amitkgupta84

Feels like GitHub issues needs states other than Open or Closed. How about:

Resolved - issue has been fixed/addressed

Won’t Resolve - addressing the issue is not aligned with the direction of the project; it is not and will not be resolved

Paused - issue is valid but no one is continuing to discuss it, explore it, or work on it at this time; someone can resume the issue if they have new data or ideas.

Maybe issues aren’t just “tasks” but they’re part of your work, and you need some tools to prioritize your work and help communicate to yourself and others what you’re focusing on and what state things are in.

I’m still struggling to find the context for this whole thing. Where did you find DDD mentioned?

Do you have a link to some docs supporting that? How exactly is revenue tied to usage?

As a content creator, once you've recorded and edited your content, you have to solve for a few kinds of things:

1) hosting, content delivery, authentication and access control, styling and UI, etc. 2) discoverability, which requires things like a search engine, recommendation engine, a community/social network, links, etc. 3) some degree of independence, and the freedom to decide what content to show, what content to monetize, etc. free from whatever agenda/motives a company like YouTube/Alphabet might have.

One way to look at it is that PeerTube starts to make (1) a lot more accessible without something like YouTube. (2) is where YouTube shines, but is possible via existing structures in the Internet if you're just using PeerTube (plus PeerTube has some sort of federation, though I haven't looked into it much). (3) is where PeerTube shines.

There doesn't have to be one answer for the entire market. YouTube is probably better for smaller creators looking to build their audience and be discovered, but once you reach a sufficient size, and don't need to rely on the YouTube recommendation engine for people to find you, something that offers greater independence and self-determination would seem desirable. Of course, I'm only talking about tendencies, a very small content creator may have a strong desire for independence, and a a very large content creator may be perfectly fine coloring inside whatever lines YouTube draws.

I started working on a Helm [1] chart for PeerTube if someone wants to try out running PeerTube on a Kubernetes [2] cluster (I've only tried this on GKE [3]).

[1] https://www.helm.sh/ [2] https://kubernetes.io/ [3] https://cloud.google.com/kubernetes-engine/

What would a user of Kubernetes (as opposed to the person setting up Kubernetes) gain from this? How much of the underlying containerization technology is exposed through the Kubernetes APIs such that one could actually leverage features in rkt that aren't in Docker, or vice versa?

This blog post is painfully stupid. His point #1 is that posting answers to trivial questions one could easily answer themselves with a Google search is detrimental to good pedagogy. He then goes on to claim that most of his points came from doing this very thing. What's the logic here? Behaviour x is bad, I do x on StackOverflow, therefore StackOverflow is bad? No, SO in itself is not the problem, the author of that post is the problem, and judging by his score, one of the biggest contributors to said problem.

His second point is on the poor reward system. There is no reward system, there are only fake Internet points (and maybe a few things like additional posting or moderation privileges, but nothing of any consequence). Any "reward" system you feel there to be is what your imagination makes of the fake Internet points. The gripe about the "cool kids table" just demonstrates a basic ignorance of supply and demand. The complaint that he's scored many points after being inactive on the site is just as inane; if your "work" has value to people even years after producing it, why should you not be "rewarded" for it?

Last is his prediction that his 14k reputation would be diminished by the "petty children" who make up the SO community, presumably after they read and became angry with his blog post. Well, his reputation is now over 19k, so... I suppose it's possible that his current score is actually the work of petty children up-voting his questions and answers out of spite for him and his prediction.

That would be harsh. Who's doing that?

A good course. There are MOOCs that are good courses. This one is so-so. I don't understand your question.

I'm taking this course right now, I'm a little ambivalent about it. They cover various machine learning algorithms, which one can learn anywhere, but they also talk about how to deal with these things in a massive data context. The pragmatic tools needed to wrangle large amounts of data so that you can apply your usual ML algorithms to it is very nice to see.

That said, I don't feel like I'm learning concepts. So far, the techniques have felt like: break up the data into chunks this way, apply a bunch of hash functions that way, this is what ended up working for this particular problem. I guess if you work in the field, the tools you're exposed to will inspire things in your own work, and you'll feel more like you're building a general framework.

The homeworks are terrible. There are no mandatory programming assignments, and the one optional one does nothing to gradually work up to applying the stuff they teach you, it's just, here's a massive zip that won't fit on your hard drive, here's an uninteresting computational question to answer about it, go for it.

The remaining (basic) homeworks are quizzes and they're incredibly tedious. (There are advanced homeworks as well, but it hasn't been that inspiring). One of the recent homeworks was really just a rehash of some high school linear algebra, another one involved doing some computations with a bunch of different points. The points weren't provided in a list, they were drawn onto a jpeg so you had to manually copy all of them down. That's the kind of course it's been.

It's a very light weight course, which is nice if you're working. If your basic math skills are good and you already have some familiarity with ML and distributed computing, 5 hrs/wk is enough to watch the videos (at 2x speed plus occasionally hitting the 10-second-fast-forward) and do the basic homeworks.

I haven't read the entire thing, my barometer for deciding to read the whole thing was the section on limits. The intuition the author tries to develop is very wrong. The whole section on the limit being the smallest impossible number to reach is more misleading than it is instructive. It's dead wrong for probably the most trivial kind of function, the constant functions. If f is a constant function, it's limit (as x approaches any value) is not only not an impossible value for f to obtion, it's the only possible value! Ignoring constant functions, it breaks down with even trivial examples, e.g. the limit of x sin(1/x) as x approaches 0. The limit is 0, but 0 is not impossible to obtain as x approaches 0, in fact f(x) = 0 for infinitely many x in any arbitrarily small neighbourhood of 0.

The real idea is much simpler than "what's the biggest impossible number", it's "what number am I getting closer to."

Also, there wasn't even an example of when a limit doesn't exist, that's quite a significant omission. The function f(x) = sin(1/x) is a good contrast to the previous function; in this case, the limit does not exist as x approaches 0, and it's easy to see here that it's because f(x) doesn't get closer to any value, it keeps oscillating wildly between -1 and 1.