How I AI

Most teams are still passing static design files back and forth, and most Figma files are already out of date by the time they reach engineering.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
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Brought to you by:
Optimizely—Your AI agent orchestration platform for marketing and digital teams
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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
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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
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Brought to you by:
Vanta—Automate compliance and simplify security
Lovable—Build apps by simply chatting with AI
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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
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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
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Brought to you by:
WorkOS—Make your app enterprise-ready today
Rovo—AI that knows your business
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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
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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:
How Jesse set up five OpenClaw agents, each with its own role, persona, SOUL.md file, and dedicated Mac Mini
The workflow for photographing an entire curriculum book and having an agent generate formatted, ready-to-teach lesson plans from the images
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)
Why Jesse treats agent onboarding like employee onboarding
The “decision file” trick and other incantations for managing agents that actually stick
Where multi-agent collaboration breaks down, and why no current messaging platform handles agent-to-agent handoffs well
How photographing every toy, book, and supply in the house lets the AI recommend real physical materials during lesson planning
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/
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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 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:
How Brian built a shared Next.js app that serves as a collaborative prototyping environment for Notion’s design team
Why encountering “reality” early in the design process leads to better products
How to use Claude Code’s “plan mode” to get better results when prototyping
The power of custom Claude slash commands and skills to automate repetitive tasks
How to transform Figma designs into working code with a single prompt
Why AI-powered products can’t be effectively designed in static tools like Figma
Brian’s rule for working with AI: “When Claude asks you to do something, teach it to do that thing itself”
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Brought to you by:
WorkOS—Make your app enterprise-ready today
Orkes—The enterprise platform for reliable applications and agentic workflows
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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
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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].