How I AI

I’ve been off Claude for months. Not because it got dumb, but because it got annoying. The rambling, the hedging, the preachy little disclaimers on tasks that didn’t need them. I moved most of my daily work to Codex and I didn’t miss it. Then Anthropic shipped Opus 5.5: 40% cheaper than Opus 5, faster, and with what they’re calling a fundamentally different alignment approach. I ran it for a week across real work, including four long-running agentic tasks, a full ChatPRD homepage redesign, an SVG benchmark, and one very firm refusal, and I’m ready to give you the honest verdict. There’s a lot to like. There are still two things that drive me a little crazy. And there’s one capability I genuinely wasn’t expecting.

What you’ll learn:
Why I walked away from Claude entirely, and what it took for me to come back
The real cost math on Opus 5.5 and why pricing matters more for agentic work than single prompts
What happened when I ran four long-running agentic tasks, including one that tried to manipulate Claude mid-run
Why Opus 5.5 is now my go-to for frontend prototyping, and where it still lets me down
The one capability I genuinely didn’t see coming, and no other model in my stack can match it
The moment Opus 5.5 told me flat-out no, and what that says about where Anthropic’s safety posture actually lands in practice
Where Codex still wins, and how I’m splitting my model stack after a full week of testing
—
In this episode:
(00:00) Why I stopped using Claude
(01:02) What Anthropic says Opus 5.5 is
(01:54) Cost, speed, and benchmark overview
(03:20) Safety, alignment, and the cybersecurity limits
(05:02) How I AI bench
(05:39) Voice test: is it actually not annoying?
(07:54) Long-running agentic task results
(10:50) Frontend prototyping
(17:23) Writing voice and email
(19:41) SVG illustrations
(20:46) Video editing
(21:42) My verdict: what it’s good at, what it still isn’t
—
Tools referenced:
• Claude Opus 5.5: https://www.anthropic.com/claude-opus-5-5
• ElevenLabs MCP connector: https://elevenlabs.io/mcp
• Codex (OpenAI): https://openai.com/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 jordan@penname.co.

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:
Why a software factory is more than a coding agent
The public Slack → Linear → GitHub → QA workflow
Human interactions per PR as a signal of automation and throughput
Why human review is still the bottleneck
Scoring agent runs, finding failure modes, and self-improving agent workflows
Replaying real tasks to choose model cost and quality tradeoffs
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 jordan@penname.co.

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:
Why Muse is the best-designed personal agent I’ve tested, and what specifically made it feel that way
The one-shot family PDF Muse produced that Claude and Codex never quite nailed
How Muse’s permission model works, and why it’s different from every other agent I’ve used
Why I set up a sleep training goal in Muse, and what it revealed about agent tone
The activity feed feature I immediately wished Codex and Claude Code had
Where Muse failed, and what it says about the limits of this category right now
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 jordan@penname.co.

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

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