I’ve been running an experiment. Frank is an AI agent running via OpenClaw in a container, on a dedicated Fedora laptop sitting on my desk. While this started as an experiment, the use case was real: Could I enable Frank to be a true digital assistant for my photography business, Trail Nutz Media? When we talk about AI use cases, we often forget a core truth: AI is successful only when we have people who have actually done the job, who recognize when the job is done well, and who understand when it has gone sideways.

Note: I originally shared this experiment on LinkedIn, but I am archiving the “Frank” journey here as we build out Enterprise-Grade AI use cases I just met Frank. He works for about $5 a day in tokens. He doesn’t need a desk, he doesn’t take lunch, and he puts out the same volume of work as some full-time employees. (Before you come for me: that says more about the “roles” we’ve created than it does about the tech).

Building a Resilient Future: The Power of People, Process, and Technology While attending KubeCon in Salt Lake City, I heard an incredible story from a former colleague now working for a major tech company. They shared insights into the Kubernetes infrastructure supporting their cloud storage service—a system spanning multiple clouds. For just one of these clouds, their monthly spend was estimated at over $20 million, supporting an infrastructure with hundreds of thousands of compute nodes.