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I don't know much about deep learning. Just noticed that there are 40+ upvotes and 0 comments. I propose the HN Bikeshedding effect theory. Take the number of comments and divide it by the number of upvotes.

<0.1 = Too technical for even HN audience 0.1-1.0 = At the right level for the HN audience >1 = The topic is similar to painting the bike shed.




The high amount of upvotes and low ratio of votes/comments on deep learning/big data posts is unfortunately accurate.

It's not a problem that HN has topics which are frequently upvoted; topics such as employment and Rust are popular memes.

It is a problem, however, if the upvote-for-the-title crowd upvote articles which are bad and would not get upvotes if they were about another topic. That's a legit hard problem to solve (what makes a good submission?), unfortunately, but one I've been looking into.

(For clarity, this submission is a good submission, but I've seen quite a few top-ranking HN submissions that are just a bar chart on a controversial topic that is poorly sourced. And linkbait about deep leaning tends to get upvotes, but flagged too.)


There's a problem with upvoting on HN: upvote is also the only saving mechanism available: If I run across some click-baity title, but I don't have time to read it right now I will click upvote, but what I really mean by that is "save for later".

Maybe HN just needs to separate bookmarking and upvoting?


... HN has added favorites recently ("favorite" link, on the submission/individual comments page) and thus has both. And apparently really needs a more public changelog.


I was just making an observation, certainly didn't intend it as criticism.


Have you considered that some posts may simply be a good read? I often don't comment because the post itself made a good job of stating a point. There is no need to reiterate what was said in a post just to get HN points.


HN is like Leslie Knope and has an opinion about everything :-)

Jokes aside, I agree with your comment.


I've created two scrapbooks commemorating this comment. Where I should ship them to? ;-)


Burt Macklin, FBI.


That's already how HN works. Articles with too many comments get a huge penalty and get pushed off the front page quickly.

It has the effect of stifling a lot of interesting and important discussions and topics. I make an effort to up vote posts with too many comments to help them.


Sounds about right !

But actually all Deep Learning stuff, tutorials, videos are upvoted pretty quickly those times.




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