That's incredibly concise. I see you're perturbing vx and vy randomly and updating location accordingly on every frame. Is the perturbation also per-frame or is that block of code just executed once? Is randomf a floating-point number between 0 and 1?
Your point of view is very clear: it's not a feature you're interested in. But it doesn't mean that it's not a good feature for the person who has opened the issue.
Instead of convincing them that it's a bad feature or talking about why it's uninteresting to you, just point them to a CONTRIBUTING.md and stop engaging with them after that. If they're actually serious about the feature, they can implement it in their own fork (which then you can request as a PR if you want it in your repo).
You're right! Even I tried to post it. This is what I observed:
1. After posting, I'm redirect to https://news.ycombinator.com/newest where the link works
2. I click to open the discussion, now on this page link no longer works
3. Going back to /newest, the link no longer works!
1. Would it be correct to describe memoized DOM approach as combination of direct manipulation (like hand-written jquery, or what svelte compiles to) + a mechanism to avoid invoking selectors by caching references to DOM elements? Or is there more to it?
2. It'd be a good experiment to separate out the memoized DOM implementation from imba codebase in a way it can be used by different frameworks, just as virtual DOM libraries got popular after react. If someone were to attempt this, where would you recommend that they start with the imba codebase?
I've always viewed Julia as a language for scientific computing professionals [1].
The article pronounces Julia's death only based on popularity relative to other languages. Yet, it's not clear what the author is comparing it to.
The comparisons I see are MATLAB and FORTRAN, to which Julia seems to stand third in TIOBE Index [2] that the author is using. The author doesn't seem to focus on this.
The author mentions
> Julia’s target user is harder to define. I have struggled with this while writing Learn Julia.
I wonder if it may not be the case that the author has developed his own notion of what Julia ought to be. And I'll agree that Julia may have failed his grand vision to displace large parts of Python, but I do not think that that vision is based in reality. Python users that want to use frameworks written in other, faster languages (like C++) will forever continue to use Python and enjoy the vast libraries that it offers which aren't centred around scientific computing.
[1]: There seems to be a list on https://juliacomputing.com/. Arguably their needs might be very different than the author's. But I can't say because the article's arguments are not based on technical shortcomings.
[2]: In the TIOBE Index (as a proxy for popularity) MATLAB gets 1.04%, Fortran 0.83%, and Julia 0.41% (GNU's Octave, the main FOSS Matlab competitor, is nowhere to be seen). I do not know what these percentages mean though https://www.tiobe.com/tiobe-index/