Genie 3 is a groundbreaking AI world model developed by DeepMind that generates interactive, explorable 3D environments in real-time. Unlike traditional video generation tools, Genie 3 produces consistent, physics-aware worlds with dynamic event prompts that modify environments while maintaining realism. Used primarily for AI agent training, educational simulations, creative media, and robotics development, it supports sustained multi-minute interactions with environmental consistency at high visual quality.

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.

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.

Java-Action-Shape is a dedicated action library designed to extend the LightJason multi-agent framework with advanced geometric capabilities. It provides agents with out-of-the-box actions to instantiate common shapes (circle, rectangle, polygon), apply transformations (translate, rotate, scale), and perform analytical computations (area, perimeter, centroid). Each action is thread-safe and integrates with LightJason’s asynchronous execution model, ensuring efficient parallel processing. Developers can define custom shapes by specifying vertices and edges, register them within the agent’s action registry, and include them in plan definitions. By centralizing shape-related logic, Java-Action-Shape reduces boilerplate code, enforces consistent APIs, and accelerates the creation of geometry-driven agent applications, from simulations to educational tools.