The field of tactile sensing is witnessing a significant shift towards more adaptable, context-driven, and scalable solutions. Recent advancements focus on developing systems that can be rapidly configured for various applications, such as musical gloves, gait monitoring, and smart home systems, emphasizing the need for open-source platforms that facilitate quick prototyping. Additionally, there is a growing interest in creating tactile skins that can conform to the specific shapes of robots and adapt to their operational contexts, moving away from modular, one-size-fits-all designs. This trend is supported by the introduction of computational design pipelines that enable the procedural generation of 3D printed tactile skins tailored to individual robot topologies and application domains. Furthermore, the integration of machine learning for contact localization in variable density three-dimensional tactile skins is emerging as a robust solution for complex, non-uniform sensor distributions, enhancing the versatility and accuracy of tactile sensing systems. These developments collectively push the boundaries of tactile sensing technology, making it more versatile, precise, and applicable across a broader range of robotic and human-environment interaction scenarios.