pnc

#!/usr/bin/python

import socket
import struct
import sys

# We want unbuffered stdout so we can provide live feedback for
# each TTL. You could also use the "-u" flag to Python.
class flushfile(file):
    def __init__(self, f):

bgallagh3r

#!/bin/bash -e
clear
echo "============================================"
echo "WordPress Install Script"
echo "============================================"
echo "Database Name: "
read -e dbname
echo "Database User: "
read -e dbuser
echo "Database Password: "

mitjat

import base64
import json
import matplotlib, matplotlib.pyplot
import numpy
import types 

def show_plot(width, height=None):
    """
    A decorator -- show the matplotlib plot after `f` completes.
    Takes optional parameters (width, height) determining the size of the plot.

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

rossant

"""
MIT License

Copyright (c) 2017 Cyrille Rossant

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

vladignatyev

# The MIT License (MIT)
# Copyright (c) 2016 Vladimir Ignatev
#
# Permission is hereby granted, free of charge, to any person obtaining 
# a copy of this software and associated documentation files (the "Software"), 
# to deal in the Software without restriction, including without limitation 
# the rights to use, copy, modify, merge, publish, distribute, sublicense, 
# and/or sell copies of the Software, and to permit persons to whom the Software 
# is furnished to do so, subject to the following conditions:
# 

maraoz

OP_DEPTH 3 OP_EQUAL
OP_IF
  OP_HASH160 <hash160(R)> OP_EQUALVERIFY
  OP_0 2 <AlicePubkey1> <BobPubkey1> 2 OP_CHECKMULTISIG
OP_ELSE
  OP_0 2 <AlicePubkey2> <BobPubkey2> 2 OP_CHECKMULTISIG
OP_END

echohack

ffmpeg -i input_file.mp4 -vf scale=320:-1:flags=lanczos,fps=30 frames/ffout%03d.png
convert -loop 0 frames/ffout*.png output_file.gif

johnny5550822

Can you imagine a computer can generate an nba article?

The following is some nba articles fully-automatically generated by char-cnn, a recurrent-neural-network library thanks to Andrej Karpathy [link]. The library is awesome to easy, and very user-friendly. You should try it! :)

Basically, I wrote a python script [link] to extract past archives . And use that as the training set for the recurrent neural network.

The articles below are generated by a network trained with rougly about 2 millions character (which is an okay size; not big enough though). You can see that the generated article contains artificial author names, speeches, etc; similar to an nba archive (although the logic has to be improved, it is FUN.)

You can tune the parameter and train with a even bigger dataset using my script. And you will probably get better result! Have fun:)

vchollati