where are openai/anthropic employees buying clothes from post IPO

Getting the most out of Claude Fable 5, Anthropic’s powerful new model, you need to maximize your ambition: It’s built for full task delegation—you leave it looping for hours or overnight and come back to a finished product. If you want to get the most out of it, you need to relearn what software engineering is and how to step away to let the model do its work. That’s why I invited @mikeyk, head of Anthropic Labs, on @every’s AI & I. Mike’s been using Mythos-class models for a few months now internally at Anthropic, and he’s learned a ton of new tricks to make its increased powers work for him. And, as a co-founder of Instagram, he can reflect on how software engineering has changed over the last 15 years and what it means going forward. We get into: - Why the right workflow for Fable 5 is overnight delegation, not back-and-forth iteration—Mike ends his workday by briefing the model, then wakes up to a completed task. When a remote service went down mid-task, Fable 5 wrote a workaround, documented it, and forged ahead - The gap between what’s in your head and what exists in the world is closing fast—given access to Fable 5 and a set of internal MCPs, an Anthropic recruiter described the experience as, "The first time in my life where I feel like the thing that's in my head and the thing that exists in the world are right next to each other. I can just do it." - Software engineering isn’t dead, but the role has been reinvented—the PM/eng split is blurring, and the better engineers Mike talks to are holding two feelings at once: loss for the craft and shock at what’s now possible - Verification is the new bottleneck—Mike gives Fable video captures of its own work so it can catch animation glitches that screenshots would miss This is a must-watch for anyone building software and trying to figure out their role now that the models can handle so much. Watch below! Timestamps Introduction: 00:00:03 How Fable completely reshaped Mike's workflow: 00:01:48 When to use Sonnet versus Fable: 00:04:48 What the media tracker Mike built over a weekend reveals about agent-native architecture: 00:10:06 The cost to build has collapsed: 00:15:00 Is software engineering over?: 00:19:03 How Anthropic's engineering teams work today: 00:21:48 The mechanics of verification: 00:38:39 Dynamic workflows: 00:47:24 What people should use the model to build: 00:44:39

claude found a 2023 black friday coupon code for every and somehow it still works. annual went from $288 to $144. we are no longer prompting models. we are sending them to do coupon archaeology.

"Codex, where would you say I fall on @every's eight levels of AI adoption?" 👀

Fascinating results Anthropic running away with it right now So many people want to start their own company Google over OpenAI Vercel, Linear, Every, PostHog overperforming A great list if you're trying to figure out where to go work 👇

Here's the template Codex prompt we use to draft GTM plans at Every. When you've already done the big picture thinking in meetings and @SlackHQ, the next step is just prompting an agent to make a plan for you and the agent to review together.

Automation is a lie. CLIs are over. The SaaSpocalypse is dumb. A year ago @danshipper came on the podcast to predict where AI was heading. He was remarkably right—including the call that everyone was sleeping on Claude Code. Dan has a unique lens into where things are going because his team at @every is possibly the most AI-pilled group of people in tech. I always learn a ton talking to Dan. So I brought him back for round two. We'll score these in exactly a year: 🔸 Every company will have one “super-agent” in Slack. 🔸 Codex and Claude Code will become the new operating system for knowledge work. 🔸 The AI job apocalypse is not happening. 🔸 PMs and designers will thrive. 🔸 We will read way more AI-generated writing and we will like it. 🔸 "I would buy SaaS stocks right now." Listen now 👇 youtube.com/watch?v=4D3hDmGh…

We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automation