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How to download your Udemy course videos using youtube-dl

$ youtube-dl --list-extractors | grep udemy

Steps

Get link to the course to download. e.g. https://www.udemy.com/course-name/
Login into udemy website, save the cookie from chrome using Chrome (Cookie.txt)[1] export extension. Save it to file udemy-cookies.txt
Get the link of the video that you want to download. usually in format. Use the command provided below where you have to replace the {course_link} and {path_to_cookies_file} with respective paths.

$ youtube-dl {course_link} --cookies {path_to_cookies_file}

Mute volume

osascript -e "set volume with output muted"

Unmute volume

osascript -e "set volume without output muted"

Complete Recent Discord Quest

Note

This no longer works in browser!

Note

This no longer works if you're alone in vc! Somebody else has to join you!

How to use this script:

Accept the quest under User Settings -> Gift Inventory

So you want to write a sync system for a web app with offline and realtime support? Good luck. You might find the following resources useful.

Overview articles

Database in a browser, a spec (Stepan Parunashvili)

What problem are we trying to solve with a sync system?
The web of tomorrow (Nikita Prokopov)

Tampermonkey userscript - Soundcloud Downloader Clean

An ad-less, multilingual, clean Soundcloud downloader with robust code. Adds a 'Download' button to all single track views.

Features

No third-party embeds, redirects or ads, directly uses the Soundcloud API.

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	{
	"$schema": "https://zed.dev/schema/themes/v0.1.0.json",
	"name": "Visual studio code themes",
	"author": "Microsoft",
	"themes":[
	{
	"name": "Light (Visual Studio)",
	"appearance": "light",
	"style": {
	"border": null,

	##### Windows
	# Windows thumbnail cache files
	Thumbs.db
	Thumbs.db:encryptable
	ehthumbs.db
	ehthumbs_vista.db

	# Dump file
	*.stackdump

	# chain DataFrame.withColumnRenamed() calls for each df.schema.fields
	df = reduce(lambda chain, column: chain.withColumnRenamed(*column),
	map(lambda field: (field.name, str.lower(field.name)),
	df.schema.fields),
	df)