Dran

Thank you for letting me know what software not to use; good bot

agree in principal, but in practice:

1. parents who live across the state

2. plexamp for music

The problem with this is it doesn’t work for home users that want to pay for their software. Crazy… I know… but those people do exist.

For people with “that one game” there is a middle ground. Mine is Destiny 2 and they use a version of easy anticheat that refuses to run on Linux. My solution was to buy a $150 used Dell on eBay, a $180 GPU to be able to output to my 4 high-res displays, and install Debian + moonlight on it. I moved my gaming PC downstairs and a combination of wake-on-lan + sunshine means that I can game at functionally native performance, streaming from the basement. In my setup, windows only exists to play games on.

The added bonus here is now I can also stream games to my phone, or other ~thin clients~ in the house, saving me upgrade costs if I want to play something in the living room or upstairs. All you need is the bare minimum for native-framerate, native-res decoding, which you can find in just about anything made in the last 5-10 years.

Can you think of a better proxy for financial stability that isn’t obtrusively invasive?

I’m not saying it’s perfect; I’m just saying I don’t have a better idea. The intent is certainly reasonable.

“Open source” in ML is a really bad description for what it is. “Free binary with a bit of metadata” would be more accurate. The code used to create deepseek is not open source, nor is the training datasets. 99% of “open source” models are this way. The only interesting part of the open sourcing is the architecture used to run the models, as it lends a lot of insight into the training process, and allows for derivatives via post-training

It’s a little deeper than that, a lot of advertising works on engagement -based heuristics. Today, most people would call it “AI” but it’s fundamentally just a reinforcement learning network that trains itself constantly on user interactions. It’s difficult-to-impossible to determine why input X is associated with output Y, but we can measure in aggregate how subtle changes propagate across engagement metrics.

It is absolutely truthful to say we don’t know how a modern reinforcement learning network got to the state it’s in today, because transactions on the network usually aren’t journaled, just periodically snapshot for A/B testing.

To be clear, that’s not an excuse for undesirable heuristic behavior. Somebody somewhere made the choice to do this, and they should be liable for the output of their code.

If your bootloader is unlocked, you can fastboot boot an image and that will only run in memory the one time, however, it will share a data partition (apps, preferences, etc) so it might not behave as well as a native install would have.