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Posts
3
Comments
298
Joined
4 yr. ago

  • I agree with being pragmatic, but the opinion of hating Microsoft isn't unfounded. There are pragmatic reasons to avoid building up and entrenching yourself in tooling that doesn't respect you as a user or is controlled by companies that has interests that don't align with yours.

  • We need more need to normalize companies stepping up to pay for security development for opensource products they utilize. If companies aren't putting FTEs to cover their risk of using a product or service then they should be held liable for any damages that causes them or their customers. This is for more than FOSS and for more than CVEs but also critical errors that cause delays in business continuity.

    The issue is many c suite are just now under standing this and many justice systems seem behind on this.

  • That's not really the case for all ML systems, it the fact that programers can generate content that they themselves did not make, collect, or anticipate themselves by creating models that generate thier own decision trees based on input data.

  • Exactly! If it were just out putting exact data they wouldn't care about making new works and just pivot as the world's greatest source of compression.

    Though there is some work researchers have done to heavily modify these models to over fit to do exactly this.

  • Arguably neither GNU utils or the Linux kernel are OSs since they don't operate anything on their own. They more accurately parts of system. I think distros are more accurately called OSs

  • I've described it as cost flexible, because you should be funding or ensure developers are funded to a level appropriate level of risk to operations if a vulnerability is discovered or a critical failure prevents a correct operation.

    That's for big business and governments at least. Small businesses also has the same concerns but the risk matrix for them is just different.

  • It'll be interesting to see if this gets used in places where the cost of dedicated hardware out ways the bandwidth available. Video calls to Antarctica, shipping vessels, airplanes, space, etc. At least that's something that comes to mind. Could also see a next interation of CDNs using it, if the numbers check out.

  • Training tends to be more compute intensive while inference is more likely to be able to be ran on a smaller hardware foot print.

    The neater idea would be a standard model or set of models, so that a 30G program can be used on ~80% of target case, games and video seem good canidates for this.

  • I like Linux for a lot of reasons, but the reason I was dualbooting the most was more packages for AI and the like just worked on it and I was programming.

    The reason I deleted my windows partition though was I had a faulty drive that on windows ment I would crash all the time, but my Linux boot just worked for like another year on the failing disk with no issue. When I got a new drive I just installed Linux and didn't bother getting Windows again.

    I have to Linux for work sometimes and the biggest pet peeve for me is that the app search bar is always slow or broken. Like it is so good on KDE, I default to superkey, search app, enter compared to opening any lists of menus.