How I AI

Brian Grinstead is a distinguished engineer at Mozilla, where he’s worked on Firefox and the web platform since 2013 (he joined to help launch Firefox DevTools). Recently he and his team pointed an agentic bug-finding pipeline at Firefox—a codebase with tens of thousands of files and tens of millions of lines of code—and shipped a record month of security fixes. The viral chart everyone saw gave the credit to Anthropic’s new Mythos model. Brian’s take is that the harness and pipeline did just as much of the work, and he walks through exactly how it runs and how anyone can build a starter version.

What you’ll learn:

1. How to build a basic bug-finding harness by running Claude Code or Codex with one prompt and the -p flag, no SDK required
2. Why pointing an agent at a whole codebase fails, and how an LLM judge can score and rank files before you spend any compute
3. How a verifier subagent kills false positives by catching the agent when it cheats
4. The goal-loop pattern: give an agent a tightly scoped problem, a clear pass/fail signal, and let it retry far past the point a human would quit
5. Why teams that already invested in fuzzing, CI, and dev tooling are so far ahead
6. How to weigh model versus harness, and why Brian splits the credit close to 50-50
7. How a non-engineer can reuse the same score, verify, and fix the loop for design quality, conversion rate, or tech debt
8. Why AI-generated patches still can’t ship on their own, and where humans stay in the loop

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Brought to you by:

WorkOS—Make your app enterprise-ready today

Metaview—The agentic recruiting platform for winning teams

—

In this episode, we cover:

(00:00) Introduction to Brian Grinstead

(02:43) The viral chart: Firefox Security Bug Fixes by Month

(05:32) How the custom harness works

(10:22) Goal loops and guardrails

(14:45) How they built it

(16:55) Real bugs, including a 15-year-old one

(23:00) Open-sourcing it

(26:26) Why humans still review every fix

(32:30) Live demo and prioritizing files

(40:18) Mobilizing the team and recap

(42:33) Lightning round

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Tools referenced:

• Claude Code: https://claude.ai/code

• Claude Agent SDK: https://code.claude.com/docs/en/agent-sdk/overview

• Codex: https://openai.com/index/openai-codex/

• OpenAI Agent SDK: https://developers.openai.com/api/docs/guides/agents

• VS Code: https://code.visualstudio.com/

• Docker: https://www.docker.com/

• Firefox: https://www.mozilla.org/firefox/

• Address Sanitizer: https://github.com/google/sanitizers

• RLBox: https://rlbox.dev/

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Other references:

• Mozilla Bug Bounty Program: https://www.mozilla.org/security/bug-bounty/

• Mozilla GitHub: https://github.com/mozilla

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Where to find Brian Grinstead:

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

GitHub: https://github.com/bgrins

—

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 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:

1. The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigm
2. The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow
3. How to think about loop design using the “onboarding an employee” mental model
4. The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking
5. How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team
6. How to set up a weekly skills-identification automation in Codex that spawns its own validating subagents
7. Why goal-based loops are the hardest to write well—and where most people burn tokens for nothing
8. The two warning signs that your loop is going to get expensive before it gets useful

—

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

—

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].

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:

1. How Ankur uses Codex to run week-long benchmark experiments across database indexes, column store formats, and execution engines to speed up slow queries
2. Why he argues there’s no excuse to skip rigorous benchmarking now that agents can run them tirelessly
3. The “agent line” framework: how to decide which decisions, directions, and interactions you can hand off to an agent
4. How I think about the practical vs. theoretical quality of AI on hard technical problems, and why human attention decays on tedious work
5. 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”
6. How to build a scoring function live and let an agent improve your prompt inside a safe playground
7. How Ankur turned his designer David’s taste into a repeatable eval so quality scales beyond one person
8. 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

—

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/

—

Where to find Ankur Goyal:

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

—

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].

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

—

Other reference:

• SWBench Pro benchmark: https://www.swebench.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].

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:

1. How to build a Claude Project with custom instructions for vetting brands based on heritage, craftsmanship, and return policies
2. The shopping criteria that help surface century-old manufacturers over trendy direct-to-consumer brands
3. How to use Claude to search through trusted vendor websites that have terrible UX
4. Why AI actually helps small artisans and heritage brands compete against Amazon’s infrastructure
5. How to use Claude Cowork to automate returns by finding receipts in your email and drafting refund requests
6. The technique for getting Claude to analyze whether a brand is legitimate or just a drop-shipping operation
7. How to shop within a specific budget or with gift cards using AI assistance

—

Brought to you by:

Orkes—The enterprise platform for reliable applications and agentic workflows

Metaview—The agentic recruiting platform for winning teams

—

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/

—

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].

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:

1. How to create an AI avatar using Google Flow in under five minutes
2. Why video AI tools unlock creative possibilities for people with zero video production skills
3. The step-by-step process of generating a full storyboard using AI as your creative producer
4. How to use character consistency features to generate multiple video scenes with the same avatar
5. The uncanny-valley moments you’ll encounter when your AI clone doesn’t quite nail emotions or physics
6. How to stitch together AI-generated scenes into a complete video using built-in editing tools

—

Brought to you by:

Merge—Connective infrastructure for production AI

Jira Product Discovery—Prioritize with insights, build with confidence

—

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

—

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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:

1. How to build and ship an iPhone app using Replit without any coding knowledge
2. The step-by-step process for creating custom AI-generated workout videos by combining Gemini images with real exercise footage
3. How to use Claude as your technical architect and Claude Code as your software engineer
4. How to navigate App Store submission requirements (including fixing rejection feedback)
5. Why being hyper-literal in your prompts unlocks better AI results
6. Why a beginner’s mind is actually an advantage when building with AI tools

—

Brought to you by:

WorkOS—Make your app enterprise-ready today

Metaview—The agentic recruiting platform for winning teams

—

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

—

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 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:

1. Where Opus 4.8 excels: greenfield prototypes, one-shot features, and fast execution
2. Where it struggles: the last 10%, edge cases in existing codebases, and hallucinations
3. How Opus 4.8 compares to Opus 4.7 on business strategy work
4. Why I’m still reaching for Opus 4.7 on data-heavy strategy and roadmap work
5. The new features shipping alongside the model: dynamic workflows with parallel subagents and effort control in Claude.ai and Cowork
6. 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

—

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

—

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].

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:

1. What Goals are and how they differ from standard prompts
2. How I used /goal to eliminate hundreds of error logs in my codebase over a five-hour autonomous run
3. 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
4. How to write effective /goal prompts with measurable outcomes, verification methods, and constraints
5. When not to use Goals and what makes a strong versus weak Goal
6. Why Goals represent a fundamental shift in how we work with AI, from babysitting the model to managing it

—

Brought to you by:

Mercury—Radically different banking loved by over 300K entrepreneurs

—

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

—

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].

Felix Rieseberg is the engineering lead for Claude Cowork and Claude Code Desktop at Anthropic. He previously spent five years at Slack building developer tools. In this episode, Felix demonstrates how he uses Claude to solve real-life problems: analyzing floor plans to build interactive 3D house walkthroughs, automatically tracking promises he makes on Twitter, and building a $20 hardware device that physically approves Claude actions with a button press.

What you’ll learn:

1. How to use Claude Cowork to turn a 2D floor plan into an interactive 3D walkthrough where you can move furniture around
2. The “go one abstraction layer up” philosophy: why you should never manually enter data Claude can find itself
3. How to use your email as an inventory database for furniture, clothing, and personal purchases
4. When to use Opus vs. Sonnet 4.6 (hint: it’s about how well you can scope the problem, not technical complexity)
5. How live artifacts work and why they’re powerful for dashboards that refresh with real-time data from your connectors
6. The product philosophy behind making latency delightful
7. How to build your own $20 hardware device using Claude Code (no hardware experience required)
8. Why Felix never reads the code Claude writes and judges it purely on output

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Brought to you by:

Magic Patterns—Prototypes that look like your product

Guru—The AI layer of truth

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In this episode, we cover:

(00:00) Introduction to Felix Rieseberg

(02:40) Felix’s role at Anthropic

(03:25) The multiple tabs in Claude and why they exist

(05:55) Using Claude Cowork to design a new house using floor plans

(09:52) When to use Opus versus Sonnet 4.6

(12:37) Building an interactive 3D furniture planner

(14:30) Using your email as a source of truth for personal inventory

(15:58) The anti-to-do list: going one abstraction layer up

(23:14) Introduction to live artifacts

(26:02) Building a personal dashboard with live data

(28:37) Being polite to Claude (and why it matters for your humanity)

(30:28) Claude interaction tips

(32:33) Looking at the daily dashboard

(33:55) How live artifacts work with connectors

(35:02) Redesigning the dashboard

(37:55) The biggest gap: people don’t know what problems AI can solve

(41:52) The reverse interview

(42:30) Making latency delightful through asynchronous design

(44:05) The redesigned dashboard

(45:28) AI should free up your creative energy

(46:44) Building a $20 hardware Claude buddy

(52:33) Why kids are magical AI users

(54:30) Recap and final thoughts

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Tools referenced:

• Claude Cowork: https://www.anthropic.com/product/claude-cowork

• Claude Code: https://claude.ai/code

• Claude for Chrome: https://code.claude.com/docs/en/chrome

• Claude Desktop: https://claude.ai/download

• Live Artifacts: https://support.claude.com/en/articles/14729249-use-live-artifacts-in-claude-cowork

• Connectors (Spotify, Gmail, Calendar, Notion): https://claude.ai/settings/connectors

• Slack: https://slack.com/

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Where to find Felix Rieseberg:

Website: https://felixrieseberg.com/

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

X: https://x.com/felixrieseberg

GitHub: https://github.com/felixrieseberg

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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 [email protected].