Marcio Vinicius dos Santos mvsusp

One challeging problem amuzed me today: the best way to work with short lived attributes on long lived objects. Like the iron throne, sometimes the attribute, in this case the Westeros King changes constantly and you don't want to check always the value, in this case the current king:

public class IronThrone{
  private KingChecker _kingChecker;

  public IronThrone(KingChecker kingChecker){
    _kingChecker = kingChecker;
  }

	#!/usr/bin/env ruby

	system "mkdir dump" unless File.directory? "dump"
	%w(rubygems csv nokogiri open-uri active_support ruby-debug pdfkit).each do \|req\|
	require req
	end

	def generate_page_uri(page)
	return nil if page.empty?
	base_url = "http://gatherer.wizards.com/Pages/Search/Default.aspx?"

	//Returns the number of elements between the two words, or null if at least one of the words hasn't an occurrence in the array
	public Integer shortestPath(String firstWord, String secondWord, String pathToWordFile){

	//Just checking if the two words are the same
	if( firstWord.equals(secondWord) ){
	return 0;
	}

	String[] words = new Scanner(pathToWordFile).nextLine().split(" ");

	public Node floor(Node x, Key key){
	if( x == null) return null;

	Integer cmp = key.compareTo(x.key);

	if(cmp < 0) return floor(x.left, key);
	else if(cmp == 0) return x;

	Node t = floor(x.right, key);
	if(t != null ) return t;

	var express = require('express');

	var router = express.Router();

	router.get('/crawler', function(req, res, next) {
	var elements,
	data,
	util = require('util'),
	spawn = require('child_process').spawn,
	ls = spawn('phantomjs', ['motor.js', 'http://www.medium.com']);

	using UnityEngine;
	using UnityEditor;

	public class AlignWithGround : MonoBehaviour {

	// Use this for initialization
	void Start () {

	}

	import tensorflow as tf
	from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
	from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
	from tensorflow.python.keras._impl.keras.engine.topology import InputLayer
	from tensorflow.python.keras._impl.keras.layers import Conv2D, Activation, MaxPooling2D, Dropout
	from tensorflow.python.keras._impl.keras.models import Sequential
	from tensorflow.python.keras._impl.keras.optimizers import rmsprop
	from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS

	HEIGHT = 32

	from tensorflow.python import pywrap_tensorflow


	def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors,
	all_tensor_names):
	"""Prints tensors in a checkpoint file.
	If no `tensor_name` is provided, prints the tensor names and shapes
	in the checkpoint file.
	If `tensor_name` is provided, prints the content of the tensor.
	Args:

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function
	from __future__ import print_function

	import sys

	import numpy as np
	import os
	import tensorflow as tf