Imagine effortlessly designing a 3D room, simply by showing an AI a few examples of the style you want. Researchers have developed SceneMotifCoder (SMC), an AI system that learns to generate complex 3D object arrangements from just a handful of examples. Unlike previous methods that often produce unrealistic or mismatched scenes, SMC leverages the power of large language models (LLMs) to 'understand' spatial relationships between objects. Here's how it works: SMC first converts an example 3D scene into a simple program. Then, it uses an LLM to analyze patterns in that program, creating a more general 'meta-program.' This meta-program acts like a template that can be reused with different objects and layouts. For example, if you show SMC a stack of plates, it can then generate stacks of books, bowls, or anything else, all while maintaining realistic physics and spacing. This innovation marks a shift away from complex, data-hungry AI models toward a more efficient and intuitive design process. By combining example-based learning with the reasoning capabilities of LLMs, SMC makes 3D scene creation accessible to everyone, opening up new possibilities for interior design, game development, and virtual reality experiences. While SMC currently focuses on smaller object arrangements, future research could extend its capabilities to encompass entire rooms and larger furniture items. Imagine arranging an entire living room or kitchen simply by providing a few inspiring snapshots. This technology has the potential to revolutionize the way we design and interact with virtual worlds.