This live demonstration showcases GAPses, an ultra-low-power smart-glasses platform based on an ultra-low power RISC-V multicore processor (GAP9), enabling always-on, real-time, energy-efficient edge processing of electrooculography (EOG) and electroencephalography. GAPses performs on-device signal processing and machine-learning inference, converting raw biosignals into events without cloud compute or continuous high-bandwidth streaming, enabling energy-scalable and privacy-preserving operation. In the demo, dry electrodes integrated into the glasses frame capture horizontal/vertical EOG, and an on-device lightweight CNN running on GAP9 classifies saccadic eye movements from these EOG signals in real time. The resulting eye-movement events are transmitted via BLE to a laptop running a visualization application, which displays the CNN outputs alongside filtered EOG traces. The classification stream drives multiple interactive scenarios, including grid control, a Tetris game, and live class-probability visualization. During the demo session, we will run the complete pipeline live: a team member will wear the glasses and perform a sequence of saccades to trigger on-device CNN inference. The GUI updates in real time with predicted classes and EOG traces, allowing attendees to observe latency, robustness, and privacy benefits of RISC-V-based embedded biosignal inference in a practical wearable form factor. Overall, the demo highlights GAPses as an open, fully wearable research platform and illustrates how parallel RISC-V compute enables always-on neural interfaces by executing sensing, inference, and event-level decisions locally without cloud dependence or continuous high-bandwidth streaming.