Good question! PyXL today is aimed more at embedded and real-time systems. For server-class use, I'd need to mature heap management, add basic concurrency, a

Good question! PyXL today is aimed more at embedded and real-time systems. For server-class use, I'd need to mature heap management, add basic concurrency, a simple network stack, and gather real-world benchmarks (like requests/sec). That said, I wouldn’t try to fully replicate CPython for servers — that's a very competitive space with a huge surface area. I'd rather focus on specific use cases where deterministic, low-latency Python execution could offer a real advantage — like real-time data preprocessing or lightweight event-driven backends. When I originally started this project, I was actually thinking about machine learning feature generation workloads — pure Python code (branches, loops, dynamic types) without heavy SIMD needs. PyXL is very well suited for that kind of structured, control-flow-heavy workload. If I wanted to pitch PyXL to VCs, I wouldn’t aim for general-purpose servers right away. I'd first find a specific, focused use case where PyXL's strengths matter, and iterate on that to prove value before expanding more broadly.