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Joined 2 years ago
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Cake day: June 16th, 2023

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  • Pocket. A closed source binary blob in a “open source” project.

    Orbit: AI productivity tool.

    Anonym: Ad server.

    Locking down extensions.

    Cutting 250 jobs while raising executive pay 400%.

    In 2021 the CEO made 5.5 million. They got about 7 million in donations that year.

    80% of their revenue is from google. But google encourages them to waste the money on stuff not related to the browser because it’s competition to chrome. Their job us to look like a viable competitor but not be good one.

    The browser is constantly getting worse on performance, user experience, and customizability.

    They have gone from 34% user share to 2.2%. So clearly I’m not alone in my opinion of the current state of the browser.







  • For ntsc vhs players it wasnt a component in the vcr that was made for copy protection. They would add garbled color burst signals. This would desync the automatic color burst sync system on the vcr.

    CRT TVs didn’t need this component but some fancy tvs would also have the same problem with macrovission.

    The color burst system was actually a pretty cool invention from the time broadcast started to add color. They needed to be able stay compatible with existing black and white tv.

    The solution was to not change the black and white image being sent but add the color offset information on a higher frequency and color TVs would combine the signals.

    This was easy for CRT as the electron beam would sweep across the screen changing intensity as it hit each black and white pixel.

    To display color each black and white pixel was a RGB triangle of pixels. So you would add small offset to the beam up or down to make it more or less green and left or right to adjust the red and blue.

    Those adjustment knobs on old tvs were in part you manually targeting the beam adjustment to hit the pixels just right.

    VCRs didn’t usually have these adjustments so they needed a auto system to keep the color synced in the recording.


  • The solution for this is usually counter training. Granted my experience is on the opposite end training ai vision systems to id real objects.

    So you train up your detector ai on hand tagged images. When it gets good you use it to train a generator ai until the generator is good at fooling the detector.

    Then you train the detector on new tagged real data and the new ai generated data. Once it’s good at detection again you train the generator ai on the new detector.

    Repeate several times and you usually get a solid detector and a good generator as a side effect.

    The thing is you need new real human tagged data for each new generation. None of the companies want to generate new human tagged data sets as it’s expensive.












  • It was something around 40 TB X2 . We were doing a terrain analysis of the entire Earth. Every morning for 25 days I would install two fresh drives in the cluster doing the data crunching and migrate the filled drives to our file server rack.

    The drives were about 80% full and our primary server was mirrored to two other 50 drive servers. At the end of the month the two servers were then shipped to customer locations.