joshdover

Idiomatic React Testing Patterns

Testing React components seems simple at first. Then you need to test something that isn't a pure interaction and things seem to break down. These 4 patterns should help you write readable, flexible tests for the type of component you are testing.

Setup

I recommend doing all setup in the most functional way possible. If you can avoid it, don't set variables in a beforeEach. This will help ensure tests are isolated and make things a bit easier to reason about. I use a pattern that gives great defaults for each test example but allows every example to override props when needed:

harlow

aaron,erin,ron,ronnie
abel,ab,abe,eb,ebbie
abiel,ab
abigail,abby,gail,gail,nabby
abner,ab
abraham,ab,abe
abram,ab
adaline,ada,addy,delia,dell,lena
adelaide,addy,adele,dell,della,heidi
adeline,ada,addy,delia,dell,lena

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

oelmekki

1. Gemfile

gem 'browserify-rails', '1.5.0' # until fix: https://github.com/browserify-rails/browserify-rails/issues/101
gem 'react-rails'

Browserify-rails allows to use browserify within assets pipeline. React-rails is here only to allow to use #react_component (and thus, prerendering).

Note that jquery-rails can be removed from Gemfile, the npm version of jquery and jquery-ujs will be used instead.

GilLevi

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

iandees

I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.

After a day or two figuring out how to compile the examples, I finally made some progress:

Compiling dlib C++ on a Mac with Homebrew

1. Clone dlib from Github to your local machine:

ChuckJHardy

DigitalOcean Ubuntu 14.04 x64 + Rails 4 + Nginx + Unicorn + PostgreSQL + Capistrano 3

Create a Droplet

SSH into Root

$ ssh [email protected]

Change Root Password

juliandunn

Deep Dive: PostgreSQL on AWS

When loading data

• disable backups (backup_retention=0)
• disable multi-AZ and autovacuum
• pg_dump -Fc (compressed) and pg_restore -j (parallel)
• Increase maintenance_work_mem

melnikaite

# lib/capistrano/tasks/assets.rake

Rake::Task['deploy:assets:precompile'].clear

namespace :deploy do
  namespace :assets do
    desc 'Precompile assets locally and then rsync to remote servers'
    task :precompile do
      local_manifest_path = %x{ls public/assets/manifest*}.strip

xdamman

#!/bin/bash

# Bash script to install latest version of ffmpeg and its dependencies on Ubuntu 12.04 or 14.04
# Inspired from https://gist.github.com/faleev/3435377

# Remove any existing packages:
sudo apt-get -y remove ffmpeg x264 libav-tools libvpx-dev libx264-dev

# Get the dependencies (Ubuntu Server or headless users):
sudo apt-get update