The field of online scheduling and clustering is moving towards more efficient and robust algorithms that can handle dynamic environments and uncertain inputs. Recent developments have focused on improving the competitive ratios of online algorithms, as well as designing new approximation schemes that can provide near-optimal solutions with bounded migration factors. Notably, researchers have made progress in establishing provably efficient dynamic scheduling algorithms for blockchain sharding systems and designing simpler and more efficient algorithms for online transportation problems. Additionally, new results have been obtained for robust scheduling on uniform machines, including the development of Efficient Polynomial Time Approximation Schemes (EPTASs) with constant amortized migration factors. Furthermore, competitively consistent clustering algorithms have been designed to maintain approximately optimal clustering solutions at all times while minimizing recourse. Some noteworthy papers in this area include: On the Efficiency of Dynamic Transaction Scheduling in Blockchain Sharding, which establishes provably efficient dynamic scheduling algorithms for blockchain sharding systems. Competitively Consistent Clustering, which designs algorithms that maintain an O(β)-approximate solution at all times with bounded recourse. Robust Scheduling on Uniform Machines, which proposes EPTASs with constant amortized migration factors for scheduling jobs on uniform machines.