Alessandro Re akiross

Git sees every file in your working copy as one of three things:

tracked - a file which has been previously staged or committed;
untracked - a file which has not been staged or committed; or
ignored - a file which Git has been explicitly told to ignore.

Ignored files are usually build artifacts and machine generated files that can be derived from your repository source or should otherwise not be committed. Some common examples are:

dependency caches, such as the contents of /node_modules or /packages
compiled code, such as .o, .pyc, and .class files

Nix Flake MVP

Goals

To provide Nix repositories with an easy and standard way to reference other Nix repositories.
To allow such references to be queried and updated automatically.
To provide a replacement for nix-channel, NIX_PATH and Hydra

Tmux TrueColor (a.k.a. 24-bit color) Support

Prerequisites

To enable TrueColor for Tmux, you must make sure that you have two things:

Tmux version 2.2 or higher. You can find out your version by running tmux -V
You have a terminal that supports TrueColor (it may also be referenced by 24 bit color. To check if you do, run

Create a Wi-Fi hotspot on Linux using `nmcli`

Original post : https://unix.stackexchange.com/a/310699

nmcli con add type wifi ifname wlan0 con-name Hostspot autoconnect yes ssid Hostspot
nmcli con modify Hostspot 802-11-wireless.mode ap 802-11-wireless.band bg ipv4.method shared
nmcli con modify Hostspot wifi-sec.key-mgmt wpa-psk
nmcli con modify Hostspot wifi-sec.psk "veryveryhardpassword1234"

	from line_profiler import LineProfiler

	def do_profile(follow=[]):
	def inner(func):
	def profiled_func(args, *kwargs):
	try:
	profiler = LineProfiler()
	profiler.add_function(func)
	for f in follow:
	profiler.add_function(f)

	# Use this script to test that your Telegram bot works.
	#
	# Install the dependency
	#
	# $ gem install telegram_bot
	#
	# Run the bot
	#
	# $ ruby bot.rb
	#

	def train_network(samples, neural_network, nr_epochs=10, batch_size=64):
	optimizer = optim.Adam(neural_network.parameters())
	neural_network.train()

	for epoch_nr in range(nr_epochs):
	sample_ids = np.random.shuffle(range(len(samples)))

	for start in range(0, len(samples) // batch_size, batch_size):
	mini_batch = samples[sample_ids[start: start + batch_size]]
	boards, pis, vs = zip(*mini_batch)


	environment = environments.GoEnvironment(board_size=19)
	player_mcts = mcts.MCTS(
	environment,
	networks.NeuralNetwork(board_size=environment.getStateSize(), action_size=environment.getActionSize()),
	)

	training_samples = collections.deque(maxlen=opt.training_samples_buffer_size)

	for iteration_number in range(opt.num_iters):

	import dropbox
	import dropbox.files


	CHUNK_SIZE = 4 * 1024 * 1024


	def upload_stream(dbx, readable, dest_path):
	d = readable.read(CHUNK_SIZE)
	offset = len(d)