Abstract-- Dexterous in-hand manipulation remains a foun-dational challenge in robotics, with progress often constrained by the prevailing paradigm of imitating the human hand. This anthropomorphic approach creates two critical barriers: 1) it limits robotic capabilities to tasks humans can already perform, and 2) it makes data collection for learning-based methods exceedingly difficult. Both challenges are caused by traditional force-closure which requires coordinating complex, multi-point contacts based on friction, normal force, and gravity to grasp an object. This makes teleoperated demonstrations unstable and amplifies the sim-to-real gap for reinforcement learning. In this work, we propose a paradigm shift: moving away from replicating human mechanics toward the design of novel robotic embodiments. We introduce the Suction Leap-Hand (SLeap Hand), a multi-fingered hand featuring integrated fingertip suction cups that realize a new form of suction-enabled dexterity. More importantly, this suction-based embodiment unlocks a new class of dexterous skills that are difficult or even impossible for the human hand, such as one-handed paper cutting and in-hand writing. Our work demonstrates that by moving beyond anthropomorphic constraints, novel embodiments can not only lower the barrier for collecting robust manipulation data but also enable the stable, single-handed completion of tasks that would typically require two human hands. Dexterous manipulation, the ability to reconfigure objects within a single hand, remains a grand challenge in robotics [1], [2]. The dominant paradigm for achieving this goal has been data-driven learning on anthropomorphic hands, an approach that has led to successes in grasping and reorientation [3], [4], [5].