The inspiration for this series reaches back to the Model Garage articles I read as a kid. Stories that started teaching me about troubleshooting; and sometimes it is as much about human intuition as it was about the iron. When those first articles appeared, video was far more mechanical than electrical; Zworykin and Farnsworth had not yet introduced their all-electronic systems. You can get a sense of that era in this very first Model Garage story from July 1925.
My primary goal with this project was to learn the complexities of SMPTE ST 2110 myself. I realized early on that my initial approach: a linear, systematic study plan wasn't clicking. I needed a way to make it easier for others to learn by grounding the theory in the messy reality of the field. By revamping the DoubleMCX landing page into a hub for real-world scenarios, I found that a narrative approach to learning was not only more effective but far more engaging.
Interestingly, this process of building a narrative-driven curriculum for 2110 taught me something equally important: how to leverage AI. Using AI to synthesize these technical challenges has become central to how I develop my frameworks and troubleshoot these systems, turning the act of learning into a collaborative effort between human experience and machine intelligence.
In the tradition of the Model Garage, these stories feature a rotating cast of personalities: the 2110 Geek, who sometimes overlooks the simple physical cause; the Broadcast Veteran, whose analog/SDI-style intuition is both a superpower and a blind spot; and the retired CTO, who keeps a trained eye on the system-level architecture, who can step back and look at the whole field, when everyone else gets too deep into the weeds. Together, they navigate the five distinct phases of 2110 implementation:
Welcome to The 2110 Case Files.
By Jim Boston — Broadcast Systems Engineer & Creator of DoubleMCX
When I decided to build the DoubleMCX interactive ST 2110 training and reference platform, I wasn’t just learning about PTP, NMOS, SDP, and IP media workflows — I was simultaneously learning how to collaborate with AI tools as a mentor and co-pilot. With decades of hands-on broadcast experience, I turned to AI to accelerate development of visualizations, pop-ups, roadmaps, dashboards, and educational content while keeping my practical “human-in-the-loop” expertise at the center.
People often use “AI” and “Machine Learning” interchangeably, but they are distinct. AI is the broad field of creating tools that perform human-like tasks. Machine Learning is a key technique for training those tools by finding patterns in data.
Today’s models often combine these. In my case, this enabled rapid prototyping while I verified technical accuracy.
I followed the “Three C’s” (Concise, Clear, Consistent) and persona + task + context + format. Techniques like powerful phrases (“Think step-by-step”) and prompt chaining helped break down complex features.
I never fed sensitive data into public tools. I documented my process, verified every output, and maintained transparency. This “human-in-the-loop” approach — my decades of experience guiding AI — is what makes DoubleMCX practical and trustworthy.
Final Thought: AI was my mentor and accelerator, but the vision, accuracy, and educational value come from a veteran engineer who lived through major industry transitions.