[jo_]

Forgive my ignorance, but it seems like this is just attempting to take advantage of the optimization done by LLVM, yes?

What I would love is a simple way of writing standalone functions that compile into a cross-platform LLVM file that I can call from a variety of other languages on a variety of other systems. In particular, if I train a recurrent network on text data for a chat bot, I want to be able to use that LLVM file + model in a game I release for the PC and for Android without worrying about the NDK/gcc/clang/Windows/OSX build nightmare. The ability to easily and quickly define a model in TensorFlow, write a Python function that takes an array of data, and spits out an array of data would be incredible and would mean that all the work I'm doing for a native Rust library is unneeded.

Admittedly, with Bazel I could create a C++ wrapper for the function which loads the library. It's just... that produces a 150mb shared library with all the dependencies and it's also a pain in the ass.

[perone]

This is actually easy to do, you just need to generate the IR and then merge them into a Module, after that you apply passes over the entire module to optimize, to do function inlining, etc.