Luffy uses neuroplastic AI for real-time adaptive control. Its neural networks are trained in simulation, without the need for large training data sets, and refined in reality, where they can achieve up to 400x greater efficiency than traditional deep learning. The lightweight architecture is ultra-energy efficient and self-refining, meaning there is no constant retraining from the cloud. This inherent adaptiveness, combined with the ability to be embedded in existing hardware, makes Luffy's models ideal for complex edge use cases, such as industrial motors, VFDs, thermal control and robotics.