AI & I

Dan Shipper

Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves. For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest.

  1. לפני יומיים (2)

    Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million

    If your MCP server has dozens of tools, it's probably built wrong. You need tools that are specific and clear for each use case—but you also can't have too many. This creates an almost impossible tradeoff that most companies don't know how to solve.That's why we interviewed Alex Rattray, the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. We get into MCP and the future of the AI-native internet.  [Disclosure: Dan is a small investor in Stainless.] If you found this episode interesting, please like, subscribe, comment, and share. To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipper Get started with Braintrust at https://www.braintrust.dev/  Timestamps: 00:01:15 - Introduction 00:05:09 - APIs and MCP, the connectors of the new internet 00:11:00 - Why MCP exists 00:17:15 - Why MCP servers are hard to get right 00:20:24 - Design principles for reliable MCP servers 00:25:06 - Using MCP for business ops at Stainless 00:40:57 - Alex's take on the security model for MCP 00:44:42 - How one-off AI actions become permanent production software Links to resources mentioned in the episode:Alex Rattray: Alex Rattray (@RattrayAlex), Alex RattrayStainless: https://www.stainless.com/Inside Stainless: The Developer Tools Startup Anthropic Just Bought for $300 Million

    ‏51 דק׳

  2. 13 במאי

    Claude Code Can Be Your Second Brain

    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. If you found this episode interesting, please like, subscribe, comment, and share!  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) / X⁠Alephic, 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/sabotageThe apps Noah used to set up Claude Code on his phone: ⁠Termius⁠, ⁠Tailscale⁠

    שעה אחת ו-10 דקות

  3. 8 במאי

    The Secrets of Claude's Platform From the Team Who Built It

    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. If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps:00:01:48 - How the Claude platform evolved from API to agents00:04:09 - The primitives that make up Claude Managed Agents00:10:37 - Why the harness and the model are becoming a single unit00:18:49 - The infrastructure wall that kills most agent projects in production00:24:49 - Why team agents need a different shape than individual productivity tools00:26:36 - How Anthropic's legal team uses an agent to review marketing copy00:34:24 - Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms00:35:50 - How to measure agent success with outcome and budget as the end state00:39:11 - What the platform looks like a year from now, when Claude writes its own harness

    ‏43 דק׳

  4. 6 במאי

    Why We Switched From Claude Code to Codex

    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 Introduction00:00:57 How Codex went from a tool for senior engineers to a daily driver for knowledge work00:02:42 How Claude Code proved that a great coding agent works for any knowledge work00:07:24 Austin's switch to Codex00:13:48 How Austin set up Codex with folders, keys, and reviewer agents00:18:24 Using Codex to brainstorm automations across Gmail, Slack, and Notion00:22:42 How Austin manages the human review step when Codex is drafting communications00:28:54 Using Codex to build specialized agents inspired by product executive Claire Vo00:31:09 Synthesizing meeting transcripts and Slack threads into a go-to-market plan00:40:15 Building a live KPI tracker in Notion that agents can read00:44:54 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

    ‏58 דק׳

  5. 29 באפר׳

    How Stripe Is Building for an Agent-native World

    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/subscribeFollow him on X: https://twitter.com/danshipper Head to http://granola.ai/every and get 3 months free with the code EVERY Timestamps00:00:45 Introduction00:01:27 New rules for an agent-driven economy00:03:57 Compute theft is the new payment fraud00:10:00 How Stripe expanded fraud detection from checkout to the full customer lifecycle00:19:48 Why AI companies are scaling way faster than top SaaS companies00:23:27 Outcome-based billing is replacing seat-based pricing00:29:57 Where AI spending is coming from00:36:45 How the developer experience changes when agents are the builders00:41:00 The agentic commerce spectrum, from assisted buying to autonomous purchasing00:51:06 Meet Link, a consumer wallet for delegated agent purchases Links to resources mentioned in the episode:Emily Glassberg Sands on X: https://x.com/emilygsandsStripe: https://stripe.comStripe Radar: https://stripe.com/radarStripe Link: https://link.comLovable: https://lovable.dev

    ‏54 דק׳

  6. 22 באפר׳

    The AI Sandwich: Where Humans Excel in an AI World

    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-pluginCompound engineering guide: https://every.to/source-code/compound-engineering-the-definitive-guideCompound 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

    ‏29 דק׳

  7. 15 באפר׳

    The AI Model Built for What LLMs Can't Do

    Most AI companies are racing to build bigger LLMs. Eve Bodnia thinks that's the wrong approach. Eve is the founder and CEO of Logical Intelligence, which is developing an alternative to the transformer-based models dominating the industry. Her argument: LLMs’ architecture makes them fundamentally unsuited for some mission-critical tasks. A system that generates output one token at a time, with no ability to inspect its own reasoning mid-process or guarantee its results, shouldn't be trusted to design chips, analyze financial data, or even fly a plane. Her alternative is the energy-based model (EBM), a form of AI rooted in the physics principle of energy minimization, not language prediction. Rather than guessing the next probable word, an EBM maps every possible outcome across a mathematical landscape, where likely states settle into valleys and improbable ones sit on peaks.  Dan Shipper talked with Bodnia for AI & I about why she believes LLM progress is plateauing, what it means for AI to actually understand data rather than just pattern-match across it, and how her team is building toward formally verified code generated in plain English—no C++ required. 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  Timestamps:  00:00:51 - Introduction 00:02:09 - Why correctness and verifiability matter in AI 00:09:33 - What an energy-based model is 00:14:21 - How EBMs construct energy landscapes to understand data 00:19:00 - Why modeling intelligence through language alone is a flawed approach 00:26:54 - What it means for a model to "understand" data 00:37:21 - How EBMs solve the vibe coding problem and enable formally verified code 00:43:21 - Why LLM progress is plateauing 00:49:54 - Mission-critical industries haven't adopted LLMs, and how EBMs could fill that gap

    ‏54 דק׳

  8. 8 באפר׳

    We Gave Every Employee an AI Agent. Here's What Happened.

    While walking to the office, our COO Brandon Gell had his AI agent call him and go over his emails in his inbox one by one. When he arrived, he opened Gmail and confirmed she'd done everything he'd asked. "My jaw is on the floor," he messaged me.That was the moment Every got serious about setting up each employee with their own agent. Today, it's a reality—and it has completely changed how we work.Dan Shipper talked to Every COO Brandon Gell and head of platform Willie Williams for Every's AI & I about what happens when everyone at a company gets their own AI sidekick.  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  Visit https://scl.ai/dialect to learn more about Dialect, a new system from Scale AI. Timestamps:  00:00 Introduction00:02:21 How Brandon built Zosia, an AI agent to run his household00:07:09 Brandon's aha moment re: using agents for work00:09:39 What happened when everyone on the team got their own agent00:12:42 How agents take on their owners' personalities, and why that matters inside an org00:23:51 Why it's important for agents to do work in public00:30:51 What we're still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem00:40:45 How we built Plus One, our hosted OpenClaw product00:47:27 The cultural shift required to make agents work at scale

    ‏50 דק׳
הצגת הכול (114)

טריילר

אודות

Learn how the smartest people in the world are using AI to think, create, and relate. Each week I interview founders, filmmakers, writers, investors, and others about how they use AI tools like ChatGPT, Claude, and Midjourney in their work and in their lives. We screen-share through their historical chats and then experiment with AI live on the show. Join us to discover how AI is changing how we think about our world—and ourselves. For more essays, interviews, and experiments at the forefront of AI: https://every.to/chain-of-thought?sort=newest.

מידע

  • יוצרים
    Dan Shipper
  • שנות פעילות
    2023 - 2026
  • פרקים
    114
  • סיווג
    תוכן בוטה
  • זכויות יוצרים
    © Dan Shipper
  • אתר התכנית
    AI & I

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