I give an overview of the many uses of flows in probabilistic modeling and inference. I focus on settings in which flows are used to speed up or otherwise improve inference (i.e. settings in which flows are not part of the model specification), including applications to Optimal Experimental Design, Hamiltonian Monte Carlo, and Likelihood-Free Inference. I conclude with a brief discussion of how flows enter into probabilistic programming language (PPL) systems and suggest research directions that are important for improved PPL integration.