[-] V699@kbin.social 32 points 1 year ago

This is my biggest fear. The hidden weakness of the fediverse is that the largest implementation gets to set the rules of federation

[-] V699@kbin.social 9 points 1 year ago

Self imposed gatekeeping. Damn that's real

[-] V699@kbin.social 7 points 1 year ago
3
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

Would be kinda cool if there was a system to donate money to a magazine and then have that revenue automatically get split between highly ranked content of that magazine.

[-] V699@kbin.social 5 points 1 year ago

People over complicate federation. I write federated software so lemme break it down. Federation just means data sharing. When you post something on a federation enabled website it sends a copy of your post to everyone who follows you and tells their service to store your data in their database in addition to their own data. What this means is that you can't just blow up a server to shut it down because everyone in the game has a copy.

[-] V699@kbin.social 5 points 1 year ago

I use the micro blogging feature inside magazines where I'm asking for help that doesn't require a link to anything. It acts like a standard forum in that mode

[-] V699@kbin.social 8 points 1 year ago

I realized why I like kbin more while I was writing a front end for Lemmy. On Lemmy you have very limited control over filtering and sorting e.g
"Top" granularity is only at the day level whereas in kbin you can do hours. Lemmy only has a single "hotness" filter whereas kbin has the same granularity for hotness as it does top.

[-] V699@kbin.social 7 points 1 year ago

Race conditions are the worst

[-] V699@kbin.social 14 points 1 year ago

We got the smart people from Reddit

[-] V699@kbin.social 15 points 1 year ago

this is a kbin magazine heh

4
Free user limit | GitLab (docs.gitlab.com)
submitted 1 year ago by V699@kbin.social to c/tech@kbin.social

Documentation for GitLab Community Edition, GitLab Enterprise Edition, Omnibus GitLab, and GitLab Runner.

1
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

Image upload isn't working for me

#kbinMeta

[-] V699@kbin.social 8 points 1 year ago

Startups inside companies usually get shut down once the bill gets too high. I've experienced this first hand

1
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

I don't seem to be getting notifications for interactions with my content

#kbinMeta

1
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

Can you post to multiple magazines at once?

#kbinMeta

[-] V699@kbin.social 8 points 1 year ago

I actually like the UX of this site better than reddit it doesn't have anything to do with politics

8
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

Why the name "magazine"? a magazine isn't a community

#kbinMeta

2
submitted 1 year ago by V699@kbin.social to c/kbinMeta@kbin.social

#Feedback Please allow us to click through to a link to read article before we read comments. I find myself double clicking a lot just to get to article.

#kbinMeta

4
submitted 1 year ago by V699@kbin.social to c/tech@kbin.social
12
submitted 1 year ago by V699@kbin.social to c/tech@kbin.social

Identifying hit songs is notoriously difficult. Traditionally, song elements have been measured from large databases to identify the lyrical aspects of hits. We took a different methodological approach, measuring neurophysiologic responses to a set of songs provided by a streaming music service that identified hits and flops. We compared several statistical approaches to examine the predictive accuracy of each technique. A linear statistical model using two neural measures identified hits with 69% accuracy. Then, we created a synthetic set data and applied ensemble machine learning to capture inherent non-linearities in neural data. This model classified hit songs with 97% accuracy. Applying machine learning to the neural response to 1st min of songs accurately classified hits 82% of the time showing that the brain rapidly identifies hit music. Our results demonstrate that applying machine learning to neural data can substantially increase classification accuracy for difficult to predict market outcomes.

1
submitted 1 year ago by V699@kbin.social to c/fediverse@kbin.social

testing federation @flancian

#fediverse

22
submitted 1 year ago by V699@kbin.social to c/world@lemmy.world

President Joe Biden said on Monday the threat of Russian President Vladimir Putin using tactical nuclear weapons is "real", days after denouncing Russia's deployment of such weapons in Belarus. On Saturday, Biden called Putin's announcement that Russia had deployed its first tactical nuclear weapons to Belarus "absolutely irresponsible".

10
TypeScript 5.2's New Keyword: 'using' (www.totaltypescript.com)
submitted 1 year ago by V699@kbin.social to c/tech@kbin.social

TypeScript 5.2 introduces 'using', a keyword that disposes anything with a Symbol.dispose function upon leaving scope, making resource management easier.

[-] V699@kbin.social 14 points 1 year ago

At first I was like...uh... heh

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V699

joined 1 year ago