How I AI

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
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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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

This week I’m doing a solo breakdown of everything xAI and Cursor have shipped recently, including Grok Bot, Cursor Origin, and the Grok 4.6 model. I set up five Grok Bots, ran Grok 4.6 through my Claire Weighted Index against GPT-5.6 Sol, Claude Sonnet 5, and Opus 5, and spent time actually using Origin as a GitHub replacement. Here’s what’s worth your attention, what’s overhyped, and where I’m personally putting my time.

What you’ll learn:
The one Grok Bot feature no other agent platform has shipped yet, and why it made me actually use the product
What a week of real Grok Bot use revealed, and why I still reach for my OpenClaws
Whether Cursor Origin is a GitHub replacement or just a pretty redesign
Where Grok 4.6 landed on the Claire Index, and the one category where it genuinely surprised me
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Why everyone’s quietly switching to Grok
(01:52) Grok Bot overview and setup
(03:22) My 5 Grok Bots
(04:30) The killer feature: multi-account connectors
(06:07) Grok Bot’s virtual machine and how it actually works
(06:41) Experience overview
(07:35) What I don’t love about Grok Bot
(10:08) Grok Bot use cases and my honest verdict
(12:20) Cursor Origin: the agent-native GitHub replacement
(13:47) What Origin actually looks like in practice
(14:59) Why I’m not switching from GitHub yet
(17:42) What would get me to move over
(18:52) Grok 4.6 and the How I AI Vibe bench
(20:41) Claire Index results: where Grok 4.6 ranked
(23:03) Design evals: where Grok surprised me
(25:00) My conclusion and how I’m splitting my time now
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Tools referenced:
• Grok Bot: https://x.ai/bot
• Cursor: https://cursor.com/home
• Cursor Origin: https://cursor.com/origin
• OpenClaw: https://openclaw.ai/
• 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 jordan@penname.co.

Yana Welinder is the solo founder of Yana Bana, an AI-native fashion brand built with AI as her technical co-founder, starting from hand-drawn sketches and ending with runway photos, CAD files for 3D printing, and a live Stripe-connected pre-order site—no engineers required. A former product leader, she brings an operator’s rigor to her creative process: her “fashion prompt” is a detailed spec covering silhouette, volume, fabric behavior, movement, and sound, and watching her use Codex plus computer use to navigate 3D design software that’s entirely new to her is a clarifying demo of what today’s toolset actually makes possible.

What you’ll learn:
How Yana uses a custom fashion prompt as a technical spec to get consistent, realistic, on-design outputs
Why ChatGPT Images 2.0 outperforms other models for fashion design
How she uses Codex plus computer use to operate CAD and fashion software she’s never personally learned
The workflow for taking a garment from hand-drawn sketch to product photo, runway photo, and influencer shot in a single session
How she ran vendor outreach end to end using deep research and browser use
How she built a full e-commerce site with voting, databases, and Stripe integration
Why she’s testing human patternmakers and Codex in parallel
—
Brought to you by:
Merge—Connective infrastructure for production AI
Jira AI SDLC—Get your tokens’ worth with Jira
—
In this episode, we cover:
(00:00) Introducing Yana Welinder and Yana Bana
(02:38) Tour of the Yana Bana site
(05:20) The fashion prompt stack
(07:39) Live demo: generating a jacket from a prompt in ChatGPT
(10:01) Why Image Gen 2.0 beats other models
(11:51) The “prompt as spec” principle
(14:02) Iterating the design
(17:12) Using Codex and computer use to build CAD files in 3D software
(20:50) Vendor research, outreach emails, and Superhuman browser use
(23:34) Building the full e-commerce site
(27:40) Quick recap and what’s still hard
(30:05) How Yana prompts when AI pushes back
(31:15) Where to find Yana and how to vote on her garments
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Tools referenced:
• ChatGPT (Images 2.0): https://chat.openai.com
• Codex (OpenAI): https://openai.com/codex
• CLO 3D (fashion pattern software): https://www.clo3d.com
• Vercel: https://vercel.com
• GitHub: https://github.com
• Stripe: https://stripe.com
• Superhuman: https://superhuman.com
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Other references:
• Ruth Asawa: https://ruthasawa.com
• SFMOMA (Ruth Asawa): https://www.sfmoma.org/artist/Ruth_Asawa/
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Where to find Yana Welinder:
LinkedIn: https://www.linkedin.com/in/ywelinder/
X: https://x.com/yanabana
Website: https://www.yanabana.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.

Grace Clarke is an AI educator and former marketing consultant who taught herself Claude Code earlier this year and built a curriculum out of the process. She now runs her entire service business on tools she’s built with Claude, including a pipeline operator, a proposal maker, and a Gmail replacement she created in under 30 minutes, and teaches individuals and teams to do the same.

What you’ll learn:
How to build an hourly pipeline in Claude that moves clients through your process automatically
Why Grace ditched traditional proposals for password-protected, interactive HTML documents built in Claude
How she uses a “voice guide” skill file so every Claude output sounds like her, not like AI slop
The two-step forcing function she teaches non-technical clients to build the muscle of opening Claude
Why she started building in Claude Code, then handed the work off to Cowork via a Markdown session file
How she replaced Gmail entirely with a custom inbox
Why she teaches “intent engineering” instead of prompt engineering, and what that looks like in practice
How she uses Claude on her phone, on walks, to track workouts and manage plants alongside client work
—
Brought to you by:
Bolt.new—Turn your idea into a real product
Hyperagent—Deploy fleets of agents that handle real work
—
In this episode, we cover:
(00:00) Grace’s background and why she started building with Claude
(04:48) The pipeline operator: what it is and how it runs her business every hour
(08:48) Building the muscle memory to use AI
(12:02) What goes into building a skill file (voice guide, proposal rules, versioning)
(13:50) How she built her proposal maker
(16:15) The voice guide: teaching Claude how she thinks, not just how she writes
(21:22) Live demo of the custom Gmail replacement built in Cowork
(30:44) Workout tracking, plant photos, and tiny daily Claude habits
(34:51) The biggest misconception holding people back from adopting AI
(38:36) What Grace does when Claude is not giving her what she wants
(40:38) Claude builds a proposal for Claire in real time
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Tools referenced:
• Claude: https://claude.ai
• Claude Code: https://claude.ai/code
• Netlify: https://www.netlify.com
• Google Forms: https://forms.google.com
• Google Sheets: https://sheets.google.com
• Google Cloud (for service accounts and custom connectors): https://cloud.google.com
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Other reference:
• Stratechery by Ben Thompson: https://stratechery.com
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Where to find Grace Clarke:
LinkedIn: https://www.linkedin.com/in/gracegclarke/
X: https://x.com/graceclarke
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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.

AI writes most of my code now, and that created a new problem: a PR queue I couldn’t keep up with. In this episode, I walk through how I built Merge Mommy, a Vercel Eve agent that reads every PR after checks pass, scores it across six risk dimensions, auto-approves the low-risk ones, and pings me in Slack for anything that needs a human. I built the whole thing in one Codex session, it’s SOC 2 compatible, and it’s already cleared my backlog.

What you’ll learn:
Why AI-generated PRs create a review bottleneck and why the answer isn’t reviewing all of them
How Intercom 5x’d PR approval speed and reduced revert rates by putting AI in the review loop
Why Vercel Eve is the simplest framework I’ve found for deploying AI agents in Slack and GitHub
How I built a full PR review agent in Codex with one prompt and a few steering turns
The six components I use to score PR risk (blast radius, reversibility, data security, ops impact, verification gap, and change surface)
How I used Chrome browser use to handle Slack bot and GitHub app configuration so I never had to click through setup screens manually
Why auto-approved PRs can be SOC 2 compliant as long as the process is auditable, queryable, and in your risk policy
How to set up Slack escalation so low-risk PRs become a two-click merge with no manual review
—
Brought to you by:
WorkOS—Make your app Enterprise Ready today
—
In this episode, we cover:
(00:00) The PR review backlog problem nobody’s talking about
(02:35) Why you don’t have to review every AI-generated PR
(05:14) How Intercom built AI-approved PRs (and proved they’re safer)
(06:10) How the Eve framework works (directory, skills, channels, connectors)
(09:16) The Codex prompt I used to build the entire bot
(11:36) What the agent actually does: read, score, approve, or escalate
(13:07) Setting up your Eve agent
(15:47) The six-component risk scoring model
(17:23) Merge Mommy in action: three live PR examples
(21:10) Recap and how to build your own version
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Tools referenced:
• Vercel Eve: https://vercel.com/eve
• Vercel AI SDK: https://sdk.vercel.ai/
• Vercel Chat SDK: https://chat-sdk.dev/
• Codex (OpenAI): https://openai.com/codex
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Other references:
• AI is approving our pull requests: Here’s how we made it safe: https://www.intercom.com/blog/ai-is-approving-our-pull-requests-heres-how-we-made-it-safe/
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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.