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@kfcobrien
kfcobrien / .BASH-PROMPT.png
Last active September 4, 2023 14:55
Clean informative bash prompt with git info
.BASH-PROMPT.png
@setzer22
setzer22 / README.md
Last active December 2, 2023 14:03
Linux command to hibernate and then reboot into a different OS

Linux command to hibernate and then reboot into a different OS

In this document I describe my setup about how to add a menu shortcut that will hibernate and reboot the system into a different OS (in my case, Windows), and then restore linux on the next reboot.

For this, I'll be using Arch Linux with systemd-boot as my boot manager, but in practice any bootloader that handles the LoaderEntryOneShot (a.k.a. BootNext) efivar should work.

NOTE It is advisable to not do this with window's fast boot feature active, since alternating the hibernation of two systems can cause shared partitions to get corrupted. However, if additional steps are taken in order to ensure the shared partition are unmounted before reboot, or no partitions are shared between the two OSs, this can be made to work with fastboot enabled which should give quite a boost in Windows' startup time.

Set the LoaderEntryOneShot/BootNext EFI variable

@max-mapper
max-mapper / bibtex.png
Last active March 10, 2024 21:53
How to make a scientific looking PDF from markdown (with bibliography)
bibtex.png
@notdodo
notdodo / install_printer
Last active February 7, 2024 09:07
Install a printer on Arch Linux with cups using command line
#!/bin/bash
################################################################
# Install a printer on Arch Linux with cups using command line #
# Used for a HP PSC 1510 with default driver #
################################################################
sudo pacman -S cups
sudo systemctl start org.cups.cupsd
@tristanwietsma
tristanwietsma / adaboost.py
Created April 30, 2013 01:13
AdaBoost Python implementation of the AdaBoost (Adaptive Boosting) classification algorithm.
from __future__ import division
from numpy import *
class AdaBoost:
def __init__(self, training_set):
self.training_set = training_set
self.N = len(self.training_set)
self.weights = ones(self.N)/self.N
self.RULES = []