fizixer

As an experienced programmer, I have spent enough time trying to understand and make sense of some aspects of pure/higher math, specifically logic, that I feel qualified to say this:

- Experienced programmers are better trained at formality and precision than mathematicians, in some respects, and are able to ask questions that make experienced mathematicians go "why are you asking this question?" or "just get used to the idea (because that's what everyone does)"

- Much of higher math study advice (such as the one posted here) is aimed at laymen. And experienced programmers are no laymen.

- Mathematicians are laymen in many aspects compared to experienced programmers. An experienced programmer will have an easier time learning and using a proof assistant. Mathematicians (most of them) run away as fast as they can the moment they hear the phrase 'proof assistant.'

Touch typing is not that hard. You can learn, and then try yourself and see if you like mouseless work.

You don't touch type I assume?

Touch typing is not only about typing faster. It gives you this other ability of "running your control panel" out of the home keys only (four fings on each hand resting on a,s,d,f and j,k,l,;). When you have to lift one of the hands off the home keys and move it to the mouse to do anything, and I mean anything, it's super annoying and frustrating.

Those who never learned to touch type are not likely to grok the appeal of mouseless work.

Well. Looks like the hacks, gimmickers, and salesmen have succeeded wonderfully.

> Is there a reason to make this distinction here?

The original method, by Whitted, that looked much better than any previous methods, and yet looks horrible by today's standards, is ray tracing. It had no global illumination.

The method based on solving, explicitly or implicitly, the rendering equation by James Kajiya, and one that has built-in global illumination, is path tracing.

The distinction is not important for buzzword hijacking hacks, marketing gimmickers, and snake oil salesmen.

As always, path tracing is not ray tracing.

You missed one comparison: C infra (no C++).

(well strictly speaking, additionally you could also have hand-written assembly infra).

Machine learning is nowhere close to replacing the human coding activity.

Nonetheless, machine learning is creeping up into newer and newer areas of application in surprising, unpredictable, and unprecedented ways, and ignoring it would put you at peril as a tech worker. This is what I mean when I use the term 'software 2.0'.

Can't you spin up your own software in a docker instance on the cloud? what does official support mean here?

New to cloud.

I just want to point out one thing, for the record.

I'm sorry "Stop Monday Morning Quarterbacking (MMQ)" isn't directed at you per se, but the general direction of whoever is reading my comment.

MMQ is, unfortunately, practiced widely in many prediction/retrospection circles like startups, finance, economics, politics.

I'm speaking from a the point of view of scientific rigor, or at least quantitative data analysis. No one does that when it comes to opining about the cause-and-effect of an event in the past. If you're "the winner", anything you say about why you won, would be taken as gospel. "Winner is always right". Rigorous analysis is very hard, and costly, and to what end? Just so you could say "my reasoning is based on analysis"? That's a very boring thing to say. Unless rigorous-analysis finds utility in applications like decision-making for future startups, and is shown to work over and over again (maybe we'll need AI for that), no one is going to bother with it.