One of Lua's goals has been extreme portability, and the main implementation works on anything that has a C compiler, going to the extreme of compiling cleanly on C89, C99, and even compiling as C++ (no extern "C"). Remember that Lua is popular in the embedded space too, so this is a big feature.

Pallene isn't designed to be a new native language on its own. Pallene is designed to be a companion language for Lua, specializing in a subset of performance.

But as importantly, Pallene isn't just compiling to C. Pallene is generating C code that directly manipulates the underlying Lua internals, which are in C.

The research thesis is that many bottlenecks are due to boxing and unboxing going through an FFI. Memory safety also incurs overhead. Python is an extreme example of how excruciatingly slow this can be, but even Lua incurs costs for this. A core tenant of the Pallene compiler is that it can generate C code that gets to cheat like crazy. Pallene gets to directly access Lua internals and things like arrays directly manipulate underlying C arrays deep inside, which sidesteps boxing/unboxing. The compiler can do the analysis to make sure it doesn't cheat in a way that is unsafe. Finally, the C optimizer now also has a chance to perform optimizations. And now operations such as crunching math on arrays of numbers may get much faster because now you get generated code that is more CPU friendly and may benefit more from prefetching and cache locality.

Pallene is built from the the extreme compatibility goals as Lua since it is designed to work with it. It it only depends on any C compiler and Lua itself. If you get Lua compiled, then you can get Pallene working. That means any existing project that uses Lua (5.4) could start adding Pallene modules to their project for new features or to try to improve performance in key areas. Since Pallene just outputs a Lua modules, it looks like any other Lua module implemented in C, so it won't create new portability constraints that you didn't have before. This is different than say LuaJIT, where not all platforms may allow JIT or you may be targeting a new CPU architecture that LuaJIT does not support.

Both Teal and Pallene were started by grad students of Roberto's. Since Roberto has started giving talks himself on Pallene, I'm assuming they are joining in.