This case study delves into the evolution of a feed algorithm for Company X, a social media platform aiming to enhance user engagement. The transition from a chronological feed to a personalized and dynamic algorithm involved a multifaceted approach. Key objectives included personalization, relevance, diversity, and real-time adaptability. The process involved data collection, content analysis, user behavior modeling, collaborative filtering, dynamic ranking, and A/B testing. Challenges, such as data privacy, algorithm bias, and scalability, were addressed through anonymization, diversity incorporation, and scalable infrastructure. The results demonstrated a substantial increase in user engagement, affirming the efficacy of the adaptive feed algorithm. This case study underscores the importance of a holistic and iterative approach to algorithm development for sustained user satisfaction in the digital content landscape.