[MiroF]

I don't quite follow.

I am not too experienced with Julia, but my understanding was that it uses LLVM to jit itself. Since the LLVM jit compiler is also an API available to C++, anything that can be done in Julia can be done with jit to LLVM api in C++.

Then you just compile the methods that you'll actually use with LLVM right before using them.

[ddragon]

Sorry, you're right in that Julia is written in C/C++, so everything Julia does can be solved in those by writing a language (like Julia itself, and not unlike Tensorflow original interface) and compiling it on demand and finding a way to eval the new code and recover the results. I was talking along the ways of how to make it sort of convenient (at least viable to implent unlike the former), as an extension to the C++ compiler itself where you can just tell the compiler what stays AoT and what is JIT'd but otherwise keep the same C++ syntax.

Not to mention if you want to reimplement Julia's logic in C++ you'll have to develop it's sophisticated type inference, since Julia compiler is so aggressive that it will compile at once entire blocks of program (the entire program if it can) as long as it can infer what types are used downstream, which is why it can compete with AoT compiled languages (it's basicaly a "Just Ahead of Time Compiler")

[shele]

The crux is that which are "the methods that you'll actually use" is very difficult to answer. A lot of effort is put into this on the Julia "Package compiler" and "Compiler" projects.