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Mohamed3on / cloudSettings
Last active May 30, 2019
Visual Studio Code Settings Sync Gist
View cloudSettings
Mohamed3on /
Last active Aug 6, 2018
Inspect Cordova/Ionic apps with Chrome Devtools
  1. Connect your Android device
  2. Make sure it's working correctly with adb and you can find it when you run adb devices
  3. In the directory of your app, run cordova run android --device, Now the app will run on your device
  4. Go to chrome://inspect/#devices on your Chrome browser, and select your app and click inspect
  5. Now you can inspect the HTML/CSS, and debug your JS with ease!
Mohamed3on /
Last active Oct 3, 2019
Run prettier on all JS files in a directory
  1. Install prettier
  2. Make a .prettierignore file, and add directories you'd like prettier to not format, for example: **/node_modules
  3. Run prettier --write "**/*.js" *Don't forget the quotes.
  4. Optional: if you want to format JSON/SCSS files too, replace js with json/scss.
View hyper.js
// Future versions of Hyper may add additional config options,
// which will not automatically be merged into this file.
// See for all currently supported options.
module.exports = {
config: {
// default font size in pixels for all tabs
fontSize: 14,
// font family with optional fallbacks
Mohamed3on / The Technical Interview Cheat
Last active May 26, 2019 — forked from TSiege/The Technical Interview Cheat
This is a fork to fix the markdown errors in the original gist.
View The Technical Interview Cheat

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics



  • Stores data elements based on an sequential, most commonly 0 based, index.
  • Based on tuples from set theory.
View nnCostFunction.m
function [J grad] = nnCostFunction(nn_params, ...
input_layer_size, ...
hidden_layer_size, ...
num_labels, ...
X, y, lambda)
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
% [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
% X, y, lambda) computes the cost and gradient of the neural network. The
% parameters for the neural network are "unrolled" into the vector
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