Teaching AI to perform parkour is an impressive testament to how far robotics and artificial intelligence have come in mimicking human physical abilities. From day 1 to day 100, the progression involves a complex learning process where the AI gradually develops skills such as balance, agility, and precise timing by interacting with its environment. This incremental training approach—similar to how a human might learn parkour—helps the AI understand how to assess obstacles and execute movements like jumps, climbs, and vaults. In practical terms, this involves training the AI with simulations and real-world data, employing reinforcement learning algorithms to reward successful maneuvers while correcting mistakes. The robot's sensors constantly collect information about its position and speed, feeding back to the AI system to refine motor control and planning. Over weeks, the AI becomes more adept at overcoming challenges that would initially have seemed insurmountable. This milestone-based approach to teaching AI parkour offers exciting possibilities not only in entertainment and sports robotics but also in search and rescue missions where robots need to navigate complex environments quickly and safely. From personal experience, watching an AI develop such fluid and dynamic movement capabilities demonstrates how quickly machine learning can bridge the gap between computational power and physical execution. By tagging #ai, #parkour, #teaching, and #robot, the content highlights the intersection of these fields, inspiring enthusiasts interested in how AI can learn and adapt motor skills traditionally thought to be uniquely human. The journey from day 1 to day 100 reflects continuous improvement and learning—a process that many learners of any skill can relate to.