The modular design of gsplat allows researchers and developers to easily integrate and test new densification strategies by providing a flexible API that supports algorithmic customization. This design facilitates the implementation of novel methods by enabling users to create their own densification strategies or modify existing ones without altering the core library. For instance, researchers can experiment with hybrid approaches that combine elements from the current strategies, such as Adaptive Density Control (ADC) and Markov Chain Monte Carlo (MCMC), to develop a more robust densification heuristic. Additionally, the clear documentation and well-structured codebase of gsplat make it easier for contributors to understand the underlying mechanisms of Gaussian Splatting, thus promoting collaborative research and innovation. By encouraging contributions from the open-source community, gsplat can evolve to include a wider array of densification techniques, potentially leading to breakthroughs in 3D reconstruction and novel view synthesis.