Lewis Cowles Lewiscowles1986

Required applications

the remarkable toolchain
make ssh scp

Install xovi

Follow https://github.com/asivery/rmpp-xovi-extensions/blob/master/INSTALL.MD
Copy the qt-resource-rebuilder.so in the extensions.zip to /home/root/xovi/extensions.d(via scp)
Remember to refresh the hashtables https://github.com/asivery/rmpp-xovi-extensions/blob/master/INSTALL.MD#to-update-hashtab-required-for-ui-mods

Docker for Mac

On M1 machines, Docker for Mac is running a lightweight linux ARM VM, then running containers within that, so containers are essentially running natively. Don't be fooled by the fact the UI or binary CLI tools (e.g. docker) might require Rosetta.

Within that VM is an emulation layer called QEmu. This can be used by docker to run Intel containers. This does not use Rosetta at all, and has a roughly 5-6X performance penalty. (If you just upgraded your CPU this may result in a similar performance to your old machine!)

Pulling and running with Docker

Many images in public registries are multi-architecture. For instance at the time of writing on Docker Hub the php:8.0-cli image has the following digests:

Multiple PHP versions using `phpenv` and `php-build`

Install dependecies

Debian/Ubuntu users

sudo apt install \
  autoconf \
 bison \

	// g++ ping_pong.cpp -o ping_pong -std=c++20 -O3
	#include <algorithm>
	#include <cerrno>
	#include <cstdint>
	#include <cstring>
	#include <cstdlib>
	#include <iostream>
	#include <vector>
	#include <chrono>

	document.addEventListener('DOMContentLoaded', () => {
	const entryElement = document.querySelector('.entry');
	if (navigator.canShare && entryElement) {
	const shareButton = document.createElement('button');
	shareButton.textContent = 'Share this article';
	shareButton.onclick = async () => {
	try {
	await navigator.share({
	url: location.href,
	title: document.title

	library(rayrender)
	library(glue)

	y_dist = 8

	circle <- function(x = 0, y = 0 + 20, rad = 1, nvert = 500, ...){
	rads <- seq(0,2*pi,length.out = nvert)
	xcoords <- cos(rads) * rad + x
	ycoords <- sin(rads) * rad + y
	cbind(x = xcoords, y = y_dist, z = ycoords)

	add_filter( 'woocommerce_cart_no_shipping_available_html', 'myplugin_no_shipping_available_message' );
	add_filter( 'woocommerce_no_shipping_available_html', 'myplugin_no_shipping_available_message' );
	/**
	* Update the Woocommerce No Shipping message to include contact details.
	*/
	function myplugin_no_shipping_available_message( $message ) {

	$country = WC()->customer->get_shipping_country();
	$mailto = 'mailto:' . get_option( 'admin_email' ); // Could also be 'woocommerce_stock_email_recipient'.
	$link = sprintf( wp_kses( __( '<a href="%s">contact us</a>', 'my-plugin' ), array( 'a' => array( 'href' => array() ) ) ), esc_url( $mailto ) );

	/* So how does this work?
	I'm using ANSI escape sequences to control the behavior of the terminal while
	cat is outputting the text. I deliberately place these control sequences inside
	comments so the C++ compiler doesn't try to treat them as code.*/
	//[2K[2D[A[2K[A[2K[A[2K[A[2K[A
	/*The commands in the fake code comment move the cursor to the left edge and
	clear out the line, allowing the fake code to take the place of the real code.
	And this explanation uses similar commands to wipe itself out too. */
	//[2K[2D[A[2K[A[2K[A[2K[A
	#include <cstdio>

	module GADTMotivation

	(*
	Here is a simple motivational example for GADTs and their usefulness for library design and domain modeling. Suppose we
	need to work with settings which can be displayed and adjusted in a GUI. The set of possible setting "types" is fixed
	and known in advance: integers, strings and booleans (check-boxes).
	The GUI should show an example value for each possible setting type, e.g. 1337 for an integer setting and "Hello" for a
	string setting. How can we model this small domain of setting types and computing example values?
	*)

	import json
	import base64
	import os

	# make sure the output directory exists before running!
	folder = os.path.join(os.getcwd(), "imgs")

	with open("scr.har", "r") as f:
	har = json.loads(f.read())