Did you know you can contribute to the future of robotics? Every task on Continuum Hub is more than a task. It’s a data bounty that creates a complete trajectory showing how a robot was controlled, what happened in the environment, and whether the task succeeded. That’s how @continuumlabs_ turns individual robot interactions into an ever-improving data engine for Physical AI. The lifecycle moves through Pre-training, Training and Post-training. First, contributors provide human demonstrations to build a diverse dataset. Once the required slots are filled, a policy is trained. During Post-training, contributors supervise the trained policy and correct its mistakes, creating valuable on-policy data for the next iteration. Slots count data contributions, not unique users, and each task can be completed up to five times. Every run is evaluated independently, while your Points value is based on the average quality of your completions, so repeating a task only makes sense if you can improve the result. After submission, each trajectory is uploaded, replayed in simulation, automatically scored for success and motion quality, then signed onchain with a unique Data ID. Accepted demonstrations and corrections feed directly back into training, creating a continuous loop where better policies reveal new failures and those failures generate the next wave of training data.