The project employs a convolutional neural network (CNN) to classify STM tip sharpness, with custom image processing and contour extraction. Data augmentation techniques bolster model resilience despite a limited dataset. An ROI-based cross-scan algorithm further enhances accuracy, ensuring reliable classifications and forming the foundation for full automation in STM-based fabrication.

Surplus Share utilizes a Python backend with a heuristic genetic algorithm to optimize delivery routes for drivers. This algorithm leverages the Google Maps Distance Matrix API to calculate travel times and distances between locations, taking into account real-time traffic conditions. Additionally, a distribution algorithm ensures efficient allocation of food resources based on dietary restrictions. Firebase Firestore serves as the database, storing information about businesses, individuals, and available food. The frontend, built with Nuxt.js and TailwindCSS, provides a user-friendly interface for both businesses and individuals.

The app leverages OpenAI's davinci-003 model to create personalized workout plans based on the user's preferences and objectives. For backend operations, it utilizes Firestore for document-based storage and user authentication. We used Nuxt for the frontend web app, with TailwindCSS as our styling tool.