proposal-types-as-comments VS 0.30000000000000004

As per the proposal:

Not runtime ones. https://github.com/giltayar/proposal-types-as-comments/issues/45

I do wonder about a future where JavaScript gets non-enforced optional type annotation syntax[1] which is and if said syntax will be slightly incompatible with TypeScript. That would be a little awkward for Deno, wouldn’t it. However I hope that if JS gets type annotation syntax that it would be a strict subset of TypeScript—or at the very least future compatible—for this very reason.

1. https://github.com/giltayar/proposal-types-as-comments

I assumed the joke was about this

It's actually Gil Taylor

The proposal on GitHub

https://0.30000000000000004.com/

Although it would be good to move in the direction of using a BigDecimal equivalent by default when ingesting unknown data.

There's no such thing as precision for floats. Floating-point calculations are always inaccurate: read this: https://0.30000000000000004.com/

Edit: This specific example even has its own website: https://0.30000000000000004.com/

In everyday math, we know adding 0.6 + 0.3 equals 0.9, right? But when we turn to computers it results in 0.8999999999999999. Surprisingly, this doesn’t just happen only in JavaScript; it’s the same in many programming languages like Python, Java, C too. Also, it’s not just about this specific calculation. There are many more decimal calculations showing similar not-quite-right answers.

Surprisingly common values like 0.1 don't have a precise representation in binary for most formats, including standard floating point number formats. See https://0.30000000000000004.com/ for more detail than you can shake a stick at.

Also if the local tax code states using 5 decimal places for intermediate values when you will introduce “errors” using formats that give greater precision as well as those that give less precision. Having work on mortgage and pension calculations I can state that the (very) small errors seen at individual steps because of this can balloon significantly through repeated calculations.

Furthmore, the name floating point gives away the other issue. Floating point numbers are accurate to a given number of significant figures not decimal places. For large numbers any decimal places you have in the result are at best an estimate, and as above any rounding errors at each stage can compound into a much larger error by the end of a calculation.

You'll love this https://0.30000000000000004.com/