Altabel Group's Blog

Posts Tagged ‘Facebook

What is the hottest trend in artificial intelligence right now? Machine Learning is the right answer! Thanks to technological advances and emerging frameworks, Machine Learning may soon hit the mainstream. Because of new computing technologies, Machine Learning today is not like Machine Learning of the past. While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Every single day it’s become clear that Machine Learning is already forcing massive changes in the way companies operate. Every Fortune 500 company is already running more efficiently — and making more money — because of Machine Learning. But how this “phenomenon” helps business bring money and attract new and new customers?

Problems that can be easily solved using ML

Every single business some time or other can face with definite problems. But there are some kinds of business problems Machine Learning can prevent if not handle at all:

Email spam filters
Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.

Speech recognition
There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – Machine Learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.

Face detection
It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Credit card purchase fraud detection
Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

Product / music / movie recommendation
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.

Here is enumerated not all but just a few problems that can be solved. And with the course of time this list will only expand.

Industries that already use ML in action

Most industries working with large amounts of data have recognized the value of Machine Learning technology. The adoption of Machine Learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, health care, and etc. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. In some cases, it will help transform the way companies interact with customers.

Retail industry
Machine Learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform. Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or Machine Learning, and it’s expected that many shopping centers and retailers will start to explore similar options this year.

Financial services
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Health care
Machine Learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Machine Learning can be used to understand risk factors for disease in large populations. For instance, Medecision company developed an algorithm that is able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective, and many others thing that you can do using ML. For example ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. Exxon Mobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges.

Government
Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft. Chicago’s Department of Public Health is early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned.

Marketing and sales
Websites recommending items you might like based on previous purchases are using Machine Learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail. PayPal, for example, is using Machine Learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation and other transportation organizations. In some cases, mathematical models are used to optimize shipping routes. By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving every day. For a company like UPS, a reduction of one mile per day per driver would equal a savings of as much as $50 million a year in fuel.

Have you ever worked with ML? Was it useful for your business? Or maybe you are still thinking about whether it costs to implement Machine Learning in your business? Will it be relevant and defensibly? If you have an answer on at least one question – share with me your experience. We will be happy to discuss it in comments. But if you don’t have an answer, always remember – Big companies are investing in Machine Learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.

 

Yuliya Poshva

Business Development Manager

E-mail: yuliya.poshva@altabel.com
Skype: juliaposhva
LI Profile: Yuliya Poshva

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Artificial Intelligence, Machine Learning are new buzzwords that are actively discussed in the tech world. Do you remember how our future was described in the movies some time ago: Terminator, Skynet, AI rules the world? General AI machines have remained in the movies and science fiction novels however narrow AI technologies are gradually evolving from the science fiction era to the reality and are already around us. Google uses Machine Learning to filter out spam messages from Gmail. Facebook trained computers to identify specific human faces nearly as accurately as humans do. Deep Learning is used by Netflix and Amazon to decide what you want to watch or buy next.

AI, machine learning, and deep learning are not quite the same thing but these terms are often used haphazardly and interchangeably, and that sometimes leads to some confusion. So let`s see what is the difference between each type of technology.
 
Artificial Intelligence (AI)

Artificial intelligence, which has been around since the 1950s, has seen ebbs and flows in popularity over the last 60+ years. But today, with the recent explosion of big data, high-powered parallel processing, and advanced neural algorithms, we are seeing a renaissance in AI—and companies from Amazon to Facebook to Google are scrambling to take the lead.

AI is the broadest way to think about advanced, computer intelligence. It can refer to anything from a computer program playing a game of chess, to a voice-recognition system like Amazon’s Alexa interpreting and responding to speech. The technology can broadly be categorized into three groups: Narrow AI (that is focused on one narrow task), artificial general intelligence or AGI (a machine with the ability to apply intelligence to any problem, rather than just one specific problem), and superintelligent AI (when its equal to humans or even surpasses them).

Pardoe believes that “we’ve just entered the “Fourth Industrial Revolution”, and while the adoption of AI has just started, the next few years will transform many sectors.
 
Machine learning

Machine learning is one subfield of AI. Or let`s say it`s the field of AI which today is showing the most promise at providing tools that industry and society can use to drive change. The core principle here is that machines take data and “learn” for themselves. Unlike hand-coding a software program with specific instructions to complete a task, ML allows a system to learn to recognize patterns on its own and make predictions.
 

 
Here are some of the popular machine learning methods:

-supervised learning: the “trainer” will present the computer with certain rules that connect an input (an object’s feature, like “smooth,” for example) with an output (the object itself, like a marble), and the algorithm learns by comparing its actual output with correct outputs to find errors. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

-unsupervised learning: the computer is given inputs and is left alone to discover patterns. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.

-reinforcement learning: the algorithm discovers through trial and error which actions yield the greatest rewards. This type of learning has three primary components: the agent (the learner or decision maker, for instance, the driverless car), the environment (everything the agent interacts with, for instance the road) and actions (what the agent can do).
 
Deep Learning

Deep learning is a brunch of Machine Learning, let`s see it as the cutting-edge of the cutting-edge. It uses some ML techniques to solve real-world problems by tapping into neural networks that simulate human decision-making.

Deep Learning involves feeding a computer system with a lot of data, which it can use to make decisions about other data. This data is fed through neural networks. These networks are logical constructions which ask a series of binary true/false questions, or extract a numerical value, of every bit of data which pass through them, and classify it according to the answers received.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

The machine revolution has certainly started and the AI revolution is sure to pave the way for some significant changes in our lives. Machines will gradually improve, slowly replacing jobs that require repetitious behavior. But what happens when one day the machines become smarter than us?

 

Anna Kozik

Business Development Manager

E-mail: Anna.Kozik@altabel.com
Skype: kozik_anna
LI Profile: Anna Kozik

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Artificial intelligence gives us more and more opportunities every day. Chatbots and their developing for client-communication are the hottest topics today. Many experts say that chatbots are the future. Let’s get a short review.
 
Who are they?

Chatbots are essentially programs pretending to be people that you can interact with through text or even voice. They are closely connected with a wide messenger’s distribution and I believe everyone has been already acquainted with them. It’s obvious that chat- bots’ popularity is starting to take off.
 
How can they enhance e-commerce?

1. First of all chat bot doesn’t get tired, being late or absent – it assists your clients 24/7 365 days per year and get significant workload on its shoulders. That is why they seem to be the best answer for e-commerce business owners to manage thousands of one-to-one conversations with customers.

2. The second point is that chat bots can understand natural language and communicate with people in the same manner making conversations realistic and trustworthy. So chat bots give online shop owners an opportunity to provide pleasant shopping experience for customers.

3. Chatbots are proactive. They can understand what the particular customer wants not only through a simple conversation, but also through analysis and collecting of personal and profile page data ( smartphone data, cookies and so on). All this information might help to improve the marketing strategy and provide customers with the best user experience.

4. Additionally, chatbots simply save customers’ time. You don’t need to waste time for searching the appropriate item, click on mass of characteristics, study a market and etc. You can simply say : Hey, I want to buy an efficient computer for my child. Bot does it for you with a great pleasure and at a quick pace.

5. Finally, all this points give more advantages for businesses: services become much more better and faster, the conversion becomes higher, sales increase, operational costs and salary charges reduce.

 

 
Fresh examples of using

The most outstanding example is already occurring in China, where consumers are using WeChat to fulfill their daily living and commerce needs. Soon, this system will gain prominence in the United States.

Further more brands like Walmart and Hyatt are testing customer service and shopping within apps on the new Facebook Messenger for Business App. A free messaging app, called Kik, has a bot shop for companies such as Sephora, Vine, and H&M. So it can be said that the part of the technology is already there.

Amazon’s voice bot messaging has brought the idea of personal assistant to the next level. Customers can talk to the bot and ask it to order items through Amazon Prime, get a pizza, purchase flowers and call for an Uber. Voice commands are making shopping easier than ever, but there are still many issues to work out.

 

 
What’s Coming in the Future?

Voice-based AI and mobile chatbots are the dominant trend right now, but it’s not crazy to think that this could evolve into something more personal and user-based. E-commerce data is there to help you segment and automate email messaging to certain customers, so it’s not silly to assume that this data will eventually merge with bots.

Imagine a personalized bot that talks to you, and only you, when you shop on the Target website. They’d know the last items you bought, deliver the most relevant products and even stock your shopping cart with suggestions. The days of taking hours to shop online will soon knock the pad. In the near future, all the work will be done for you.

Are you ready for this?
 

Kate Kviatkovskaya

Kate Kviatkovskaya

Business Development Manager

E-mail: Kate.Kviatkovskaya@altabel.com
Skype: kate.kviatkovskaya
LI Profile: Kate Kviatkovskaya

 
altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

For almost 15 years ASP.NET has been one of the best web development technologies and many developers consider it to be the best offering from Microsoft. ASP.Net evolves to bring in better features and functionality, which helps businesses scale better. Each year developers see few new trends that enhances development and shortens the time-to-market the solution. Here we will discuss a few trends that will benefit both developers and businesses indulging in ASP.Net.

React

React.js is a JavaScript library for building user interfaces, built by top engineers at Facebook. Facebook’s dev team built React to solve one problem: building large applications with data that changes over time. React lets you express how your app should look at any given point, and can automatically manage all UI updates when your underlying data changes. React.js is declarative, which means that React conceptually hits the “refresh” button any time data changes, and knows to only update the changed parts. React was used in-house at Facebook before being released as an open-source project to the public, so you can be certain it knows how to handle an astronomical amount of data. React was created by Facebook in 2013, and then released as an open-source project. This means that Facebook’s developers solved React’s major problems first, and then made the code available to the world.

Let’s have a glimpse at it benefits.

Data Flow in One Direction – Properties are passed to component to render HTML tags. Component itself cannot change the property; instead, it requires a callback function to modify the property values.

Virtual DOM – is a JavaScript tree of React elements and components. React renders the virtual DOM to the browser to make the user interface visible. React observes the virtual DOM for changes and automatically mutates browser DOM to match the virtual DOM.

JSX – is a Javascript XML syntax transform, which helps in using HTML and rendering its sub-components. It is a preprocessor step that adds XML syntax to JavaScript. You can definitely use React without JSX but JSX makes React a lot more elegant. Just like XML, JSX tags have a tag name, attributes, and children. If an attribute value is enclosed in quotes, the value is a string. Otherwise, wrap the value in braces and the value is the enclosed JavaScript expression.

Easy to Integrate – React can be simply integrated with other tools or frameworks like Jest, Angular.js or Backbone.js.

Xamarin

Xamarin is highly popular mobile development framework with the rule write-once-run-everywhere coding for three leading mobile platforms: Windows, Android and iOS. It empowers developers to write in a single language on a single code base for their app to reach over billions of smart devices irrespective of the platform. Xamarin delivers perfect look and feel of any given platform’s native UI with power-packed functionality and native app performance. Xamarin eliminates the need to manage separate development teams or having to choose one platform over another.

Following are few more benefits of Xamarin:

Xamarin uses the C# programming language
C# is capable of doing anything you could do in Java, Objective-C, and Swift – and it works on platforms that use any of these. Most applications can share 75% or more of their coding, helping to make development on multiple platforms easier than ever before. Many functions unique to each device are mapped at runtime to correspond to that specific device, resulting in an end-user experience that works the way they expect it to work.

Xamarin can import and convert existing code
Do you have existing Objective-C or Java code? Xamarin uses an automatic binding generator to match code like custom controls and frameworks to your new app, and a little bit of testing is usually enough to fix any glitches that occur. By importing your existing code, you can hit the ground running and reduce the time it will take to roll out your improved app.

Xamarin offers same-day support for new OS releases
One of the biggest problems with apps is updating them when a new operating system comes out. These changes can cause major disruptions in the way some functions work, but this particular developer has been able to offer same-day updates that allow you to start taking advantage of new features and capabilities. These updates also mean that you can deal with any major disruptions to your app and get it back up and running if anyone was broken – your business can’t afford to have its tools stuck in limbo, and working with a company offering active support is one of the best ways of ensuring your investment won’t be lost at a crucial time.

Elasticsearch

Elasticsearch is the most popular enterprise search engine followed by Apache Solr based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elastic search was first released in February 2010, and is a free and open source distributed inverted index created by Shay Banon. It is developed in Java, so it is a cross-platform.

Below you can find major highlights of Elastic Search:

Real-Time Data Analysis – All data is immediately made available for search and analytics.

Distributed approach – Indices can be divided into shards, with each shard able to have any number of replicas. Routing and rebalancing operations are done automatically when new documents are added.

Multi-Tenancy – Multiple indices can be maintained by single cluster and can execute queries individually or as a group. Also, maintain alias of indices and keep them updated.

Full-Text Search – Elastic Search implements a lot of features: customized splitting text into words, customized stemming, facetted search, and more. Powerful, developer-friendly query API supports multilingual search, geolocation, contextual did-you-mean suggestions, autocomplete, and result snippets.

Easy-To-Use RESTful API – Elastic Search is API driven; actions can be performed using a simple Restful API.

Open Source – Elasticsearch is available freely, under the most adoptable and trusted open source license of Apache 2.

In addition, the Microservices, Azure, and AngularJS are also trending in Asp .Net. Nowadays, enterprise applications are in high demand, and these tools are playing a key role to hit the ground and running.

Thanks for reading!

Want to know more about Xamarin and React? Feel free to explore Altabel’s blog and find more information about the hottest trends in IT world!

 

Svetlana Pozdnyakova

Business Development Manager

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

 

“Computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty.”
Donald Knuth, 1974

 

It’s better to start your journey into the career of programming by answering the question “Do you really need programming?” This question does not apply to those, who majored in computer programming or was close to it. If at school you were good at math, if you like to spend a lot of time sitting in front of the computer, if you want to learn something new, then programming is for you. What is more, this area is now in demand and highly paid in the world, job vacancies for the post of programmers are always open. Isn’t it the best time to be a programmer? 🙂

Everyone knows that the future programmer should be able to think broadly and to present the project from different perspectives before its implementation and realization. Unfortunately, the machine does not understand a human language. Of course, I’m not talking about Siri and other voice recognition — I’m talking about the creation of new software. To create the calculator, the computer needs to be given the task in the same way as the foreman explains to workers how to lay bricks. That’s why you can’t do anything without understanding the programming languages. Well, first you need to decide what kind of programming languages we should start with.

And here everyone chooses a language which will be useful for him. It depends on the kind of products you are going to develop. Most of us studied Turbo Pascal at school, and it’s no news that this language is practically not used anymore. So, if you want to join the team of programmers in the nearest future, the choice of language should be made sensibly.

Among the most popular programming languages in 2016 are Java, followed by C languages, then Python, JavaScript, PHP, Ruby, etc. It should come as no surprise that the more popular language is, the more chances you have to find work in the future. So, you’d better start with Java or C#, as these are the best paid and relatively simple learning languages of writing code. If you can’t cope with them, then you should try to learn Python. This language suits for quick and effective programming.

But if you have no programming experience at all you can start with something more simple for understanding. Good examples can be the basics of HTML and CSS.

Why? These two languages are essential for creating static web pages. HTML (Hypertext Markup Language) structures all the text, links, and other content you see on a website. CSS is the language that makes a web page look the way it does—color, layout, and other visuals we call style. Well, if you are interested in making websites, you should definitely start with HTML and CSS.

Let’s move to JavaScript. It is the first full programming language for many people. Why? It is the next logical step after learning HTML and CSS. JavaScript provides the behavior portion of a website. For example, when you see that a form field indicates an error, that’s probably JavaScript at work.

JavaScript has become increasingly popular, and it now lives outside web browsers as well. Learning JavaScript will put you in a good place as it becomes a more general-purpose language.

Some people also suggest choosing Python as the first programming language because Python’s program code is readable, first of all. You don’t even need to be a programmer to understand what is happening in the program. Due to the simple syntax of Python you will need less time for writing programs than in Java, for example. A huge base of libraries will save you a lot of strength, nerves and time. Large technology companies are working with Python: Yandex, Google, Facebook and YouTube. It is used for web applications, game development, software for servers.

Java can also be a good choice for a beginner. This language is more popular than Python, but a bit more complicated. At the same time, the development tools are much better designed. Java is one of the most popular languages for the backend development of modern enterprise web applications. It is used in Amazon, eBay, LinkedIn and Yahoo! With Java and the frameworks based on it, developers can create scaling web apps for a wide range of users. Java is also the primary language used for developing Android applications for smart phones and tablets. Moreover, after Java you will be able to work with low level programming languages.

PHP is one more popular language. The PHP language, along with databases (e.g. MySQL) is an important tool for creating modern web applications. Most of the sites developed on PHP are focused on a large amount of data. It is also a fundamental technology of powerful content management systems like WordPress. There are no normal imports in PHP, there are many solutions to one and the same problem. And it makes training more complicated.

 

 
The languages C and C# are a bit complicated for a beginner. But if you develop software for embedded systems, work with system kernels or just want to squeeze out every last drop from all available resources, C is what you need.

Ruby has begun to gain popularity since 2003, when the framework Rails appeared. Used widely among web startups and big companies alike, Ruby and Rails jobs are pretty easy to come by. Ruby and Rails make it easy to transform an idea into a working application, and they have been used to bring us Twitter, GitHub, and Treehouse.

Choosing a programming language may still seem challenging. It shouldn’t. You can’t go wrong. As long as you choose a language that is regularly used in technology today, you’re winning. When you are starting out, the goal is to become solid in the basics, and the basics are pretty similar across almost all modern programming languages.

Part of learning to code is learning a language’s syntax (its grammatical or structural rules). A much bigger part of learning to code, the part that takes longer and gives you more headaches, is learning to solve problems like a programmer. You can learn the grammatical structure of the English language pretty quickly; however, you won’t truly understand the language until you put that grammatical structure to use in a conversation. The same is true in programming. You want to learn the core concepts in order to solve problems. Doing this in one language is similar to doing it in another. Because the core concepts are similar from language to language, I recommend sticking with whichever language you choose until your understanding of the core concepts is solid. If you have a clear idea of your reasons for learning to program, and know exactly what you want to accomplish with your new coding skills, then you’ll be able to make the right choice.

How did you guys get into programming? What are the best programming languages for first-time learners?

Please, share with us your experience and opinion here below 🙂

 

Kate Kviatkovskaya

Kate Kviatkovskaya

Business Development Manager

E-mail: Kate.Kviatkovskaya@altabel.com
Skype: kate.kviatkovskaya
LI Profile: Kate Kviatkovskaya

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

Let`s start from a bit of history. React.js is a JavaScript library for building UIs. It was created by Facebook development team to deal with large applications with data that changes over time: react.js hits the “refresh” button any time data changes, and knows to only update the changed parts. Firstly, react was used in-house at Facebook and then it was released as an open-source project and it has quickly gained popularity among developers.

Facebook is not the only one to use React:

Instagram is 100% built on React, both public site and internal tools ;

Yahoo`s mail client is made in React;

Netflix – the biggest paid video-streaming service;

Sberbank, bank #1 in Russia, is built with React;

Khan Academy uses React for most new JS development.

React in comparison to Angular.js isn`t a complete framework. However we can`t say that React.js is only “V” in the MVC. After a closer look, you can actually see that React.js is more than just “V”, it has quite some features of the C (controller) part as well. This is why React is so confusing to understand.

Let`s see why React.js stands out from the crowd:

Convenient architecture

Flux – is highly competitive to MVC. One-way data flow provides maintainability and efficient arrangement of data and DOM elements.

Virtual DOM

React developers suggested using “virtual DOM” in order to solve performance issue for websites with too dynamic DOM. All changes in a document are made there first, and then React looks for the shortest path to apply them in a real DOM tree. This approach makes the framework fast.

Components

React is fundamentally different than other front-end frameworks in that each asset is made up of many isolated components. Want a button changed across the whole platform? Change it once and voilà it`s changed everywhere.

By making the creation, distribution and consumption of isolated reusable components more straightforward, developers are better able to save time by using and creating common abstractions. This is true of both low level elements like buttons and high level elements such as accordions.

JSX

React.js uses a special syntax called JSX, which allows to mix HTML with Javascript. Markup and code are composed in the same file. This means code completion gives you a hand as you type references to your component’s functions and variables.

SEO  friendly

React is significantly more SEO friendly than most JavaScript MVC frameworks. As it is based on a virtual DOM you can use it on the server without needing a headless browser on the server such as Phantom.js to render pages to search engine bots.

React.js is a new interesting emerging Javascript library. It does have some drawbacks however it`s an excellent alternative for building large apps where data changes quickly. We are curious to hear about your experience in using React.jsJ Have you tried it?

 

Anna Kozik

Anna Kozik

Business Development Manager

E-mail: Anna.Kozik@altabel.com
Skype: kozik_anna
LI Profile: Anna Kozik

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

jsframework

Whether you’re building apps for the browser, mobile or desktop, Aurelia can enable you to not only create amazing UI, but do it in a way that is maintainable, testable and extensible.

Retrospective and today

Aurelia is a project of Rob Eisenberg, the author of a very popular MV * – framework for Caliburn.Micro XAML-platforms, Durandal. Understanding all the disadvantages of Durandal, Eisenberg engaged in the development of so-called NextGen framework. In 2014 he began to work in Angular team on the second version of the framework. However, several months later, Rob decided to leave the Angular team since the direction of Angular 2, in his opinion, had changed a lot. He gathered a large team and returned to work on the framework of his dreams. And Aurelia is the result of that work.

JavaScript of tomorrow?

By using modern tooling Aurelia was written from the ground up in ECMAScript 2016. This means you have native modules, classes, decorators and more at disposal.
Aurelia is written in modern and future JavaScript, it takes a nowadays approach to architecture. It’s built as a series of collaborating libraries, which form a powerful and robust framework for building Single Page Apps (SPAs). However, Aurelia’s libraries can often be used individually, in traditional web sites or even on the server-side through technologies like NodeJS.
Aurelia’s code is open sourced under the MIT License, a very permissive license used by many popular web projects today. The starter kits are available under the Creative Commons 0 license. There is also a Contributor for those who wish to join the team in working on Aurelia. Ultimately, this means that you can use Aurelia without fear of legal repercussions and it can be build in the same confidence.

Benefits of Aurelia

Clean and Unobtrusive – Aurelia is the only framework that lets you build components with plain JavaScript. It stays out of your way so your code remains clean and easy to evolve over time.

Convention over Configuration – Simple conventions help developers follow solid patterns and reduce the amount of code they have to write and maintain. It also means less fiddling with framework APIs and more focus on their app.

Simple, But Not Simplistic – Because of the simple design developers are able to learn a very small set of patterns and APIs that unlock limitless possibilities.

Promotes the “-ilities” – Testability, maintainability, extensibility, learnability, etc.- Aurelia’s design helps developers to naturally write code that exhibits these desirable characteristics.

Amazingly Extensible – Aurelia is highly modular and designed to be customized easily, so developers will never hit a roadblock or have to “hack” the framework to succeed.

Web Standards Focused – Focused on next generation JavaScript and Web Components, and avoiding unnecessary abstractions that obscure the underlying web, Aurelia is the cleanest and most standards-compliant framework today.

Integrates Well with Others – Easily integrated with any 3rd party library or framework: for instance, with jQuery, React, Polymer, Bootstrap, MaterializeCSS and much more.

TypeScript Support – Each Aurelia library is released with its own d.ts files. There are also official TypeScript beginner kits and production quality starter kits.

An Official Product with Commercial Support – Being an official product of Durandal Inc., it has commercial and enterprise support available, so you can use Aurelia for building core technology for your business.

Thriving Community and Ecosystem – Having one of the largest developer gitter channels in the JavaScript world, oodles of contributors and a huge core team, Aurelia has been used to build just about every type of application and is used by large, well-known multi-national companies and enterprises.
 
Aurelia, Angular and React.js – what’s common and what’s different?
 

Aurelia vs. Angular

Similarities between Aurelia and Angular 2:

  • Aurelia offers ES6-support out of the box and supports all forms of alternative abstraction syntax such as TypeScript and CoffeeScript. Migration documentation about migrating from Angular 1 and 2 have been put on the roadmap.
  • The basis of both Angular 2 and Aurelia application comprise components associated with the corresponding template.
  • Differences in vision details and options range:

  • The syntax is much simpler and more explicit (i.e. self-explanatory) than Angular 2 and looks a lot like standard JS syntax. ES6 and JSPM are used by default and a gulp file with a custom-built system to transpile ES6 to ES5 using the Babel transpiler is included in the standard package.
  • Aurelia also uses conventions instead of its own syntax and boilerplate code. No special characters like the ones in Angular 2 (*, (), [] en #) here.
  • Aurelia is built in a modular way making it very pluggable. You can plug in internationalization, routing, virtualization, animation, … Besides that, third party plugins are available as well such as the aurelia-flux plugin adding the Flux dispatcher to Aurelia.
  • The presence of a root-component is necessary; it represents an application (app). The metadata may / should be attached to components by using decorators. Component initialization is performed by using dependency injection. In addition, each component has a declared lifecycle, which can be built by using the lifecycle hooks. The components may be formulated into a hierarchical structure.
  • Communication between the component and the template is performed by using data binding. The process of template rendering to the final HTML can be integrated by using pipes (Angular) or value converters + binding behaviours (Aurelia).
  • The main advantage of Aurelia in comparison to Angular is an advanced composition mechanism and template parts. Aurelia is designed with an emphasis on unobtrusive, the number of framework structures in the final code is minimal. Aurelia is more compact, while Angular sometimes simply forces to produce copy-paste.
  • Aurelia is new to the market while Angular has a big user base because it’s already been around for 6 years. On the other hand, Aurelia has great documentation available, it’s an official product of Durandal Inc, and the company has a long term vision for the product, something the Angular team doesn’t seem to have and is blamed for a lot.

Aurelia vs. React.js

  • While on the surface it might not seem fair to compare Aurelia to React.js, they’re both being used for the same things. Despite the fact that React.js is a fully-fledged and functionally released product without the early preview alpha tag and Aurelia is not, at current stage they are both pretty at the same level. You can achieve the same tasks within both, just in different ways.
  • As for React components and Aurelia’s ViewModel’s, they are both quite similar in that you’re essentially using a class to define properties and methods bound to a particular view. The primary difference between them is React doesn’t separate the logic from the view, meaning in React the View and ViewModel are both within the same file.
  • However, that’s not to say that Aurelia doesn’t allow you to achieve the same thing by rendering the View from within the ViewModel as well and forgoing a traditional View.
  • The original intent behind React.js was not to be a competitor to the likes of Angular or Aurelia, but rather be the library that everyone uses with their SPA framework like Angular to improve performance.
  • Therefore, this means you can easily use React.js within Aurelia. Aurelia and React.js can be used together and in doing so, it provides you with a level of power other frameworks cannot without subsequent complexity and strict convention like EmberJS.

Aurelia vs. Angular and React

  • Two-way binding is provided out of the box and the framework does so very precisely. By default, 1-way databinding is used except for form controls, a clear plus when compared to React. Do keep in mind that two-way data binding can only be done through explicit syntax, as is the case in Angular 2.
  • The learning curve for Aurelia is comparable to that of Angular 2 and thus a lot steeper than React’s. Luckily, the extensive documentation makes up for that a great deal.
  • Angular 2 and Aurelia Architecture is very similar. Aurelia looks a lot like Angular 2 in the sense that it’s a complete framework that relies on the web standards. It’s as pluggable as React is and as Angular 2 will be.
  • While Angular was created by Google and React by Facebook, they don’t provide commercial or enterprise support, something that Aurelia will do.

 
Conclusion

It goes without saying why these three frameworks are so popular. They all have a lot of strong advantages. Eventually, I’m favoring Aurelia: there’s solid documentation available and the overall philosophy is the same with Angular 2, but Aurelia is a better choice from the syntax and execution point of view. The architecture and syntax vision of Aurelia team seems to be more clear than the vision of the Angular team. The company and enterprise support of Aurelia is also a big pro.
What is your personal experience with these frameworks? Which one would you choose for your projects and why? What’s your prediction “who” will win the crown in the nearest future? Please feel free to share your thoughts with us.

Thank you in advance!

 

Darya Bertosh

Darya Bertosh

Business Development Manager

E-mail: darya.bertosh@altabel.com
Skype: darya.bertosh
LI Profile: Darya Bertosh

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com


%d bloggers like this: