In this mini episode, I do a full walkthrough of the AI design tools that dropped in April 2026: Anthropic’s new Claude Design, OpenAI’s GPT Images 2.0, and Google Labs’ open-source DESIGN.md format. I import a full design system from Lenny’s Newsletter, build a landing page, turn my own article into a polished deck, generate a brand kit for ChatPRD, and run a personal color analysis from a photo.

What you’ll learn:

1. How Claude Design handles design system imports and whether it can actually replace Figma
2. The three best use cases for Claude Design: marketing landing pages, slide decks, and creative redesigns
3. Why ChatGPT Images 2.0 is a breakthrough for brand kits and layout work
4. Google’s new DESIGN.md standard
5. The practical limits of AI design tools (spoiler: you’ll hit credit limits fast)

—

Brought to you by:

WorkOS—Make your app enterprise-ready today

Rippling—Stop wasting time on admin tasks, build your startup faster

—

In this episode, we cover:

(00:00) Welcome and what’s in the spring 2026 AI design drop

(01:45) Claude Design overview

(03:05) Importing Lenny’s Newsletter design system into Claude Design

(04:06) How Claude Design structures a design system

(05:42) Google Labs’ DESIGN.md standard

(06:41) Building Lenny Doc, a PRD generator landing page using the Lenny design system

(09:44) Why the three-variation output is Claude Design’s smartest UX choice

(10:20) Hitting the Claude Design limit and paying $200 to keep going

(11:05) Where Figma still wins

(13:20) Reviewing Lenny Doc

(16:19) Turning an Open Claude article into a branded slide deck

(17:57) The ’90s GeoCities “Lenny’s Product Zone” redesign

(19:44) Claude Design recap

(20:15) ChatGPT Images 2.0 and what makes it the first “thinking” image model

(21:25) Generating a multi-page brand kit for ChatPRD and iterating with reference images

(23:43) Personal color analysis demo

(26:02) Recap

—

Detailed workflow walkthroughs from this episode:

• How I Put Claude Design and GPT Images 2.0 to the Test: Building Landing Pages, Slides, and Brand Kits: https://www.chatprd.ai/how-i-ai/claude-design-and-gpt-images-2-building-landing-pages-slides-and-brand-kits

• How to Generate a Professional Brand Kit with GPT Images 2.0: https://www.chatprd.ai/how-i-ai/workflows/how-to-generate-a-professional-brand-kit-with-gpt-images-2-0

• How to Convert an Article into a Polished Slide Deck with AI: https://www.chatprd.ai/how-i-ai/workflows/how-to-convert-an-article-into-a-polished-slide-deck-with-ai

• How to Build a High-Fidelity Landing Page with Claude Design: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-high-fidelity-landing-page-with-claude-design

—

Tools referenced:

• Claude Design: https://claude.ai/design

• ChatGPT Images 2.0: https://openai.com/index/introducing-chatgpt-images-2-0/

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

—

Other references:

• Google’s DESIGN.md: https://stitch.withgoogle.com/docs/design-md/overview

• Lenny’s Newsletter: https://www.lennysnewsletter.com/

• Jamie Gannon “How I AI” episode on reference styles: https://www.lennysnewsletter.com/p/mastering-midjourney-how-to-create

• Brand prompt inspiration: https://x.com/riomadeit/status/2046682442791071787

• Figma team “How I AI” episode on design systems: https://www.lennysnewsletter.com/p/from-figma-to-claude-code-and-back

—

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 Scanlan is a senior principal engineer at Intercom, where he’s led the company’s transformation to AI-first engineering. In just nine months, Intercom doubled their R&D throughput while maintaining code quality, with 100% of engineers—plus designers, PMs, and TPMs—now shipping code via Claude Code.

What you’ll learn:

1. How Intercom doubled their merged PRs per R&D employee in just nine months using Claude Code
2. The telemetry infrastructure they built to measure AI adoption and quality across hundreds of engineers
3. Why they built a skills repository with hooks that enforce engineering standards automatically
4. How they’re preparing their product for an agent-first world with CLIs, MCPs, and ephemeral APIs
5. The permission and accountability framework that enabled rapid AI adoption
6. Why backlog zero is now achievable and what that means for engineering culture

—

Brought to you by:

Celigo—Intelligent automation built for AI

Cursor—The best way to code with AI

—

In this episode, we cover:

(00:00) Introduction to Brian Scanlan

(02:40) Why Intercom went all-in on AI for both product and engineering

(05:01) The breakthrough moment with Opus 4.6 and Christmas break 2025

(07:02) Demo: Intercom’s merged PRs per R&D head

(12:50) Agent-first work as a fundamental reimagining of technical workflows

(14:27) The cost tradeoff: treating AI spend as an investment

(16:47) Measuring quality

(21:22) Demo: Shipping a redirect in the Rails monolith with Claude Code

(24:03) Creating a custom PR skill

(26:33) Building a software factory with predictable quality standards

(30:15) Telemetry infrastructure: Honeycomb for skill usage tracking

(32:10) Session data collection and personalized usage insights

(36:08) Quick overview

(39:20) Walking through Intercom’s skills repository

(42:16) Deep dive: The flaky spec skill and how it reached 100x capability

(46:44) The “and then” workflow for building comprehensive skills

(52:31) The live website and overview of workflows

(53:32) How internal AI experience informs customer product decisions

(56:18) Making SaaS products agent-friendly with CLIs and helpful hints

(01:03:49) Why conversion drop-off is invisible in agent-driven workflows

(01:05:28) Lightning round and final thoughts

—

Detailed workflow walkthroughs from this episode:

• How Intercom Doubled Engineering Output: Brian Scanlan's 4 AI Workflows for Claude Code: https://www.chatprd.ai/how-i-ai/how-intercom-doubled-engineering-output-brian-scanlan-ai-workflows-for-claude-code

• Design an Agent-Friendly CLI to Automate SaaS Product Onboarding: https://www.chatprd.ai/how-i-ai/workflows/design-an-agent-friendly-cli-to-automate-saas-product-onboarding

• Build a Self-Improving AI Agent to Automatically Fix Flaky Tests: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-agent-to-automatically-fix-flaky-tests

• Automate High-Quality Pull Request Descriptions with a Custom AI Skill: https://www.chatprd.ai/how-i-ai/workflows/automate-high-quality-pull-request-descriptions-with-a-custom-ai-skill

—

Tools referenced:

• Claude Code: https://claude.ai/code

• Cursor: https://cursor.com/

• Honeycomb: https://www.honeycomb.io/

• Snowflake: https://www.snowflake.com/

• Fin AI: https://www.intercom.com/fin

• Vercel: https://vercel.com/

—

Other references:

• Intercom GitHub Repo: https://github.com/intercom

• Google API Go Client Repo: https://github.com/googleapis/google-api-go-client

—

Where to find Brian Scanlan:

X: https://x.com/brian_scanlan

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

Company: https://www.intercom.com

—

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].

JJ Englert leads community enablement at Tenex. In this episode, JJ provides a complete zero-to-one tutorial on Claude Cowork, Anthropic’s desktop tool that sits between simple chat and full terminal-based coding.

What you’ll learn:

1. How to create your first Claude Cowork project by connecting a folder on your computer and building context over time
2. The “brain” file strategy: how to create a preferences document that Claude reads every time to understand who you are and how you work
3. Why one-click connectors to Gmail, Slack, Notion, and Google Calendar unlock AI that actually does work instead of just suggesting it
4. How to analyze your sent emails to build a writing skill that perfectly matches your tone and style
5. The sub-advisory-board technique: spinning up three AI agents with different personas to review your work from multiple perspectives
6. How to set permissions for each connector so Claude only drafts (never sends) or always asks before taking action
7. The scheduled-task workflow that creates a morning debrief by reading your email, Slack, and calendar every day at 7:30 a.m.
8. Why projects with shared memory beat individual chat threads for consistent, high-quality AI outputs

—

Brought to you by:

Tines—Start building intelligent workflows today

Cursor—The best way to code with AI

—

In this episode, we cover:

(00:00) Introduction to JJ Englert

(02:48) What Cowork is and who it’s for

(05:49) Getting started: Opening the Cowork tab in Claude Desktop

(07:04) Understanding projects as folders on your computer

(07:54) Creating your “brain” file, with working preferences and context

(10:24) Demo: Building a daily operating system project from scratch

(12:18) How to prompt Cowork when starting a new project

(14:54) Understanding the project interface and shared memory

(18:37) Setting up connectors to Gmail, Slack, Google Calendar, and other tools

(21:00) Using connectors to analyze your emails and build personalized writing skills

(24:21) Creating a thinking-partner skill for decision support

(26:18) Cowork vs. OpenClaw

(27:18) Building a sub-advisory skill with multiple AI personas for feedback

(34:03) Advanced skill example: Multi-step newsletter creation with research and evaluation

(36:08) Setting up scheduled tasks for morning debriefs

(37:57) Going beyond one-off tasks with AI

(41:00) Progressive trust and the tradeoff of information for productivity

(44:08) Different use cases beyond work productivity

(46:08) Lightning round

—

Tools referenced:

• Claude Code: https://claude.ai/code

• Wispr Flow: https://whisperflow.ai/

• Monologue: https://www.monologue.to/

• Domo: https://www.domo.com/

• Pencil.dev: https://pencil.dev/

• Remotion: https://www.remotion.dev/

• Obsidian: https://obsidian.md/

• OpenClaw: https://openclaw.com/

• Notion: https://notion.so/

—

Other references:

• Get Started with Claude Cowork: https://support.claude.com/en/articles/13345190-get-started-with-cowork

—

Where to find JJ Englert:

YouTube: https://www.youtube.com/channel/UCv2ovDhYVtlJw4QMidLFP8Q

X: https://twitter.com/jjenglert

LinkedIn: https://www.linkedin.com/in/jj-englert-a08836a6/

—

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].

Yash Tekriwal is the head of education at Clay. A self-described hyper-optimizer, Yash has built multiple custom productivity applications using Perplexity Computer and OpenClaw to manage his overwhelming daily workflow—including a Slack digest system that categorizes over 150 daily notifications into actionable priorities, and a consolidated news/email/Slack dashboard that serves as his personal command center.

What you’ll learn:

1. How Yash built a custom Slack digest that categorizes 150+ daily notifications into action-required, need-to-read, and FYI buckets
2. Why Perplexity Computer beats Claude Code and Codex for building personal productivity apps
3. His “anti-to-do list” framework: spending an hour daily automating tasks you never want to do again
4. How to use AI for deterministic tasks (APIs, structured data) vs. subjective tasks (categorization, summarization)
5. Why the SaaS apocalypse narrative is wrong—and why we’re about to see an explosion of micro-software
6. How his team uses Perplexity Computer to prototype design systems and communicate with cross-functional partners

—

Brought to you by:

Guru—The AI layer of truth

ThoughtSpot—Build AI-powered analytics into your product

—

In this episode, we cover:

(00:00) Introduction to Yash

(02:38) The burden of 150 daily Slack notifications

(05:45) When to use AI for tasks vs. building deterministic code

(06:38) Building the Slack digest with OpenClaw

(11:33) Introducing Perplexity Computer and the visual dashboard

(14:28) Three reasons Perplexity Computer beats Claude Code

(16:14) Using connectors to automate meeting follow-ups across Notion and Asana

(18:21) The Kanban-style Slack dashboard

(20:15) The long tail of customer requests and the future of micro-software

(24:09) The anti-to-do list framework

(26:21) Building a consolidated news, email, and Slack digest

(29:48) How Perplexity Computer handles authentication and deployment

(31:46) Team use case: Prototyping persona-based learning journeys for Clay University

(35:49) Lightning round and final thoughts

—

Tools referenced:

• Perplexity Computer: https://www.perplexity.ai/computer/new

• OpenClaw: https://openclaw.ai/

• Discord: https://discord.com/

• Claude Code: https://claude.ai/code

• Codex: https://openai.com/codex/

• Asana: https://asana.com/

• Airtable: https://airtable.com/

• Figma: https://www.figma.com/

• Vercel: https://vercel.com/

• ChatGPT: https://chat.openai.com/

—

Other references:

• Slack: https://slack.com/

• Notion: https://www.notion.so/

• Superhuman: https://superhuman.com/

• Clay University: https://www.clay.com/university

• Kanban boards: https://en.wikipedia.org/wiki/Kanban_board

—

Where to find Yash Tekriwal:

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

X: https://x.com/yash_tek

Company: https://www.clay.com/

—

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].

Al Chen is a field engineer at Galileo, an observability platform for AI applications, where he works on the front lines with enterprise customers asking highly technical questions. Despite never having held an engineering role, Al has built a system using Claude Code to query Galileo’s 15 separate repositories, combine that with Confluence documentation and customer-specific quirks, and deliver hyper-personalized answers that would otherwise require constant engineering support.

What you’ll learn:

1. How to use Claude Code to query multiple repositories simultaneously for customer support
2. Why code is often a better source of truth than documentation
3. How to combine repository context with Confluence and Slack using MCPs
4. The “customer quirks” system that creates hyper-personalized deployment guides
5. How to build virtuous loops that turn single customer questions into scalable knowledge
6. Why information organization matters less in the AI era
7. A simple 16-line script (written by Claude Code) that pulls the latest main branch across all your repositories to keep your context current
8. How to reduce engineering interruptions to near-zero by empowering customer-facing teams to query the codebase directly

—

Brought to you by:

Orkes—The enterprise platform for reliable applications and agentic workflows

Tines—Start building intelligent workflows today

—

In this episode, we cover:

(00:00) Introduction to Al Chen

(02:50) The problem: documentation wasn’t enough

(04:23) Pulling 15 repos into VS Code

(06:03) How Claude Code queries the entire codebase

(08:00) Why current code beats documentation

(08:31) The pull script that keeps everything updated

(09:54) Opening projects at the multi-repo level

(11:40) Live demo: answering deployment questions

(13:25) The customer quirks system

(15:00) Living in chaos: why organization matters less now

(17:03) Competing on customer experience, not just product

(18:20) Should customers be able to query the code directly?

(20:05) Where humans still add value

(25:46) Using AI for reactive Slack support

(29:16) The “and then” workflow discovery

(32:07) Scaling processes across the team

(34:07) Lightning round and final thoughts

—

Tools referenced:

• Claude Code: https://claude.ai/code

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

• Pylon: https://usepylon.com/

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

—

Other references:

• Slack: https://slack.com/

• Kubernetes: https://kubernetes.io/

• Stack Overflow: https://stackoverflow.com/

• Intercom: https://www.intercom.com/

—

Where to find Al Chen:

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

Company: https://www.rungalileo.io

—

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].

Hilary Gridley is an entrepreneur, former product leader, and new mom who previously appeared on the podcast discussing AI for managers. She returns to share how she's transformed her approach to personal productivity using Claude Code as her primary tool for managing both professional work and life admin. Hilary demonstrates her "anti-system system"—a philosophy that prioritizes simplicity over complex setup, allowing AI to learn preferences through observation rather than upfront configuration.

What you’ll learn:

1. How to capture to-dos instantly using a simple iPhone back-tap shortcut that requires zero app switching
2. The “10x impact framework” for deciding what tasks to automate versus where to invest your human effort
3. How to use Claude Code’s observation capabilities to build a preference file that improves over time without manual setup
4. Why the “yappers API” (talking about what you’re doing while working) eliminates the need for complex OAuth integrations
5. A workflow for breaking down overwhelming tasks into 10-minute first steps that actually get completed
6. How to create Claude Skills by simply describing problems rather than writing code or following tutorials
7. Techniques for using “recording mode” to demo workflows without exposing personal information

—

Brought to you by:

WorkOS—Make your app Enterprise Ready today

Lovable—Build apps by simply chatting with AI

—

In this episode, we cover:

(00:00) Introduction to Hilary Gridley

(02:43) The opportunity cost of time as a new mom and entrepreneur

(07:11) Philosophy of the anti-system system

(08:05) Demo: Planning your day with Claude Code

(10:00) Setting up simple iPhone shortcuts for task capture

(11:48) How Claude organizes reminders and learns preferences automatically

(16:19) Breaking down overwhelming tasks into manageable first steps

(23:40) The yappers API: talking to Claude instead of building integrations

(25:28) Daily logging and observation patterns

(27:45) Quick summary

(30:50) The power of screenshots

(32:55) 10x impact framework for automation decisions

(37:51) Applying the framework to different career stages

(39:29) Building a “recording on” skill for anonymizing demos

(44:11) Building a returns tracking skill from scratch

(48:31) Building the muscle memory to reach for AI tools

(50:18) Where to find Hilary

—

Tools referenced:

• Claude Code: https://claude.ai/code

• Obsidian: https://obsidian.md/

• iPhone Shortcuts: https://support.apple.com/guide/shortcuts/welcome/ios

• Cursor: https://cursor.sh/

—

Other references:

• Figma file Hilary demo’ed: https://www.writerbuilder.com/howiai

—

Where to find Hilary Gridley:

Substack: https://hils.substack.com/

Website: https://writerbuilder.com

—

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].

Steve Kaliski is a software engineer at Stripe who has spent the past six and a half years building developer tools and payment infrastructure. He’s part of the team that created “minions”—Stripe’s internal AI coding agents, which now ship approximately 1,300 pull requests per week with minimal human intervention beyond code review. In this episode, Steve demonstrates how Stripe engineers activate development work from Slack and leverage cloud-based development environments for parallel agent workflows, and demos machine-to-machine payments where AI agents transact autonomously with third-party services.

What you’ll learn:

1. How Stripe’s “minions” write 1,300 pull requests per week with minimal human intervention
2. Why a good developer experience for humans creates better outcomes for AI agents
3. The critical role of cloud development environments in unlocking AI-powered engineering velocity
4. The machine payment protocol that lets AI agents spend money to accomplish tasks
5. The code review strategy for handling thousands of agent-written PRs
6. Why non-engineers at Stripe are starting to use minions to ship code
7. The future of software businesses built primarily for agent consumers

—

Brought to you by:

Optimizely—Your AI agent orchestration platform for marketing and digital teams

Rippling—Stop wasting time on admin tasks, build your startup faster

—

In this episode, we cover:

(00:00) Introduction to Steve

(02:39) Stripe’s minions and their effect on Stripe as a whole

(04:42) Why activation energy matters more than execution

(05:44) What is a minion? The technical architecture

(06:52) Demo: Activating a minion from Slack with an emoji

(09:04) Why good developer experience benefits both humans and agents

(11:22) Walking through the agent loop and system prompts

(13:42) Why Stripe chose Goose as their agent harness

(16:00) The role of Stripe’s developer productivity team

(17:15) Why cloud environments unlock multi-threaded AI engineering

(21:14) One-shot prompting: from Slack to shipped PR

(22:04) How Stripe handles code review for 1,300 AI-written PRs weekly

(23:44) Non-engineers using minions across the company

(24:53) Demo: Planning a birthday party with Claude and machine payments

(32:15) Quick recap

(35:08) The future of ephemeral, API-first businesses for agents

(36:36) Lightning round and final thoughts

—

Detailed workflow walkthroughs from this episode:

• How Stripe's AI 'Minions' Ship 1,300 PRs Weekly from a Slack Emoji: https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji

• How to Build an Autonomous AI Agent That Pays for Services to Complete Tasks: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-an-autonomous-ai-agent-that-pays-for-services-to-complete-tasks

• How to Automate Code Generation from a Slack Message into a Pull Request: https://www.chatprd.ai/how-i-ai/workflows/how-to-automate-code-generation-from-a-slack-message-into-a-pull-request

—

Tools referenced:

• Goose (AI agent harness): https://github.com/block/goose

• Claude Code: https://claude.ai/code

• Cursor: https://cursor.sh/

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

• Slack: https://slack.com/

• Browserbase: https://browserbase.com/

• Parallel AI: https://www.parallel.ai/

• PostalForm: https://postalform.com/

• Stripe Climate: https://stripe.com/climate

—

Other references:

• Stripe machine payments: https://docs.stripe.com/payments/machine

• Blue-Green Deployment: https://martinfowler.com/bliki/BlueGreenDeployment.html

• Git worktrees: https://git-scm.com/docs/git-worktree

—

Where to find Steve Kaliski:

Twitter: https://twitter.com/stevekaliski

LinkedIn: https://www.linkedin.com/in/steve-kaliski-079a7710/

—

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].

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:

1. How to use Warp to manage Azure resources and assign permissions without navigating complex web interfaces
2. Techniques for automating document scanning and processing directly from the terminal
3. Methods for analyzing and compressing video files using AI-generated FFmpeg commands
4. How to create simple rules that dramatically improve AI performance for specialized tasks
5. Ways to build triggered workflows in Microsoft 365 Copilot that automatically respond to emails
6. How to configure ChatGPT to perform scheduled tasks like checking for new content
7. Strategies for creating consistent AI interactions using AutoHotkey shortcuts

—

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

—

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

—

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/

—

Other references:

• NAPS2: https://www.naps2.com/

• PyPDF2: https://pypdf2.readthedocs.io/

• FFmpeg: https://ffmpeg.org/

—

Where to find Marco Casalaina:

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

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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].

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:

1. How to set up a dual-agent Claude Code system (builder + reviewer)
2. Why being a “picky customer” is the right mindset for non-technical builders
3. How Daniel prioritizes features using AI-ranked impact vs. build time
4. Why saving everything as Markdown files creates long-term context
5. The importance of branch-based development—even when AI writes the code
6. How Daniel ships to the App Store without formal engineering experience
7. His end-of-day “What did I drop the ball on?” Copilot workflow

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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 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/

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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].

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:

1. How to use Figma’s MCP to pull production code directly into Figma files
2. A workflow for pushing design changes from Figma back into your codebase using Claude Code without manual CSS adjustments
3. 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
4. Why AI has shifted design work upstream to planning and downstream to craft, eliminating the rushed middle phase of execution
5. How to create custom skills that automate pre-flight checks, lint fixes, and CI monitoring before pushing code to production
6. 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].