Last week I wrote about how I structured my agents to learn—the two-file system, the feedback loop, the accumulation of knowledge over time. But the technical architecture is only half the story. The more interesting part is what happens when agents actually learn. Something shifted after the first few

Most AI agents have amnesia. Every conversation starts fresh. You teach them something useful, and by tomorrow it's gone. I wanted agents that actually get better over time. Not through fine-tuning or retraining—through structured learning from use. Here's how I built it. The Two-File System Every agent in my

Last week I wrote about my Chief of Staff agent—a system that orchestrates specialized agents across my calendar, email, tasks, and content. It's changed how I work. But the bigger idea is what comes next. My agents are starting to work with other agents. Right now, it's simple: my

I built a Chief of Staff agent. It runs my life. Not metaphorically. Every morning, it pulls my calendar, scans my email, checks my tasks, and tells me what matters today. It knows what I'm working toward this week, this month, this quarter. It prioritizes ruthlessly. And it gets better

Everyone's talking about the 1000x engineer. What about the 1000x non-engineer? I've been building personal agents for the past few weeks. A Chief of Staff agent that orchestrates my day. A Content agent that plans and schedules my writing. A Calendar agent, an Email agent, a Planning agent that tracks

This blog post was written almost entirely by an agent. Not a chatbot. Not a one-shot prompt. An agentic workflow—a system of specialized agents that read context, make decisions, and execute across multiple tools and services. I've been building personal agents for the past few weeks. A Chief of