AI & I

From time to time, we will republish episodes that you might have missed. This episode originally aired in September 2025.
Noah Brier uses Claude Code as his second brain—it’s the coolest notetaking setup we’ve ever seen.
He has Claude running on a server in his basement hooked up to a VPN. It stores, reads, and writes to thousands of notes in his Obsidian vault. He does it all from his phone.
We had him on the show to tell us exactly how he’s pulling this off. 
Dan and Noah get into:
The nuts and bolts of the Claude Code-Obsidian setup: Noah set up Claude Code on top of his Obsidian root directory, and he walked me through how he uses it to prep for an upcoming speech—creating a project folder, pulling in relevant research from his notes, saving transcripts from chats with other LLMs, and generating daily progress updates.
The “thinking partner” that lives inside Noah’s second brain: Noah points out that in the hype around AI’s ability to write, the fact that it can read is overlooked. That’s why he has an agent inside Claude Code with strict guardrails to stay in “thinking mode.” It logs his questions, tracks insights, and catches him up on research if he returns to a project after a few days away.
How Noah does deep work on his phone: Noah rigged a home server in his basement, put his Obsidian vault in it—and then runs Claude Code on top. Noah says that being able to think, write, research, and ship code from his phone has fundamentally changed the way he works.
This episode is a must-watch for anyone curious about who wants to learn how to use Claude Code to build a true second brain.
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Timestamps: 
00:00:52 - Introduction 
00:02:10 - How you can do deep work on your phone 
00:05:30 - Why Noah thinks Grok has the best voice AI 
00:11:11 - The nuts and bolts of Noah's Claude Code-Obsidian setup 
00:26:05 - Using an agent in Claude Code as a "thinking partner"
 00:30:23 - Noah's Thomas' English Muffin theory of AI 
00:39:47 - The white space still left to explore in AI 
00:48:44 - How Noah is preparing his kids for AI 
01:00:06 - How he brought his Claude Code setup to mobile
Links to resources mentioned in the episode:
Noah Brier: ⁠https://www.noahbrier.com/⁠, ⁠Noah Brier (@heyitsnoah) / X⁠
Alephic, his AI strategy consultancy: ⁠alephic.com⁠ 
The conference he leads about marketing and AI: ⁠http://BRXND.AI⁠ 
A newsletter he writes about AI: ⁠newsletter.brxnd.ai⁠  
The declassified relic from World War II they talk about: ⁠https://www.alephic.com/sabotage
The apps Noah used to set up Claude Code on his phone: ⁠Termius⁠, ⁠Tailscale⁠

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget.

That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. 

On this week’s AI & I from @every, I talk with Angela Jiang (@angjiang), head of product for the Claude platform, and Katelyn Lesse (@katelyn_lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production.

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To hear more from Dan Shipper:
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Follow him on X: https://twitter.com/danshipper
Timestamps:
00:01:48 - How the Claude platform evolved from API to agents
00:04:09 - The primitives that make up Claude Managed Agents
00:10:37 - Why the harness and the model are becoming a single unit
00:18:49 - The infrastructure wall that kills most agent projects in production
00:24:49 - Why team agents need a different shape than individual productivity tools
00:26:36 - How Anthropic's legal team uses an agent to review marketing copy
00:34:24 - Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms
00:35:50 - How to measure agent success with outcome and budget as the end state
00:39:11 - What the platform looks like a year from now, when Claude writes its own harness

In January, Dan Shipper wrote that whoever wins vibe coding wins how you work on your computer—and OpenAI had some serious catching up to do.
Three months and the release of GPT-5.5 later, Codex has more than caught up. Austin Tedesco, Every's head of growth, now spends about 80 percent of his working time inside the Codex desktop app, doing everything from drafting go-to-market plans from a stack of meeting transcripts to rebuilding the company's KPI dashboard.
On this episode of AI & I, Dan sat down with Austin to discuss why the agent management interface—a desktop app built on top of a coding agent—is becoming the new operating system for knowledge work, and why Codex has become his daily driver.
If you found this episode interesting, please like, subscribe, comment, and share!
To hear more from Dan Shipper:
Subscribe to Every: every.to/subscribe
Follow him on X: twitter.com/danshipper
Join the membership for Where You Live at joinbilt.com/dan
Timestamps for YouTube:
00:00:00 Introduction
00:00:57 How Codex went from a tool for senior engineers to a daily driver for knowledge work
00:02:42 How Claude Code proved that a great coding agent works for any knowledge work
00:07:24 Austin's switch to Codex
00:13:48 How Austin set up Codex with folders, keys, and reviewer agents
00:18:24 Using Codex to brainstorm automations across Gmail, Slack, and Notion
00:22:42 How Austin manages the human review step when Codex is drafting communications
00:28:54 Using Codex to build specialized agents inspired by product executive Claire Vo
00:31:09 Synthesizing meeting transcripts and Slack threads into a go-to-market plan
00:40:15 Building a live KPI tracker in Notion that agents can read
00:44:54 Using Codex for recruiting
Links to resources mentioned in the episode:
Austin on X: @tedescau
Dan's January essay on OpenAI's catch-up problem: every.to/chain-of-thought/openai-has-some-catching-up-to-do
Every's vibe check on GPT-5.5: every.to/vibe-check/gpt-5-5

Emily Glassberg Sands leads data and AI at Stripe, which processes roughly 2% of global GDP, giving her a bird’s-eye view into how AI is upending the internet economy. 
Dan Shipper talked with Glassberg Sands for Every's AI & I about what the data on Stripe's network actually shows: AI companies are scaling three times faster than the top SaaS cohort of 2018, fraud has moved from the checkout to the full funnel, and agents have started buying things, although mostly low-stakes commodities like Halloween costumes. 
The conversation covers the new fraud types unique to AI companies, the AI-on-AI arms race between bad actors and fraud detectors, where AI revenue growth is actually coming from, and how Stripe is rebuilding the payments infrastructure for a world where the buyer is an agent.

If you found this episode interesting, please like, subscribe, comment, and share!

To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper

Head to http://granola.ai/every and get 3 months free with the code EVERY
Timestamps
00:00:45 Introduction
00:01:27 New rules for an agent-driven economy
00:03:57 Compute theft is the new payment fraud
00:10:00 How Stripe expanded fraud detection from checkout to the full customer lifecycle
00:19:48 Why AI companies are scaling way faster than top SaaS companies
00:23:27 Outcome-based billing is replacing seat-based pricing
00:29:57 Where AI spending is coming from
00:36:45 How the developer experience changes when agents are the builders
00:41:00 The agentic commerce spectrum, from assisted buying to autonomous purchasing
00:51:06 Meet Link, a consumer wallet for delegated agent purchases

Links to resources mentioned in the episode:
Emily Glassberg Sands on X: https://x.com/emilygsands
Stripe: https://stripe.com
Stripe Radar: https://stripe.com/radar
Stripe Link: https://link.com
Lovable: https://lovable.dev

Most frameworks for working with AI agents assume humans should stay in the loop at every phase. That’s the wrong approach, says Cora general manager Kieran Klaassen.
Kieran is the creator of Every's AI-native engineering methodology, compound engineering. His four-step framework—plan, work, review, compound—rebuilds how engineers work with agents. The insight, worked out with collaborator Trevin Chow, is about when to be in the loop and when to step away and let the model handle it. "LLMs are very good at just following steps, doing deep work, working for hours—days even now," Kieran says. "That thing is kind of solved."
Kieran and Trevin describe an AI workflow as a sandwich. Agents are the workhorse filling, and humans are the bread, responsible for framing the problem at the start and reviewing the outputs at the end. 
Every CEO Dan Shipper talked with Kieran for AI & I about why setting the frame of a problem is still hard for agents, why simulated personas won't replace human judgment, Dan's bar for AGI—an agent worth running 24/7 with no off switch—and what Kieran's background as a classical composer taught him about performance, polish, and finding the parts of work that bring you joy.
If you found this episode interesting, please like, subscribe, comment, and share!
Head to http://granola.ai/every and get 3 months free with the code EVERY
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe 
Follow him on X: https://twitter.com/danshipper 

Discover more resources in the episode
Compound engineering plugin: https://github.com/EveryInc/compound-engineering-plugin
Compound engineering guide: https://every.to/source-code/compound-engineering-the-definitive-guide
Compound engineering camp: https://every.to/source-code/compound-engineering-camp-every-step-from-scratch

Timestamps:  
 00:00:00 – Introduction and the AI sandwich metaphor
 00:02:33 – What compound engineering is and how it’s evolved
 00:04:27 – The "work" phase of agentic coding is essentially solved
 00:06:27 – Why humans belong at the beginning and the end of an AI workflow
 00:11:06 – Dan's argument for why agents can't change frames—and how this will keep us employed
 00:16:51 – Full automation is a moving target
 00:23:21 – Musical composition as a model for human-AI collaboration
 00:26:39 – Find your place in an AI-accelerated world by leaning into what brings you joy