Reservoir computing is a popular scheme to utilize the naturally occurring computational power of dynamical systems and natural phenomena. Originally inspired by recurrent neural networks, it is now applied to a wide variety of substrates and systems. It can be used for many typical machine learning tasks, such as time series prediction or classification, but in recent years it has gathered particular interest for the ease with which a reservoir computer can emulate another dynamical system in the so called "autonomous mode" or "closed loop" configuration. This talk will introduce a new method that extends the existing research, by changing the "target system" class from merely modelling autonomous dynamical systems, to explicitly allowing the reservoir computer to also try and emulate "driven dynamical systems". Of great interest here is the fact, that the "reservoir" itself is a driven dynamical systems. Thus, within this new framework of "semi-closed loop operation", we can turn the reservoir computing scheme on itself and emulate reservoirs with other reservoirs. Many open questions remain, in particular on what the ordering of "emulatable" reservoirs is and how to measure this efficiently.