Many science and engineering models reduce to problems involving huge sparse matrices -- matrices where most of the values are zeroes. Such computations are resource-intensive (time, memory, energy), and much research was devoted into data structures ("formats") and algorithms leading to fast sparse matrix operations. Yet, most such formats are highly specialized and seldom make it into a solid software package apt for general use. The RSB (Recursive Sparse Blocks) data structure is a format that addresses performance concerns for current shared-memory multicore CPUs, while also avoiding dead ends in terms of usability. The LIBRSB library implements RSB with all the necessary operations to manipulate sparse matrices in most computations, in the most popular programming languages, and on many hardware platforms. This talk will give an overview of the concepts behind LIBRSB and its main usage modes. Intended audience: Developers of Linear Systems Solvers based on Iterative Methods, or General Computing Packages. Expected prior knowledge: Familiarity in any of C, C++, Fortran, Python, GNU Octave.