Young star clusters emerge from their parent molecular cloud heavily hierarchically substructured and asymmetric. Typical fractal dimensions hover around 3/2 in position space as revealed by Gaia data and reproduced by hydrodynamical simulations. Yet frequently dynamicists run N-body simulations of star clusters starting with simple spherically symmetric models. Can we do better? Here I explore various machine learning strategies to generate realistic star cluster initial conditions.