Worked on refining emotional modeling for the MaiMBot repository, focusing on delivering more stable and nuanced conversational responses. The approach involved adjusting valence and arousal parameters for multiple emotions and revising the decay logic, which reduced the bot’s susceptibility to negative emotional fluctuations. This feature, implemented in Python and leveraging AI/ML and backend development skills, aimed to enhance user experience by improving consistency and reliability in sentiment handling. The month’s work prioritized robust feature delivery over bug fixes, demonstrating depth in affective computing and stateful modeling to align the bot’s behavior with business goals of higher engagement and conversational stability.