How I AI

John Bai and Peng Zheng are designers on the Grok Bot team at SpaceXAI, where they’re building one of the most talked-about AI products right now. John writes publicly about his design process (his piece “Designing Grok Bot with Grok Bot” has already made the rounds) and shares bot templates with the design community. Peng brings a product-design sensibility to personal tools, and his website doubles as a live demo of what he builds.

What you’ll learn:
How Peng built a self-updating personal website using Grok Bot as the entire backend pipeline, with no CMS and no Figma file
The exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automatically
How John’s Figma Bro bot handles production design tasks while he’s at the gym
How John uses voice memos to direct Figma work through an MCP connection without opening his laptop
The “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager or engineer
The “trash can method” of software development
How both designers organize their personal bot ecosystems
What John and Peng actually think AI means for the future of design as a craft
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Vanta—Automate compliance and simplify security
—
In this episode, we cover:
(00:00) Introducing John and Peng
(02:53) The Grok Bot hype train
(04:35) Peng’s self-updating personal website built with Grok Bot
(15:12) How AI makes design more accessible
(19:35) Website update result
(20:13) John’s Figma Bro bot
(23:35) Creating marketing materials for the bot marketplace
(26:00) DevBot: from shower thoughts to working prototypes
(28:48) The trash can method of software development
(31:19) Other bots John and Peng are using
(39:05) Practical tips for when bots don’t do what you want
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Tools referenced:
• Grok Bot (xAI): https://x.ai/bot
• Figma: https://www.figma.com
• Figma MCP server: https://www.figma.com/mcp-catalog/
• Google Places API: https://developers.google.com/maps/documentation/places/web-service
• Notion: https://www.notion.so
• Swarm (Foursquare): https://www.swarmapp.com
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Other references:
• Designing Grok Bot with Grok Bot: https://x.ai/bot/guides/designing-grok-bot-with-grok-bot
• Figma Bro bot template (shared by John Bai): https://x.ai/bot/marketplace/bots/figma-bro
• From zero coding background to hardware hacker: How Cursor + a Raspberry Pi makes AI fun: https://www.lennysnewsletter.com/p/from-zero-coding-background-to-hardware?utm_source=publication-search
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Where to find John and Peng:
John Bai on X: https://x.com/johnbai
Peng Zheng on X: https://x.com/pengzheng_
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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.

Sharadh Krishnamurthy is an engineering manager at Stripe, where he helped build Kai, the company’s internal AI agent used by more than 10,000 employees every week. He’s worked across several of Stripe’s core infrastructure teams, including data and developer experience, which gives him a grounded, systems-level perspective on what it actually takes to make AI work at enterprise scale. He’s currently focused on the governance, skills, and infrastructure layers that let every Stripe employee use AI safely and effectively, regardless of their technical background.

What you’ll learn:
Why Stripe built Kai from scratch instead of buying, and what tipped the decision
What Kai knows about you by default and what you actually control
Why “projects” at Stripe are a governance mechanism, not just a folder
How Stripe structured its data layer so agents can query safely at scale
Why the infrastructure Stripe built for human developers turned out to be exactly what agents needed
How Kai’s skills platform lets any employee package a workflow, and what happens when you have 2,000 of them
What Sharadh learned the hard way when agents nearly took down production systems
—
Brought to you by:
DX—Engineering intelligence for the AI era
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Introducing Sharadh
(02:46) Why Stripe built an AI agent (Kai) instead of buying tools
(05:18) What Kai knows about you (and what you can turn off)
(06:51) Projects as a governance layer
(10:04) Live demo: Kai builds a dashboard
(12:18) Tools, skills, and the secure sandbox
(17:22) Why Stripe has benefited so much from AI
(19:20) Agentic identity, load shedding, and rogue agents
(20:41) Iterating on the dashboard
(25:01) How they rolled out Kai across the team
(29:07) How projects work
(34:18) Bespoke agents for bespoke use cases
(35:58) The skill builder workflow
(40:40) Skill quality, evals, and telemetry
(43:01) Recap
(45:13) Lightning round
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Tools referenced:
• Trino: https://trino.io/
• Anthropic: https://www.anthropic.com/
• Gemini: https://gemini.google.com/
• Cursor: https://www.cursor.com/
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Where to find Sharadh Krishnamurthy:
LinkedIn: https://www.linkedin.com/in/sharadhk
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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 jordan@penname.co.

I got early access to GPT-6 Astra: when I say this model broke through tasks I couldn’t crack with 5.6 Sol or Fable, I mean it specifically: the ChatPRD product intelligence feature, building 3D games, the hardware hack, and a handful of one-shot coding projects I’d tried and failed on repeatedly.

What you’ll learn:
Why Astra’s computer use feels different, and which production tools I’m trusting it with
The one feature I’d thrown every model at for six months, and what finally got it to 90%
How I’m using browser use for QA, not building, and what it found that I would’ve missed
Why I think UI is genuinely back, and what that means for SaaS and MCPs
The hardware hack I’d been chasing since GPT-5.5, and how Astra finally cracked it
What Astra built me in Blender in one shot, and why 3D is my new capability benchmark
The AIM-style Mac app Astra made in one shot, and what it signals about desktop development now
An honest take on speed, cost, and whether Astra is worth making your daily driver
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In this episode, we cover:
(00:00) GPT-6 Astra overview
(03:44) Browser/computer use test on my CRM
(09:08) Flora thumbnail generation
(13:00) Browser use for QA
(15:20) Coding: ChatPRD product intelligence feature, finally one-shotted
(18:36) Hardware hack: Divoom MiniToo CLI and live streaming display
(22:23) Building an AIM-style Mac app
(24:24) Blender and 3D assets: Barbie Bench and the kids’ family app
(28:52) Summary: what Astra is great at and what to try first
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Tools referenced:
• GPT-6 Astra: https://openai.com/index/gpt-6-astra/
• Codex: https://openai.com/codex
• Flora (node-based AI image/video editing): https://flora.ai/
• Figma: https://www.figma.com
• Blender: https://www.blender.org
• GPT Image 2: https://developers.openai.com/api/docs/models/gpt-image-2
• Divoom MiniToo: https://divoom.com/products/minitoo
• cxo.dev: https://www.cxo.dev/
—
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 jordan@penname.co.

I’m running about 30 active agents at any given moment, and in this episode I break down my full Grok Bot setup: what it is, how it compares to OpenClaw, and the nine bots I’ve built for work and my personal life. We go deep on Chief (my chief-of-staff bot sweeping six inboxes and multiple Slack workspaces), TradBot (the family agent that prints a kitchen-table newspaper for my kids), two engineering bots handling my PR queue and SOC 2 compliance monitoring, Holly Helpdesk, and a handful of personal bots I didn’t expect to actually love. I also walk through how I migrated everything from OpenClaw, including the script I used to export and transplant each agent’s identity and schedule.

What you’ll learn:
The three primitives Grok Bot is built on, and why one of them changes what agents can actually do
How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage
The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near my inbox
Why I created a family agent, what it produces every morning, and the design principle I used that has nothing to do with a screen
The two engineering bots doing work I used to do myself, and how one of them handles compliance in a way that surprised me
How Holly Helpdesk started getting five-star reviews from customers who had no idea they were talking to a bot
The personal bots I built mostly on a whim, and the one I now look forward to every Monday morning
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Why I migrated from OpenClaw to Grok Bot
(02:10) Grok Bot overview: the three core primitives
(07:41) Chief: my chief-of-staff bot
(11:51) OpenClaw vs. Grok Bot
(12:41) TradBot: my family agent
(19:51) LGTM the PR Closer
(22:03) Lockdown: SOC 2 control monitoring bot
(24:00) Holly Helpdesk: customer support
(26:58) Penny Pincher: subscription audit, insurance negotiation, Rolex shopping
(29:37) ShopZilla and Sylvie Style: personal shopping and wardrobe bots
(32:54) How to migrate your OpenClaws
(34:26) Final take
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Tools referenced:
• Grok Bot (SpaceXAI multi-agent platform): https://x.ai/news/introducing-grok-bot
• OpenClaw (previous agent platform): https://openclaw.ai/
—
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 jordan@penname.co.

Daniel Blum is a product manager at Melio, a B2B payments company, and one of the most systematic thinkers I’ve had on the show when it comes to personal AI infrastructure. He’s spent the past year building a Claude- and Cowork-based productivity system that manages his Notion board, processes his Slack and email, and runs self-improvement loops every week without needing to be prompted. Beyond his own workflow, Daniel built and scaled a “Workstation” onboarding plugin that gets any Melio employee up and running with a personalized Claude setup in about 15 minutes.

What you’ll learn:
Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick
How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it
The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down
Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing
The self-improvement loop that watches Daniel’s edits, spots recurring friction, and suggests new skills to build
How he uses a skill called “Improve” to filter the endless flood of AI tips without drowning in them
What he built to scale his personal system to every PM at Melio, and the UX lesson he learned the hard way
The capability gap that’s still keeping him from running 100% of his work through Claude
—
Brought to you by:
Optimizely—Your AI agent orchestration platform for marketing and digital teams
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Daniel’s background and the PM overhead problem he needed to solve
(03:30) His AI stack at Melio
(05:00) The two rules that make any AI system genuinely powerful
(06:00) The Notion board Cowork built for him (and manages on his behalf)
(07:30) How he contextualizes Claude with voice memos, links, and recurring updates
(09:00) His weekly prep automation
(11:00) His morning brief
(15:00) How Claude flags unknown internal terms and saves them to context
(17:30) Running 70% to 80% of his workday through Cowork
(19:00) Chrome connector vs. MCPs for tools without integrations
(20:00) The real ROI question: why the early weeks feel slow, and why you push through anyway
(25:00) Scaling the system to the team with the Workstation plugin
(26:30) The self-improvement loop
(31:00) How the Improve skill separates actually useful AI tips from the hype
(32:00) The Workstation onboarding flow, and the UX lesson from distributing “Spectacular”
(38:00) The 20% Claude still can’t do, and what changes when it can
(41:00) What Daniel spends his reclaimed time on
(42:30) Claude rage
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Tools referenced:
• Claude: https://claude.ai
• Notion: https://notion.so
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Other references:
• From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin: https://www.lennysnewsletter.com/p/from-a-690-newsletter-to-3m-api-how?utm_source=publication-search
• How the founder of Morning Brew built a Claude content machine that never runs out of ideas and never sounds like slop | Alex Lieberman: https://www.lennysnewsletter.com/p/how-the-founder-of-morning-brew-built?utm_source=publication-search
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Where to find Daniel Blum:
LinkedIn: https://www.linkedin.com/in/blumd/
Website: https://www.imdanielblum.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 jordan@penname.co.