Predation accounts for a relatively low rate of nest failure: only 34% compared to an average of 80% for birds in similar habitats. This may be enabled by their well camouflaged nests, or simply the lack of local predators.
Not out of range for moral support. I'm sure there's an alien out there having a shitty day. Like, maybe his spaceship got towed and he lost his job at the Dyson sphere. I'm rooting for you, buddy!
I don't think having well-defined precision is a rare requirement, it's more that most devs don't understand (and/or care) about the pitfalls of inaccuracy, because they usually aren't obvious. Also, languages like JavaScript/PHP make it hard to do things the right way. When I was working on an old PHP codebase, I ran into a popular currency library (Zend_Currency) that used floats for handling money, which I'm sure works fine up until the point the accountants call you up asking why they can't balance the books. The "right way" was to use the bcmath extension, which was a huge pain.
I work at big tech (not MS) and yes, the comp package really is that good, though not as good as it used to be. I immediately doubled my total comp when I came here from my last job, and now it's ~5x. I could retire right now if I wanted, so I don't care about layoffs anymore.
Yeah, it got really popular when the Bible dropped in the 2nd century BCE. The Noah flood story was basically a copy-and-paste of the Epic of Gilgamesh. Bible nerds were annoying af.
Implementation restriction: although numeric values have arbitrary precision in the language, implementations may implement them using an internal representation with limited precision. That said, every implementation must:
Represent integer values with at least 256 bits.
Represent floating-point values with a mantissa of at least 256 bits and a signed binary exponent of at least 16 bits.
Give an error if unable to represent an integer value precisely.
Give an error if unable to represent a floating-point value due to overflow.
Round to the nearest representable value if unable to represent a floating-point value due to limits on precision. These requirements apply to the result of any expression except for builtin functions, for which an unusual loss of precision must be explicitly documented.
That works until you realize your calculations are all wrong due to floating point inaccuracies. YAML doesn't require any level of precision for floats, so different parsers on a document may give you different results.
YAML doesn't require any level of accuracy for floating point numbers, and that doc appears to have numbers large enough to run into problems for single-precision floats (maybe double too). That means different parsers could give you different results.
But the company has already invested so much into the CEO, they can't just let him go because he doesn't understand the sunk cost fallacy! /s