shane.stars shanev

Senior Swift Backend Developer (Freelance, 100% Remote)

I am searching experienced backend developers (freelance, remote) with a ❤️ for Swift for Moonshot Energy, the world’s first AI-driven renewable energy company located in Germany. Moonshot Energy is financially backed by Innogy SE, one of Europe's largest energy providers.

At Moonshot you will play a major role in making the world greener, the future brighter and energy distribution smarter by using latest technologies like server-side Swift, microservices, artificial intelligence 🤖 and machine learning and new interfaces like chatbots and dialog-based systems (NLP).

Since our team is working on European timezones it would be good if you would live in the EU or if you are an early bird living in US-East 🇪🇺🇺🇸.

Your job:

build our internal microservices

Operator Mono w/ Italics on OSX Vim

https://github.com/ReactiveCocoa/ReactiveCocoa

https://github.com/Alamofire/Alamofire

https://github.com/antitypical/Result

https://github.com/square/Aardvark

https://github.com/optonaut/ActiveLabel.swift

	var _fetchedResultsController: NSFetchedResultsController<Entity>? = nil
	var blockOperations: [BlockOperation] = []

	var fetchedResultController: NSFetchedResultsController<Entity> {
	if _fetchedResultsController != nil {
	return _fetchedResultsController!
	}

	let fetchRequest: NSFetchRequest<Entity> = Entity.fetchRequest()
	let managedObjectContext = (UIApplication.shared.delegate as! AppDelegate).managedObjectContext!

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	import UIKit

	@IBDesignable
	class BorderButton: UIButton {

	@IBInspectable var cornerRadius: CGFloat = 0 {
	didSet {
	layer.cornerRadius = cornerRadius
	layer.masksToBounds = cornerRadius > 0
	}

	var mongoose = require('mongoose');
	mongoose.connect('mongodb://localhost/foobar');

	// bootstrap mongoose, because syntax.
	mongoose.createModel = function(name, options) {
	var schema = new mongoose.Schema(options.schema);
	for (key in options.self) {
	if (typeof options.self[key] !== 'function') continue;
	schema.statics[key] = options.self[key];
	}

	//
	// CollectionViewDataSource.swift
	// Khan Academy
	//
	// Created by Andy Matuschak on 10/14/14.
	// Copyright (c) 2014 Khan Academy. All rights reserved.
	//

	import UIKit