I _accidentally_ fell into a vipassana state of mind after the first time trying a particular religious chant, seriously, from out of the pains of boredom as a 20 year old.

At the time I lived nearby mountains, and after 'coming to', I saw them through the window. I cannot describe to you the overwhelming feeling of beauty and awe that crashed over me. It was as though this was the very first time I had ever seen mountains. I think the best description I can manage comes from 'Both Sides Now' by Joni Mitchell.

'I've looked at clouds from both sides now From up and down, and still somehow It's cloud illusions I recall I really don't know clouds at all'

The experience was so disorienting. As if I was seeing the mountains for what they were directly through my senses and not mediated by my knowledge of what mountains were like, linguistically.

It changed my entire life -- weeks later later I lost my job and fell into a 5 year extreme depression / anxiety spell in the attempt to reconcile my adolescent religious upbringing with the continued insight from that experience. Absolutely worthwhile, in hindsight, but it cost me near everything for it.

Buffet is well known to play long, big, safe, predictable bets, with recent exception with airlines with bad COVID timing. All this is consistent with his personality and history as an investor.

C = A + B

Python with Numpy perfectly service just that need. We all have our grief with the status quo, but Python needs data processing acceleration from somewhere. In my view, Python needs to implement a JIT to alleviate 95% of the need for Numpy.

This is feasible to reduce interpreter overhead considerably by using array programming:

c = a + b #This is valid Python code.

Where a and b are two same sized numpy arrays. Numpy typically handles the add in an optimized SIMD function.

I see this generalization often, that Godel showed that all formal systems share this restriction, but doesn't the formal system have to be at least be able to express the properties of integers?

Is the ability to formally express integers relatively a particularly demanding constraint on choice of axioms?

Otherwise, Clang has access to the same optimizations that Numba has as they both share LLVM as their optimizing compiler. Beyond that, I think a fairer comparison is C w/ OpenMP vs Numba for parallel processing if syntactic brevity is the metric.

If your data changes, your problem changes. We only ever solve particular problems, given the distribution, shape and density characteristics of input data.

Then, follow your nose through the bibliography. =)