A client has hired me to build a streaming platform where viewers will be able to subscribe, watch live programming, and return to available recordings afterward. Think of a Netflix-style service built around live events and on-demand viewing, with the operational tools needed to prepare and manage those broadcasts.
My responsibility covers the engineering, interface design, and code. I’m translating the client’s requirements into a connected service, from the viewing experience to the systems that prepare and deliver each event.
This chapter is dated to the project’s August 10, 2026 kickoff. The accompanying mockups show later development concepts. Design and functionality may change; displayed content and data are illustrative. Client branding has been removed for this article.
AWS provides the cloud infrastructure behind the build. To explain how I’m approaching the event setup, I think of a stack of plates.
Each stack represents one live event. Each plate represents a layer of its setup, such as how the live feed arrives, where it needs to go, or how the event is recorded. Now imagine each plate is available in several colors. Choosing a color represents choosing how that particular layer should behave.
Putting an event together means selecting the right combination of plates and colors for that production. The next event gets its own combination. Some choices may carry over, while others change with the requirements of the show.
That flexibility is central to the engineering. In production, every event has its own requirements. The platform needs to accommodate those differences while keeping the setup understandable to the person operating it. The plates also have to work together: changing one choice can affect what is needed elsewhere in the stack.
My production experience helps me think through those relationships. I’m considering how an event is prepared, what needs to be checked before going live, what information operators need during the broadcast, and what happens to the recording afterward. The interface needs to make those decisions manageable without requiring the operator to understand every AWS service underneath them.
AI is a significant part of my development process. I’m using it to help write and review code, explore implementation approaches, and work through technical problems. I remain responsible for the architecture, design decisions, and verification of the resulting system.
My target is to bring a build I estimate would otherwise take more than a year down to roughly three months. AI accelerates the implementation and gives me more opportunities to test and refine an approach. The resulting workflows still need to be checked, including whether the event configuration produces the intended viewing experience.
The platform remains in development, and I’m keeping the client’s identity and detailed commercial plans private. The assignment is to bring that configurable production system together with a straightforward subscription streaming service.
For the production team, the choices behind each event need to be visible and manageable. For the viewer, that work should resolve into something simple: finding the program, opening it, and watching.