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CHWs Augment: Quantization & Edge AI
A live walkthrough of building an Android AI app for community health workers, covering model quantization, edge deployment, code design, and equitable accessibility.
So, I’ll be presenting my journey building my first Android APK, CHWs Augment. The focus will be on augmentation, because with the rise of AI, people often think of it as a replacement tool—but my project explores AI as an augmentor, designed to enhance human work rather than replace it.
Technically, the project dives into quantization, Edge AI, and responsible AI, with an emphasis on equity: How can someone in a remote area benefit from AI just like someone in the city? How can we build free AI solutions that run efficiently on low-budget phones and scale augmentation to improve efficiency for CHVs?
I’ll be doing a live demo of the APK, showing its workflow and the tangible ways it helps CHVs in remote areas. The focus is on impact, not revenue: building scalable AI that improves work and accessibility. Along the way, I’ll share the internals—the code, model design, and deployment choices—so the audience sees not just what it does, but how it was built.
This project is imperfect and experimental, which is intentional: sharing raw builds accelerates learning, sparks collaboration, and pushes the boundaries of AI deployment in resource-constrained settings.