I also know literally nothing about this particular project, but why not? If you support a small restricted subset of Python it's completely doable under certain conditions for specific types of programs. E.g., Numba can easily outperform Python 100-1000x in numerical applications (done it myself multiple times), simply because it jit-compiles the code by first translating it to LLVM IR.
(minor contributor) I’ve been following the project for a while and pleasantly surprised by the ability to manually convert Python programs to Seq without needing to make too many changes. Note, most of my experimentation has been with smallish programs I’ve written. I like that I can still think “Pythonically” and compose mostly correct Seq code using familiar idioms, e.g. list/set/dict comprehensions. The standard library is very readable and a source for “from import” type functionality. Some of the other features I’ve come to appreciate: pipeline operator |>, JIT compile or create an executable (seqc run, seqc build), match statements, and strong typing.
> If you support a small restricted subset of Python
That's why I quoted their claim that the "vast majority" of Python programs run unmodified. Even PyPy barely achieves that. To really get 100x performance over Python (and even supposedly beat C) with a compiler that works on most unmodified Python code would be an extraordinary achievement.
That seems misrepresenting the original points: it can run the vast majority of python programs unmodified AND in some cases outperform Python - not at the same time.
