The field's tools forecast whether a trial will enroll. Almost nobody asks whether the population is real. A multi-panel adversarial run killed our lead population assumption in a day.

Everyone's got a jet pack now and productivity still hasn't moved. This is about the fast, fragile little machines people are building with them, none of it composed, nobody steering, and what to do next.

I thought the constraint had flipped — from internal bandwidth to external pace. It's more complicated than that.

Most people are using LLMs as question-answering machines. That's not wrong, it's just not the interesting part.

What most people call AI is often just a chat interface sitting on top of a much bigger capability problem.

Why the AI studio metaphor stopped being enough for me, and how I'm starting to think in terms of AI Forge, Speedrift, and a bounded dark factory.

What I've learned about working with LLMs in CLI contexts—the patterns, the pitfalls, and why understanding matters more than tooling.

A curated list of repos, configs, and practices for CLI-based AI coding—road-tested by people doing real work.

Why my LLM work feels like running an AI studio, and how the environment, connectors, and mindset fit together.

The playbook I'm using to embed AI agents across meetings, CRM, messaging, and build workflows for business acceleration.

A tour of the tmux, Tailscale, and Claude agent setup that keeps my remote development environment humming.