I built a Python runtime that loads precompiled MLIR artifacts from a closed-source compiler

I’ve been building Fluno, a closed-source compiler/runtime experiment for extracting selected hot regions from Python/PyTorch-style continuous inference loops and running them as precompiled native artifacts.

The public repo is not the compiler. It is the audit/runtime surface:

- a Python package ("fluno_runtime") that loads precompiled artifacts

- manifest/schema/hash/expiry validation before dynamic library loading

- a Windows x86_64 live artifact package

- benchmark docs and claim boundaries

- zero-compiler-internals public package structure

The current L-size continuous inference benchmark shows:

- PyTorch optimized repeated: 84.673 ms

- Fluno "hot_vector_repeated": 4.061 ms

- Fluno "hot_run_repeated": 7.245 ms

- max absolute error: 0.0 within the published 11-element "partial_summary_vector" scope

Important limitation: Fluno does not currently beat the handwritten Rust/C++ references on this row. The point of the current public release is not “faster than C++”; it is showing a Python-callable artifact runtime boundary with fail-closed validation and native-class latency.