A recent paper introduces a novel algorithm designed to select demonstration examples efficiently in in-context learning, with the primary goal of improving downstream inference speed. This algorithm employs gradient estimation techniques to rapidly identify the most relevant examples from a larger pool, thereby enhancing computational efficiency. The approach is notable for its linear-time complexity, which marks a significant advancement over previous methods. By enabling quicker and more effective example selection, the algorithm opens up new possibilities for applications in areas such as prompt tuning and reasoning tasks. The authors claim that this innovation not only improves efficiency but also facilitates new use cases within the broader field of machine learning. This development aligns with ongoing research efforts to optimize in-context learning frameworks and supports the growing demand for scalable AI solutions. Overall, the algorithm represents a promising step toward more practical and versatile AI inference methods.