>It used to be that Python's lack of performance didn't matter because disks and networks were so slow things were IO bound. In more and more cases that's just not true anymore.

That's generally a valid statement. But it's why PyPy and Nuitka exist. Pyston and Pyjion are up and coming in this area.

>And it baffles me that anyone would even consider writing 100KLOC+ project in something as lax as Python. That's just asking for trouble.

That's why Python 3.5 has type annotations. It increases the amount of sane usecases for Python going forward.

Let me add I'm with you though in your general thought. The DB/IO bottleneck myth needs to die. We do need more performance, on the order of Elixir. CPU performance at that level or above removes a whole class of application issues. Not sure C's performance is needed though unless you're doing systems programming.

I just happen to like Python as a language, so happy to fly the flag on the various solutions to make it fit that truly needed aspect.

> As someone who has spent almost 20 years (on and off) writing C and C++, I don't believe it. C/C++ lets you go as close to hardware as you would possibly want. Want SIMD? Easy. Want custom memory allocation (a big deal if you allocate/deallocate a ton)? Sure, why not. Want to profile and optimize cache locality and memory layout? Knock yourself out. Memory alignment? Yup. Branch hints? Of course. I could continue with this, but as someone who has written performance-sensitive code you already know most if not all of this. It's not a coincidence that e.g. high performance linear algebra libs are written in C.

Which you can use in Python. I'm a C guy myself, but this bullshit "do it all in C" attitude needs to be calmed down a bit. Python is a great language for many reasons, and you can make up for its downsides by using Python libraries that are implemented in C.

Have you seen numba? Compiles numerical python approaching fortran speeds