Building effective recommender systems can be challenging, but a variety of resources are available to aid in this process. Experts like Jacopo Tagliabue, Ronay Ak from Nvidia, and Serdar Kadioglu from Fidelity have contributed insights and tools for improving these systems. Key resources include Nvidia Merlin, an open-source framework for high-performing recommenders, and RecList, a library for behavioral testing. Fidelity's Mab2Rec is designed for creating contextual multi-armed bandit recommenders. Additionally, the RecSys reproducibility paper at TMLR’22 addresses the non-deterministic behavior of Thompson Sampling, providing strategies to mitigate it. The Association for Computing Machinery offers terminology on reproducibility, while various conferences and communities, including the Comet ML Slack community, provide platforms for further learning and collaboration.