aunyks

import hashlib as hasher
import datetime as date

# Define what a Snakecoin block is
class Block:
  def __init__(self, index, timestamp, data, previous_hash):
    self.index = index
    self.timestamp = timestamp
    self.data = data
    self.previous_hash = previous_hash

ziadoz

#!/usr/bin/env bash
# https://developers.supportbee.com/blog/setting-up-cucumber-to-run-with-Chrome-on-Linux/
# https://gist.github.com/curtismcmullan/7be1a8c1c841a9d8db2c
# https://stackoverflow.com/questions/10792403/how-do-i-get-chrome-working-with-selenium-using-php-webdriver
# https://stackoverflow.com/questions/26133486/how-to-specify-binary-path-for-remote-chromedriver-in-codeception
# https://stackoverflow.com/questions/40262682/how-to-run-selenium-3-x-with-chrome-driver-through-terminal
# https://askubuntu.com/questions/760085/how-do-you-install-google-chrome-on-ubuntu-16-04

# Versions
CHROME_DRIVER_VERSION=`curl -sS https://chromedriver.storage.googleapis.com/LATEST_RELEASE`

markreid

Git Flow (with rebasing)

See http://nvie.com/posts/a-successful-git-branching-model/

Features

revelt

a
abi
abidvi
abiejose
abiejų
abiejuose
abiem
abipus
abu
abudu

devStepsize

'''
This is an example of how to send data to Slack webhooks in Python with the
requests module.
Detailed documentation of Slack Incoming Webhooks:
https://api.slack.com/incoming-webhooks

'''

import json
import requests

kevindavenport

samim23

# "Colorizing B/W Movies with Neural Nets", 
# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
# BACKGROUND: http://tinyclouds.org/colorize/
# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
# USAGE:
# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/
# 2. Use FFMPEG or such to extract frames from video.
# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
# mogrify -resize 224x224 *.jpg
# mogrify -gravity center -background black -extent 224x224 *.jpg

craffel

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

hest

def get_count(q):
    count_q = q.statement.with_only_columns([func.count()]).order_by(None)
    count = q.session.execute(count_q).scalar()
    return count

q = session.query(TestModel).filter(...).order_by(...)

# Slow: SELECT COUNT(*) FROM (SELECT ... FROM TestModel WHERE ...) ...
print q.count()

perrygeo

TODO

implement security measures
git config
config files
full sublimetext config
set up openvpn
rdesktop and network drive to terra
set up evolution
RStudio