One sort-of counterexample would be The Unreasonable Effectiveness of Mathematics in the Natural Sciences, where a lot of Math has been surprisingly accurate even when the assumptions where violated.
The Mathematical Theory of Communication by Shannon and Weaver. It's an extended version of Shannon's original paper that established Information Theory, with some extra explanations and background. 144 pages.
Atiyah & McDonald's Introduction to Commutative Algebra fits. It's 125 pages long, and it's possible to do all the exercises in 2-3 weeks – I did them over winter break in preparation for a course.
Lang's Algebra and Eisenbud's Commutative Algebra are both supersets of Atiyah & McDonald, I've studied each of those as well and thought A&M was significantly better.
Unfortunately, I think it isn't very compatible with the way management works at most companies. Normally there's pressure to get your tickets done quickly, which leaves less time for "refactor as you go".
I've heard this a lot, but I've worked at 8 companies so far, and none of them have had this kind of time pressure. Is there a specific industry or location where this is more common?
A big piece is that companies are extremely siloed by default. It's pretty easy for a team to improve things in their silo, it's significantly harder to improve something that requires two teams, it's nearly impossible to reach beyond that.
Uber is particularly siloed, they have a huge number of microservices with small teams, at least according to their engineering talks on youtube. Address validation is probably a separate service from anything related to maps, which in turn is separate from contacts.
Because of silos, companies have to make an extraordinary effort to actually end up with good UX. Apple was an example of these, where it was literally driven by the founder & CEO of the company. Tumblr was known for this as well. But from what I heard, Travis was more of a logistics person than a UX person, etc.
(I don't think silos explain the bank validation issue)
using vector syntax is much faster than loops in Python
To generalize this slightly, using Python to call C/C++ is generally much faster than pure Python. For example, built-in operations in Pandas tend to be pretty fast, while using .apply() is usually pretty slow.
I didn't know about that, thanks!
I found Loop Hero much better with higher speed, which you can fix by modifying a variables.ini file: https://www.pcinvasion.com/loop-hero-speed-mod/
I've used Optim.jl for similar problems with good results, here's an example: https://julianlsolvers.github.io/Optim.jl/stable/#user/minimization/