onnyyonn

#!/bin/bash
export_dir="Export"
share_dir="Share"

mkdir -p "$share_dir"

overwrite=false
if [[ "$1" == "-overwrite" ]]; then
    overwrite=true
fi

onnyyonn

#! /usr/bin/env nix-shell
#! nix-shell -i bash -p git bitwarden-cli jq
# This script downloads my SSH key from Bitwarden and then clones my private NixOS config repository from Github
# Assumptions:
#   1. You have only one item named "SSH Keys" stored in Bitwarden
#   2. The private key is saved in that item as an attachment named "id_rsa"
#   3. The public key is added to Github account

mkdir -p ~/.ssh

onnyyonn

FZF Scripts

• ksend: send files to other devices using kdeconnect
• fz-open: navigate directory structure to find and open files
• editdot: open dotfiles tracked by yadm in $EDITOR
• dotlog: browse git log of dotfiles tracked by yadm, preview diff and copy commit hash

onnyyonn

Wordle

Note

I created this list in 2022, at the height of the Wordle craze. It was yet to be bought by the NYT. Wordle-likes were popping up every day, and as I kept track of them, this list was born. I’m delighted to see that new Wordle-likes are still being developed, and I still get comments suggesting new games to add. But I don’t intend to add any new links to this list, nor do I want to remove ones that have stopped working. I want to keep the list frozen as a snapshot in time. That said, please feel free to comment with new games for others to discover. I just wanted to clarify that they won’t be added to the list, to set the expectation.

Homepage: https://www.powerlanguage.co.uk/wordle/

Clones

• YAWDLE: Shareable puzzles. Code
• Hello Wordl: 4 to 11 letter puzzles and variable difficulty. Code

onnyyonn

# Sample script to generate freqencies of a list of words from Google Ngram Viewer

import pandas as pd, numpy as np
import re, requests
from ast import literal_eval

# Import list of words
df = pd.read_csv("wordle_full.csv")
df.rename( columns={'Unnamed: 0':'word', 'Unnamed: 1':'frequency'}, inplace=True )

onnyyonn

onnyyonn

import numpy as np
from statsmodels.distributions.empirical_distribution import ECDF

def bias_correct(obs_data_train, model_data_train, model_data_test):
    """
    Function for bias correcting model_data_test, based on obs_data_train and madel_data_train CDF.
    All three should be numpy array. Can be multi-dimensional.
    
    obs_data_train = observation / ground truth
    

onnyyonn

|=  i=@
`@ub`i

onnyyonn

• Go to about:config, change browser.display.background_color value to #000000 and toolkit.legacyUserProfileCustomizations.stylesheets value to true
• Open profile folder in about:profiles under “Root Directory”
• go to / create chrome subfolder, open / create userChrome.css and append the following lines: #browser vbox#appcontent tabbrowser, #content, #tabbrowser-tabpanels, browser[type=content-primary], browser[type=content] > html { background: var(--in-content-page-background)!important }

onnyyonn

### Keybase proof

I hereby claim:

  * I am onnyyonn on github.
  * I am anamitra (https://keybase.io/anamitra) on keybase.
  * I have a public key ASB16eMLCTYZzbmzoGU96qzJsxj6j7XSR9qJ-njEU3GnWwo

To claim this, I am signing this object: