As many other modern programming languages, Pharo spreads its applications into computationally demanding fields such as machine learning, big data, cryptocurrency, etc. This raises a need for fast numerical computation libraries. In this work, we propose to speed up the low-level computations by calling the routines from highly optimized external libraries, e.g., LAPACK or BLAS through the foreign function interface (FFI). As a proof of concept, we build a prototype implementation of linear regression based on the DGELSD routine of LAPACK. Using three benchmark datasets of different sizes, we compare the execution time of our algorithm agains pure Pharo implementation and scikit-learn - a popular Python library for machine learning. We show that LAPACK & Pharo is up to 2103 times faster than pure Pharo. We also show that scikit-learn is 8-5 times faster than our prototype, depending on the size of the data. Finally, we demonstrate that pure Pharo is up to 15 times faster than the equivalent implementation in pure Python. Those findings can lay the foundation for the future work in building fast numerical libraries for Pharo and further using them in higher-level libraries such as pharo-ai.