Deep Dive AI • Michigan Milestone 54,000 Views Is an Entire Battle Creek A number on an analytics screen became much easier to understand when I stopped thinking about page views—and started picturing an entire Michigan city. The Number Became Real There are numbers we understand mathematically and numbers we understand instinctively. For months, 54,000 blog views belonged to the first category. I could see the counter rising inside Blogger. I could compare one month against another. I could watch an older article suddenly wake up in search. But 54,000 still felt like an abstraction—the kind of number a dashboard reports without giving you any real sense of scale. Then I compared it with Battle Creek. 54,000+ roughly city-sized attention Deep Dive AI has now recorded a number of page views comparable in scale to the population of a small M...

Deep Dive AI • Content Intelligence 54,000 Views Later: The Blog Is Starting to Talk Back After 921 posts, the interesting number is no longer the lifetime total. The interesting part is that the archive is finally large enough to show us what deserves to become a system. All-time views 54,301 The archive now has enough traffic history to expose patterns. Published posts 921 Not a small sample anymore. This is a content dataset. Last month 5,485 Recent traffic is operating well above the early baseline. Yesterday 542 One day is not a trend, but it shows the ceiling is higher. There is a point in a long-running project where the numbers stop being decoration and start becoming instruction. The archive is no longer just content. It is starting to reveal what the audience actually wants. That is the milestone that matters to me. D...

Deep Dive AI • MRI Safety Workflow MRI Safety Navigator: From Implant Search to a Defensible MRI Safety Workflow The goal is not to replace MRI safety judgment. It is to make the research process faster to organize, easier to verify, and much harder to lose track of when the patient is waiting. After years of researching implants, reading manufacturer conditions, tracking model numbers, checking old documentation, and trying to make sure every important condition was accounted for before a patient entered the magnet, I kept coming back to the same problem: the information exists, but the workflow is scattered. That is why I built MRI Safety Navigator . It is not intended to be another implant list. It is designed as a practical workspace for the part of MRI safety that happens between receiving incomplete device information and reaching a documented, evidence-based decision. Open MRI Safety Navigator Read the How-To Guide The core idea MRI s...

Deep Dive AI Factory • September 2026 Today We Bolt Hermes Onto the AI Factory After months of building production systems one piece at a time, the Deep Dive AI Factory is getting something it has never had before: a persistent supervisory intelligence. Factory V2 READY OpenMontage ACCEPTED Regression Tests 108 PASS Next Layer HERMES This is one of the biggest architecture changes we have made since the Deep Dive AI Factory stopped being a collection of scripts and started becoming an actual production system. Today we begin integrating Hermes Agent as the supervisory head of the Factory. We are not replacing the Factory with Hermes. We are giving the Factory a supervisor. That di...

MRI Safety Navigator: Turning Implant Research Into a Practical MRI Safety Workflow MRI safety information has become more detailed over the years. That should make implant screening easier. In practice, it often does the opposite. A patient arrives for an MRI with an implant, device card, model number, surgical history—or sometimes almost no useful information at all. The MRI technologist then has to determine exactly what the device is, locate reliable manufacturer documentation, establish its MR status, identify every applicable scanning condition, compare those conditions with the scanner and planned examination, document the decision, and decide whether anything still requires escalation. That is not simply a search problem. It is a workflow problem . That is why I created MRI Safety Navigator . Try MRI Safety Navigator Open MRI Safety Navigator The goal is not to replace the MRI technologist, radiologist, MR Medical Director, manufacturer documentation, institutional po...

The AI Drift Problem: Your Agent Worked Perfectly Last Month—So Why Is It Failing Today? An AI workflow can pass every test. It can produce clean results, follow the format, use the right files, meet the checklist, and appear ready for production. Then a month later, without anyone intentionally changing the workflow, the quality starts slipping. The output gets a little less consistent. The format breaks more often. The sources are not quite as strong. The retries increase. The human corrections take longer. The cost per finished result quietly rises. Nothing exploded. Nothing obviously broke. The system drifted. That is the AI drift problem. In traditional software, we are used to thinking in terms of bugs. Something works, then someone changes the code, then something breaks. AI systems are different. They live inside a moving environment. The model can change. The inputs can change. The APIs can change. The websites can change. The user’s needs can change. ...