How I AI

Nicole Ruiz is a writer and parent who has built a comprehensive AI-powered shopping system to help her family buy high-quality, long-lasting items while avoiding the noise of drop-shipping brands, paid ads, and poorly made products. She writes an interview series on Substack about how technology is changing the household.

What you’ll learn:
How to build a Claude Project with custom instructions for vetting brands based on heritage, craftsmanship, and return policies
The shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brands
How to use Claude to search through trusted vendor websites that have terrible UX
Why AI actually helps small artisans and heritage brands compete against Amazon’s infrastructure
How to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requests
The technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operation
How to shop within a specific budget or with gift cards using AI assistance
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Brought to you by:
Orkes—The enterprise platform for reliable applications and agentic workflows
Metaview—The agentic recruiting platform for winning teams
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In this episode, we cover:
(00:00) Introduction to Nicole and AI-powered shopping
(02:29) The problem
(04:55) Building a Claude Project for household purchasing
(07:44) The “anti-to-do list” concept for reducing mental overhead
(10:30) Shopping for a can opener: the system in action
(15:53) How AI helps century-old brands with terrible websites
(18:45) Processing returns with Claude Cowork
(25:06) Using gift cards strategically
(26:33) Vetting brands
(29:40) Recap, lightning round, and final thoughts
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Tools referenced:
• Claude: https://claude.ai/
• Claude Cowork: https://www.anthropic.com/product/claude-cowork
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Other references:
• Boston General Store: https://bostongeneralstore.com/
• L.L.Bean: https://www.llbean.com/
• Manufactum: https://www.manufactum.com/
• 5 OpenClaw agents run my home, finances, and code | Jesse Genet: https://www.lennysnewsletter.com/p/5-openclaw-agents-run-my-home-finances
• 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
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Where to find Nicole Ruiz:
X: https://x.com/nwilliams030
Substack (The Third Oikos): https://www.thirdoikos.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
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

In this experimental episode, I document my real-time attempt to create an AI avatar of myself using Google Flow and the new Gemini Omni video generation model. I walk through the entire process—from scanning my face with my phone to generating a complete one-minute hype video for the podcast, all in about 15 minutes.

What you’ll learn:
How to create an AI avatar using Google Flow in under five minutes
Why video AI tools unlock creative possibilities for people with zero video production skills
The step-by-step process of generating a full storyboard using AI as your creative producer
How to use character consistency features to generate multiple video scenes with the same avatar
The uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physics
How to stitch together AI-generated scenes into a complete video using built-in editing tools
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Brought to you by:
Merge—Connective infrastructure for production AI
Jira Product Discovery—Prioritize with insights, build with confidence
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In this episode, we cover:
(00:00) Getting started with Google Flow and Gemini Omni
(01:38) The avatar creation process: scanning and photo capture
(02:55) Using Flow to brainstorm a hype video storyboard
(06:59) Generating the first video scene with the avatar
(08:41) Troubleshooting: accidentally generating images instead of videos
(09:32) Generating all seven scenes for the complete video
(11:37) Reviewing the avatar videos
(13:13) Stitching the videos together in the browser-based editor
(14:32) The complete How I AI hype video
(15:32) What worked and what didn’t
(19:04) Final thoughts
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Tools referenced:
• Google Flow: https://labs.google/fx/tools/flow
• Gemini Omni: https://gemini.google/overview/video-generation/
• Veo 3: https://deepmind.google/technologies/veo/
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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.

Bryce Rattner Keithley has spent her career in talent and recruiting, working with technical leaders but never writing a line of code herself. Yet she managed to build Daily Hundred—a fitness app featuring custom AI-generated videos of anthropomorphic animals demonstrating exercises—and ship it to the App Store before her software engineer friends. Using Replit, Claude, Gemini, and a relentless beginner’s mindset, Bryce proves that in the AI era, execution is no longer the constraint on good ideas.

What you’ll learn:
How to build and ship an iPhone app using Replit without any coding knowledge
The step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footage
How to use Claude as your technical architect and Claude Code as your software engineer
How to navigate App Store submission requirements (including fixing rejection feedback)
Why being hyper-literal in your prompts unlocks better AI results
Why a beginner’s mind is actually an advantage when building with AI tools
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Brought to you by:
WorkOS—Make your app enterprise-ready today
Metaview—The agentic recruiting platform for winning teams
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In this episode, we cover:
(00:00) Introduction to Bryce and Daily Hundred
(04:48) Building with Replit
(06:16) The beginner’s mindset advantage
(11:17) Creating anthropomorphic animals
(22:55) Moving from static image to video
(27:15) The floating genie and other anthropomorphic animal generations
(30:46) Shifting from web app to App Store submission
(36:24) User feedback
(37:41) Lightning round and final thoughts
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Tools referenced:
• Replit: https://replit.com/
• Lovable: https://lovable.dev/
• Claude: https://claude.ai/
• Claude Code: https://claude.ai/code
• Gemini: https://gemini.google.com/
• Higgsfield: https://higgsfield.ai/
• Kling: https://kling.ai/
• Railway: https://railway.app/
• TestFlight: https://developer.apple.com/testflight/
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Other references:
• How a 91-year-old vibe coded a complex event management system using Claude and Replit | John Blackman: https://www.lennysnewsletter.com/p/how-a-91-year-old-vibe-coded-a-complex
• What Got You Here Won’t Get You There: https://www.amazon.com/What-Got-Here-Wont-There/dp/1401301304
• How Women Rise: https://www.amazon.com/How-Women-Rise-Holding-Careers/dp/0316440124
• A Whole New Mind: https://www.amazon.com/Whole-New-Mind-Right-Brainers-Future/dp/1594481717
• How to Win Friends and Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034
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Where to find Bryce Rattner Keithley:
LinkedIn: https://www.linkedin.com/in/brycerattner/
GitHub: https://github.com/brk-bot/
Daily Hundred on the App Store: https://apps.apple.com/us/app/daily100-fitness-challenge/id6762108062
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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 a few hours of early-access testing with Anthropic’s newly released model Opus 4.8. I walk through real coding, design, and strategy tasks across Claude Code and Claude Cowork, and give you my unfiltered view on what impressed me and what didn’t.
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What you’ll learn:
Where Opus 4.8 excels: greenfield prototypes, one-shot features, and fast execution
Where it struggles: the last 10%, edge cases in existing codebases, and hallucinations
How Opus 4.8 compares to Opus 4.7 on business strategy work
Why I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap work
The new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and Cowork
The prompting and harness strategy I’d use to get the most out of it
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In this episode, we cover:
(00:00) Introduction to Opus 4.8 
(00:44) Benchmark performance and pricing
(01:53) First coding test: Building a prototyping tool
(03:00) Where it failed: The last 10% problem
(03:27) The hallucination problem
(04:23) Testing Opus 4.8 on existing codebases
(05:24) The ambition test: Building games for a 9-year-old
(07:03) Business strategy test: 4.7 vs 4.8
(08:23) The roadmap test
(09:17) Final verdict
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References:
• System Card: Claude Opus 4.8: https://cdn.sanity.io/files/4zrzovbb/website/c886650a2e96fc0925c805a1a7ca77314ccbf4a6.pdf
• Introducing Claude Opus 4.8 on X: https://x.com/claudeai/status/2060042702150930686?s=20
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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.

In this 30-minute episode, I walk through my favorite feature in Codex: the /goal command. I show how Goals transform AI from a turn-based assistant that needs constant ‘what’s next?’ prompting into an autonomous agent that can work for hours on complex, multi-step tasks. I share three real examples: eliminating thousands of Sentry errors, cleaning 3,900 emails down to 68, and organizing hundreds of Linear tasks.

What you’ll learn:
What Goals are and how they differ from standard prompts
How I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous run
The non-technical use cases that make Goals incredibly powerful: cleaning up 3,900 emails in under four hours and organizing hundreds of project management tasks in Linear
How to write effective /goal prompts with measurable outcomes, verification methods, and constraints
When not to use Goals and what makes a strong versus weak Goal
Why Goals represent a fundamental shift in how we work with AI, from babysitting the model to managing it
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Brought to you by:
Mercury—Radically different banking loved by over 300K entrepreneurs
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In this episode, we cover:
(00:00) Introduction
(01:50) What is /goal and when should you use it?
(02:45) The difference between prompts and Goal-based loops
(04:06) Claire’s first five-hour 45-minute autonomous coding task
(05:05) How to manage a Goal lifecycle: view, pause, resume, and clear
(06:06) How to write strong goals: outcomes vs. outputs
(07:34) The six components of effective Goals
(08:57) Example: Reducing P95 checkout latency with /goal
(09:36) Demo: Using /goal to eliminate Sentry errors in ChatPRD
(13:18) Demo: Burning down Vercel API errors
(17:28) Non-technical use case: Cleaning 3,900 emails with /goal
(21:24) Demo: Using /goal to clean up Linear project tasks
(24:41) When not to use /goal
(26:10) Why /goal changes everything
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Tools referenced:
• Codex: https://openai.com/codex/
• Sentry: https://sentry.io/
• Vercel: https://vercel.com/
• Linear: https://linear.app/
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Other reference:
• OpenAI blog post “Using Goals in Codex”: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex
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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.