How I AI

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:

1. How to use Zapier’s MCP server to create custom collections of tools that work seamlessly with Claude, ChatGPT, and other AI assistants
2. A workflow for using Claude Projects to provide detailed instructions for tool usage, improving reliability and consistency
3. How to automate CRM updates and meeting preparation by connecting AI to your calendar, notes, and internal knowledge bases
4. A system for creating a virtuous cycle of customer feedback by automatically analyzing support tickets and updating knowledge bases
5. Why thinking about “what your AI could do while you sleep” is a powerful framework for identifying high-impact automation opportunities
6. 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:

1. How to install and set up Clawdbot (and why it’s not as simple as the “one-liner” suggests)
2. The security implications of giving an autonomous AI access to your computer and accounts
3. How to safely limit Clawdbot’s permissions while still making it useful
4. Why Clawdbot struggles with basic time concepts but excels at research tasks
5. The future of AI assistants—and who might build the consumer-friendly version
6. How to use voice messaging with AI agents for on-the-go productivity
7. 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

—

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:

1. How to use mermaid diagrams to preload context into Claude Code for faster, more accurate coding assistance
2. Creating custom hooks in Claude Code to automatically check for TypeScript errors and commit working code
3. Building efficient command-line aliases and tools to streamline your AI workflows
4. Techniques for using AI to generate documentation that works for both humans and machines
5. How to leverage AI for code investigation and orientation when tackling unfamiliar codebases
6. 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].

Teresa Torres is the author of Continuous Discovery Habits and an internationally acclaimed speaker and coach. In this episode, Teresa demonstrates how she’s built a personalized productivity system using Claude Code to manage her tasks, automate research collection, and improve her writing. She shows how non-developers can leverage AI tools to create personalized workflows that match their unique needs and thinking style.

What you’ll learn:

1. How Teresa built a personalized task management system in Claude Code that matches her exact workflow needs
2. Why she moved from Trello to a markdown-based system that gives her complete control and searchability
3. How she automated academic research collection with daily digests of relevant papers
4. Her strategy for organizing context files to make Claude more effective without overwhelming it
5. Why “pair programming” with Claude has become her approach to everything from writing to task management
6. How she uses Claude as a writing partner while maintaining her authentic voice
7. The power of slash commands and automation to reduce friction in daily workflows

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Brought to you by:

Brex—The intelligent finance platform built for founders

Graphite—The next generation of code review

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In this episode, we cover:

(00:00) Introduction to Teresa Torres

(02:10) Why Claude Code became Teresa’s productivity tool of choice

(03:00) The evolution from browser-based AI to terminal-based workflows

(04:14) Demo: Creating a personalized task management system

(07:52) How the task system works with markdown files and Obsidian

(12:56) Quick recap

(14:13) Taking notes within tasks for better searchability

(15:54) Demo: Automated research digest workflow

(19:32) How the research plugin searches and summarizes academic papers

(24:43) Filtering overwhelming information sources

(29:00) Using small, focused context files instead of one large document

(32:58) Claude as a writing partner: review, research, and refinement

(35:34) Recap of workflows and lightning round

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Tools referenced:

• Claude Code: https://claude.ai/

• Obsidian: https://Obsidian.md/

• VS Code: https://code.visualstudio.com/

• Descript: https://www.descript.com/

• ChatGPT: https://chat.openai.com/

• Trello: https://trello.com/

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Other references:

• Continuous Discovery Habits: https://www.producttalk.org/continuous-discovery-habits/

• Google Scholar: https://scholar.google.com/

• Claude Code: What It Is, How It’s Different, and Why Non-Technical People Should Use It: https://www.producttalk.org/claude-code-what-it-is-and-how-its-different

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Where to find Teresa Torres:

Blog: https://producttalk.org/

Podcast: https://justnowpossible.com/

Book: https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309

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

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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].

Alexander Embiricos, the product lead for Codex at OpenAI, shares practical workflows for getting the most out of this AI coding agent. In this episode, he demonstrates how both non-technical users and experienced engineers can leverage Codex to accelerate development, from making simple code changes to building production-ready applications. Alex walks through real examples of using Codex in VS Code and terminal environments, implementing parallel workflows with Git worktrees, and creating detailed implementation plans for complex projects. He also reveals how OpenAI uses Codex internally, including how they built the Sora Android app in just 28 days, and offers insights on automated code review and the future of AI-assisted development.

What you’ll learn:

1. How to set up and use Codex in VS Code and terminal environments for both simple and complex coding tasks
2. A practical workflow for running multiple Codex instances in parallel using Git worktrees to avoid conflicts
3. How to create detailed implementation plans using the Plans.md technique for complex engineering projects
4. Why context is critical when prompting Codex—and how to provide the right information for better results
5. How OpenAI uses automated code review to accelerate development while maintaining high quality standards
6. The key differences between vibe coding for prototypes versus building production-ready applications with AI
7. How the new GPT-5.2 model improves Codex’s capabilities with faster reasoning and better problem-solving

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Brought to you by:

Brex—The intelligent finance platform built for founders

Graphite—Your AI code review platform

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Detailed workflow walkthrough from this episode:

https://chatprd.ai/how-i-ai/advanced-codex-workflows-with-openai-alex-embiricos

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In this episode, we cover:

(00:00) Introduction to Alex and Codex

(02:06) Getting started with Codex

(04:54) Using Codex for parallel tasks

(07:34) Understanding Git worktrees

(09:51) Terminal shortcuts and command-line efficiency

(12:16) How OpenAI built the Sora Android app with Codex

(15:37) Using PLANS.md for problem solving

(17:57) The importance of high agency

(22:22) Deciding between what needs a plan and what doesn’t

(26:42) How to multiply the impact of Codex

(28:08) Implementing automated code review with GitHub

(31:58) Delivering the benefits of AGI to all humanity

(34:35) Accelerating developer productivity

(36:38) Recap and final thoughts

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Tools referenced:

• Codex: https://openai.com/blog/openai-codex

• VS Code: https://code.visualstudio.com/

• Cursor: https://cursor.com/

• Git: https://git-scm.com/

• GitHub: https://github.com/

• Atlas: https://openai.com/atlas

• ChatGPT: https://chat.openai.com/

• Slack: https://slack.com/

• Linear: https://linear.app/

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Other references:

• Sora Android app: https://openai.com/blog/sora

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

• SWE-bench: https://openai.com/index/introducing-swe-bench-verified/

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Where to find Alexander Embiricos:

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

X: https://x.com/embirico

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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].

Wade Foster is the co-founder and CEO of Zapier. In this episode, Wade shows how he uses meeting transcripts, Zapier agents, and even Grok to analyze company culture, evaluate interview candidates, and source talent from unexpected places. He explains why CEOs need to lead by example when it comes to AI adoption and shares practical workflows that any executive can implement to make hiring more effective and efficient.

What you’ll learn:

1. How to use meeting transcripts to extract your company’s “unspoken culture” and compare it against your stated values
2. A workflow for creating AI interview evaluators that assess candidates against your job descriptions and company values
3. How to use Zapier agents to provide objective feedback on candidate interviews while checking your own biases
4. Why CEOs should participate in AI “hackathons” and “show and tells” rather than just delegating AI adoption
5. A surprising technique for using Grok to find “diamonds in the rough” talent outside traditional recruiting channels
6. How AI enables companies to complete tasks that were previously not economically viable

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This entire episode is brought to you by:

Brex—The intelligent finance platform built for founders

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In this episode, we cover:

(00:00) Introduction to Wade Foster

(02:32) Zapier’s AI adoption

(06:50) Creating AI fluency rubrics

(08:37) Using Granola to extract company culture from meeting transcripts

(10:49) Practical use cases for company culture rubrics

(13:38) Building an AI interview evaluation agent in Zapier

(16:50) Using Copilot to improve agent prompts

(18:49) Ideas for enhancing the interview agent

(22:31) Mistakes people make when using agents

(25:11) Using Grok to find talent on social media platforms

(33:39) Recap of AI workflows for recruiting and hiring

(34:40) Lightning round and final thoughts

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Tools referenced:

• Zapier: https://zapier.com/

• Zapier Agents: https://zapier.com/agents

• Granola: https://granola.ai/

• Grok: https://grok.x.ai/

• ChatGPT: https://chat.openai.com/

• Copilot: https://copilot.microsoft.com/

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Other references:

• Zapier values: https://zapier.com/about

• How Zapier’s EA built an army of AI interns to automate meeting prep, strengthen team culture, and scale internal alignment | Cortney Hickey: https://www.lennysnewsletter.com/p/how-zapiers-ea-built-an-army-of-ai

• How this CEO turned 25,000 hours of sales calls into a self-learning go-to-market engine | Matt Britton (Suzy): https://www.lennysnewsletter.com/p/how-this-ceo-turned-25000-hours-of

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Where to find Wade Foster:

Zapier: https://zapier.com/

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

X: https://twitter.com/wadefoster

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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].

Rachel Wolan, the chief product officer at Webflow, has embraced AI not just as a product leader but as a hands-on builder. A coder since age 16, Rachel has returned to her technical roots by creating a custom AI chief-of-staff application that helps manage her executive workload. In this episode, she demonstrates how she uses personal AI software to prep for meetings, triage her calendar, manage emails, and even get brutally honest feedback about how she’s spending her time.

What you’ll learn:

1. How Rachel built a custom AI chief-of-staff application that integrates with her calendar, email, and more
2. Why building personal software can be a gateway to understanding AI’s capabilities for executives
3. How her AI agents help her prep for podcasts, dinners, and meetings with just-in-time information
4. The technical approach to building personal AI software using markdown files, API tokens, and multiple LLM interfaces
5. How Rachel organized company-wide “builder days” that dramatically increased AI tool adoption across her organization
6. Why she believes executives must lead by example in AI adoption to authentically drive organizational change

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Brought to you by:

Graphite—Your AI code review platform

Atlassian for Startups —From MVP to IPO

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In this episode, we cover:

(00:00) Introduction to Rachel Wolan

(02:26) Why Rachel started leaning into AI

(06:26) Building an AI chief of staff

(08:17) Prepping for the podcast

(10:00) Rachel’s morning flow with her AI chief of staff

(14:14) Designing a personalized interface with custom note cards

(16:34) Getting “brutal truth” feedback from your AI assistant

(19:34) Email triage and management workflows

(23:31) Prepping for networking dinners and events

(28:18) The result of building an AI chief of staff

(30:09) Organizing “builder days” to drive AI adoption

(35:38) Measuring the impact of AI adoption initiatives

(38:00) Lightning round and final thoughts

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Tools referenced:

• Claude: https://claude.ai/

• Claude Code: https://claude.ai/code

• Cursor: https://cursor.com/

• Google Calendar API: https://developers.google.com/calendar

• Gmail API: https://developers.google.com/gmail

• Webflow: https://webflow.com/

• Figma: https://www.figma.com/

• Make: https://www.make.com/

• Hex: https://hex.tech/

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Where to find Rachel Wolan:

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

X: https://x.com/rachelwolan

Webflow: https://webflow.com

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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].

Brian Greenbaum is a Senior Staff Product Designer at Pendo who led a company-wide AI transformation after a personal epiphany while on paternity leave. After experiencing the power of AI coding tools firsthand, he created a structured approach to help his entire product organization adopt AI. In this episode, Brian shares his complete playbook for driving AI adoption across teams, measuring success, and navigating the organizational challenges that come with new technology adoption.

What you’ll learn:

1. The exact Slack message Brian sent while on paternity leave that kickstarted his company’s AI transformation
2. How to structure both synchronous and asynchronous AI learning opportunities for maximum adoption
3. The two-pronged approach that dramatically increased AI tool usage across teams
4. Why becoming your company’s AI champion is one of the best career moves you can make right now
5. How to measure AI adoption success with sentiment surveys and clear metrics
6. The critical role of creating a “golden path” for AI tool usage with legal, security, and finance teams

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Brought to you by:

Google Gemini—Your everyday AI assistant

Lovable—Build apps by simply chatting with AI

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In this episode, we cover:

(00:00) Introduction to Brian Greenbaum

(01:38) Brian’s paternity leave epiphany that sparked an AI initiative

(05:00) Sending the message that launched a transformation

(12:25) The two-pronged approach: synchronous and asynchronous learning

(17:29) Encouraging experimentation and creative exploration

(18:41) How AI enables designers to move beyond MVP thinking

(22:00) Quick summary of the two-pronged approach

(24:43) Measuring AI adoption

(33:48) Creating a centralized AI knowledge center

(35:58) Building an MCP server to demonstrate AI’s potential

(44:08) Why technical understanding is crucial for non-technical roles

(46:01) Final thoughts

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Tools referenced:

• Cursor: https://cursor.com/

• Bolt.new: https://bolt.new/

• Claude: https://claude.ai/

• ChatGPT: https://chat.openai.com/

• Midjourney: https://www.midjourney.com/

• Gemini: https://gemini.google.com/

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Other references:

• Pendo: https://www.pendo.io/

• Confluence: https://www.atlassian.com/software/confluence

• Slack: https://slack.com/

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Where to find Brian Greenbaum:

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

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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].

Cortney Hickey is the executive assistant to the CEO at Zapier, where she’s leveraging AI to transform traditional EA responsibilities into scalable, organization-wide systems. In this episode, she demonstrates how she’s built AI workflows that automate meeting preparation, reinforce company culture through automated feedback, and democratize strategic knowledge across the organization. Her approach shows how EAs can use AI not to replace their roles but to elevate them—working on higher-impact initiatives while creating systems that benefit the entire company.

What you’ll learn:

1. How to build an automated meeting prep system that researches participants, checks CRM data, and delivers actionable insights before important meetings
2. A framework for creating AI-powered culture reinforcement through automated meeting feedback aligned with company values and operating principles
3. How to develop an AI-powered document review system that helps teams align with executive expectations before formal reviews
4. A strategy for creating a centralized knowledge base that makes company strategy accessible and interactive for all employees
5. Why “progress over perfection” is the key mindset for building effective AI workflows that evolve over time
6. How EAs can use AI automation to work themselves out of repetitive tasks and into higher-impact strategic roles

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Brex—The intelligent finance platform built for founders

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In this episode, we cover:

(00:00) Introduction to Cortney

(02:48) Overview of meeting prep automation with Zapier Agents

(04:43) How the meeting prep agent works

(10:21) An example of the meeting prep agent in practice

(12:16) Creating a culture reinforcement system through meeting feedback

(15:45) EAs’ unique position to leverage these tools

(18:12) Building an automated meeting coach

(24:03) Developing an executive document review system

(33:15) Creating a centralized strategy companion in NotebookLM

(36:18) How AI is transforming the EA role, not replacing it

(40:00) Lightning round and final thoughts

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Tools referenced:

• Zapier: https://zapier.com/

• Zapier Agents: https://zapier.com/agents

• Todoist: https://todoist.com/

• Slack: https://slack.com/

• HubSpot: https://www.hubspot.com/

• ChatGPT: https://chat.openai.com/

• Google NotebookLM: https://notebooklm.google/

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Where to find Cortney Hickey:

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

—

Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

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

X: https://x.com/clairevo

—

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

Michal Peled is a Technical Operations Engineer at HoneyBook who specializes in building internal tools and automations that eliminate friction for teams. In this episode, Michal demonstrates three practical AI use cases: using ChatGPT’s agent mode to automate LinkedIn recruiting, transforming customer research into interactive AI personas, and creating a custom calendar solution for a very San Francisco–specific problem—avoiding expensive parking during Giants games.

What you’ll learn:

1. How to use ChatGPT agent mode to automate LinkedIn recruiting and find high-quality candidates that manual searches missed
2. The step-by-step process for turning static customer research into interactive AI personas that product and marketing teams can actually use
3. Why NotebookLM excels at creating prompts from source material with proper citations
4. How to structure agent-mode prompts to create effective “little helpers” that follow your exact workflow
5. A practical framework for improving your prompts when AI tools aren’t giving you the results you want
6. How internal tools teams can drive massive impact by focusing on eliminating friction in everyday workflows

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Brought to you by:

Brex—The intelligent finance platform built for founders

Google Gemini—Your everyday AI assistant

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In this episode, we cover:

(00:00) Introduction to Michal and ChatGPT agent mode

(02:10) Using agent mode for LinkedIn recruiting automation

(05:14) Creating effective prompts for agent mode

(10:50) Demo of agent mode searching LinkedIn profiles

(16:29) Results and team reception of the recruiting automation

(19:53) The outcome of implementing on Michal’s team

(23:50) Creating custom GPT personas from customer research

(28:43) Using NotebookLM to transform research into persona prompts

(35:00) Adding guardrails to custom GPT personas

(37:20) Demo of interacting with custom-persona GPTs

(41:02) Creating a calendar automation for parking during baseball games

(48:15) Lightning round and final thoughts

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Tools referenced:

• ChatGPT: https://chat.openai.com/

• NotebookLM: https://notebooklm.google.com/

• Claude: https://claude.ai/

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Other references:

• Google Calendar: https://calendar.google.com/

• HoneyBook: https://www.honeybook.com/

• LinkedIn: https://www.linkedin.com/

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Where to find Michal Peled:

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

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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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