How I AI

Zach Lloyd is the co-founder and CEO of Warp, an AI-powered terminal and software factory platform used by tens of thousands of engineers. Before Warp, he spent nearly a decade at Google, including time as a principal engineer on Google Sheets. He built Warp from the ground up as a modern, AI-native alternative to legacy terminals, and the team has since expanded into software factories: a full cloud-based system that takes an idea in Slack all the way through to a merged PR.

In this episode:

1. Why a software factory is more than a coding agent
2. The public Slack → Linear → GitHub → QA workflow
3. Human interactions per PR as a signal of automation and throughput
4. Why human review is still the bottleneck
5. Scoring agent runs, finding failure modes, and self-improving agent workflows
6. Replaying real tasks to choose model cost and quality tradeoffs
7. CEO workflows with Figma MCP, Granola, and research agents

—

Brought to you by:

DX—Engineering intelligence for the AI era

OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more

—

In this episode, we cover:

(00:00) Intro

(02:35) Warp’s AI software factory, Wilson

(09:23) Automatic factory triggers

(11:12) The engineering leader dashboard Zach wishes he’d had

(15:18) How code review is changing in an AI factory

(17:08) Tracking cost per PR across model configs

(18:47) Using LLM-as-a-judge to score every agent run

(20:02) Catching redundant tests

(22:19) How the factory self-improves from failed runs

(26:03) Quick recap

(28:33) Building a cost-quality Pareto chart for model selection

(31:35) How Zach uses AI for non-technical CEO work

(32:10) Figma MCP demo

(35:43) Granola MCP demo

(36:41) GOG CLI demo

(38:20) Thinking in parallel tasks instead of sequential ones

(40:42) Zach’s prompting strategy for factory tasks

(44:48) Where to find Zach

—

Tools referenced:

• Warp (AI terminal and software factories): https://warp.dev

• Warp Factories: https://warp.dev/factories

• Linear (project and issue tracking): https://linear.app

• GitHub (version control and PR management): https://github.com

• Slack (team communication and factory input layer): https://slack.com

• Sentry (crash reporting and automated issue triggers): https://sentry.io

• Figma (design, used via Figma MCP): https://figma.com

• Granola (AI meeting notes and MCP integration): https://granola.so

• Grok Bot (fast inference, cost/quality trade-off): https://x.ai/bot/guides/grok-bot-101

—

Where to find Zach:

X: https://x.com/ZachLloydTweets

—

Where to find Claire:

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].

I spent a few hours putting Meta’s Muse, its new personal AI agent, through a real first-pass test: onboarding, calendar management, goal setting, a one-shot family morning newsletter, browser-based shopping, and the animated avatar that honestly surprised me.

What you’ll learn:

1. Why Muse is the best-designed personal agent I’ve tested, and what specifically made it feel that way
2. The one-shot family PDF Muse produced that Claude and Codex never quite nailed
3. How Muse’s permission model works, and why it’s different from every other agent I’ve used
4. Why I set up a sleep training goal in Muse, and what it revealed about agent tone
5. The activity feed feature I immediately wished Codex and Claude Code had
6. Where Muse failed, and what it says about the limits of this category right now
7. The animated avatar decision that showed me what top-of-craft AI product design actually looks like

—

Brought to you by:

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

OpenArt—An all-in-one AI creation platform for images, videos, music, audio, and more

—

In this episode, we cover:

(00:00) What Muse is and who it’s actually built for

(04:41) Signing in and the onboarding flow

(07:16) The activity feed and its task lineage

(08:24) First real task: managing the family calendar and deleting soccer practice

(09:48) Requesting a morning newsletter PDF

(14:29) The personalized news feed and how I set it up

(16:05) The “Ideas” feature as an out-of-the-box prompt library

(17:10) Setting up personal goals (water, shoes, and sleep training)

(21:40) Library: documents, websites, images, videos, and podcasts

(23:11) Quick recap and what I love

(23:56) Activity feed design deep dive: tool calls and step-by-step lineage

(25:18) How Muse handles permissions

(26:12) The animated avatar: Polly becomes Slime, the teal dragon

(29:34) Browser use test: shopping for New Balance 9060s (not great)

(31:15) Browser use test 2: buying IMAX tickets for The Odyssey (much better)

(33:34) TL;DR and what I’ll actually use Muse for going forward

—

Tools referenced:

• Muse: https://muse.ai/

• Stripe Link (payment method featured in Muse): https://link.com

• 1Password (future Muse integration mentioned): https://1password.com

• OpenClaw (Claire’s previous personal agent setup): https://openclaw.ai/

• Grok Bot (Grok-based agent from prior stack): https://x.ai/news/introducing-grok-bot

• Codex (OpenAI coding agent, comparison point): https://openai.com/codex

• NotebookLM (Google, comparison to Muse’s podcast generation): https://notebooklm.google.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].

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:

1. How Peng built a self-updating personal website using Grok Bot as the entire backend pipeline, with no CMS and no Figma file
2. The exact check-in bot setup that lets Peng send a photo or a place name and have his portfolio update itself automatically
3. How John’s Figma Bro bot handles production design tasks while he’s at the gym
4. How John uses voice memos to direct Figma work through an MCP connection without opening his laptop
5. The “shower thought to prototype” workflow John uses with DevBot to test interaction ideas without first going through a product manager or engineer
6. The “trash can method” of software development
7. How both designers organize their personal bot ecosystems
8. 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

—

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

—

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

—

Where to find John and Peng:

John Bai on X: https://x.com/johnbai

Peng Zheng on X: https://x.com/pengzheng_

—

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].

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:

1. Why Stripe built Kai from scratch instead of buying, and what tipped the decision
2. What Kai knows about you by default and what you actually control
3. Why “projects” at Stripe are a governance mechanism, not just a folder
4. How Stripe structured its data layer so agents can query safely at scale
5. Why the infrastructure Stripe built for human developers turned out to be exactly what agents needed
6. How Kai’s skills platform lets any employee package a workflow, and what happens when you have 2,000 of them
7. 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

—

Tools referenced:

• Trino: https://trino.io/

• Anthropic: https://www.anthropic.com/

• Gemini: https://gemini.google.com/

• Cursor: https://www.cursor.com/

—

Where to find Sharadh Krishnamurthy:

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

—

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].

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:

1. Why Astra’s computer use feels different, and which production tools I’m trusting it with
2. The one feature I’d thrown every model at for six months, and what finally got it to 90%
3. How I’m using browser use for QA, not building, and what it found that I would’ve missed
4. Why I think UI is genuinely back, and what that means for SaaS and MCPs
5. The hardware hack I’d been chasing since GPT-5.5, and how Astra finally cracked it
6. What Astra built me in Blender in one shot, and why 3D is my new capability benchmark
7. The AIM-style Mac app Astra made in one shot, and what it signals about desktop development now
8. An honest take on speed, cost, and whether Astra is worth making your daily driver

—

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

—

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 [email protected].

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:

1. The three primitives Grok Bot is built on, and why one of them changes what agents can actually do
2. How Chief, my general-purpose chief of staff, handles a scope I didn’t think a single bot could manage
3. The writing quirk I noticed immediately with the Grok model, and what I did about it before letting it near my inbox
4. 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
5. The two engineering bots doing work I used to do myself, and how one of them handles compliance in a way that surprised me
6. How Holly Helpdesk started getting five-star reviews from customers who had no idea they were talking to a bot
7. 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

—

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 [email protected].

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:

1. Why Daniel says the two rules that make any AI system powerful aren’t about the tool you pick
2. How his weekly prep automation fills an entire Notion board from scratch every Sunday, without his touching it
3. The morning brief feature that teaches Claude new internal terms on its own, so company jargon never slows it down
4. Why he describes Notion as “read-only” now, and what that says about how PM workflows are changing
5. The self-improvement loop that watches Daniel’s edits, spots recurring friction, and suggests new skills to build
6. How he uses a skill called “Improve” to filter the endless flood of AI tips without drowning in them
7. What he built to scale his personal system to every PM at Melio, and the UX lesson he learned the hard way
8. 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

—

Tools referenced:

• Claude: https://claude.ai

• Notion: https://notion.so

—

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

—

Where to find Daniel Blum:

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

Website: https://www.imdanielblum.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].

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:

1. Why Ryan manages 15 concurrent Devin agents with a folder system and a piece of paper, not a dashboard
2. The Watchdog playbook: what he built to replace a customer success team across every law firm account
3. How his LAN PR skill closes the loop on 40 daily PRs without a QA team reviewing a single one
4. Why he moved off local agents almost entirely, and the one situation where he still reaches for Codex
5. The design workflow we’re both using: Claude Design into a Markdown spec, then Codex to build the real thing
6. What he found when he got away from his computer and met a real customer, and why it changed his entire product direction
7. How he’s hiring his first engineer without a single phone screen or interview
8. 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

—

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/

—

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

—

Where to find Ryan Carson:

X: https://x.com/ryancarson

Untangle: https://untangle.us

—

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].

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:

1. The one Grok Bot feature no other agent platform has shipped yet, and why it made me actually use the product
2. What a week of real Grok Bot use revealed, and why I still reach for my OpenClaws
3. Whether Cursor Origin is a GitHub replacement or just a pretty redesign
4. 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

—

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

—

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].

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:

1. How Yana uses a custom fashion prompt as a technical spec to get consistent, realistic, on-design outputs
2. Why ChatGPT Images 2.0 outperforms other models for fashion design
3. How she uses Codex plus computer use to operate CAD and fashion software she’s never personally learned
4. The workflow for taking a garment from hand-drawn sketch to product photo, runway photo, and influencer shot in a single session
5. How she ran vendor outreach end to end using deep research and browser use
6. How she built a full e-commerce site with voting, databases, and Stripe integration
7. 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

—

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

—

Other references:

• Ruth Asawa: https://ruthasawa.com

• SFMOMA (Ruth Asawa): https://www.sfmoma.org/artist/Ruth_Asawa/

—

Where to find Yana Welinder:

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

X: https://x.com/yanabana

Website: https://www.yanabana.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].