We Are Learning revolutionizes content creation with its user-friendly platform, designed to produce high-quality 3D animations and interactive simulations in minutes. Whether for training, educational purposes, or storytelling, the platform's intuitive interface allows users to craft engaging, immersive experiences without the need for technical expertise. The platform is equipped with numerous templates and an AI assistant, Aico, to expedite the creation process and ensure professional-level outputs.

Archetype AI specializes in scenario generation and simulation creation, enabling users to design interactive experiences tailored to specific needs. It supports various applications, including training simulations for professionals, virtual environments for educational purposes, and complex scenario modeling for researchers. Leveraging state-of-the-art AI technologies, it ensures high fidelity and realism in generated scenarios, allowing users to analyze outcomes and improve decision-making processes.

Medical Realities is an innovative platform transforming the way medical professionals are trained through immersive experiences. By utilizing the latest VR and AR technologies, the platform offers engaging simulations and interactive content that allow users to practice surgical skills and knowledge in a safe environment. With access to a vast library of high-definition videos and simulations, learners can experience real-life scenarios that enhance their educational journey. The comprehensive tools available on Medical Realities streamline the training process, making it more effective and efficient for healthcare professionals around the globe.

Multiagent_system offers a comprehensive toolkit for constructing and managing multi-agent environments. Users can define custom simulation scenarios, specify agent behaviors, and leverage pre-implemented algorithms such as DQN, PPO, and MADDPG. The framework supports synchronous and asynchronous training, enabling agents to interact concurrently or in turn-based setups. Built-in communication modules facilitate message passing between agents for cooperative strategies. Experiment configuration is streamlined via YAML files, and results are logged automatically to CSV or TensorBoard. Visualization scripts help interpret agent trajectories, reward evolution, and communication patterns. Designed for research and production workflows, Multiagent_system seamlessly scales from single-machine prototypes to distributed training on GPU clusters.