A developer shared a test project on GitHub that demonstrates running a CoreML model entirely offline on Apple silicon hardware. The repository, maintained by user fordnox, includes a script that downloads a multilingual CoreML model, initializes a Python environment, installs required dependencies, and executes inference without network connectivity. The workflow involves creating a directory, setting up a virtual environment via uv, adding the laya-coreml[demo] package, fetching model files to a local models/snake path, and launching the laya-coreml-snake inference command. The project serves as a practical example of offline machine learning deployment on macOS, highlighting compatibility with Apple's silicon architecture. No specific performance metrics or user counts were disclosed in the shared code.