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@marktheunissen
marktheunissen / pedantically_commented_playbook.yml
Last active February 7, 2026 19:32 — forked from phred/pedantically_commented_playbook.yml
Insanely complete Ansible playbook, showing off all the options
This playbook has been removed as it is now very outdated.
@statico
statico / gist:3172711
Created July 24, 2012 21:15
How to use a PS3 controller on Mac OS X 10.7 (Lion)

How to use a PS3 controller on Mac OS X 10.7 (Lion)

  1. Open Apple menu -> System Preferences -> Bluetooth and disable Bluetooth on Mac as well as any other nearby Macs or devices which will try to pair with and confuse the controller.

  2. Reset PS3 controller by inserting paperclip into pinhole near L2 button.

  3. Connect PS3 controller to Mac with USB cable.

  4. Enable Bluetooth.

@rtomaszewski
rtomaszewski / example_paramiko_with_tty.py
Created August 19, 2012 19:49
example paramiko script with interactive terminal
import paramiko
import time
import re
bastion_ip='ip'
bastion_pass='pass'
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy( paramiko.AutoAddPolicy() )
ssh.connect(bastion_ip, username='root', password=bastion_pass)
@dergachev
dergachev / README.md
Created October 10, 2012 16:49
Vagrant tutorial

Vagrant Setup

This tutorial guides you through creating your first Vagrant project.

We start with a generic Ubuntu VM, and use the Chef provisioning tool to:

  • install packages for vim, git
  • create user accounts, as specified in included JSON config files
  • install specified user dotfiles (.bashrc, .vimrc, etc) from a git repository

Afterwards, we'll see how easy it is to package our newly provisioned VM

@dsparks
dsparks / twitter_search.R
Created December 5, 2012 22:08
Twitter search
doInstall <- TRUE
toInstall <- c("twitteR", "lubridate")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
searchTerms <- c("New York", "Los Angeles", "Chicago", "Houston", "Philadelphia",
"Phoenix", "San Antonio", "San Diego", "Dallas", "San Jose",
"Jacksonville", "Indianapolis", "Austin", "San Francisco",
"Columbus", "Fort Worth", "Charlotte", "Detroit", "El Paso",
"Memphis")
@benwaldie
benwaldie / 2013-02-24-TUAW_Waldie-1.applescript
Created February 24, 2013 20:33
TUAW > AppleScript > Generate OmniFocus Email Followups from Contacts
-- "using terms from" is necessary to let AppleScript know that these event handlers are terminology that belongs to the Contacts app.
using terms from application "Contacts"
-- This handler returns the Contacts property for which the plug-in should function.
on action property
return "email"
end action property
-- This handler returns the name of the plug-in to be displayed in the Contacts property popup menu.
on action title
@omz
omz / Evernote Installer.py
Created February 27, 2013 15:07
Evernote Installer
# Simple installer script for using the Evernote SDK in Pythonista
#
# This script should be run from the root directory. In order to keep things
# tidy, it installs the module and all its dependencies in a directory named
# 'evernote-sdk'. In order to be able to import it, you have to add that to
# your import path, like this:
#
# import sys
# sys.path.append('evernote-sdk')
#
@ccstone
ccstone / BBEdit-TextWrangler_RegEx_Cheat_Sheet.txt
Last active June 15, 2025 17:57
BBEdit-TextWrangler Regular Expression Cheat-Sheet
————————————————————————————————————————————————————————————————————————————————————————————————————
BBEdit / BBEdit-Lite / TextWrangler Regular Expression Guide Modified: 2018/08/10 01:19
————————————————————————————————————————————————————————————————————————————————————————————————————
NOTES:
The PCRE engine (Perl Compatible Regular Expressions) is what BBEdit and TextWrangler use.
Items I'm unsure of are marked '# PCRE?'. The list while fairly comprehensive is not complete.
# coding=UTF-8
from __future__ import division
import re
# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013
class SummaryTool(object):
@zacstewart
zacstewart / classifier.py
Last active September 19, 2024 23:56
Document Classification with scikit-learn
import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score
NEWLINE = '\n'