• pixelscript@lemmy.mlEnglish
    14·
    1 year ago

    The replacement for the JavaScript Date API is on the cusp of finalization.

    They just got an RFC proposal approved by the IETF for an extension to the way datetime strings should be serialized that adds support for non-Gregorian calendar systems. That seems to have been the last round of red tape holding them back. Now it’s just a handful of bugfix PRs to merge and browsers can begin shipping implementations unflagged.

    You can watch the progress here if you find it interesting. In the meantime, there is a polyfill out now if you want to get started with it.

  • mercano@lemmy.world
    8·
    1 year ago

    All numbers in JS are stored as 64-bit floats, so past a certain point, precision starts to degrade.

      • UnknownFryingObject@feddit.de
        13·
        1 year ago

        Well that’s how floating point units work.

        The following is more a explanation about the principle than a precise description of float values in programming, since working with binary values has its own quirks, especially with values lower than one, but anyways:

        Think about a number noted by a base and an exponent, like
        1.000.000
        can be represented as 1*10^6.

        1.000.001 now becomes 1,000001*10^6.

        If you want more precision or bigger numbers maintaining the same precision, you will have to add further and further decimal places and that hits a limit at a certain amount.

        So basically you can either get really high numbers in a floating point unit or you can store really precise small numbers. But you cannot achieve both at the same time.

        • Traister101@lemmy.today
          6·
          1 year ago

          Alternatively as both floats (32 bit) and doubles (64 bit) are represented in binary we can directly compare them to the possible values an int (32 bit) and a long (64 bit) has. That is to say a float has the same amount of possible values as an int does (and double has the same amount of values as a long) . That’s quite a lot of values but still ultimately limited.

          Since we generally use decimal numbers that look like this 1.5 or 3.14. It’s setup so the values are clustered around 0 and then every power of 2 you have half as many meaning you have high precision around zero (what you use and care about in practice) and less precision as you move towards negative infinity and positive infinity.

          In essence it’s a fancy fraction that is most precise when it’s representing a small value and less precise as the value gets farther from zero