Discover gists

Inspired by dannyfritz/commit-message-emoji

Go - The Ultimate Folder Structure

Organizing your Go (Golang) project's folder structure can help improve code readability, maintainability, and scalability. While there is no one-size-fits-all structure, here's a common folder structure for a Go project:

project-root/
    ├── cmd/
    │   ├── your-app-name/
    │   │   ├── main.go         # Application entry point
    │   │   └── ...             # Other application-specific files

Debugging PHP with XDebug v3 inside Docker using VSCode

Assumptions / Prerequisites

XDebug v3+ inside Docker (e.g. php:7.3-apache Docker image)
Running Docker v20.10+
VSCode with PHP Debug Extension (Felix Becker)
Using Docker Compose for orchestration

Objective

In some cases, only these lines will work

for product in IntelliJIdea WebStorm DataGrip PhpStorm CLion PyCharm GoLand RubyMine; do
    rm -rf ~/.config/$product*/eval 2> /dev/null
    rm -rf ~/.config/JetBrains/$product*/eval 2> /dev/null
done

But if not, try these

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!

>>> docker exec -it CONTAINERID /bin/sh
/app # telnet
/bin/sh: telnet: not found

/app # apk update
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/main/x86_64/APKINDEX.tar.gz
fetch http://dl-cdn.alpinelinux.org/alpine/v3.7/community/x86_64/APKINDEX.tar.gz
v3.7.0-243-gf26e75a186 [http://dl-cdn.alpinelinux.org/alpine/v3.7/main]
v3.7.0-229-g087f28e29d [http://dl-cdn.alpinelinux.org/alpine/v3.7/community]

Commit type	Emoji
Initial commit	🎉 `:tada:`
Version tag	🔖 `:bookmark:`
New feature	✨ `:sparkles:`
Bugfix	🐛 `:bug:`

	--[[
	release.luau - A Lune script for publishing Roblox games
	MPL 2.0 License
	(c) 2024, Zack Ovits

	usage: lune run release
	--]]

	-- Lune libraries
	local stdio = require("@lune/stdio")

	import os
	from typing import Dict, List, Optional

	import requests
	from jose import jwt, jwk
	from jose.utils import base64url_decode

	JWK = Dict[str, str]
	JWKS = Dict[str, List[JWK]]

	"""MkDocs hook for performance debugging

	The idea behind it is to provide a minimal curated performance log for a `mkdocs build` run.
	Of course anyone can use a proper profiler, but the output often contains a lot of non-critical data
	that the user has to first filter to get a clearer picture. This hook takes care of this and allows
	to quickly see where a bottleneck is occurring. It also has a lower overhead than a proper profiler.

	It creates a file in the current working directory, it can be configured easily with variables set
	at the bottom of the file. There are also the timing categories included.

	// PLIST in NSData to NSDictionary
	NSDictionary * result = [NSPropertyListSerialization propertyListWithData:data options:0 format:&format error:outError];