How I AI

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
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Brought to you by:
DX—Engineering intelligence for the AI era
Hyperagent—Deploy fleets of agents that handle real work
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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
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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/
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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’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/
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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.

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.

Ryan Carson is a five-time founder and the current solo founder of Untangle, a B2B SaaS platform for family law firms. Before Untangle, he co-founded Treehouse, an online coding education platform, and has spent the better part of two decades building and leading tech companies. He’s active on X, where he shares his solo founder journey in real time, including what he actually spends on AI tools each month.

What you’ll learn:
Why Ryan manages 15 concurrent Devin agents with a folder system and a piece of paper, not a dashboard
The Watchdog playbook: what he built to replace a customer success team across every law firm account
How his LAN PR skill closes the loop on 40 daily PRs without a QA team reviewing a single one
Why he moved off local agents almost entirely, and the one situation where he still reaches for Codex
The design workflow we’re both using: Claude Design into a Markdown spec, then Codex to build the real thing
What he found when he got away from his computer and met a real customer, and why it changed his entire product direction
How he’s hiring his first engineer without a single phone screen or interview
Why we both think more AI output is actually the wrong goal, and what to optimize for instead
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Introduction to Ryan Carson
(02:55) Ryan’s update: Untangle, the divorce PMF pivot, and B2B growth
(07:35) Ryan’s current Devin stack: folders, P0 threads, and the paper list
(16:29) Watchdog playbook: account monitoring across every firm
(18:09) Managing agent decision fatigue: Ryan’s method vs. Claire’s
(20:32) Producing more output does not make a better product
(22:47) Using cloud agents for ops beyond just code
(25:14) When to use Codex vs. Devin vs. Claude Code
(27:15) Merge Mommy recap
(28:15) LAN PR skill: review loops, video walkthrough, and auto-merge
(30:20) Slack vs. Devin threads for async team communication
(35:58) Claude Design plus Codex for building a technical design system
(39:00) EA tools: Polly the OpenClaw vs. Claude Code on a Mac Mini
(42:09) Quick recap and final thoughts
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Tools referenced:
• Devin (Cognition): https://www.cognition.ai/
• Codex (OpenAI): https://openai.com/codex
• Claude Code/Claude Design (Anthropic): https://www.anthropic.com/claude
• OpenClaw (Claude-based desktop client): https://openclaw.ai
• Cursor: https://www.cursor.com/
• BugBot (Devin’s built-in PR review): https://cursor.com/bugbot
• Sentry (error monitoring referenced in Watchdog): https://sentry.io/
• Ugmonk (analog to-do system): https://ugmonk.com/
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Other references:
• Devin playbooks/skills documentation: https://docs.cognition.ai/
• Jack Dorsey/Buzz (async-first communication referenced): https://buzz.new/
• Merge Mommy (Claire’s Eve agent for PR risk scoring, deployed on Vercel): https://www.lennysnewsletter.com/p/build-an-ai-code-review-bot-in-30
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Where to find Ryan Carson:
X: https://x.com/ryancarson
Untangle: https://untangle.us
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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.