Turn this cute YouTube cat video into a briefer-but-still-cute GIF:
- youtube-dl is a command-line tool for quickly downloading video files from a given YouTube URL
-- http://www.blackwasp.co.uk/SQLDisableConstraints.aspx | |
ALTER TABLE TableName NOCHECK CONSTRAINT ALL | |
ALTER TABLE TableName CHECK CONSTRAINT ALL | |
ALTER TABLE TableName NOCHECK CONSTRAINT FK_Table_RefTable | |
ALTER TABLE TableName CHECK CONSTRAINT FK_Table_RefTable | |
Windows Registry Editor Version 5.00 | |
[HKEY_CURRENT_USER\Software\SimonTatham\PuTTY\Sessions\Slight] | |
"Colour0"="240,240,240" | |
"Colour1"="255,255,255" | |
"Colour2"="63,63,65" | |
"Colour3"="103,103,103" | |
"Colour4"="0,0,0" | |
"Colour5"="0,255,0" | |
"Colour6"="21,23,26" |
convert -density 256x256 -background transparent favicon.svg -define icon:auto-resize -colors 256 favicon.ico |
import socket | |
class Netcat: | |
""" Python 'netcat like' module """ | |
def __init__(self, ip, port): | |
self.buff = "" | |
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) |
# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn | |
Out of Memory (OOM) refers to a computing state where all available memory, including swap space, has been allocated. | |
Normally this will cause the system to panic and stop functioning as expected. | |
There is a switch that controls OOM behavior in /proc/sys/vm/panic_on_oom. | |
When set to 1 the kernel will panic on OOM. | |
A setting of 0 instructs the kernel to call a function named oom_killer on an OOM. | |
Usually, oom_killer can kill rogue processes and the system will survive. | |
The easiest way to change this is to echo the new value to /proc/sys/vm/panic_on_oom. | |
# cat /proc/sys/vm/panic_on_oom 1 |
Turn this cute YouTube cat video into a briefer-but-still-cute GIF:
""" | |
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) |
precision lowp float; | |
uniform sampler2D channel0; | |
uniform float time; | |
varying vec2 vTextureCoord; | |
// rendering params | |
const float sphsize = 0.8; // planet size | |
const float dist = 0.08; // distance for glow and distortion |