tap shortcut that requires zero app switchingThe “10x impact framework” for deciding what tasks to automate versus where to invest your human effortHow to use Claude Code’s observation capabilities to build a preference file that improves over time without manual setupWhy the “yappers API” (talking about what you’re doing while working) eliminates the need for complex OAuth integrationsA workflow for breaking down overwhelming tasks into 10-minute first steps that actually get completedHow to create Claude Skills by simply describing problems rather than writing code or following tutorialsTechniques for using “recording mode” to demo workflows without exposing personal information—Brought to you by:WorkOS—Make your app Enterprise Ready todayLovable—Build apps by simply chatting with AI—In this episode, we cover:(00:00) Introduction to Hilary Gridley(02:43) The opportunity cost of time as a new mom and entrepreneur(07:11) Philosophy of the anti-system system(08:05) Demo: Planning your day with Claude Code(10:00) Setting up simple iPhone shortcuts for task capture(11:48) How Claude organizes reminders and learns preferences automatically(16:19) Breaking down overwhelming tasks into manageable first steps(23:40) The yappers API: talking to Claude instead of building integrations(25:28) Daily logging and observation patterns(27:45) Quick summary(30:50) The power of screenshots(32:55) 10x impact framework for automation decisions(37:51) Applying the framework to different career stages(39:29) Building a “recording on” skill for anonymizing demos(44:11) Building a returns tracking skill from scratch(48:31) Building the muscle memory to reach for AI tools(50:18) Where to find Hilary—Tools referenced:• Claude Code: https://claude.ai/code• Obsidian: https://obsidian.md/• iPhone Shortcuts: https://support.apple.com/guide/shortcuts/welcome/ios• Cursor: https://cursor.sh/—Other references:• Figma file Hilary demo’ed: https://www.writerbuilder.com/howiai—Where to find Hilary Gridley:Substack: https://hils.substack.com/Website: https://writerbuilder.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.

Steve Kaliski is a software engineer at Stripe who has spent the past six and a half years building developer tools and payment infrastructure. He’s part of the team that created “minions”—Stripe’s internal AI coding agents, which now ship approximately 1,300 pull requests per week with minimal human intervention beyond code review. In this episode, Steve demonstrates how Stripe engineers activate development work from Slack and leverage cloud-based development environments for parallel agent workflows, and demos machine-to-machine payments where AI agents transact autonomously with third-party services.What you’ll learn:How Stripe’s “minions” write 1,300 pull requests per week with minimal human interventionWhy a good developer experience for humans creates better outcomes for AI agentsThe critical role of cloud development environments in unlocking AI-powered engineering velocityThe machine payment protocol that lets AI agents spend money to accomplish tasksThe code review strategy for handling thousands of agent-written PRsWhy non-engineers at Stripe are starting to use minions to ship codeThe future of software businesses built primarily for agent consumers—Brought to you by:Optimizely—Your AI agent orchestration platform for marketing and digital teamsRippling—Stop wasting time on admin tasks, build your startup faster—In this episode, we cover:(00:00) Introduction to Steve(02:39) Stripe’s minions and their effect on Stripe as a whole(04:42) Why activation energy matters more than execution(05:44) What is a minion? The technical architecture(06:52) Demo: Activating a minion from Slack with an emoji(09:04) Why good developer experience benefits both humans and agents(11:22) Walking through the agent loop and system prompts(13:42) Why Stripe chose Goose as their agent harness(16:00) The role of Stripe’s developer productivity team(17:15) Why cloud environments unlock multi-threaded AI engineering(21:14) One-shot prompting: from Slack to shipped PR(22:04) How Stripe handles code review for 1,300 AI-written PRs weekly(23:44) Non-engineers using minions across the company(24:53) Demo: Planning a birthday party with Claude and machine payments(32:15) Quick recap(35:08) The future of ephemeral, API-first businesses for agents(36:36) Lightning round and final thoughts—Detailed workflow walkthroughs from this episode:• How Stripe's AI 'Minions' Ship 1,300 PRs Weekly from a Slack Emoji: https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji• How to Build an Autonomous AI Agent That Pays for Services to Complete Tasks: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-an-autonomous-ai-agent-that-pays-for-services-to-complete-tasks• How to Automate Code Generation from a Slack Message into a Pull Request: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-code-generation-from-a-slack-message-into-a-pull-request—Tools referenced:• Goose (AI agent harness): https://github.com/block/goose• Claude Code: https://claude.ai/code• Cursor: https://cursor.sh/• VS Code: https://code.visualstudio.com/• Slack: https://slack.com/• Browserbase: https://browserbase.com/• Parallel AI: https://www.parallel.ai/• PostalForm: https://postalform.com/• Stripe Climate: https://stripe.com/climate—Other references:• Stripe machine payments: https://docs.stripe.com/payments/machine• Blue-Green Deployment: https://martinfowler.com/bliki/BlueGreenDeployment.html• Git worktrees: https://git-scm.com/docs/git-worktree—Where to find Steve Kaliski:Twitter: https://twitter.com/stevekaliskiLinkedIn: https://www.linkedin.com/in/steve-kaliski-079a7710/—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.

Marco Casalaina, VP of Core AI Products and AI Futurist at Microsoft, demonstrates how he uses AI tools to automate administrative tasks that typically consume valuable time. Rather than using Warp as a coding assistant (its primary marketed purpose), Marco leverages it to manage Azure resources, scan documents, compress videos, and more. He shows how these “micro-agents” can reduce friction in everyday workflows, allowing him to focus on higher-value activities. Marco also demonstrates how Microsoft 365 Copilot and ChatGPT can create triggered workflows that respond to emails or check for information on a schedule, highlighting how the line between consuming and building AI agents is blurring.What you’ll learn:How to use Warp to manage Azure resources and assign permissions without navigating complex web interfacesTechniques for automating document scanning and processing directly from the terminalMethods for analyzing and compressing video files using AI-generated FFmpeg commandsHow to create simple rules that dramatically improve AI performance for specialized tasksWays to build triggered workflows in Microsoft 365 Copilot that automatically respond to emailsHow to configure ChatGPT to perform scheduled tasks like checking for new contentStrategies for creating consistent AI interactions using AutoHotkey shortcuts—Brought to you by:Rovo—AI that knows your businessLovable—Build apps by simply chatting with AI—In this episode, we cover:(00:00) Introduction to Marco Casalaina(02:14) Why Marco chose Warp for administrative tasks(03:57) Demo: Using Warp to manage Azure resources and permissions(06:00) How CLI tools eliminate GUI friction for complex tasks(07:18) Creating rules to improve AI performance for specialized tasks(10:28) Demo: Document scanning automation(13:00) Combining odd and even pages using a Python automation(15:04) The value of ephemeral AI solutions vs. permanent tools(17:12) Video compression using FFmpeg commands(20:22) The concept of “ad hoc agents” for specific tasks(22:31) Demo: Creating triggered workflows in Microsoft 365 Copilot(25:51) Demo: Setting up scheduled tasks in ChatGPT(27:17) How AI automation changes time management(29:14) Teaching AI skills to the next generation(30:30) Strategies for improving AI performance with AutoHotkey—Detailed workflow walkthroughs from this episode:• How Microsoft's AI VP Automates Everything with 5 Micro-Agent Workflows: https://www.chatprd.ai/how-i-ai/microsofts-ai-vp-automates-everything-with-5-micro-agent-workflowsHow to Create an Automated Meeting Scheduler with Microsoft • 365 Copilot: https://www.chatprd.ai/how-i-ai/workflows/how-to-create-an-automated-meeting-scheduler-with-microsoft-365-copilot• How to Scan and Merge Two-Sided Documents into a Single PDF with AI: https://www.chatprd.ai/how-i-ai/workflows/how-to-scan-and-merge-two-sided-documents-into-a-single-pdf-with-ai• How to Automate Azure User Role Management with AI in the Terminal: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-azure-user-role-management-with-ai-in-the-terminal—Tools referenced:• Warp: https://www.warp.dev/• Microsoft Azure: https://azure.microsoft.com/en-us• Azure CLI: https://learn.microsoft.com/en-us/cli/azure/• Microsoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365/copilot• ChatGPT: https://chat.openai.com/—Other references:• NAPS2: https://www.naps2.com/• PyPDF2: https://pypdf2.readthedocs.io/• FFmpeg: https://ffmpeg.org/—Where to find Marco Casalaina:LinkedIn: https://www.linkedin.com/in/marcocasalaina/—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.

Daniel Roth, editor in chief at LinkedIn, went from business writer to iOS app developer, without ever learning how to code. Using Claude Code, Daniel built and shipped multiple production-ready iOS apps to the App Store, including Commutely, a personalized train-tracking app for New York commuters.What you’ll learn:How to set up a dual-agent Claude Code system (builder + reviewer)Why being a “picky customer” is the right mindset for non-technical buildersHow Daniel prioritizes features using AI-ranked impact vs. build timeWhy saving everything as Markdown files creates long-term contextThe importance of branch-based development—even when AI writes the codeHow Daniel ships to the App Store without formal engineering experienceHis end-of-day “What did I drop the ball on?” Copilot workflow—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 Daniel Roth(02:46) Daniel’s AI development workflow overview(05:56) Using Claude to prioritize feature ideas(08:58) Building vs. marketing(09:47) Creating a retention plan for his app(10:38) Introducing Bob the Builder and Ray the Reviewer(13:50) How Bob and Ray work together to build features(14:37) Why Daniel focuses on learning the process(16:34) The importance of using branches for development(17:39) Managing AI agents like managing a team(21:12) Navigating the App Store(23:06) Being a “picky customer” rather than a PM(25:00) Testing in Xcode and shipping to the App Store(28:14) Quick recap(30:00) Creating terminal aliases with Claude(31:38) Demo of his Commutely app(32:10) Using Copilot to manage work responsibilities(35:05) How Daniel talks to AI without personifying it—Tools referenced:• Claude: https://claude.ai/• Claude Code: https://claude.ai/code• Cursor: https://cursor.sh/• Xcode: https://developer.apple.com/xcode/• Canva: https://www.canva.com/• Microsoft Copilot: https://copilot.microsoft.com/• Terminal: https://support.apple.com/guide/terminal/welcome/mac• Obsidian: https://obsidian.md/—Other reference:• Commutely (iOS app): https://apps.apple.com/us/app/commutely/id6755789873—Where to find Daniel Roth:LinkedIn: https://www.linkedin.com/in/danielroth1/Newsletter: https://www.linkedin.com/newsletters/forward-deployed-editor-7378272989982683137/—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.

Most teams are still passing static design files back and forth, and most Figma files are already out of date by the time they reach engineering. Gui Seiz (designer) and Alex Kern (engineer) from Figma walk through the exact workflow their team uses to bridge that gap with AI, live onscreen. They demo how to pull a running web app directly into Figma using the Figma MCP, edit it collaboratively, and push it back to code. The old linear waterfall workflow is gone. What replaces it is a fluid, bidirectional loop where design and code inform each other in real time.What you’ll learn:How to use Figma’s MCP to pull production code directly into Figma filesA workflow for pushing design changes from Figma back into your codebase using Claude Code without manual CSS adjustmentsHow to export multiple code states (like all five states of a signup flow) into Figma so designers can work with what actually exists in productionWhy AI has shifted design work upstream to planning and downstream to craft, eliminating the rushed middle phase of executionHow to create custom skills that automate pre-flight checks, lint fixes, and CI monitoring before pushing code to productionHow to structure your codebase so AI can write 90% of your code more effectively—Brought to you by:Optimizely—Your AI agent orchestration platform for marketing and digital teams—In this episode, we cover:(00:00) Introduction to Gui and Alex from Figma(02:56) How AI has transformed Figma’s internal workflows(05:17) The collapse of linear design-to-code workflows(07:28) Demo: Pulling production code into Figma using MCPs(10:49) Using Figma for precise design manipulation and team collaboration(14:10) Demo: Pushing Figma designs back into code with Claude Code(16:06) How AI has changed the role of software engineers(18:43) The shift to upstream planning and downstream craft(22:31) Demo: Exporting multiple code states back into Figma(25:23) Synchronous vs. asynchronous collaboration with AI(28:00) Eliminating design and engineering toil with AI(29:03) Demo: Custom skills for automating pre-flight checks(34:06) Code first or design first?(35:24) Using AI to learn and explore codebases—Tools referenced:• Figma: https://www.figma.com/• From Claude Code to Figma: Turning production code into editable Figma designs: https://www.figma.com/blog/introducing-claude-code-to-figma/• Codex: https://codex.ai/• Claude Code: https://claude.ai/code• Buildkite: https://buildkite.com/—Other references:• Balsamiq: https://balsamiq.com/—Where to find Gui Seiz:X: https://x.com/guiseizLinkedIn: https://www.linkedin.com/in/guiseiz/—Where to find Alex Kern:X: https://x.com/kernioLinkedIn: https://www.linkedin.com/in/alexanderskern/—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.

Jamey Gannon is an AI creative director who specializes in creating consistent, beautiful brand imagery using AI tools. In this episode, Jamey demonstrates her streamlined workflow for generating cohesive brand assets using Midjourney, Nano Banana, and other AI image tools. She walks through her process of creating mood boards, using style references, developing personalization codes, and strategically iterating to achieve a consistent aesthetic. Rather than relying on complex prompts, Jamey shows how visual references and strategic shortcuts can produce better results with less effort.What you’ll learn:How to create effective mood boards that communicate your desired aesthetic to AI image generation toolsWhy style references (SREFs) often produce more consistent results than general mood boards in MidjourneyA systematic approach to testing and refining your visual styleHow to use personalization codes in Midjourney to develop your own unique aesthetic preferencesTechniques for combining image references, style references, and minimal prompting to achieve consistent brand imageryA workflow for using Nano Banana to fix specific elements in Midjourney-generated images without extensive editingHow to package and deliver your brand imagery system to clients so they can continue generating consistent assets—Brought to you by:Vanta—Automate compliance and simplify securityLovable—Build apps by simply chatting with AI—In this episode, we cover:(00:00) Introduction to Jamey Gannon(02:31) Creating mood boards as the foundation for AI image generation(08:45) Using SREFs for better consistency(11:15) Test prompts for evaluating style consistency(12:33) The iterative process of creating and refining images(24:28) Combining techniques for consistent brand imagery(28:25) Scaling out your aesthetic across different subjects(35:48) Using Nano Banana for targeted image refinements(38:23) Creating realistic AI self-portraits for content(43:04) Building a visual reference library for inspiration(46:50) Troubleshooting techniques when AI isn’t cooperating—Tools referenced:• Midjourney: https://www.midjourney.com/• Nano Banana: https://gemini.google/overview/image-generation/• Flora: https://flora.ai/• Pinterest: https://www.pinterest.com/• Cosmos: https://www.cosmos.so/—Other reference:• Style references (SREFs) in Midjourney: https://docs.midjourney.com/hc/en-us/articles/32180011136653-Style-Reference—Where to find Jamey Gannon:Website: https://www.brand-sprints.com/linksLinkedIn: https://www.linkedin.com/in/jameygannon/X: https://x.com/jameygannonInstagram: https://www.instagram.com/jameygannonMaven Course (get 10% off with this link): https://bit.ly/4b18RfM—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.

Chintan Turakhia is Senior Director of Engineering at Coinbase, where he’s led the transformation of a 1,000-plus-engineer organization to embrace AI tools at scale. When tasked with rewriting Coinbase’s self-custody wallet into a consumer social app in just six to nine months, Chintan turned to AI as a force multiplier. His team has achieved remarkable efficiency gains, including reducing PR review times from 150 hours to just 15 hours, and dramatically compressing the cycle from user feedback to shipped features.What you’ll learn:How to drive AI adoption in large, established engineering organizationsThe “speed run” technique that got 100 engineers to push 70 PRs in 15 minutesHow to identify and replicate the behaviors of AI power usersWhy engineering leaders must get hands-on with AI tools to drive adoptionHow to build custom AI agents that integrate with your existing workflowsThe metrics that actually matter when measuring AI’s impact on engineering velocityHow to compress the cycle from user feedback to shipped features—Brought to you by:WorkOS—Make your app enterprise-ready todayRovo—AI that knows your business—In this episode, we cover:(00:00) Introduction to Chintan(02:38) How Coinbase approached rewriting their app with AI assistance(08:00) The importance of leadership conviction and hands-on demonstration(10:30) The “PR speed run” technique that transformed team adoption(17:57) Measuring success(19:20) Demo: Real-time feedback-to-feature implementation(23:14) Using Cursor to analyze AI adoption patterns(33:15) Quick recap and appreciation(36:00) Demo: Building a live feedback capture system using AI transcription(40:50) Using custom Slack bots to automate engineering workflows(47:10) Advice for driving AI adoption within your organization(50:00) Personal use case: AI for wine selection based on taste preferences(55:23) Lightning round and final thoughts—Tools referenced:• Cursor: https://cursor.sh/• Linear: https://linear.app/• Slack: https://slack.com/• ChatGPT: https://chat.openai.com/• Claude: https://claude.ai/• GitHub Copilot: https://github.com/features/copilot—Other references:• Coinbase: https://www.coinbase.com/• React Native: https://reactnative.dev/• How custom GPTs can make you a better manager | Hilary Gridley (Head of Core Product at Whoop): https://www.lennysnewsletter.com/p/how-custom-gpts-can-make-you-a-better-manager—Where to find Chintan Turakhia:LinkedIn: https://www.linkedin.com/in/chintanturakhia/X: https://x.com/chintanturakhiaBase App (formerly Coinbase Wallet): https://base.app/—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.

Jesse Genet is a homeschooling parent and entrepreneur who runs her household with five specialized OpenClaw agents. She layers them on top of her Obsidian “second brain,” deploys each on its own Mac Mini, and assigns every agent a distinct role—homeschool, finance, scheduling, development, and operations—so each one operates with clear scope and responsibility.What you’ll learn:How Jesse set up five OpenClaw agents, each with its own role, persona, SOUL.md file, and dedicated Mac MiniThe workflow for photographing an entire curriculum book and having an agent generate formatted, ready-to-teach lesson plans from the imagesUsing a coding agent to build a custom kids’ TV app from scratch and ship it to a real television in four days (with zero prior terminal experience)Why Jesse treats agent onboarding like employee onboardingThe “decision file” trick and other incantations for managing agents that actually stickWhere multi-agent collaboration breaks down, and why no current messaging platform handles agent-to-agent handoffs wellHow photographing every toy, book, and supply in the house lets the AI recommend real physical materials during lesson planningThe hands-free printing loop that took Jesse from scan → upload → email → print to “Sylvie, print this” in 30 seconds flat—Brought to you by:Optimizely—Your AI agent orchestration platform for marketing and digital teams—In this episode, we cover:(00:00) Meet Jesse and her “after Claw” life(02:30) Layering OpenClaw on top of Obsidian(04:44) Logging homeschool lessons automatically(07:12) Turning books into a structured curriculum(13:09) Using SOUL.md files to give each agent a personality(14:39) Running multiple specialized AI agents(16:43) Agent collaboration(18:19) Partitioning data across Mac Minis(27:00) Building a custom YouTube app with AI(37:00) Creating a physical inventory from cupboard photos(41:00) Printing from voice: reducing friction(44:00) Managing agent memory and decision files—Tools referenced:• OpenClaw: https://openclaw.ai/• Obsidian: https://obsidian.md• Slack: https://slack.com• QuickBooks: https://quickbooks.intuit.com• Google Gemini: https://gemini.google.com/• Mac Mini: https://www.apple.com/mac-mini/—Other references:• Claude Code for product managers: research, writing, context libraries, custom to-do system, and more | Teresa Torres: https://www.lennysnewsletter.com/p/claude-code-for-product-managers—Where to find Jesse Genet:X: https://x.com/jessegenetLinkedIn: https://www.linkedin.com/in/jessegenet/—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 Lovin is a designer at Notion AI who has transformed how the design team builds prototypes, by creating a shared code environment powered by Claude Code. Instead of designers working in isolated repositories or limited to static Figma designs, Brian built a collaborative “prototype playground” where the entire team can create, share, and iterate on functional prototypes. In this episode, Brian demonstrates how AI-assisted coding has dramatically accelerated the design process and why code-based prototyping is essential for building AI-powered products.What you’ll learn:How Brian built a shared Next.js app that serves as a collaborative prototyping environment for Notion’s design teamWhy encountering “reality” early in the design process leads to better productsHow to use Claude Code’s “plan mode” to get better results when prototypingThe power of custom Claude slash commands and skills to automate repetitive tasksHow to transform Figma designs into working code with a single promptWhy AI-powered products can’t be effectively designed in static tools like FigmaBrian’s rule for working with AI: “When Claude asks you to do something, teach it to do that thing itself”—Brought to you by:WorkOS—Make your app enterprise-ready todayOrkes—The enterprise platform for reliable applications and agentic workflows—In this episode, we cover:(00:00) Introduction to Brian(02:36) Building for B2B SaaS(04:42) Notion’s prototype playground: what it is and how it works(08:01) The technical background of designers using the playground(10:52) Demo: building a podcast player prototype(16:00) Actionable tips for better Claude Code results(20:16) Analyzing the result(20:30) Creating slash commands to simplify the workflow(23:03) Turning Figma designs into production-ready code(25:06) MCP frustrations and tips(30:54) Demo: creating a custom “find icon” skill(35:03) Demo: Creating a deploy command to simplify GitHub workflows(41:09) Quick recap(41:59) How code-based prototyping is changing design at Notion(46:48) Brian’s tool preferences(48:42) Prompting techniques when AI is not listening—Tools referenced:• Claude Code: https://claude.ai/• Cursor: https://cursor.sh/• Next.js: https://nextjs.org/• Figma: https://figma.com/• Monologue: https://www.monologue.to/• GitHub: https://github.com/• GitHub Desktop: https://desktop.github.com/• Tailwind CSS: https://tailwindcss.com/• Bun: https://bun.sh/—Other references:• Claude Skills explained: How to create reusable AI workflows: https://www.lennysnewsletter.com/p/claude-skills-explained—Where to find Brian Lovin:Website: https://brianlovin.com/LinkedIn: linkedin.com/in/brianlovinX: https://twitter.com/brian_lovin—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.

Joe McCormick is a principal software engineer at Babylist who lost most of his central vision due to a rare genetic disorder right before starting college. He pivoted from mechanical engineering to computer science and now leads AI enablement at Babylist. Joe demonstrates how he uses AI to build micro Chrome extensions that make his everyday work and life more accessible, showing how personal software can address accessibility needs that mainstream products often overlook.What you’ll learn:How to build custom Chrome extensions in under 25 minutes using Claude CodeA practical workflow for creating AI-powered accessibility toolsHow to use Claude Skills to accelerate repetitive development tasksTechniques for making Claude Code more screen reader accessibleWhy personal software is becoming increasingly viable with AI assistanceHow multimodal AI is transforming accessibility for visually impaired users—Brought to you by:Tines—Start building intelligent workflows today—Detailed workflow walkthroughs from this episode:• How I AI: Building Custom AI Accessibility Tools for Slack with Joe McCormick & Claude Code: https://www.chatprd.ai/how-i-ai/custom-ai-accessibility-tools-for-slack-claude-code• Build a Slack Link Summarizer from Scratch using Claude Code: https://www.chatprd.ai/how-i-ai/workflows/slack-link-summarizer-using-claude-code• Create a Fast, Accessible AI Spell Checker for Any Website: https://www.chatprd.ai/how-i-ai/workflows/accessible-ai-spell-checker-for-any-website• Build a Custom AI Tool to Describe Images in Slack: https://www.chatprd.ai/how-i-ai/workflows/ai-tool-to-describe-images-in-slack—In this episode, we cover:(00:00) Introduction to Joe and his background(02:34) Joe’s journey into computer science after vision loss(04:50) The concept of personal software for accessibility(06:09) Demo of image description Chrome extension for Slack(10:40) Demo of AI-powered spell checker extension(13:12) The efficiency of keyboard shortcuts for accessibility(14:37) Live building a link summarization extension(20:28) Using Claude Skills to extract common patterns(25:30) Reviewing and modifying the development plan(27:45) Removing cognitive friction for users through repeating patterns(31:40) How to get fluent with AI tools(34:55) Loading the extension into Chrome in developer mode(36:19) Testing and debugging the extension(40:44) Quick recap(42:12) Lightning round and final thoughts—Tools referenced:• Claude Code: https://claude.ai/code• VS Code: https://code.visualstudio.com/• Gemini: https://gemini.google.com/• ChatGPT: https://chat.openai.com/• Meta Ray-Ban Smart Glasses: https://www.meta.com/smart-glasses/—Other references:• Chrome Extensions Documentation: https://developer.chrome.com/docs/extensions/• ARIA (Accessible Rich Internet Applications): https://developer.mozilla.org/en-US/docs/Web/Accessibility/ARIA• Windows Subsystem for Linux: https://learn.microsoft.com/en-us/windows/wsl/• Screen Readers: https://www.afb.org/blindness-and-low-vision/using-technology/assistive-technology-products/screen-readers• Claude Skills explained: How to create reusable AI workflows:https://www.lennysnewsletter.com/p/claude-skills-explained—Where to find Joe McCormick:LinkedIn: https://www.linkedin.com/in/joemccormickjr/Company: https://www.babylist.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.