AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.

AI Receptionist is a Python and Flask-based web app integrating OpenAI’s GPT models and Google Calendar API to serve as a virtual front-desk agent. Visitors interact through a chat widget or voice interface, receiving instant answers to FAQs, custom information about services, and seamless meeting scheduling. The system logs visitor details, sends email notifications, and maintains a searchable history of interactions. It supports configuration of custom greeting scripts, automated follow-ups, and multilingual responses, making it an adaptable solution for startups, clinics, co-working spaces, and remote offices seeking modern, scalable reception automation.