Multi-Agent Drone Environment is a Python package offering a customizable multi-agent simulation for UAV swarms, built on OpenAI Gym and PyBullet. Users define multiple drone agents with kinematic and dynamic models to explore cooperative tasks such as formation flying, target tracking, and obstacle avoidance. The environment supports modular task configuration, realistic collision detection, and sensor emulation, while allowing custom reward functions and decentralized policies. Developers can integrate their own reinforcement learning algorithms, evaluate performance under varied scenarios, and visualize agent trajectories and metrics in real time. Its open-source design encourages community contributions, making it ideal for research, teaching, and prototyping advanced multi-agent control solutions.

PaintIt.ai is a revolutionary platform utilizing AI to transform the interior design process, making it accessible, enjoyable, and affordable. Users can personalize their space by choosing from various design styles and seeing real-time visualizations of their ideas. Whether for homeowners looking to redecorate, designers seeking innovative tools, or real estate agents wanting to showcase properties, PaintIt.ai simplifies the design process and enhances creativity with AI-powered customization.

RinSim provides a modular simulation environment focused on modeling dynamic logistics scenarios with multiple autonomous agents. Users can define road networks via graph structures, configure fleets of vehicles including electric models with battery constraints, and simulate stochastic request arrivals for pickup and delivery tasks. The discrete-event architecture ensures precise timing and event management, while built-in routing algorithms and customizable agent behaviors allow extensive experimentation. RinSim supports output metrics such as travel time, energy consumption, and service level, and includes visualization modules for real-time and post-simulation analysis. Its extensible design enables integration of custom algorithms, scaling up to large fleets, and reproducible research workflows essential for academia and industry optimization of mobility strategies.

TryOnHaul is a virtual try-on application that enables users to visualize how various clothing items will look on them without physically trying them on. The app employs advanced augmented reality technology to provide a realistic view of clothing fit and style. Users can easily browse through a wide range of fashion items, select their size, and see a real-time visual of themselves in the selected attire. Whether shopping for casual wear, formal outfits, or accessories, TryOnHaul enhances the online shopping experience by minimizing the guesswork associated with sizing and fit.

AgentSimJS is designed to simplify the creation and execution of large-scale agent-based models in JavaScript. With its modular architecture, developers can define agents with custom states, sensors, decision-making functions, and actuators, then integrate them into dynamic environments parameterized by global variables. The framework orchestrates discrete time-step simulations, manages event-driven messaging between agents, and logs interaction data for analysis. Visualization modules support real-time rendering using HTML5 Canvas or external libraries, while plugins enable integration with statistical tools. AgentSimJS runs both in modern web browsers and Node.js, making it suitable for interactive web applications, academic research, educational tools, and rapid prototyping of swarm intelligence, crowd dynamics, or distributed AI experiments.

AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.