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 jordan@penname.co.

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

What you’ll learn:

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

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

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

What you’ll learn:

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/

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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 jordan@penname.co.

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

What you’ll learn:

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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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.