Zhitao Gong gongzhitaao

Instructions

CloudFlare is an awesome reverse cache proxy and CDN that provides DNS, free HTTPS (TLS) support, best-in-class performance settings (gzip, SDCH, HTTP/2, sane Cache-Control and E-Tag headers, etc.), minification, etc.

Make sure you have registered a domain name.
Sign up for CloudFlare and create an account for your domain.
In your domain registrar's admin panel, point the nameservers to CloudFlare's (refer to this awesome list of links for instructions for various registrars).
From the CloudFlare settings for that domain, enable HTTPS/SSL and set up a Page Rule to force HTTPS redirects. (If you want to get fancy, you can also enable automatic minification for text-based assets [HTML/CSS/JS/SVG/etc.], which is a pretty cool feature if you don't want already have a build step for minification.)
If you

I installed Font Awesome and uses it to make my Emacs mode line look cool:

Use the align-left icon for auto-fill-mode.
Use the thumbs-up and thumbs-down icons for flymake status (we need to reimplement flymake-report-status to make this happen).
Use the tags icon for gtags-mode.

Requirements: diminish.el - I installed it from MELPA.

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

	;; org-archive-subtree-hierarchical.el
	;;
	;; version 0.2
	;; modified from https://lists.gnu.org/archive/html/emacs-orgmode/2014-08/msg00109.html
	;; modified from https://stackoverflow.com/a/35475878/259187

	;; In orgmode
	;; * A
	;; ** AA
	;; *** AAA


	get_latest_release() {
	curl --silent "https://api.github.com/repos/$1/releases/latest" \| # Get latest release from GitHub api
	grep '"tag_name":' \| # Get tag line
	sed -E 's/."([^"]+)"./\1/' # Pluck JSON value
	}

	# Usage
	# $ get_latest_release "creationix/nvm"
	# v0.31.4

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	""" An rbm implementation for TensorFlow, based closely on the one in Theano """

	import tensorflow as tf
	import math

	def sample_prob(probs):
	"""Takes a tensor of probabilities (as from a sigmoidal activation)
	and samples from all the distributions"""
	return tf.nn.relu(
	tf.sign(

	FROM debian:jessie

	ENV USER=boatswain USER_ID=1000 USER_GID=1000

	# now creating user
	RUN groupadd --gid "${USER_GID}" "${USER}" && \
	useradd \
	--uid ${USER_ID} \
	--gid ${USER_GID} \
	--create-home \