How I AI

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:

1. How to set up a dual-agent Claude Code system (builder + reviewer)
2. Why being a “picky customer” is the right mindset for non-technical builders
3. How Daniel prioritizes features using AI-ranked impact vs. build time
4. Why saving everything as Markdown files creates long-term context
5. The importance of branch-based development—even when AI writes the code
6. How Daniel ships to the App Store without formal engineering experience
7. His end-of-day “What did I drop the ball on?” Copilot workflow

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Vanta—Automate compliance and simplify security

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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

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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/

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Other reference:

• Commutely (iOS app): https://apps.apple.com/us/app/commutely/id6755789873

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Where to find Daniel Roth:

LinkedIn: https://www.linkedin.com/in/danielroth1/

Newsletter: https://www.linkedin.com/newsletters/forward-deployed-editor-7378272989982683137/

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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 [email protected].

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:

1. How to use Figma’s MCP to pull production code directly into Figma files
2. A workflow for pushing design changes from Figma back into your codebase using Claude Code without manual CSS adjustments
3. How to export multiple code states (like all five states of a signup flow) into Figma so designers can work with what actually exists in production
4. Why AI has shifted design work upstream to planning and downstream to craft, eliminating the rushed middle phase of execution
5. How to create custom skills that automate pre-flight checks, lint fixes, and CI monitoring before pushing code to production
6. How 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

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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/

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Other references:

• Balsamiq: https://balsamiq.com/

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Where to find Gui Seiz:

X: https://x.com/guiseiz

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

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Where to find Alex Kern:

X: https://x.com/kernio

LinkedIn: 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 [email protected].

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:

1. How to create effective mood boards that communicate your desired aesthetic to AI image generation tools
2. Why style references (SREFs) often produce more consistent results than general mood boards in Midjourney
3. A systematic approach to testing and refining your visual style
4. How to use personalization codes in Midjourney to develop your own unique aesthetic preferences
5. Techniques for combining image references, style references, and minimal prompting to achieve consistent brand imagery
6. A workflow for using Nano Banana to fix specific elements in Midjourney-generated images without extensive editing
7. How 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 security

Lovable—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

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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/

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Other reference:

• Style references (SREFs) in Midjourney: https://docs.midjourney.com/hc/en-us/articles/32180011136653-Style-Reference

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Where to find Jamey Gannon:

Website: https://www.brand-sprints.com/links

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

X: https://x.com/jameygannon

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

Maven 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 [email protected].

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:

1. How to drive AI adoption in large, established engineering organizations
2. The “speed run” technique that got 100 engineers to push 70 PRs in 15 minutes
3. How to identify and replicate the behaviors of AI power users
4. Why engineering leaders must get hands-on with AI tools to drive adoption
5. How to build custom AI agents that integrate with your existing workflows
6. The metrics that actually matter when measuring AI’s impact on engineering velocity
7. How to compress the cycle from user feedback to shipped features

—

Brought to you by:

WorkOS—Make your app enterprise-ready today

Rovo—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

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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

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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

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Where to find Chintan Turakhia:

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

X: https://x.com/chintanturakhia

Base 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 [email protected].

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:

1. How Jesse set up five OpenClaw agents, each with its own role, persona, SOUL.md file, and dedicated Mac Mini
2. The workflow for photographing an entire curriculum book and having an agent generate formatted, ready-to-teach lesson plans from the images
3. Using 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)
4. Why Jesse treats agent onboarding like employee onboarding
5. The “decision file” trick and other incantations for managing agents that actually stick
6. Where multi-agent collaboration breaks down, and why no current messaging platform handles agent-to-agent handoffs well
7. How photographing every toy, book, and supply in the house lets the AI recommend real physical materials during lesson planning
8. The hands-free printing loop that took Jesse from scan → upload → email → print to “Sylvie, print this” in 30 seconds flat

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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

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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/

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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

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Where to find Jesse Genet:

X: https://x.com/jessegenet

LinkedIn: 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 [email protected].

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:

1. How Brian built a shared Next.js app that serves as a collaborative prototyping environment for Notion’s design team
2. Why encountering “reality” early in the design process leads to better products
3. How to use Claude Code’s “plan mode” to get better results when prototyping
4. The power of custom Claude slash commands and skills to automate repetitive tasks
5. How to transform Figma designs into working code with a single prompt
6. Why AI-powered products can’t be effectively designed in static tools like Figma
7. Brian’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 today

Orkes—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

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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/

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Other references:

• Claude Skills explained: How to create reusable AI workflows: https://www.lennysnewsletter.com/p/claude-skills-explained

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Where to find Brian Lovin:

Website: https://brianlovin.com/

LinkedIn: linkedin.com/in/brianlovin

X: 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 [email protected].

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:

1. How to build custom Chrome extensions in under 25 minutes using Claude Code
2. A practical workflow for creating AI-powered accessibility tools
3. How to use Claude Skills to accelerate repetitive development tasks
4. Techniques for making Claude Code more screen reader accessible
5. Why personal software is becoming increasingly viable with AI assistance
6. How multimodal AI is transforming accessibility for visually impaired users

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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

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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

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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/

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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

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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 [email protected].

I put the newest AI coding models from OpenAI and Anthropic head-to-head, testing them on real engineering work I’m actually doing. I compare GPT-5.3 Codex with Opus 4.6 (and Opus 4.6 Fast) by asking them to redesign my marketing website and refactor some genuinely gnarly components. Through side-by-side experiments, I break down where each model shines—creative development versus code review—and share how I’m thinking about combining them to build a more effective AI engineering stack.

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What you’ll learn:

1. The strengths and weaknesses of OpenAI’s Codex vs. Anthropic’s Opus for different coding tasks
2. How I shipped 44 PRs containing 98 commits across 1,088 files in just five days using these models
3. Why Codex excels at code review but struggles with creative, greenfield work
4. The surprising way Opus and Codex complement each other in a real-world engineering workflow
5. How to use Git concepts like work trees to maximize productivity with AI coding assistants
6. Why Opus 4.6 Fast might be worth the 6x price increase (but be careful with your token budget)

—

Brought to you by:

WorkOS—Make your app enterprise-ready today

—

Detailed workflow walkthroughs from this episode:

• How I AI: GPT-5.3 Codex vs. Claude Opus 4.6—Shipping 44 PRs in 5 Days: https://www.chatprd.ai/how-i-ai/gpt-5-3-codex-vs-claude-opus-4-6

• How to Combine Claude Opus and GPT-5.3 Codex for High-Velocity Code Refactoring: https://www.chatprd.ai/how-i-ai/workflows/how-to-combine-claude-opus-and-gpt-5-3-codex-for-high-velocity-code-refactoring

• How to Redesign a Marketing Website Using Claude Opus 4.6 for Creative Development: https://www.chatprd.ai/how-i-ai/workflows/how-to-redesign-a-marketing-website-using-claude-opus-4-6-for-creative-development

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In this episode, we cover:

(00:00) Introduction to new AI coding models

(02:13) My test methodology for comparing models

(03:30) Codex’s unique features: Git primitives, skills, and automations

(09:05) Testing GPT-5.2 Codex on a website redesign task

(10:40) Challenges with Codex’s literal interpretation of prompts

(15:00) Comparing the before and after with Codex

(16:23) Testing Opus 4.6 on the same website redesign task

(20:56) Comparing the visual results of both models

(21:30) Real-world engineering impact: 44 PRs in five days

(23:03) Refactoring components with Opus 4.6

(24:30) Using Codex for code review and architectural analysis

(26:55) Cost considerations for Opus 4.6 Fast

(28:52) Conclusion

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Tools referenced:

• OpenAI’s GPT-5.3 Codex: https://openai.com/index/introducing-gpt-5-3-codex/

• Anthropic’s Claude Opus 4.6: https://www.anthropic.com/news/claude-opus-4-6

• Cursor: https://cursor.sh/

• GitHub: https://github.com/

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Other references:

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

• Git: https://git-scm.com/

• Bugbot: https://cursor.com/bugbot

—

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 [email protected].

CJ Hess is a software engineer at Tenex who has built some of the most useful tools and workflows for being a “real AI engineer.” In this episode, CJ demonstrates his custom-built tool, Flowy, that transforms Claude’s ASCII diagrams into interactive visual mockups and flowcharts. He also shares his process for using model-to-model comparison to ensure that his AI-generated code is high-quality, and why he believes we’re just at the beginning of a revolution in how developers interact with AI.

What you’ll learn:

1. How CJ built Flowy, a custom visual planning tool that converts JSON files into interactive mockups and flowcharts
2. Why visual planning tools are more effective than ASCII diagrams for complex UI and animation workflows
3. How to create and use Claude Code skills to extend your development environment
4. Using model-to-model comparison (Claude + Codex) to improve code quality
5. How to build your own ecosystem of tools around Claude Code
6. The value of bypassing permissions in controlled environments to speed up development

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Brought to you by:

Orkes—The enterprise platform for reliable applications and agentic workflows

Rovo—AI that knows your business

—

Detailed workflow walkthroughs from this episode:

• How I AI: CJ Hess on Building Custom Dev Tools and Model-vs-Model Code Reviews: https://www.chatprd.ai/how-i-ai/cj-hess-tenex-custom-dev-tools-and-model-vs-model-code-reviews

• Implement Model-vs-Model AI Code Reviews for Quality Control: https://www.chatprd.ai/how-i-ai/workflows/implement-model-vs-model-ai-code-reviews-for-quality-control

• Develop Features with AI Using Custom Visual Planning Tools: https://www.chatprd.ai/how-i-ai/workflows/develop-features-with-ai-using-custom-visual-planning-tools

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In this episode, we cover:

(00:00) Introduction to CJ Hess

(02:48) Why CJ prefers Claude Code for development

(04:46) The evolution of developer environments with AI

(06:50) Planning workflows and the limitations of ASCII diagrams

(08:23) Introduction to Flowy, CJ’s custom visualization tool

(11:54) How Flowy compares to mermaid diagrams

(15:25) Demo: Using Flowy

(19:30) Examining Flowy’s skill structure

(23:27) Reviewing the generated flowcharts and diagrams

(28:34) The cognitive benefits of visual planning vs. text-based planning

(31:38) Generating UI mockups with Flowy

(33:30) Building the feature directly from flowcharts and mockups

(35:40) Quick recap

(36:51) Using model-to-model review with Codex (Carl)

(41:52) The benefits of using AI for code review

(45:13) Lightning round and final thoughts

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Tools referenced:

• Claude Code: https://claude.ai/code

• Claude Opus 4.5: https://www.anthropic.com/news/claude-opus-4-5

• Cursor: https://cursor.sh/

• Obsidian: https://obsidian.md/

• GPT-5.2 Codex: https://openai.com/index/introducing-gpt-5-2-codex/

• Google’s Project Genie: https://labs.google/projectgenie

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Other references:

• Mermaid diagrams: https://mermaid.js.org/

• Figma: https://www.figma.com/

• Excalidraw: https://excalidraw.com/

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

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Where to find CJ Hess:

LinkedIn: https://www.linkedin.com/in/cj-hess-connexwork/

X: https://x.com/seejayhess

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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

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

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:

1. How v0’s new Git workflow integration enables anyone to contribute production-ready code changes
2. Why skills.sh became a viral hub for AI skills, with 500 new submissions per hour
3. How to implement features in v0 that consider production concerns like abuse prevention and rate limiting
4. The benefits of branch previews for testing changes in a production-like environment before merging
5. How v0 eliminates development environment setup challenges for non-technical team members
6. Why the “terminal core” design aesthetic became central to skills.sh’s interface
7. How Vercel uses v0 internally to democratize code contributions across teams
8. The future of AI at Vercel, including upcoming tools for text-to-SVG and video generation

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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

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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/

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Other references:

• v0 Chess Match: https://v0-chess-match.app/

• React Native: https://reactnative.dev/

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Where to find Guillermo Rauch:

LinkedIn: linkedin.com/in/rauchg

X: https://twitter.com/rauchg

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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

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