Deterministic equivalent and error universality of deep random features learning, work done in collaboration with Dominik Schröder, Hugo Cui and Daniil Dmitriev is now out on arXiv. In this work, we prove a deterministic equivalent for the resolvent of deep random features sample covariance matrices, which allow us to establish Gaussian universality of the test error in ridge regression. We also conjecture (and provide extensive support) for the error universality for other loss functions, allowing us to derive an asymptotic formula for the asymptotic performance beyond ridge regression. Check it out!