The field of vector similarity search and cloud storage is rapidly evolving, with a focus on improving efficiency, accuracy, and scalability. Recent developments have led to the creation of new algorithms and frameworks that enable fast and accurate similarity searches, even in high-dimensional spaces. Additionally, there is a growing interest in optimizing cloud-based storage solutions, particularly in the context of elastic solid-state drives. Noteworthy papers in this area include Attribute Filtering in Approximate Nearest Neighbor Search, which presents a unified analysis of existing algorithms and evaluates their performance extensively. Another notable paper is TRIM, which enhances the effectiveness of traditional triangle-inequality-based pruning in high-dimensional vector similarity search. PGTuner is also a significant contribution, as it provides an efficient framework for automatic and transferable configuration tuning of proximity graphs. Furthermore, DiskJoin and WoW propose innovative solutions for large-scale vector similarity join and range-filtering approximate nearest neighbor search, respectively. RARO is a reliability-aware conversion scheme that improves read performance for QLC SSDs. These advancements have the potential to significantly impact various applications, from data science to cloud computing.