Imagine giving an AI a set of instructions and having it build its own simulated environment, entirely in code. This isn't science fiction, but the fascinating reality of Code World Models (CWMs). Researchers are exploring how Large Language Models (LLMs) can generate Python code that represents the rules and dynamics of a virtual world. Why code? Because unlike relying directly on an LLM for predictions, which can be slow and inconsistent, code offers precision, speed, and transparency. This approach opens doors to incredibly efficient model-based reinforcement learning, where an AI agent learns by interacting with its self-coded environment. The process involves feeding the LLM a description of the environment and some example interactions. The LLM then attempts to generate code that accurately reflects these interactions. A new technique called "Generate, Improve, and Fix with Monte Carlo Tree Search" (GIF-MCTS) guides the LLM in this process. GIF-MCTS uses a tree-search algorithm to explore different code possibilities, using feedback from the environment to refine the generated code iteratively. This method has shown promising results, outperforming other code generation techniques on standard benchmarks and successfully creating functional CWMs for various simulated environments, from simple games to complex physics simulations. While still in its early stages, the CWM approach offers a compelling vision for the future of AI. Imagine agents that can quickly adapt to new situations by building their own understanding of the world, coded from scratch. However, challenges remain. Current CWMs work best in deterministic, fully observable environments. Extending this approach to handle uncertainty and incomplete information is a key area of future research. The ability of LLMs to generate their own code-based world models represents a significant step towards more efficient, adaptable, and transparent AI systems.