Although the GFN parametrizations are designed to be general and robust, specialized applications can benefit from tailored parameters. For example, GFN1-xTB was re-fitted to DFT reference ionization potentials (IPs) and electron affinities (EAs) for the calculation of mass spectra, which yielded the IPEA-xTB version. Additionally, GFN1-xTB was also optimized for specific materials, such as organosilicon compounds (GFN1(Si)-xTB) and halide perovskites. Even tuning a single parameter can improve the description of exotic interactions. 14 Classical packages like tblite 49 or ParAMS provide support for reparametrization and manipulation of parameter files. However, some applications can benefit from a tighter integration and more transparent access to the xTB parameters. This is explored with the recently introduced dxtb package, which provides an implementation of the GFN1-xTB Hamiltonian in PyTorch. In this approach, the PyTorch framework allows to leverage automatic differentiation of the xTB energy expression, which could be applied for direct optimization of xTB parameters or in combination with machine models using xTB outputs. The latter has already proven successful in the context of other semiempirical quantum chemical methods. 3 Technical aspects Historically, the xTB methods are first made available in the program xtb, which has grown from an implementation of the xTB methods with basic functionality like geometry optimization, frequency analysis, and basic molecular dynamics to a full computational chemistry modeling package. As of today, xtb provides a wide range of functionalities, such as the pos-sibility to add constraining potentials for fixing distances, angles and dihedral angles or adding a confinement potential to avoid dissociation of clusters in molecular dynamics.