Michael michaelcoyote

Working with ansible-vault

I've been using a lot of Ansible lately and while almost everything has been great, finding a clean way to implement ansible-vault wasn't immediately apparent.

What I decided on was the following: put your secret information into a vars file, reference that vars file from your task, and encrypt the whole vars file using ansible-vault encrypt.

Let's use an example: You're writing an Ansible role and want to encrypt the spoiler for the movie Aliens.

How to figure out what the fuck is going on without social media.

Sourced breaking news

Bash key combinations and shortcuts

The bash shell uses a library called GNU Readline that provides easy and quick CLI key combination access to bash history, screen movement and line editing commands. I've collected some of these here along with some builtin bash shortcuts for history and other functions.

Note: The command bind -p will list all the keybindings and the readline functions they call.

	main(argc, argv)
	int argc;
	char *argv[];
	{
	int i;

	argc--;
	for(i=1; i<=argc; i++)
	printf("%s%c", argv[i], i==argc? '\n': ' ');
	}

	#!/usr/bin/env python
	# -- coding: utf-8 --

	'''
	usage:
	cat about.txt \| python soinput.py
	'''

	import sys

	#!/bin/bash
	# Adds the currently playing iTunes track to your prepared commit message.
	#
	# To install, save in repo as chmod +x to .git/hooks/prepare-commit-msg
	SONG=`osascript -e 'tell application "iTunes" to if player state is playing then "♬ : " & artist of current track & " / " & name of current track'`
	if [[ $SONG ]]; then
	echo -e "$(cat $1)\n\n$SONG" > $1
	fi

	#!/usr/bin/python

	from __future__ import print_function

	import requests
	import json
	import argparse
	try:
	from Queue import Queue
	except:

	#!/usr/bin/awk -f

	# This program is a copy of guff, a plot device. https://github.com/silentbicycle/guff
	# My copy here is written in awk instead of C, has no compelling benefit.
	# Public domain. @thingskatedid

	# Run as awk -v x=xyz ... or env variables for stuff?
	# Assumptions: the data is evenly spaced along the x-axis

	# TODO: moving average