  We describe our implementation of downset-manipulating algorithms used to
solve the realizability problem for linear temporal logic (LTL). These
algorithms were introduced by Filiot et al.~in the 2010s and implemented in the
tools Acacia and Acacia+ in C and Python. We identify degrees of freedom in the
original algorithms and provide a complete rewriting of Acacia in C++20
articulated around genericity and leveraging modern techniques for better
performances. These techniques include compile-time specialization of the
algorithms, the use of SIMD registers to store vectors, and several
preprocessing steps, some relying on efficient Binary Decision Diagram (BDD)
libraries. We also explore different data structures to store downsets. The
resulting tool is competitive against comparable modern tools.
