How I AI

Thariq Shihipar is an engineer at Anthropic working on the Claude Code team. He’s spent the past several months experimenting with HTML as a replacement for Markdown in planning and implementation workflows, discovering that richer visual formats lead to better human engagement—and, ultimately, better products. In this episode, filmed at Anthropic’s Code with Claude event in San Francisco, Thariq demonstrates how to use HTML artifacts to create interactive plans, build throwaway UIs for specific problems, and maintain living design systems that travel with your codebase.

What you’ll learn:
Why HTML has replaced Markdown as the ideal format for AI agent communication and planning
How to brainstorm in HTML to get visual mockups and interactive demos instead of text lists
The technique for building throwaway micro-UIs to edit specific parts of your plan
How to create a living design system in HTML that lives in your repo and travels with every project
Why “complexity has to earn its keep” and how HTML helps you stay in the loop without over-constraining Claude
The prompting technique that gives Claude flexibility while ensuring that you get what you need
Why 99% of your AI-generated tokens should go to planning, interfaces, and communication—not production code
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Brought to you by:
Celigo—Intelligent automation built for AI
Persona—Trusted identity verification for any use case
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In this episode, we cover:
(00:00) Introduction
(02:39) HTML as the new Markdown
(04:30) The compute allocator mindset
(05:51) How HTML makes specs more engaging
(06:48) Demo: Brainstorming in HTML with Claude Code
(09:24) From brainstorm to full implementation plan
(11:20) Prompting philosophy: Trust Claude but give it constraints
(13:50) The future of PRDs and tech specs
(18:16) Making HTML specs editable
(20:23) The abundance mindset
(24:17) Just-in-time documentation and throwaway software
(25:39) Using plans as artifacts for implementation
(26:39) Demo: Living design systems in HTML
(30:16) Adding comments and annotations to HTML plans
(31:42) Recap: The HTML workflow
(32:21) Lightning round and final thoughts
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Tools referenced:
• Claude Code: https://claude.ai/code
• Claude Design: https://claude.ai/design
• AWS: https://aws.amazon.com/
• Figma: https://www.figma.com/
• GitHub: https://github.com/
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Other references:
• Anthropic Code with Claude event: https://claude.com/code-with-claude
• SpaceX partnership announcement: https://www.anthropic.com/news/higher-limits-spacex
• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox
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Where to find Thariq Shihipar:
Website: https://www.thariq.io/
LinkedIn: https://www.linkedin.com/in/thariqshihipar/
X: https://x.com/trq212
GitHub: https://github.com/ThariqS
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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].

Ryan Nystrom is a software engineer at Notion. He joined in December 2024 after Notion acquired Campsite, the team communication platform he co-founded with Brian Lovin. At Notion, he’s been a core builder of Notion AI and the Custom Agents feature launched in February 2026. He manages a team of six to seven engineers while still writing code himself, currently running Project Afterburner, a push to cut Notion’s CI time to a quarter of its current duration.

What you’ll learn:
How to build a Notion AI custom agent that auto-generates your daily standup pre-read by pulling from Slack, GitHub, Honeycomb metrics, and yesterday’s meeting transcript
How to configure subagents and MCP integrations within Notion AI
How Notion’s internal “Boxy” system lets engineers @mention Codex from within Notion comments and get a full pull request with screenshots in 20 minutes
The spec-first development workflow: dictate an idea into Whisper, have Codex format it as a proper spec, commit it to the repo, and let the agent implement and verify it autonomously
Why fast CI is absolutely critical in the age of AI coding agents
How to prompt AI coding agents to defend their reasoning under pushback
Why engineering managers and even senior executives should keep writing code
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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 Ryan Nystrom
(02:48) How AI has upended 12+ years of the same working routine
(04:30) Project Afterburner: Notion’s push to cut CI time to a quarter
(09:00) Why high-frequency, high-quality meetings beat lower-frequency standups
(11:10) How automated context surfaces every engineer’s work equally
(12:15) Why cutting meeting prep is a burnout protection mechanism
(14:26) The case for engineering managers writing code
(16:13) Inside “Boxy”: Notion’s internal VM-based background agent system
(20:30) Old World vs. New World code review
(24:51) Prompting Codex from Notion comments
(29:20) The emotions around code review
(31:01) Quick recap
(32:00) Spec-first development: writing and checking agent specs into the repo
(35:10) The spec as changelog: version control for how a feature actually works
(37:53) How engineers’ roles are evolving
(39:00) Lightning round
(45:21) Where to find Ryan
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Tools referenced:
• Notion AI: https://www.notion.com/product/ai
• Notion Custom Agents: https://www.notion.com/blog/introducing-custom-agents
• Codex (OpenAI): https://openai.com/codex
• Claude Code (Anthropic): https://claude.ai/code
• Honeycomb (observability + MCP): https://www.honeycomb.io
• Whisper (OpenAI voice transcription): https://openai.com/research/whisper
• Slack: https://slack.com
• GitHub: https://github.com
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Other references:
• How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe): https://www.chatprd.ai/how-i-ai/stripes-ai-minions-ship-1300-prs-weekly-from-a-slack-emoji
• Notion 3.3 Custom Agents launch (February 24, 2026): https://www.notion.com/releases/2026-02-24
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Where to find Ryan Nystrom:
X: https://x.com/ryannystrom
LinkedIn: https://www.linkedin.com/in/ryannystrom/
GitHub: https://github.com/rnystrom
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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].

Claire breaks down the biggest announcements from Anthropic’s “Code with Claude” event and what they actually mean for builders shipping AI products today. From scheduled AI routines to outcome-based agents, multi-agent orchestration, and new memory systems, Claire walks through the features she’s most excited to use immediately—and how they could reshape the future of agentic software.

What you’ll learn:
How Claude Code routines let you automate recurring workflows on schedules or webhooks
What “Outcomes” are and how rubric-based agent grading works
How multi-agent orchestration enables specialized AI teams with different roles and tools
Why Anthropic’s new “Dreams” memory system matters for long-term agent behavior
Why increased Claude Code usage limits are a bigger deal than they sound
How Claire thinks about building practical agentic products today
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Resources:
• Code with Claude: https://claude.com/code-with-claude
• Claude Code Routines Docs: https://code.claude.com/docs/en/routines
• Define Outcomes Docs: https://platform.claude.com/docs/en/managed-agents/define-outcomes
• Dreams Docs: https://platform.claude.com/docs/en/managed-agents/dreams
• Multi-Agent Docs: https://platform.claude.com/docs/en/managed-agents/multi-agent
• Managed Agent Webhooks Docs: https://platform.claude.com/docs/en/managed-agents/webhooks#supported-event-types
• Codex (OpenAI): https://openai.com/codex
• GitHub: https://github.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].

John Kim is the co-founder and CEO of Delight.ai, a customer experience platform that’s transforming how companies deploy AI. But what makes John’s story fascinating isn’t just his product; it’s how he’s turned his entire company into an AI-native organization. His marketing team built a fully functional e-commerce swag store with Stripe integration in days. His sales team built their own CRM tools. His recruiting team automated their entire workflow. And it’s all tracked, measured, and celebrated through an internal platform called Automators.

What you’ll learn:
How Sendbird’s marketing team built a fully functional swag store with Stripe integration in a day (with no engineering support)
How the Automators platform works—an internal marketplace where anyone can request AI tools and engineers (or AI agents) can build them
How to create secure, compliant templates so non-technical teams can ship to production safely
How Sendbird built a token usage dashboard with five tiers (beginner through AI God) and why tracking the smoothness of the curve matters more than the total
Why visible leadership usage is the most powerful adoption signal
Why Sendbird rewrote job descriptions to prioritize curiosity, agency, and energy over years of experience
How John uses AI for his own learning
—
Brought to you by:
WorkOS—Make your app enterprise-ready today
ThoughtSpot—Build AI-powered analytics into your product
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In this episode, we cover:
(00:00) Introduction to John Kim
(02:45) The Delight.ai swag store built by marketing in two days
(05:51) The before times: when fun had to earn its place on the roadmap
(07:55) Demo: The Automators platform and quest system
(13:47) The AI Engineer for Internal Operations role
(16:06) Demo: The company-wide skills marketplace
(17:19) Treating AI adoption as a product
(18:43) Real wins: team-level and campaign examples
(21:51) Why SaaS isn’t dead—it’s being rebuilt internally
(23:46) Demo: The token tracking dashboard
(26:32) Measuring without fear: setting expectations, not punishments
(28:54) Quick recap
(30:51) Personal AI use cases: endless knowledge at your fingertips
(36:15) Lightning round and final thoughts
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Tools referenced:
• Claude Code: https://claude.ai/code
• Codex (OpenAI): https://openai.com/codex
• Obsidian: https://obsidian.md
• GitHub: https://github.com
• Stripe: https://stripe.com
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Other references:
• Jason Levin (CEO of Memelord) on How I AI: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how
• Konami Code: https://en.wikipedia.org/wiki/Konami_Code
• Andrew Huberman’s podcast: https://hubermanlab.com/
• Y Combinator: https://www.ycombinator.com/
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Where to find John Kim:
X: https://x.com/doshkim
Instagram: https://instagram.com/dosh
LinkedIn: https://www.linkedin.com/in/doshkim/
Company: https://delight.ai
Delight.ai Spark Conference (May 7, SF): https://delight.ai/spark
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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].

Owen Williams is a design manager at Stripe who built Protodash, an internal AI-powered prototyping platform that lets designers and PMs create high-quality Stripe dashboard prototypes without writing code. What started as a bundle of Cursor rules and React components evolved into a full web-based prototyping studio that runs in dev boxes, complete with design review modes, variant testing, and AI-powered iteration. Surprisingly, PMs now use Protodash just as much as designers, fundamentally changing how Stripe approaches prototyping, design reviews, and engineering handoffs.

What you’ll learn:
How Stripe built an internal AI prototyping tool using Cursor rules, MCPs, and their design system
Why “blurple slop” happens when designers use generic AI tools—and how to fix it
The architecture behind Protodash: React router, design system components, and MCP integrations
How Stripe prototypes in dev boxes so designers never have to worry about local setup
Why “demos, not memos” transformed Stripe’s design review culture
How Stripe built design review modes, variant testing, and AI annotation directly into your prototyping tool
Why internal tools don’t need to be production-grade to be transformative
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Brought to you by:
Celigo—Intelligent automation built for AI
Cursor—The best way to code with AI
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In this episode, we cover:
(00:00) Welcome and intro to Owen Williams
(02:19) The “blurple slop” problem with AI design tools
(03:50) Protodash: an internal vibe-coding tool for Stripe prototypes
(05:26) Why an engineering background helped Owen lower the bar for designers
(07:55) The Cursor rules that taught the Stripe design system
(09:04) Running prototypes on dev boxes vs. locally
(10:30) “Demos, not memos” and rewiring design reviews at Stripe
(14:50) Building Protodash Studio: a browser-based wrapper for prototyping
(19:04) Live demo: variants, line charts, and remixing prototypes in browser
(21:02) Self-testing prototypes that take screenshots and check their work
(23:20) Multiple variant features
(26:08) The annotate-for-AI button for in-canvas feedback
(27:21) Design review mode: comments, summaries, and AI follow-up
(29:39) Why building internal tools beats buying off-the-shelf
(32:50) PMs as the surprise power users of Protodash
(35:20) Live demo: a Black Friday/Cyber Monday pet store dashboard
(42:03) Lo-fi modes, monospace fonts, and “Comic Sans for WIP” at Shopify
(44:45) Quick recap
(45:35) The Radar prototype that changed engineering handoff
(49:08) Lightning round and final thoughts
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Blog & detailed workflow walkthroughs from this episode:
Stripe’s Owen Williams on Killing ‘Blurple Slop’ with an Internal Prototyping Studio: http://chatprd.ai/how-i-ai/stripe-owen-williams-on-buildling-internal-prototyping-studio
↳ How To Connect a Design System to an AI Code Editor for High Fidelity Prototypes: https://www.chatprd.ai/how-i-ai/workflows/how-to-connect-a-design-system-to-an-ai-code-editor-for-high-fidelity-prototypes
↳ Streamline Design Reviews with an AI-Powered Prototyping Studio: https://www.chatprd.ai/how-i-ai/workflows/streamline-design-reviews-with-an-ai-powered-prototyping-studio
↳ Build a Personal AI App to Track Purchases and User Manuals: https://www.chatprd.ai/how-i-ai/workflows/build-a-personal-ai-app-to-track-purchases-and-user-manuals
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Tools referenced:
• v0: https://v0.app/
• Cursor: https://cursor.com/
• Claude Code: https://www.claude.com/product/claude-code
• Claude Design: https://www.anthropic.com/news/claude-design-anthropic-labs
• Figma: https://www.figma.com/
• Stripe Radar: https://stripe.com/radar
• Balsamiq: https://balsamiq.com/
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Where to find Owen Williams:
X: https://x.com/ow
Website: https://owenwillia.ms/
LinkedIn: https://www.linkedin.com/in/owenpwilliams
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Where to find Claire Vo:
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
X: https://x.com/clairevo
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].