Fix a curve of probability distributions, perhaps connecting prior and posterior in Bayesian inference. In this talk, I will survey recent ideas to construct interacting particle systems, designed to follow this flow as closely as possible. We will touch on Stein variational gradient descent, kernel mean embeddings, optimal control and diffusion models.