Same here. For tough compute, I spin up a machine in some cloud service, do what I need, and shut it down. Of course, its a different issue if you need power on the desktop right there, like for A/V/Graphics work.
You'll have to bring in the 2π factor somewhere. Cant escape it. If sint is the sin function but with angle give in turns, then d/dx sint(x) = 2π cost(x). sin(x) ~ x for small x but sint(x) ~ 2πx for small x.
I wish people'd report metrics like "40 type classes, 5000 functions, 300 instances, 20 monads, 14 functors, ..." And such instead of "lines of haskell".
I wish people'd report metrics like "transactions per second, 99.x% availability with N secs/mins p99 latency, ..." when describing how practically useful and how effective a real-world banking/transaction system is, which further proves the practicality of the programming language.
Is it? I’m curious because I thought they were raising prices to pay for exorbitant training costs, not because subscribers are expensive on a unit basis.
I thought inference was cheap so there was little marginal cost of a new subscriber.
While this can give a notation for the domain, you'd still need an engine to process it. Prolong+CLPFD perhaps meets it well (not too familiar with the tax domain) and one could perhaps paraphrase Greenspun's tenth rule to this combo too.
https://joearms.github.io/published/2016-01-28-A-Badass-Way-...