Diabetes is a lifestyle disease. Patients don’t simply take their meds at the right times; they must make conscious diet and exercise choices, monitor their well-being every day, and cope with a plethora of complications. Patients often feel alone in the fight to keep their A1C stable with every action they take, resulting in lower motivation and the high occurrence of depression in the diabetic population. Blocks of Sugar is a social media for diabetic patients. Users sign up by entering their age, gender, and location. Based on these three features, they are matched to a league of diabetics in their neighborhood area with similar demographics. Within the web app, users can see relevant news articles and nearby events on a map interface and view their fellow league member’s health status updates. A leaderboard, ranking league members on how well they’re currently managing their diabetes, is included to gamify the experience and motivate users to make better choices. In addition, a group chat feature is available so users can anonymously discuss their tips and strategies for managing their disease. We built this app in HTML/CSS using the Johnson and Johnson One Touch Reveal API as a test dataset of diabetes patient profiles. We performed unsupervised machine learning using hierarchical clustering in the Beaker Notebook on the diabetes patient demographic profiles from the Johnson and Johnson dataset. We also used the Every Block API to retrieve diabetes-relevant news articles and meetups in the user’s local neighborhood league.