HiGHS is open-source optimization software for linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP). This talk will give a brief insight into the state-of-the-art techniques underlying its solvers, most of which were originally written as “gradware” by PhD students, and an overview of how HiGHS came to be developed. Independent benchmark results will be given to justify the claim that HiGHS has the world’s best open-source linear optimization solvers. The team developing HiGHS was responsible for a 2021 REF Impact Case Study, and has the potential to generate further Impact for the next REF, so observations on the creation of Impact via software development, and how to fund such work, will be given. Current solver developments will be outlined. These include a new interior point solver for LP and QP, and the exploitation of GPU computing.