Are We Underestimating Small Edge AI Models?[D]

A lot of recent discussion around Edge AI focuses on running increasingly larger local LLMs. Meanwhile modern smartphones already have enough compute for many practical computer vision tasks that don't require massive models at all. I recently built and released an Android feature that performs offline recognition of handwritten and printed Morse code from images and live camera frames. The final solution combines lightweight ML and computer vision techniques running entirely on-device. The AI module is under 5 MB, works fully offline, and runs on Android devices using LiteRT for inference. What made the project particularly interesting was that the entire ML pipeline was built from scratch: data collection, synthetic dataset generation, annotation, model training, evaluation, mobile optimization, and Android integration. Training was performed on a personal GPU workstation using TensorFlow/Keras, while annotation and dataset preparation relied on Label Studio and custom data-generation tools. While the problem itself is fairly niche, the project made me wonder whether we are overlooking a large class of small, highly specialized models that can solve practical tasks locally without requiring cloud infrastructure or large foundation models. What practical Edge AI applications do you think are currently underexplored? Demo video showing the feature running entirely on-device: • Downloading the optional AI module • Real-time camera recognition • Image recognition • Module removal [https://youtube.com/shorts/Y2qOK0N1Bvk](https://youtube.com/shorts/Y2qOK0N1Bvk)