Large-scale datasets have been key to the progress in fields like computer vision during the 21st century. Yet, the over-reliance on datasets has brought new challenges, such as various dataset biases, fixation on a few standardized tasks, failure to generalize beyond the narrow training domain, etc. It might be time to move away from the standard training set / test set paradigm, and consider data as it presents itself to an agent in the real world -- via a continuous, non-repeating stream. In this talk, I will discuss some of the potential benefits, as well as the challenges, of learning in a post-dataset world, including some of our recent work in test-time training.