A novel organic memristor can sense, process, and classify data, enabling ultra-low power, compact in-sensor edge computing that is highly accurate and requires minimal hardware. Reservoir computing, which uses nonlinear transformations to extract temporal features while limiting training to a lightweight readout layer, offers a more compact solution. However, many reservoir computing systems require different types of memristors for the reservoir and output layers, causing greater design complexity. Existing organic optoelectronic memristors can assist with low-power computing tasks but often depend on heterojunctions, which limit their scalability and reconfigurability. By demonstrating a fully analogue, forming-free, voltage- and light-controlled memristor capable of handling sensing, computing, and memory functions, the researchers provide a new framework for in-sensor edge computing.