Myoelectric signals (MES), which are electrical impulses generated during muscle contraction, are used primarily in prosthetics and rehabilitation. By detecting and interpreting these signals, prosthetic devices can be accurately controlled by individuals with limb loss or impairment. Building on several decades of research and development in prosthetic foot and hand control, as well as advances in artificial intelligence (AI) and increased computing power, the authors propose a method to leverage the AI capabilities of the prosthesis and, when available, cloud-based AI classification. They use an old technique called deferred synchronous communication to achieve seamless control.