The field of online scheduling and resource allocation is witnessing significant developments, with a focus on designing algorithms that can efficiently handle uncertain and dynamic environments. Researchers are exploring new approaches to maximize throughput, minimize costs, and optimize resource allocation in various settings, including real-time scheduling, inventory management, and joint replenishment problems. A key trend is the integration of machine learning and prediction techniques to improve the performance and robustness of online algorithms. Notably, the use of predictions is enabling the design of algorithms with improved competitive ratios and asymptotic optimality. Furthermore, researchers are making progress in solving long-standing problems, such as the general scheduling problem, with the development of quasi-polynomial time approximation algorithms. Some noteworthy papers in this area include: A Switching Framework for Online Interval Scheduling with Predictions, which introduces a unified framework for combining prediction-based and classical interval scheduling algorithms. A (2+ε)-approximation algorithm for the general scheduling problem in quasipolynomial time, which presents a significant improvement over previous results for this problem.