G\'acs' coarse-grained algorithmic entropy leverages universal computation to quantify the information content of any given physical state. Unlike the Boltzmann and Shannon-Gibbs entropies, it requires no prior commitment to macrovariables or probabilistic ensembles, rendering it applicable to settings arbitrarily far from equilibrium. For Markovian coarse-grainings, we prove a number of algorithmic fluctuation inequalities. The most important of these is a very general formulation of the second law of thermodynamics. In the presence of a heat and work reservoir, it implies algorithmic versions of Jarzynski's equality and Landauer's principle. Finally, to demonstrate how a deficiency of algorithmic entropy can be used as a resource, we model an information engine powered by compressible strings.

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