| 1. compile the above code using nasm using the command : | |
| nasm -f bin boot.asm -o boot.img | |
| 2. make a bootable usb of the image file created above. |
| import sys | |
| from socket import socket, AF_INET, SOCK_DGRAM | |
| SERVER_IP = '127.0.0.1' | |
| PORT_NUMBER = 5000 | |
| SIZE = 1024 | |
| print("Test client sending packets to IP {0}, via port {1}\n".format(SERVER_IP, PORT_NUMBER)) | |
| mySocket = socket(AF_INET, SOCK_DGRAM) |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <link href="https://raw.githubusercontent.com/novus/nvd3/master/build/nv.d3.css" rel="stylesheet" type="text/css"> | |
| <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.2/d3.min.js" charset="utf-8"></script> | |
| <script src="https://raw.githubusercontent.com/novus/nvd3/master/build/nv.d3.js"></script> | |
| <style> | |
| text { |
| var eventQueue = []; | |
| var svg; | |
| var element; | |
| var drawingArea; | |
| var width; | |
| var height; | |
| var volume = 0.6; | |
| var ULTIMATE_DREAM_KILLER = false; // https://github.com/debugger22/github-audio/pull/19 | |
| var orgRepoFilterNames = []; |
| import os, json, subprocess | |
| file = open('pycon_*.json', 'r') | |
| video = json.loads(file.read()) | |
| for i, v in video.items(): | |
| print("Installing {} ... ".format(v)) | |
| url = "youtube-dl -ciw --no-mtime -f 18 \"" + str(i)+ "\"" | |
| os.system(url) |
This tutorial was created by Shopify for internal purposes. We've created a public version of it since we think it's useful to anyone creating a GraphQL API.
It's based on lessons learned from creating and evolving production schemas at Shopify over almost 3 years. The tutorial has evolved and will continue to change in the future so nothing is set in stone.
| def _dilate(self, img, str_img=[]): | |
| '''Expands the poindary of img | |
| ''' | |
| w, h = img.shape | |
| res = np.asarray([[0 for _ in range(h)] for _ in range(w)]) | |
| # Iterating the anchor of the structure image over the original image | |
| for i, im_row in enumerate(img): | |
| for j, im_ele in enumerate(im_row): | |
| if img[i][j]: | |
| for k in range(-1, 2): |
| import matplotlib.pyplot as plt | |
| import cv2 as cv | |
| import numpy as np | |
| import os | |
| # import math | |
| from sobel import sobel | |
| OUTPUT_DIR = "outputs/" | |
| img_name = "./hough.jpg" |
| // C++ Structure | |
| /*The constructor initializes id to 42 when it's called. It's called an initliazation list. | |
| In your example, it is equivalent to | |
| */ | |
| struct TestStruct { | |
| int id; |
I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.
I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real
