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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!

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

	"""
	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)

	# https://nixos.wiki/wiki/Nixpkgs/Create_and_debug_packages#Using_nix-shell_for_package_development
	function debug_nix_package () {
	set -u
	packageName=${1:-default}
	flakeDir=${2:-$(pwd)}
	tmpdir=${3:-$(mktemp --directory --suffix=-nix-dbg)}
	system=${4:-$(uname --machine)-$(uname --kernel-name \| tr '[:upper:]' '[:lower:]')}

	cd "$tmpdir" \|\| exit
	ln -s "$flakeDir" flakeDir