jchris

Couchbase Lite with React Native

I went into this expecting a challenge but it was easy. Basically, Couchbase Lite has always had an optional HTTP listener that you can connect to from inside your app at http://lite.couchbase./mydatabase/. React Native has a fine XHR module and encourages using fetch so getting your app to sync can be as easy as adding some API calls to keep JSON in the database.

We haven't done a full example yet, but in the spirit of possiblity, here are quick instructions to connect Couchbase Lite iOS with a React Native app (generated from their cli).

• Download and unzip Couchbase Lite iOS (grep this page for ios)
• Drag and drop BOTH frameworks (CouchbaseLite.framework and CouchbaseLiteListener.framework) into your React Native app Xcode sidebar. More about the Listener capabilities in this blog post about peer to peer apps.
• Add the library dependenc

sebmarkbage

import { Component } from "React";

export var Enhance = ComposedComponent => class extends Component {
  constructor() {
    this.state = { data: null };
  }
  componentDidMount() {
    this.setState({ data: 'Hello' });
  }
  render() {

rodleviton

sudo -i
cd
apt-get install build-essential checkinstall && apt-get build-dep imagemagick -y
wget http://www.imagemagick.org/download/ImageMagick-6.8.7-7.tar.gz
tar xzvf ImageMagick-6.8.9-1.tar.gz
cd ImageMagick-6.8.9-1/
./configure --prefix=/opt/imagemagick-6.8 && make
checkinstall

traviskaufman

Better Jasmine Tests With this

On the Refinery29 Mobile Web Team, codenamed "Bicycle", all of our unit tests are written using Jasmine, an awesome BDD library written by Pivotal Labs. We recently switched how we set up data for tests from declaring and assigning to closures, to assigning properties to each test case's this object, and we've seen some awesome benefits from doing such.

The old way

Up until recently, a typical unit test for us looked something like this:

describe('views.Card', function() {

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer