This document discusses how a quantum chemist began using RDKit to automate workflows for computational chemistry. The chemist describes how RDKit helped automate tasks like generating protonation states, conformers, and finding lowest energy structures. RDKit was also used to build tools like RegioSQM and xyz2mol. More recently, the chemist used RDKit to implement a genetic algorithm approach for designing novel light-absorbing molecules. In conclusion, RDKit has significantly changed the chemist's research by enabling automation that reduces manual work and mistakes, though some "QM-needs" like improved conformational searching and solvation models could further benefit computational chemistry within RDKit.