If you want something that is mostly "doing mathematics", i.e. thinking about Galois groups, differential geometry, or the Riemann hypothesis, that is pretty tough. Many jobs have interesting mathematical content, but to get them you will probably need to convince the prospective employer that you can handle the non-mathematical content, which likely means writing non-trivial computer programs. The one exception that I can think of might be entry-level actuarial positions.
At this point, a lot of people decide to get a masters degree in operations research, statistics, or computer science. But don't completely give up and get an MBA. :-)
"By default, blas64 and lapack64 call the native Go implementations of the routines. Alternatively, it is possible to use C-based implementations of the APIs through the respective cgo packages and "Use" functions."
Performance comparison? Algorithmic equivalence? How close are the results numerically (e.g. how do they compare on badly conditioned matrices)?
tl;dr: if we assume deleterious effects from CO2-driven global warming and discount effects from long-term storage of high-level nuclear waste, nuclear is safer.
In the Dark Old Days, there was more specialization, so you could (and were expected to) become deeply knowledgeable about a few things, e.g. Cobol programming on OS/360. The computer vendor usually supplied everything, including the OS and the one language you used, and decent manuals were expected.
I find the MBTI incredibly useful. When I meet someone I immediately figure out what their MBTI type is. That saves me enormous amounts of time later on---I don't have to pay close attention to what they say or do. I just rely on their MBTI type. Sometimes I don't even bother listening to them or asking their opinion, which lets me use the time to think about other things, like HN. I used to use their astrological sign, but that required asking for their birthday, which was awkward, especially when I didn't get them a present.