In the last couple of years we've seen rapid evolution frontier, massive sized models. Yet at the same time small models have been going through an evolution of their own, using technologies developer for those frontier scaled models. In this talk we'll show how tensor frameworks and autograd made their way into Bayesian models, how massive model development is yielding smaller models, and how both of these are useful for the small data and model developers, and the organizations they support.