How I AI

Marco Casalaina, VP of Core AI Products and AI Futurist at Microsoft, demonstrates how he uses AI tools to automate administrative tasks that typically consume valuable time. Rather than using Warp as a coding assistant (its primary marketed purpose), Marco leverages it to manage Azure resources, scan documents, compress videos, and more. He shows how these “micro-agents” can reduce friction in everyday workflows, allowing him to focus on higher-value activities. Marco also demonstrates how Microsoft 365 Copilot and ChatGPT can create triggered workflows that respond to emails or check for information on a schedule, highlighting how the line between consuming and building AI agents is blurring.

What you’ll learn:
How to use Warp to manage Azure resources and assign permissions without navigating complex web interfaces
Techniques for automating document scanning and processing directly from the terminal
Methods for analyzing and compressing video files using AI-generated FFmpeg commands
How to create simple rules that dramatically improve AI performance for specialized tasks
Ways to build triggered workflows in Microsoft 365 Copilot that automatically respond to emails
How to configure ChatGPT to perform scheduled tasks like checking for new content
Strategies for creating consistent AI interactions using AutoHotkey shortcuts
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Brought to you by:
Rovo—AI that knows your business
Lovable—Build apps by simply chatting with AI
—
In this episode, we cover:
(00:00) Introduction to Marco Casalaina
(02:14) Why Marco chose Warp for administrative tasks
(03:57) Demo: Using Warp to manage Azure resources and permissions
(06:00) How CLI tools eliminate GUI friction for complex tasks
(07:18) Creating rules to improve AI performance for specialized tasks
(10:28) Demo: Document scanning automation
(13:00) Combining odd and even pages using a Python automation
(15:04) The value of ephemeral AI solutions vs. permanent tools
(17:12) Video compression using FFmpeg commands
(20:22) The concept of “ad hoc agents” for specific tasks
(22:31) Demo: Creating triggered workflows in Microsoft 365 Copilot
(25:51) Demo: Setting up scheduled tasks in ChatGPT
(27:17) How AI automation changes time management
(29:14) Teaching AI skills to the next generation
(30:30) Strategies for improving AI performance with AutoHotkey
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Detailed workflow walkthroughs from this episode:
• How Microsoft's AI VP Automates Everything with 5 Micro-Agent Workflows: https://www.chatprd.ai/how-i-ai/microsofts-ai-vp-automates-everything-with-5-micro-agent-workflows
How to Create an Automated Meeting Scheduler with Microsoft • 365 Copilot: https://www.chatprd.ai/how-i-ai/workflows/how-to-create-an-automated-meeting-scheduler-with-microsoft-365-copilot
• How to Scan and Merge Two-Sided Documents into a Single PDF with AI: https://www.chatprd.ai/how-i-ai/workflows/how-to-scan-and-merge-two-sided-documents-into-a-single-pdf-with-ai
• How to Automate Azure User Role Management with AI in the Terminal: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-azure-user-role-management-with-ai-in-the-terminal
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Tools referenced:
• Warp: https://www.warp.dev/
• Microsoft Azure: https://azure.microsoft.com/en-us
• Azure CLI: https://learn.microsoft.com/en-us/cli/azure/
• Microsoft 365 Copilot: https://www.microsoft.com/en-us/microsoft-365/copilot
• ChatGPT: https://chat.openai.com/
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Other references:
• NAPS2: https://www.naps2.com/
• PyPDF2: https://pypdf2.readthedocs.io/
• FFmpeg: https://ffmpeg.org/
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Where to find Marco Casalaina:
LinkedIn: https://www.linkedin.com/in/marcocasalaina/
—
Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Daniel Roth, editor in chief at LinkedIn, went from business writer to iOS app developer, without ever learning how to code. Using Claude Code, Daniel built and shipped multiple production-ready iOS apps to the App Store, including Commutely, a personalized train-tracking app for New York commuters.

What you’ll learn:
How to set up a dual-agent Claude Code system (builder + reviewer)
Why being a “picky customer” is the right mindset for non-technical builders
How Daniel prioritizes features using AI-ranked impact vs. build time
Why saving everything as Markdown files creates long-term context
The importance of branch-based development—even when AI writes the code
How Daniel ships to the App Store without formal engineering experience
His end-of-day “What did I drop the ball on?” Copilot workflow
—
Brought to you by:
WorkOS—Make your app enterprise-ready today
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) Introduction to Daniel Roth
(02:46) Daniel’s AI development workflow overview
(05:56) Using Claude to prioritize feature ideas
(08:58) Building vs. marketing
(09:47) Creating a retention plan for his app
(10:38) Introducing Bob the Builder and Ray the Reviewer
(13:50) How Bob and Ray work together to build features
(14:37) Why Daniel focuses on learning the process
(16:34) The importance of using branches for development
(17:39) Managing AI agents like managing a team
(21:12) Navigating the App Store
(23:06) Being a “picky customer” rather than a PM
(25:00) Testing in Xcode and shipping to the App Store
(28:14) Quick recap
(30:00) Creating terminal aliases with Claude
(31:38) Demo of his Commutely app
(32:10) Using Copilot to manage work responsibilities
(35:05) How Daniel talks to AI without personifying it
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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/
—
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:
How to use Figma’s MCP to pull production code directly into Figma files
A workflow for pushing design changes from Figma back into your codebase using Claude Code without manual CSS adjustments
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
Why AI has shifted design work upstream to planning and downstream to craft, eliminating the rushed middle phase of execution
How to create custom skills that automate pre-flight checks, lint fixes, and CI monitoring before pushing code to production
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/
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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].

Jamey Gannon is an AI creative director who specializes in creating consistent, beautiful brand imagery using AI tools. In this episode, Jamey demonstrates her streamlined workflow for generating cohesive brand assets using Midjourney, Nano Banana, and other AI image tools. She walks through her process of creating mood boards, using style references, developing personalization codes, and strategically iterating to achieve a consistent aesthetic. Rather than relying on complex prompts, Jamey shows how visual references and strategic shortcuts can produce better results with less effort.

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

Chintan Turakhia is Senior Director of Engineering at Coinbase, where he’s led the transformation of a 1,000-plus-engineer organization to embrace AI tools at scale. When tasked with rewriting Coinbase’s self-custody wallet into a consumer social app in just six to nine months, Chintan turned to AI as a force multiplier. His team has achieved remarkable efficiency gains, including reducing PR review times from 150 hours to just 15 hours, and dramatically compressing the cycle from user feedback to shipped features.

What you’ll learn:
How to drive AI adoption in large, established engineering organizations
The “speed run” technique that got 100 engineers to push 70 PRs in 15 minutes
How to identify and replicate the behaviors of AI power users
Why engineering leaders must get hands-on with AI tools to drive adoption
How to build custom AI agents that integrate with your existing workflows
The metrics that actually matter when measuring AI’s impact on engineering velocity
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/
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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].