Google AI has recently unveiled a new open source library called Jack's Pruner, designed to help researchers work on pruning and sparse training in machine learning. Jack's Pruner is built on top of Jack's, a popular open source numerical computing library, and aims to provide a fast and easy to use tool for researchers to develop and evaluate models with sparse and pruned architectures. Pruning and sparse training are techniques used to improve the efficiency of machine learning models. Pruning involves removing unnecessary connections in a neural network, while sparse training focuses on developing models with fewer connections from the start. These methods can result in faster and more efficient models, which are especially useful for large-scale applications such as natural language processing or computer vision. Jack's Pruner is designed to simplify the process of developing and testing these techniques, making it easier for researchers to incorporate pruning and sparse training into their work. The library provides a range of functions and utilities for working with sparse matrices and implementing pruning algorithms, as well as a set of pre-built models for testing and evaluation. Jack's Pruner is just the latest and growing line of open source tools and libraries designed to support machine learning research. With its focus on pruning and sparse training, it is likely to be a valuable addition to the two kit of many researchers working in this area. By making these techniques more accessible and easier to use, Jack's Pruner has the potential to accelerate progress in machine learning and artificial intelligence research.