smpl

Reminded my of what happened at the MindTheTech conference half a year ago.

https://peervideo.club/w/p/i4BetLY7RZa5yeNLJriXPW?playlistPosition=3

https://www.kenklippenstein.com/api/v1/file/fc39e78d-f510-4918-935b-95701be97310.pdf

Automatic Content Recognition (ACR) [42] is widely used for second-party tracking in smart TVs. As shown in Figure 1, ACR periodically captures frames (and/or audio), builds a fingerprint of the content, and then shares it with an ACR server for matching it against a database of known content (e.g., movies, ads, live feed). When the fingerprint matches, ACR server can determine exactly what piece of content is being watched on the smart TV.

https://arxiv.org/abs/2409.06203

Netscape Communicator, Netscape Communicator, KHTML, Netscape Communicator

@snap@lemm.ee @Sekoia@lemmy.blahaj.zone

https://codeberg.org/aketawi-study/rust-constellation-bgr

Yes, always from https://gìthub.com

As a general stance "People want me to give them free shit. I say gtfo.", I understand you.

That's just not proportional to Mozilla and Firefox. In 2022 they had a total revenue of $595 million¹. That allows them to hire 3305 software developers at a salary of $180.000. Google was responsible for 81% of that revenue¹. If you remove Google and their influence from the equation you're left with $113 millon and Mozilla can then hire 628 software developers. I think that would be more than adequate to maintain a browser.

1. https://en.wikipedia.org/wiki/Mozilla_Corporation

lol, I think we're giving too little credit to the marketing people in tech. I want to read their blogs!

You could try out Linux Mint¹, they're Ubuntu based and disable Snap by default².

1. https://linuxmint.com
2. https://linuxmint-user-guide.readthedocs.io/en/latest/snap.html

It seems that we focus our interest in two different parts of the problem.

Finding the most optimal way to classify which images are best compressed in bulk is an interesting problem in itself. In this particular problem the person asking it had already picked out similar images by hand and they can be identified by their timestamp for optimizing a comparison of similarity. What I wanted to find out was how well the similar images can be compressed with various methods and codecs with minimal loss of quality. My goal was not to use it as a method to classify the images. It was simply to examine how well the compression stage would work with various methods.

It's a pillar of democracy to protect the autonomy of the people.

It is a human right..