This paper tackles the challenge of adaptive level generation in video games, focusing on generating content that aligns with player skill.A key limitation of procedural content generation (PCG) is achieving semantic control.Specifically, generating levels of varying difficulty with limited training data.To address this problem, we propose a hybrid approach combining Large Language Models (LLMs) and Generative Adversarial Networks (GANs).An LLM is used to generate a diverse, difficulty-labeled dataset of Snake game levels, which are validated with A* pathfinding to ensure playability.These levels serve as training data for GANs that are able to efficiently generate new levels.The system is evaluated through user study and playability metrics.Results show that the LLMassigned difficulty labels correlate strongly with human perception.The achieved playability is 87% for easy levels and 36% for hard levels.Our findings demonstrate that the hybrid LLM-GAN approach enables scalable and semantically controlled content generation, balancing quality, adaptability, and computational efficiency.

The data mesh is a novel data management concept that emphasizes the importance of a domain before technology. The concept is still in the early stages of development and many efforts to implement and use it are expected to have negative consequences for organizations due to a lack of technological guidelines and best practices. To mitigate the risk of negative outcomes this paper proposes the use of the mask–mediator–wrapper architecture as a driver for a data mesh implementation. The mask–mediator–wrapper architecture provides a set of prefabricated configurable components that provide basic functionalities that a data mesh requires. This paper shows how the two concepts are compatible in terms of functionality, data modeling, evolvability, and aligned capabilities. A mask–mediator–wrapper-driven data mesh facilitates low-risk adoption trials, rapid prototyping, standardization, and a guarantee of evolvability. We demonstrate a mask–mediator–wrapper-driven data mesh by using our open-source Janus system to experimentally drive an exemplified data mesh.