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When I first started PyXL, this kind of vision was exactly on my mind. Maybe not AWS Lambda specifically, but definitely server-side acceleration — especially

When I first started PyXL, this kind of vision was exactly on my mind. Maybe not AWS Lambda specifically, but definitely server-side acceleration — especially for machine learning feature generation, backend control logic, and anywhere pure Python becomes a bottleneck. It could definitely get there — but it would require building a full-scale deployment model and much broader library and dynamic feature support. That said, the underlying potential is absolutely there.

petra 1y ago
This sounds brilliant. What's missing so you could create a demo for vc's or the relevant companies , proving the potential of this as competitive server-class core ?
- hwpythonner 1y ago
  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.
  - noosphr 1y ago
    I need to bit bang the RHS2116 at 25MHz: https://intantech.com/files/Intan_RHS2116_datasheet.pdf https://intantech.com/files/Intan_RHS2116_datasheet.pdf Right now I'm doing this with a dsl with an fpga talking to a computer. Does your python implementation let you run at speeds like that? If yes, is there any overhead left for dsp - preferably fp based?

petra 1y ago

This sounds brilliant. What's missing so you could create a demo for vc's or the relevant companies , proving the potential of this as competitive server-class core ?

hwpythonner 1y ago
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.
- noosphr 1y ago
  I need to bit bang the RHS2116 at 25MHz: https://intantech.com/files/Intan_RHS2116_datasheet.pdf https://intantech.com/files/Intan_RHS2116_datasheet.pdf Right now I'm doing this with a dsl with an fpga talking to a computer. Does your python implementation let you run at speeds like that? If yes, is there any overhead left for dsp - preferably fp based?