Discover gists

Option 1: Command-line download extension as zip and extract

extension_id=jifpbeccnghkjeaalbbjmodiffmgedin   # change this ID
curl -L -o "$extension_id.zip" "https://clients2.google.com/service/update2/crx?response=redirect&os=mac&arch=x86-64&nacl_arch=x86-64&prod=chromecrx&prodchannel=stable&prodversion=44.0.2403.130&x=id%3D$extension_id%26uc" 
unzip -d "$extension_id-source" "$extension_id.zip"

Thx to crxviewer for the magic download URL.

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。Docker 官方和国内很多云服务商都提供了国内加速器服务。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

配置加速地址

Ubuntu 16.04+、Debian 8+、CentOS 7+

Backslash Escapes

https://daringfireball.net/projects/markdown/syntax#precode

syntax

\*literal asterisks\*

output

*literal asterisks*

Useful GCC Compiler Options

A non exhaustive list of gcc compiler options.

$ gcc myprog.c -o myprog -Wall -Wextra -pedantic -std=c11 [...]

Optimization:

Neovim keybinds

Capital letters do the opposite of small letters in command (Press shift to trigger capital letters)
_ (underscore) to move the cursor at the beginning of line (doesn't switch to insert mode)
- 0 (zero) moves the cursor to the zeroth position of the line (doesn't switch to insert mode)
$ (dollar) to move the cursor at the end of line (doesn't switch to insert mode)
d$ will delete from wherever your cursor is till the end of the line
f<character> to move cursor to the first occurrence of <character>
- f( to move cursor to first occurence of (
t<character> to move cursor to upto but not on the first occurrence of <character>
t( to move cursor to first occurence of (

	"""
	This is a pure Python3 implementation of the K-means Clustering algorithm.
	It is based on a GitHub Gist which uses Python2:
	https://gist.github.com/iandanforth/5862470
	I have refactored the code and to assure the code obeys Python Enhancement Proposals (PEPs) rules.
	After reading through this code you should understand clearly how K-means works.
	This script specifically avoids using numpy or other more obscure libraries.
	It is meant to be clear not fast.
	I have also added integration with the plot.ly plotting library.
	So you can see the clusters found by this algorithm. To install plotly run:

	using System;
	using System.Collections;
	using System.Collections.Generic;

	/// <summary>
	/// Extends the Array with the Flatten method
	/// </summary>
	public static class Flattener {
	/// <summary>
	/// Given a N-dimensional array, flattens it into a new one-dimensional array without modifying the elements' order

	#############################################################################
	# Full Imports

	from __future__ import division
	import math
	import random

	"""
	This is a pure Python implementation of the K-means Clustering algorithmn. The
	original can be found here:

	// Country list source: https://www.dhl.com/en/country_profile.html#.XwODEJNKjOQ
	// Country abbreviation source: https://planetarynames.wr.usgs.gov/Abbreviations
	// Postal code: https://gist.githubusercontent.com/jamesbar2/1c677c22df8f21e869cca7e439fc3f5b/raw/21662445653ac861f8ab81caa8cfaee3185aed15/postal-codes.json
	// Postal code: https://en.wikipedia.org/wiki/List_of_postal_codes

	// Country/territory items with no postal code regexes or ranges either do not require postal codes
	// or there may not be enough information for that country/territory

	export const COUNTRY_ADDRESS_POSTALS = [{
	abbrev: 'AF',