  Colloidal heat engines are paradigmatic models to understand the conversion
of heat into work in a noisy environment - a domain where biological and
synthetic nano/micro machines function. While the operation of these engines
across thermal baths is well-understood, how they function across baths with
noise statistics that is non-Gaussian and also lacks memory, the simplest
departure from equilibrium, remains unclear. Here we quantified the performance
of a colloidal Stirling engine operating between an engineered
\textit{memoryless} non-Gaussian bath and a Gaussian one. In the quasistatic
limit, the non-Gaussian engine functioned like an equilibrium one as predicted
by theory. On increasing the operating speed, due to the nature of noise
statistics, the onset of irreversibility for the non-Gaussian engine preceded
its thermal counterpart and thus shifted the operating speed at which power is
maximum. The performance of nano/micro machines can be tuned by altering only
the nature of reservoir noise statistics.
