The article introduces a novel architecture for information processing using a single nonlinear node with delayed feedback, inspired by the brain's ability to process information efficiently. This approach, known as "reservoir computing," demonstrates that complex networks can be simplified to a single nonlinear node while maintaining excellent performance in speech recognition and time series prediction tasks. The authors experimentally and numerically show that this simple system can perform efficient information processing, paving the way for feasible and resource-efficient technological implementations of reservoir computing. The study highlights the potential of delay-dynamical systems, even in their simplest form, to handle complex tasks with high efficiency.The article introduces a novel architecture for information processing using a single nonlinear node with delayed feedback, inspired by the brain's ability to process information efficiently. This approach, known as "reservoir computing," demonstrates that complex networks can be simplified to a single nonlinear node while maintaining excellent performance in speech recognition and time series prediction tasks. The authors experimentally and numerically show that this simple system can perform efficient information processing, paving the way for feasible and resource-efficient technological implementations of reservoir computing. The study highlights the potential of delay-dynamical systems, even in their simplest form, to handle complex tasks with high efficiency.