Chimera is common framework for scale-invariant fully homomorphic schemes based on Ring-LWE, unifying the plaintext space and the noise representation. This hybrid protocol allows to use multiple libraries during the same computation and provides the possibility to take advantage of the best of three schemes (TFHE, HEAAN and B/FV). We review how different strategies developed for each of these schemes, such as bootstrapping, external product, integer arithmetic and Fourier series, can be combined to evaluate the principle nonlinear functions involved in machine learning.