Building IRIS: My Adaptive Accessibility Project

Standard web accessibility is too "one-size-fits-all." Most sites assume every user interacts with a screen the same way, forcing people with invisible disabilities to adapt to the interface rather than the interface adapting to them. This is the gap I'm trying to bridge with IRIS.

IRIS is designed as an adaptive companion that modifies media consumption based on specific user preferences. Instead of a static set of accessibility toggles, the goal is a system that personalizes text-to-speech, color themes, and layout structures dynamically to match the user's actual needs.

Since I'm currently deep-diving into Go and backend architecture, I'm using this project as a real-world deployment testbed. I'll be documenting the entire AI workflow as I build it out, specifically focusing on:

• Designing a flexible database schema to handle complex user accessibility profiles.
• Building the API layers in Go to ensure low-latency preference switching.
• Implementing the logic that triggers layout adaptations in real-time.

I'm treating this as a "build from scratch" experiment to see how far I can push personalized UX. I'll be posting the technical hurdles and architectural decisions as I move from the design phase to a functional backend.