Developer Muhammed Shafin P has outlined five theoretical scenarios for a hybrid Python/C async-native engine, a concept he previously detailed involving GIL and Non-GIL threads connected by a Shared Memory Bus. The proposal suggests using C threads for heavy lifting like data preprocessing in ML training pipelines or image processing in backend servers, while Python handles orchestration and I/O. Key ideas include zero-copy transfer to avoid serialization overhead and parallel execution to prevent Python from blocking on CPU-intensive tasks.

However, the author admits these are untested concepts facing significant hurdles, such as tensor handling and potential framework conflicts. The proposed architecture spans from ETL data pipelines to real-time simulation and ML inference servers. While the design promises high throughput and better resource utilization, Shafin questions if it can compete with established tools like Celery or Spark.

He is actively seeking community feedback on the feasibility and potential complexity of these hybrid systems.