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Offtopic group for casual talking about anything. @rules_for_python still apply (except for the ontopic rule)
but i want to know some way that if anyone you are using the macos so i wanted to know this, i wanted to buy a old macbook pro machine, so i am asking help like we install by making bootable pendrive in other window machine i can insert pendrive and reinstall os here is not like this system?
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i means do i need to have a pendrive or what what things, i haven't find iso file like when i install Ubuntu or arch in a laptop i can do easily for mac what is the way?
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This is how the distro handles things and I'm satisfied.
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And they all work together. The fact my cpu may sucks is another argument.
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repeating activities that lead to utilisation might have some insight. But it could be spontaneous 🤔
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My computer crashed today. I was lucky enough to have been able to open htop before it stopped reacting completely.
So first you wanted someone no you offer? Please read the rules NOW
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- How to reinstall macOS (covers all methods including Recovery and USB) https://support.apple.com/en-us/102655
- Create a bootable installer for macOS (the USB pendrive equivalent) https://support.apple.com/en-us/101578
- How to start up from macOS Recovery (Intel-specific key combos) https://support.apple.com/en-us/102518
I didn’t get banned after not solving captcha last time. Or at least, I was able to text in that group after captcha message disappeared.
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I have a new feature for the bot which silences welcome messages but still requires captchas. I'm looking for (at least one) volunteer to test that (outside of these groups).
Since some people think we are getting paid and can forward that to volunteers, let me be clear about it: There's no pay involved. You'd be doing it purely out of the goodness of your heart.
4 performance cores with 2 threads each, 8 ecores with one thread. 16 threads.
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It can compile packages, that would have made sense, but it didn't get that far.
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htop was sorted by CPU usage, so I couldn't see. Just that nix, the process I started before it began, was consuming about 20G.
All cores were fully busy, but that was a side effect of the memory being full. As if was doing something special. But somehow I managed to get it full.
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100%, there is something called Caching. When you first talk to the LLM, it needs to know everything about your project.
The context window size doesn't change when you change the model, but most of the context for the first LLM is already cached on the server. When the next LLM is called, the whole thing has to be sent again, and the LLM has to know the proper context.
This is why you see that the amount of tokens used for the newer model is much higher.