jrnk

[
  { "code": "aa", "name": "Afar" },
  { "code": "ab", "name": "Abkhazian" },
  { "code": "ae", "name": "Avestan" },
  { "code": "af", "name": "Afrikaans" },
  { "code": "ak", "name": "Akan" },
  { "code": "am", "name": "Amharic" },
  { "code": "an", "name": "Aragonese" },
  { "code": "ar", "name": "Arabic" },
  { "code": "as", "name": "Assamese" },

mcandre

• udp://tracker.openbittorrent.com:80
• udp://tracker.publicbt.com:80
• udp://tracker.istole.it:80
• udp://tracker.btzoo.eu:80/announce
• http://opensharing.org:2710/announce
• udp://open.demonii.com:1337/announce
• http://announce.torrentsmd.com:8080/announce.php
• http://announce.torrentsmd.com:6969/announce
• http://bt.careland.com.cn:6969/announce
• http://i.bandito.org/announce

marklit

pip install sh

ikr7

var emojis = [
	'😄','😃','😀','😊','☺','😉','😍','😘','😚','😗','😙','😜','😝','😛','😳','😁','😔','😌','😒','😞','😣','😢','😂','😭','😪','😥','😰','😅','😓','😩','😫','😨','😱','😠','😡','😤','😖','😆','😋','😷','😎','😴','😵','😲','😟','😦','😧','😈','👿','😮','😬','😐','😕','😯','😶','😇','😏','😑','👲','👳','👮','👷','💂','👶','👦','👧','👨','👩','👴','👵','👱','👼','👸','😺','😸','😻','😽','😼','🙀','😿','😹','😾','👹','👺','🙈','🙉','🙊','💀','👽','💩','🔥','✨','🌟','💫','💥','💢','💦','💧','💤','💨','👂','👀','👃','👅','👄','👍','👎','👌','👊','✊','✌','👋','✋','👐','👆','👇','👉','👈','🙌','🙏','☝','👏','💪','🚶','🏃','💃','👫','👪','👬','👭','💏','💑','👯','🙆','🙅','💁','🙋','💆','💇','💅','👰','🙎','🙍','🙇','🎩','👑','👒','👟','👞','👡','👠','👢','👕','👔','👚','👗','🎽','👖','👘','👙','💼','👜','👝','👛','👓','🎀','🌂','💄','💛','💙','💜','💚','❤','💔','💗','💓','💕','💖','💞','💘','💌','💋','💍','💎','👤','👥','💬','👣','💭','🐶','🐺','🐱','🐭','🐹','🐰','🐸','🐯','🐨','🐻','🐷','🐽','🐮','🐗','🐵','🐒','🐴','🐑','🐘','🐼','🐧','🐦','🐤','🐥','🐣','🐔','🐍','🐢','🐛','🐝','🐜','🐞','🐌','🐙','🐚','🐠','🐟','🐬','🐳','🐋','🐄','🐏','🐀','🐃','🐅','🐇','🐉','🐎','🐐','🐓','🐕','🐖','🐁','🐂','🐲','🐡','🐊','🐫','🐪','🐆','🐈','🐩','🐾',

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

bastman

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

kaleksandrov

#!/bin/bash

case $# in
   0)
      echo "Usage: $0 {start|stop}"
      exit 1
      ;;
   1)
      case $1 in
         start)

igormq

import tensorflow as tf

def beam_decoder(decoder_inputs, initial_state, cell, loop_function, scope=None,
                 beam_size=7, done_token=0
                ):
    """
    Beam search decoder
    
    Args:
      decoder_inputs: A list of 2D Tensors [batch_size x input_size].

wojteklu

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

---

General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

krivonogov

################
#   CPROFILE   #
#############################################################################

# 1 - Profile myfunc() from ipython
import cProfile
filename = 'filename.prof'
cProfile.run('myfunc()', filename)

# 2 - Convert your file to a usable kcachegrind file in your shell