Modular manifolds offer a novel approach to constraining weight matrices in neural networks to improve optimization efficiency and stability during training. By keeping weights confined to specific manifolds, such as the Stiefel manifold, and integrating normalization techniques, the proposed methods aim to enhance the predictability and robustness of learning algorithms. This article introduces the concept and potential benefits of such approaches, encouraging further exploration in this research area.