Bartosz Miselis bmiselis

## beastmode-install.md

      
              4 files
            
          
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                burkeholland
                / beastmode-install.md
            
            
              Last active
              December 6, 2025 16:46
            
              
                Beast Mode
              
          
    Beast Mode

Beast Mode is a custom chat mode for VS Code agent that adds an opinionated workflow to the agent, including use of a todo list, extensive internet research capabilities, planning, tool usage instructions and more. Designed to be used with 4.1, although it will work with any model.
Below you will find the Beast Mode prompt in various versions - starting with the most recent - 3.1
Installation Instructions


Go to the "agent" dropdown in VS Code chat sidebar and select "Configure Modes".
Select "Create new custom chat mode file"


## iterm2_switch_automatic.md

      
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                FradSer
                / iterm2_switch_automatic.md
            
            
              Last active
              April 6, 2025 09:19
            
              
                Switch iTerm2 color preset automatic base on macOS dark mode.
              
          
The latest beta (3.5) includes separate color settings for light & dark mode. Toggling dark mode automatically switches colors.

Vist iTerm2 homepage or use brew install iterm2-beta to download the beta. Thanks @stefanwascoding.


Add switch_automatic.py to ~/Library/ApplicationSupport/iTerm2/Scripts/AutoLaunch with:


## condaenv.txt
# For Windows users# Note: <> denotes changes to be made

#Create a conda environment
conda create --name <environment-name> python=<version:2.7/3.5>

#To create a requirements.txt file:
conda list #Gives you list of packages used for the environment

conda list -e > requirements.txt #Save all the info about packages to your folder

## docker-cleanup-resources.md

      
              1 file
            
          
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                bastman
                / docker-cleanup-resources.md
            
            
              Created
              March 31, 2016 05:55
            
              
                docker cleanup guide: containers, images, volumes, networks
              
          
    Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...
delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

  
## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## delete_git_submodule.md

      
              1 file
            
          
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                myusuf3
                / delete_git_submodule.md
            
            
              Created
              November 3, 2014 17:36
            
              
                How effectively delete a git submodule.
              
          
    To remove a submodule you need to:

Delete the relevant section from the .gitmodules file.
Stage the .gitmodules changes git add .gitmodules
Delete the relevant section from .git/config.
Run git rm --cached path_to_submodule (no trailing slash).
Run rm -rf .git/modules/path_to_submodule (no trailing slash).
Commit git commit -m "Removed submodule "
Delete the now untracked submodule files rm -rf path_to_submodule
	# For Windows users# Note: <> denotes changes to be made

	#Create a conda environment
	conda create --name <environment-name> python=<version:2.7/3.5>

	#To create a requirements.txt file:
	conda list #Gives you list of packages used for the environment

	conda list -e > requirements.txt #Save all the info about packages to your folder
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)