How I AI

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:
How CJ built Flowy, a custom visual planning tool that converts JSON files into interactive mockups and flowcharts
Why visual planning tools are more effective than ASCII diagrams for complex UI and animation workflows
How to create and use Claude Code skills to extend your development environment
Using model-to-model comparison (Claude + Codex) to improve code quality
How to build your own ecosystem of tools around Claude Code
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
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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:
How v0’s new Git workflow integration enables anyone to contribute production-ready code changes
Why skills.sh became a viral hub for AI skills, with 500 new submissions per hour
How to implement features in v0 that consider production concerns like abuse prevention and rate limiting
The benefits of branch previews for testing changes in a production-like environment before merging
How v0 eliminates development environment setup challenges for non-technical team members
Why the “terminal core” design aesthetic became central to skills.sh’s interface
How Vercel uses v0 internally to democratize code contributions across teams
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].

Reid Robinson, Principal AI Product Strategist at Zapier, shares how he uses Model Context Protocols (MCPs) to automate tedious tasks and create powerful workflows. He demonstrates practical workflows that combine Zapier’s more than 8,000 app connections with AI tools like Claude to create systems that work while he sleeps.

What you’ll learn:
How to use Zapier’s MCP server to create custom collections of tools that work seamlessly with Claude, ChatGPT, and other AI assistants
A workflow for using Claude Projects to provide detailed instructions for tool usage, improving reliability and consistency
How to automate CRM updates and meeting preparation by connecting AI to your calendar, notes, and internal knowledge bases
A system for creating a virtuous cycle of customer feedback by automatically analyzing support tickets and updating knowledge bases
Why thinking about “what your AI could do while you sleep” is a powerful framework for identifying high-impact automation opportunities
Personal use cases, including family calendar management and creating custom songs that demonstrate AI’s ability to bring joy beyond work
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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 Reid Robinson and his role at Zapier
(02:41) Understanding MCPs as app integrations for AI tools
(04:05) How Zapier’s approach to MCPs works with over 8,000 apps
(09:00) Using Claude Projects to improve tool usage instructions
(12:05) Post-meeting notes management
(15:25) Comparing deterministic workflows vs. agentic instructions
(18:15) Reid’s idea jammer
(20:04) Building a customer interview preparation workflow
(23:10) Using Gemini for processing file-based data
(25:05) Creating a virtuous cycle of customer feedback analysis
(29:16) The “if you could run ChatGPT in your sleep” framework
(31:48) Quick recap
(33:03) Personal use cases
(37:16) Using Notebook AI to prepare personalized interview prep
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Tools referenced:
• Reid’s Resources for How I AI: https://how-i-ai-reid.zapier.app/resources
• Claude: https://claude.ai/
• Zapier: https://zapier.com/
• Zapier MCP: https://zapier.com/mcp
• Granola: https://www.granola.ai/
• Coda: https://coda.io/
• Suno: https://suno.ai/
• Notebook AI: https://www.notebook.ai/
• Gemini: https://gemini.google.com/
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Other references:
• HubSpot: https://www.hubspot.com/
• Databricks: https://www.databricks.com/
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Where to find Reid Robinson:
LinkedIn: https://www.linkedin.com/in/reidtrobinson/
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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].

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

John Lindquist is the co-founder of egghead.io and an expert in leveraging AI tools for professional software development. In this episode, John shares advanced techniques for using AI coding tools like Claude Code and Cursor that go far beyond basic prompting. He demonstrates how senior engineers can use mermaid diagrams for context loading, create custom hooks for automated code quality checks, and build efficient command-line tools that streamline AI workflows.

What you’ll learn:
How to use mermaid diagrams to preload context into Claude Code for faster, more accurate coding assistance
Creating custom hooks in Claude Code to automatically check for TypeScript errors and commit working code
Building efficient command-line aliases and tools to streamline your AI workflows
Techniques for using AI to generate documentation that works for both humans and machines
How to leverage AI for code investigation and orientation when tackling unfamiliar codebases
Strategies for resetting AI conversations when they go off track
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Brought to you by:
WorkOS—Make your app enterprise-ready today
Tines—Start building intelligent workflows today
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In this episode, we cover:
(00:00) Introduction to John Lindquist
(03:15) Using context and diagrams to provide context to AI tools
(05:38) Demo: Mermaid diagrams
(06:48) Preloading context with system prompts in Claude Code
(10:30) The rise of specialized file formats for AI consumption
(13:23) Mermaid diagram use cases
(19:01) Demo: Creating aliases for common AI commands
(21:05) Building custom command-line tools for AI workflows
(26:39) Demo: Setting up stop hooks for automated code quality checks
(35:16) Investing in quality outputs
(36:40) Additional use cases for hooks beyond code quality
(39:19) Quick review
(41:14) Terminal UI vs. IDE
(45:35) Selling AI to skeptical teams
(51:57) Prompting reset tricks
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Tools referenced:
• Claude Code: https://claude.ai/
• Cursor: https://cursor.sh/
• Gemini: https://gemini.google.com/
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Other references:
• Zsh: https://www.zsh.org/
• GitHub: https://github.com/
• TypeScript: https://www.typescriptlang.org/
• Bun: https://bun.sh/
• Claude hooks: https://code.claude.com/docs/en/hooks
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Where to find John Lindquist:
Website: https://egghead.io
Newsletter: https://egghead.io/newsletters/ai-dev-essentials
LinkedIn: linkedin.com/in/john-lindquist-84230766
X: https://x.com/johnlindquist
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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].