merlinmann

Merlin's Wisdom Project

Or: “Everybody likes being given a glass of water.”

By Merlin Mann.

It's only advice for you because it had to be advice for me.

cmod

Super fast, keyboard-optimized, client side Hugo search

This is a fork of and builds upon the work of Eddie Webb's search and Matthew Daly's search explorations.

It's built for the Hugo static site generator, but could be adopted to function with any json index compatible with Fuse fuzzy search library.

To see it in action, go to craigmod.com and press CMD-/ and start typing.

tomhicks

/*
Copy this into the console of any web page that is interactive and doesn't
do hard reloads. You will hear your DOM changes as different pitches of
audio.

I have found this interesting for debugging, but also fun to hear web pages
render like UIs do in movies.
*/

const audioCtx = new (window.AudioContext || window.webkitAudioContext)()

publik-void

Should numbering start at 0 or 1?

Arguments for 1-based indexing and/or against 0-based indexing

• The number which denotes the index of an element is equal to the number one would get by counting up to (and including) that element.

• We are very used to labeling elements with their corresponding count number. This means 1-based indexing has a lot of inertia in our everyday lives and we

hkan

0⃣   0, keycap, zero
1⃣   1, number, one
🕜   1, 30, clock, time, one, thirty, 1:30, one-thirty
🕐   1, clock, time, one, 00, o’clock, 1:00, one o’clock
2⃣   2, number, two
🕝   2, 30, clock, time, two, thirty, 2:30, two-thirty
🕑   2, clock, time, two, 00, o’clock, 2:00, two o’clock
3⃣   3, keycap, three
🕞   3, 30, three, clock, time, thirty, 3:30, three-thirty
🕒   3, three, clock, time, 00, o’clock, 3:00, three o’clock

willwhitney

import requests
import sys
from datetime import datetime, timedelta
import pytz
from PIL import Image
from StringIO import StringIO
import os
import logging

# python himawari.py

wichmann.py

try: 
  #Wir brauchen aktuelle Versionen von BeautifulSoup und Requests
	from bs4 import BeautifulSoup;
	import requests 
	import shutil

	# Scrapen wir mal los
	n = 24 #Hier wirfst du die Anzahl der Seiten rein, die eine Kategorie hat (siehst du in der Pagination) 
	#(Mir ist voll und ganz bewusst, dass man dass auch einfach aus der Seite scrapen könnte, aber das
	#war zeitlich recht sinnlos)

levelsio

<?
/////////////////////
// slack2html
// by @levelsio
/////////////////////
//
/////////////////////
// WHAT DOES THIS DO?
/////////////////////
//

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

Jonty

The UK Houses of Parliament netblocks are publicly listed:

https://apps.db.ripe.net/db-web-ui/lookup?source=RIPE&type=inetnum&key=194.60.0.0%20-%20194.60.63.255 https://apps.db.ripe.net/db-web-ui/lookup?source=RIPE&type=inetnum&key=82.111.122.128%20-%2082.111.122.135 https://apps.db.ripe.net/db-web-ui/lookup?source=RIPE&type=inetnum&key=82.111.119.192%20-%2082.111.119.199 https://apps.db.ripe.net/db-web-ui/lookup?source=RIPE&type=inetnum&key=82.111.126.144%20-%2082.111.126.159 https://apps.db.ripe.net/db-web-ui/lookup?source=RIPE&type=inetnum&key=212.161.99.0%20-%20212.161.99.7

They were unwilling to reveal the UK parliament web proxies in an FOI request, so here's the list of every IP address that has made a Wikipedia edit from the UK parliament netblocks. Guess the proxies.