How I AI

I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time.

What you’ll learn:
The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigm
The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow
How to think about loop design using the “onboarding an employee” mental model
The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking
How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team
How to set up a weekly skills-identification automation in Codex that spawns its own validating subagents
Why goal-based loops are the hardest to write well—and where most people burn tokens for nothing
The two warning signs that your loop is going to get expensive before it gets useful
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Brought to you by:
WorkOS—Make your app enterprise-ready today
Runway—The creative AI platform for images, video, and more
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In this episode, we cover:
(00:00) Prompts are out and loops are in
(02:30) Defining a loop
(03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals
(06:03) Five things every effective loop needs
(09:26) The “onboarding an employee” framework for designing loops
(11:58) Live build #1: Daily aging PR loop in Claude Code
(17:08) Subagents inside loops
(19:00) Live build #2: Weekly skills identification loop in Codex
(22:57) Watching subagents spin up in real time
(25:28) Warning signals around loops
(27:31) What listeners are doing with loops
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Tools referenced:
• Claude Code: https://claude.ai/code
• Codex: https://chatgpt.com/codex
• OpenClaw: https://openclaw.ai/
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Other references:
• Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175
• Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/
• 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.

In this episode, I sit down with Ankur Goyal, founder and CEO of Braintrust, the AI evals and observability platform used by teams like Notion, Stripe, Vercel, and Zapier. This one is for the senior engineers, staff engineers, VPs of engineering, and CTOs in my audience. We get into how coding agents can take on deeply technical architecture and infrastructure work that no single human engineer could tackle before, and then we demystify evals so you can use them to make your AI products better without touching the implementation.

What you’ll learn:
How Ankur uses Codex to run week-long benchmark experiments across database indexes, column store formats, and execution engines to speed up slow queries
Why he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelessly
The “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agent
How I think about the practical vs. theoretical quality of AI on hard technical problems, and why human attention decays on tedious work
Why evals are the modern version of a PRD, and how to encode “what good looks like” so a model can figure out the “how”
How to build a scoring function live and let an agent improve your prompt inside a safe playground
How Ankur turned his designer David’s taste into a repeatable eval so quality scales beyond one person
Why fixing your CI is the highest-leverage way to speed up engineering velocity
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Brought to you by:
Guru—The AI layer of truth
Persona—Trusted identity verification for any use case
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In this episode, we cover:
(00:00) Introduction to Ankur Goyal
(03:00) Using AI agents for database optimization
(06:10) Running exhaustive benchmarks with coding agents
(09:03) Why staff engineers are wrong about AI limitations
(11:30) The “agent line” framework for delegation
(14:00) Ankur’s workflow: running 4 to 6 concurrent agents
(17:16) Technical setup: foreground agents, background agents, and cloud environments
(20:32) Spending time with AI tools
(23:06) Demystifying evals
(26:02) Live demo: Building an eval for documentation answers
(30:20) The alternative to evals: vibe checks and whack-a-mole
(32:09) Capturing designer taste in scoring functions
(33:13) Quick recap
(33:44) Managing velocity and throughput
(35:40) Why CI/CD investment is critical for AI-accelerated teams
(37:30) Ankur’s prompting strategy when agents fail
(39:10) Closing thoughts and how to connect
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Tools referenced:
• Braintrust: https://www.braintrust.dev/
• Codex: https://openai.com/codex/
• GPT 5.4: https://developers.openai.com/api/docs/models/gpt-5.4
• Claude: https://claude.ai/
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Other references:
• GPT 5.5 just did what no other model could: https://www.lennysnewsletter.com/p/gpt-55-just-did-what-no-other-model
• Paul Graham’s Maker vs. Manager Schedule: http://www.paulgraham.com/makersschedule.html
• tmux: https://github.com/tmux/tmux
• Chris Tate at Vercel: https://www.linkedin.com/in/ctatedev/
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Where to find Ankur Goyal:
LinkedIn: https://www.linkedin.com/in/ankrgyl/
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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.

Claude Fable 5 is the first Mythos-class intelligence model to be generally available, and I got early access to test it before launch. In this episode, I walk through what Anthropic is promising, what actually stood out when I used it on real work, and where I think it fits in your AI stack.
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In this episode, we cover:
(00:00) Introduction: Fable 5 is finally here
(00:31) What Anthropic says about the model
(05:14) Token-intensive by design
(06:28) Safety classifiers and the new fallback concept
(07:46) Is this or is this not Mythos?
(08:30) New product launches: Managed Agents and more
(09:20) Crushing benchmarks
(09:55) What it’s actually like to use (the good and the bad)
(11:40) Test 1: product graph spec
(12:56) Test 2: designing a skills registry
(14:04) Conservative on execution
(14:43) Test 3: multi-agent orchestration
(15:39) My takeaways
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Tools referenced:
• Claude Fable 5: https://www.anthropic.com/news/claude-fable-5-mythos-5
• Claude Managed Agents: https://platform.claude.com/docs/en/managed-agents/overview
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Other reference:
• SWBench Pro benchmark: https://www.swebench.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.

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
—
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.