Tech News - Programming

PyCon Conference 2018, held between May 9 -17 at Cleveland, United States, is one of the largest annual gatherings in the Python programming community. Every year features a range of important announcements and discussions, many of which could have a huge impact on the future of Python. This year featured discussions on everything from data science to cryptocurrencies. Let’s take a look at some of the highlights. Quantum Computing Quantum computers are turning into reality, 30 years after they were first theorized. Companies such as IBM, Google, Intel, and Microsoft have all jumped into the quantum computing pool by announcing their own quantum devices. Although Quantum computing is still at a very early stage, Python is slowly becoming a de facto language for programming quantum computers due to its vast ecosystem of libraries. Dr. Ravi Chityala, a Senior Engineer at Elekta Inc, gave an impressive talk on quantum computing while highlighting several caveats that distinguish a traditional and quantum computer. He also demonstrated how to program a quantum computer on stage with Python. Check out the complete presentation here. Blockchain and Cryptocurrencies Blockchain and cryptocurrencies are all the rage right now. Cryptocurrencies such as Bitcoin had attracted a lot of attention in the past one year, mostly from people who were trying to make a quick buck. But this trends seems to be settling down. People are shifting their focus to understanding the technology behind cryptocurrencies. This is where Blockchain comes into the picture. What’s important to developers and businesses is how Blockchain can be applied. The lack of knowledge about how Blockchain works is a barrier that stops many  people entering and exploring the blockchain and cryptocurrency world. The session “Getting Started with Blockchains and Cryptocurrencies in Python” at PyCon conducted by Amirali Sanatinia, aimed to solve this problem by helping them understand the nuts and bolts of Blockchain and embark their journey on this revolutionary technology. Django 2.0 Unchained Django is one of the most popular web development frameworks for Python. Last December the Django team released version 2 of the popular server-side framework. Since this was one of the major releases, it was no surprise that Django was a hot topic of discussion at PyCon 2018. Harry Percival, author of Test-Driven Development with Python, presented a couple of tutorials specifically for Python testers. His tutorial was an introduction to doing Test-Driven Development (TDD) with Django.He shed some light on how automated software testing has moved from being a niche interest to an important new way of thinking about how to optimise automation testing. Serverless Python Serverless is the latest phase in the evolution of cloud development; it has recently exploded in terms of popularity. Going serverless lets developers to focus on their core product instead of worrying about managing and operating servers or runtimes, either in the cloud or on-premises. This reduced overhead helps them to achieve the required scale without the overhead of running and managing the fleet of servers. Since Python is one of the few languages supported by AWS Lambda, developers can leverage it to build fast and cost-effective solutions. In PyCon 2018 we saw some exciting sessions which highlighted the latest developments in Serverless computing. James Saryerwinnie’s tutorial on Building serverless applications with AWS Chalice, sparked the interest of professional developers, while Michael Herman’s tutorial on Going Serverless with OpenFaaS, Kubernetes, and Python was explored how to build and deploy a full-stack application that uses Flask (client-facing app) along with OpenFaaS to handle background processes. While the list of sessions is long we can only cover so much. One of the most important reasons for the dominating success of Python’s popularity is it’s vibrant community and PyCon is a shining example of that. It truly symbolises how liked minded people come together and share the knowledge to make the software development world a better place to live. You can catch up on all the sessions, tutorials, and events at PyCon 2018 at their official website and their Youtube vlog. Should you move to Python 3? 7 Python experts’ opinions How to write high quality code in Python: 15+ tips for data scientists and researchers Why is Python so good for AI and Machine Learning? 5 Python Experts Explain    

After the release of Java Development Kit 10 in March this year, Oracle is all set to release JDK 11. Expected in September 2018 as part of Oracle’s new six-month release cadence for the standard edition of Java, Version 11 has just a handful of features announced so far. JDK 11 is set to be a long-term support unlike JDK 10, hence it will be a reference implementation of Java Platform, Standard Edition (Java SE) 11. JDK 11 is set to receive premier-level support from Oracle until September 2023 and extended support, featuring patches and security alerts, until 2026. Java 11 is also set to lose some capabilities as the support for CORBA, Java EE, and JavaFX will be removed. For now, JDK 11 is set to have four new features, although more are expected later. This post summarizes some details about each of the four JEPs (JDK Enhancement Proposal) currently targeted for JDK 11. Epsilon: An Arbitrarily Low-Overhead Garbage Collector The Epsilon garbage collector, billed as a “no-op” collector, will handle memory allocation without implementing any actual memory reclamation mechanisms. Epsilon’s use cases include testing for performance, memory pressure, and the virtual machine interface. It also could be used for short-lived jobs. Local-Variable Syntax for Lambda Parameters A local-variable syntax for lambda parameters aims to achieve one goal which is to allow var reserved word to be used to declare the formal parameters of an implicitly typed lambda expression. Dynamic Class-File Constants The Java class-file format will be extended to support a new constant pool form, CONSTANT_Dynamic. The goal is to reduce the cost and disruption of developing new forms of materializable class-file constraints. Remove the Java EE and CORBA Modules The Java EE and CORBA modules were deprecated in Java SE 9, with the intent to remove them in a later release—that is now set to be JDK 11. Changes under consideration for Java JDK 11 The builders of Java 11 are also looking at several proposed changes or additions to JDK 11: Adding raw string literals to Java This would make it easier to express character sequences in a readable form, with no Java indicators. It also would make it simpler to supply strings targeted for grammar syntaxes other than Java, as well as supply strings spanning several lines of source code without supplying special indicators. Extending the switch statement This functionality can be used as either a statement or an expression. This also would improve how switch handles nulls. These changes would simplify coding and prepare for pattern matching in switch. Nest-based access control This is a context that aligns with the current notion of nested types in Java. Nests allow classes that are logically part of the same code entity but compiled to distinct class files to access each other’s private members without needing compilers to insert accessibility-broadening bridge methods. To know more about Java 11 JDK read Oracle’s official blog. Read Next 26 new Java 9 enhancements you will love How to recognize Patterns with Neural Networks in Java How to create a standard Java HTTP Client in ElasticSearch

GNU Compiler Collection (GCC) is a popular Open source compiler system from the GNU Project. Recently the GNU Project announced the release of GCC 8.1 with added features for its support for languages like C, C++, Java, ADA, GO, Objective-C, among others. This release is the first stable release for GCC v8 with a lot of improvements over previous standards. This release brings an enhancement in C++ support and debugging for various languages. GNU has significantly improved the implementation of Go language to run smoothly with the latest version of Go i.e. v1.10. Apart from the compiler improvement, there has been an addition of garbage collector feature for Golang. GNU has also worked on one of the leading features of GCC compiler: Profile-driven optimization. This feature enables fail-safe compiling using several iterations of compiling and generating a profile of the runtime behavior. Based on the behavior of the program, GCC optimizes the compilation of the program. With the new improvement, GCC can detect code that is not compiled properly and can trigger the code in next instances. Some of the features that were worked on this release include the following: Improvement in compiler diagnostics enhancing the error and warning information at compile time Better Link-time optimization (LTO) using a new Dwarf format debug information. The libstdc++ library feature from C++17 and C++ 2a libraries C language extensions that support automatic vectorization for the latest AMD processors. One of the most important features to note is the support for C++ 20 (C++ 2a) which is currently under development. Also the support for the newer ARM CPUs, Intel CPUs, AMD HSA, among others is worth noting in this release. For more information about changes in GCC 8.1, you can see the changelog. Also, this release is available from GNU FTP servers. More news in application development: What’s new in Visual Studio 1.22 What is Mob Programming?

Slack: productivity and collaboration tool, or platform for procrastination, in-jokes and GIFs? We couldn't possibly say here at Packt. For most of us, the only thing worse than wasting time on Slack is looking like you're never on Slack at all. While you'd like to tell people it's because you're busy, you can see your colleagues eyeing you with suspicion, convinced that if you're not procrastinating in the same manner they are, you really can't be doing anything at all. Luckily, someone has invented a tool for dealing with exactly this problem.  Take a bow Will Leinweber (@leinweber) - you have made something to make us look busy. Or, at the very least thoughtful and ready to contribute to the channel chat at any moment. https://twitter.com/leinweber/status/989267343002951680 Will has put the project on GitHub. You can find it here. The only disappointment with the tool is that Will didn't include the additional feature that  "asks other people what their typing whenever they're typing." The results were pretty hilarious, and likely too distracting for anyone to do any work at all... https://twitter.com/leinweber/status/989285775165423616 Needless to say, there was a pretty strong reaction to Will's program. https://twitter.com/snail_5/status/989271471766757376 https://twitter.com/LittleMxSurly/status/989315676325085184 https://twitter.com/CodeTheWebBlog/status/990008655394189313 https://twitter.com/shandrew/status/989395097249693698 Truly, software is being used for incredible things in 2018. These are the projects we need if we're to survive a hostile and unforgiving future, forever typing into the abyss at each other, and doomed to search out reaction GIFs to every rude email and hostile expression someone sends your way. What other novelty software projects have you seen recently? Let us know in the comments, and we'll do some investigative work* *Have a look on Twitter. Read more Creating slash commands for Slack using Bottle

TensorFlow has continued its success in 2017 well into 2018. It's quickly expanding its capabilities, and we're beginning to see it used by engineers that aren't data specialists.  We've seen that in the launch of TensorFlow.js, which allows you to bring machine learning to the browser. But Swift for TensorFlow is a slightly different proposition. In fact, it does two things. On the one hand it offers a new way of approaching TensorFlow, but it also helps to redefine Swift. Let's be honest - Swift has come a long way since it was first launched by Apple back at WWDC 2014. Back then it was a new language created to reinvigorate iOS development. It was meant to make Apple mobile developers happier and more productive. That is, of course, a noble aim - and by and large it seems to have worked. If it hadn't we probably wouldn't still be talking about it. But Swift for TensorFlow marks Swift as a powerful modern programming language that can be applied to some of the most complex engineering problems. What is Swift for TensorFlow? Swift for TensorFlow was first unveiled at the TensorFlow Dev Summit in March 2018. Now it's open source, it's going to be interesting to see how it shapes the way engineers use TensorFlow - and, of course, how the toolchain might shift. But what is it exactly? Watch the video below, recorded at TensorFlow Dev Summit, to find out more. https://www.youtube.com/watch?v=Yze693W4MaU Here's what the TensorFlow team had to say about Swift for TensorFlow in a detailed post on Medium. "Swift for TensorFlow provides a new programming model that combines the performance of graphs with the flexibility and expressivity of Eager execution, with a strong focus on improved usability at every level of the stack. This is not just a TensorFlow API wrapper written in Swift — we added compiler and language enhancements to Swift to provide a first-class user experience for machine learning developers." Why did TensorFlow choose Swift? This is perhaps the key question: why did the TensorFlow team decide to use Swift for this project? The team themselves note that they are often asked this question themselves. Considering many of the features of Swift for TensorFlow can easily be implemented in other programming languages, it's a reasonable question to ask. To properly understand why TensorFlow chose Swift you need to go back to the aims of the project. And they're actually quite simple - the team want to make TensorFlow more usable. They explain: "We quickly realized that our core static analysis-based Graph Program Extraction algorithm would not work well for Python given its highly dynamic nature. This led us down the path of having to pick another language to work with, and we wanted to approach this methodically." The post on GitHub is well worth reading. It provides a detailed insight into how to best go about evaluating the advantages and disadvantages of one programming language over another. Incidentally, The TensorFlow team say the final shortlist of languages was Swift, Rust, Julia, and C++. Swift ended up winning out - there were 'usability concerns' around C++ and Rust, and compared to Julia not only was there a larger and more active community, it is also much more similar to Python in terms of syntax.

Flask is a Python microframework that has been around for a while. It was first released in April 2010; 8 years on, it finally reaches its first stable release. While this is perhaps the first major release, the framework is the most popular Python web framework on GitHub. Not bad for a framework that started life as an April fool's joke. In truth, Flask has been 'stable' for some time, even if it wasn't officially. However, the announcement of Flask 1.0 is important, as it brings with it a few key changes that might be instrumental in growing the community. What's new in Flask 1.0? More flexible CLI. In the release notes, the team explains: "FLASK_APP can point to an app factory, optionally with arguments. It understands import names in more cases where filenames were previously used. It automatically detects common filenames, app names, and factory names. FLASK_ENV describes the environment the app is running in, like development, and replaces FLASK_DEBUG in most cases." Flask can now handle concurrent requests in development. You can test an application's CLI commands with test_cli_runner. Improvements to documentation. In what is sure to improve the number of Flask users, the team have made changes to the documentation available. This represents a step towards more structure and control within the project. The team have app.logger. They write that it "has been greatly simplified and should be much easier to customize. The logger is always named flask.app, it only adds a handler if none are registered, and it never removes existing handlers." There many more updates and improvements. You can read about them in detail here. As you'll see many of the updates are around simplicity, and making Flask more accessible to new users. As Python continues to grow in popularity, it wouldn't be surprising to see Flask do the same with this new release. It might mark a new chapter in the development of the project, but it's a necessary step that will be important in maintaining the project for the future. Read more on Flask Python Web Development Frameworks: Django or Flask? Read here.

DeepCode is a tool that uses artificial intelligence to help software engineers write cleaner code. It's a bit like Grammarly or the Hemingway Editor, but for code. It works in an ingenious way. Using AI, it reads your GitHub repositories and highlights anything that might be broken or cause compatibility issues. It is currently only available for Java, JavaScript, and Python, but more languages are going to be added. DeepCode is more than a debugger Sure, DeepCode might sound a little like a glorified debugger. But it's important to understand it's much more than that. It doesn't just correct errors, it can actually help you to improve the code you write. That means the project's mission isn't just code that works, but code that works better. It's thanks to AI that DeepCode is able to support code performance too - the software learns 'rules' about how code works best. And because DeepCode is an AI system, it's only going to get better as it learns more. Speaking to TechCrunch, Boris Paskalev claimed that DeepCode has more than 250,000 rules. This is "growing daily." Paskalev went on to explain: "We built a platform that understands the intent of the code... We autonomously understand millions of repositories and note the changes developers are making. Then we train our AI engine with those changes and can provide unique suggestions to every single line of code analyzed by our platform.” DeepCode is a compelling prospect for developers. As applications become more complex, and efficiency becomes increasingly more important, a simple solution to unlocking greater performance could be invaluable. It's no surprise that it has already raised 1.1 milion in investment from VC company btov. It's only going to become more popular with investors as the popularity of the platform grows. This might mean the end of spaghetti code, which can only be a good thing. Find out more about DeepCode and it's pricing here. Read more: Active Learning: An approach to training machine learning models efficiently

Scala in its recent announcement suggested the roadmap for Scala 3.0 and what features developers can expect in Scala 3. Following this, the Scala community also announced the new release will be Scala 2.13 and mentioned that Scala 3 will succeed Scala 2.13. With the first official talk about Scala 3, the community also unveiled its secret project called Dotty. It announced that Dotty will become Scala 3. Dotty is a set of compiler technology for Scala. The suggested timeline for the preview release of Scala 3.0 is around early 2020. With this development, the community has suggested that Scala 2.13, expected to release in few months, will be an important release for migration to Scala 3. What can you expect in Scala 3? The community is working to fuse two big paradigms: Object-Oriented programming and functional programming together to make development more convenient in Scala ecosystem. Alongside this ongoing improvement, the community is focussing on the following aspects: With Scala 3, the community is emphasizing on 4 main features of Scala: consistency, safety, ergonomics, and performance. The community is working on adding types and operators to carry out these functionalities. In Scala 3 expect advanced programming paradigms like metaprogramming and generative programming. Currently, the macro systems are implemented at an experimental level to carry out metaprogramming. The community has suggested replacing the macro system with some other solution. Generative programming design with scala is currently under development. Improvement in Scala tools for building strong foundations for software design. Also removal of some components of the language that are rarely used to make the language lightweight. Automatic rewriting through the scalafix tool for efficient migration of code from Scala 2 to Scala 3. Updated Scala compiler, IDE plugins, REPL, Scaladoc, and build tools. The community is working on updating the Dotty plugins and its standard build tools SBT for working properly with Scala 3. Scala 3 will share the same standard library used in Scala 2. However, with the update, Scala 3 will not be binary compatible with Scala 2 because of the update in Scala compiler. The community is working on making the migration as smooth as possible which makes the release of Scala 2.14 pivotal. To get an early preview of Scala 3 features, developers can start working on the development of Dotty, available on the GitHub repository and report any issues on the issues page. Scala 2.12.5 is here!

Welcome to this week's programming news bulletin. There's some interesting news about Qt, and just weeks after the release of Java 10, you can check out the upcoming specs for Java 11. Programming news from the Packt Hub JetBrains ReSharper Ultimate 2018.1 is now available for download Programming news from across the the web Java 11 Specifications out for public review. Java's new release cycle is bringing updates and changes at an impressive pace. You can now review what's going to be coming in Java 11. Oracle tell Java SE 8 users that they need to buy a licence from 2019. Introducing Quart, a Python web microframework based on Asyncio. Quart is a bit like Flask. But it's been built to tackle one of Flask's few weaknesses - the fact that it is incompatible with Asyncio. Qt comes to the browser. The popular UI tool is now interoperable with WebAssembly. That should help Qt become more popular with JavaScript developers. It hasn't yet captured the web development market, but perhaps this is a step in that direction. GitLab 10.7 has been released. Vcpkg: Microsoft's C++ library manager is now available on Linux, macOS and Windows. Warehouse is the next generation Package Manager for Python. Scala 2.1.4 and Scala 3 roadmap out. Kotlin 1.2.40 is out!

Salesforce has welcomed spring with their new Salesforce Spring 2018 release. With this release, Salesforce users, admins, and developers can try out some fresh features and tools to enhance, tweak, and guide the processes that govern our Salesforce instances. This release brings in exciting enhancements to the Lightning Platform and advanced developments in artificial intelligence. Without further ado, let’s have a quick look at some of the noteworthy features that are really going to change the way you work with Salesforce. Create personalized navigation in Lightning Experience This new Lightning Experience feature in Salesforce allows one to reorder items, rename, or remove the added items. The navigation bar now contains more than just object-level items. One can add granular items, like a dashboard, list, or record, and so on. Build Interactive Salesforce Surveys Creating beautiful, easy-to-use forms for collecting feedback and data from users or customers is now easy. All survey data is stored in your org which is nothing but an entity that contains users, data, and automation corresponding to an individual organization. This unified data storage is helpful especially in creating reports, dashboards and sharing insights within your organization. Customize your Org to match your brand using themes The ability to customise the look and feel of Salesforce has never really been available. With Spring ’18, create up to 300 custom themes or clone the built-in themes provided by Salesforce with just a few clicks. Easy calculation of Opportunity Scoring using Einstein One can prioritize their way to more business with Einstein Opportunity Scoring. Einstein Opportunity Scoring generates opportunity scores based on the record details, history and related activities of the opportunity and related account. Information about the opportunity owner, such as yearly win rates, is also used to calculate the score. Stay on Top of Duplicate Records by Using Duplicate Jobs Duplicates are a pain for most organisations. With the relatively recent release of duplicate and matching rules in Salesforce, creating duplicate jobs with standard or custom matching rules to scan Salesforce business or person accounts, contacts or leads for duplicates has become easy. Job results can be shared with others and information about the duplicate jobs are logged. These logs helps you in tracking your progress in reducing the number of duplicate records in Salesforce org. Easy storage of Data Privacy Preferences Data privacy records, based on the Individual object, lets one store certain data privacy preferences for their customers. These records can help honor and respect customers’ wishes when they request only specific forms of contact from one’s company. Some laws and regulations, such as the General Data Protection Regulation (GDPR), can require you to honor your customers’ wishes. See More Relevant Objects First in Top Results Top Results list the most relevant results for most frequently used objects. The improved ordering of objects means less scrolling and clicking around to reach the object one wants. To know more about these and other releases in detail, visit the Salesforce Blog. Read More Implementing Automation Process with Salesforce CRM Build a custom Admin Home page in Salesforce CRM Lightning Experience Getting Started with Salesforce Lightning Experience

ReSharper is one of the leading productivity tools from JetBrains. It is integrated with Visual Studio and provides enhancement and automation for .NET project development. JetBrains announced the general availability of ReSharper 2018.1, making it the first important release for this year. One of the major improvement speculated was the support for C# 7.2. JetBrains have finalized the support by adding all the entities that are specifically part of C# 7.2. This will ensure that the code written with latest C# modifiers and operators does not throw an error in ReSharper. Additionally, there are significant bug fixes made to this release. One of the improvements that Jetbrains highlighted in the release, is the Navigation. ReSharper 2018.1 has tools to better filter the search string and provide a better overview of the members of the code. Major additions in ReSharper Ultimate: Debug Step Filters in ReSharper C++ helps you in search related to functions while debugging. Include Analyzer helps with managing dependencies in the header files while compilation. Easy navigation with Analysis Subject panel in dotMemory 2018.1. The redesigned continuous testing mode in dotCover 2018.1 can be made to run in the existing unit test session instead of running another continuous test session. Console runner redesigned for dotCover 2018.1 helps giving coverage analysis for web applications running on IIS server. Code formatting engine has been improved to avoid problems related to indenting, line breaks, spaces, etc. Code formatting problems can be rectified in the code editor itself. Update in managing StyleCop rules directly from StyleCop configuration files. StyleCop analyzes the C# code for style and consistency throughout the development. This update will enhance consistency in code styling for C# developers. Other handy features to make developer's life simple are: Pre-/Post- build support for ReSharper Build engine A new option, known as Remember last search for navigation New settings for parentheses in function declaration and wrapping LINQ expressions ReSharper 2018.1 redefines the code navigation and enhances development with C# 7.x version. Also, improvements in unit testing in ReSharper will certainly catch attention for this release. Latest ReSharper Ultimate 2018.1 is available for download from the official JetBrains website.  

Welcome to this week's programming bulletin. There are a number of new releases that should interest fans of Spring, a preview of the next installment of .NET Core and Entity Framework and an update from JetBrains. Remember to watch this space every Thursday for more programming updates.  Programming news from the Packt Hub New features in C# 8.0 https://hub.packtpub.com/exciting-new-features-in-c-8-0/ Programming news from across the web Python launches an updated PyPi; the legacy version is set to close April 30. Spring Cloud Stream 2.0 is now on general availability. The emerging Spring framework, which helps users build event-driven microservices capable of scaling quickly, has just announced the general release of version 2.0. With 'a complete revamp of content-type negotiation functionality to address performance, flexibility, and... consistency', plus many more exciting features, this could help push the framework forward in the world of microservices development. Chaos Monkey now available for Spring Boot . In case you don't know, Chaos Monkey is a tool for testing large-scale distributed systems that is inspired by the principles of Chaos engineering. In this instance, it works as a small library that you integrate as a dependency within your application. It then attacks various components of your app - like a monkey causing chaos. Microsoft announces a preview of .NET Core 2.1 and Entity Framework 2.1. The .NET engineering team have made a preview of the latest versions of .NET Core and Entity Framework available - the team expects to reach a final full release in the next few months. Javalin 1.6.0 released. The new release features some improvements to performance and async requests. Jetbrains releases IntelliJ IDEA 2018.1.1 https://blog.jetbrains.com/idea/2018/04/intellij-idea-2018-1-1-is-released/  

It’s been more than 20 years since Microsoft released the first version of the C# language. Over the years C# has experienced a remarkable evolution, from being called as a Java copycat to one of the most loved and used programming languages. The current developments in C# 7 ecosystem are exciting, and grabbing the attention of developers, but what about the future? Can developers take a sneak peek into the future of C#? Well of course they can! The Microsoft language design team have been developing the language features ‘in the open’ for quite some time now. They have proposed several new features for the upcoming C# 8.0 and have released several prototypes for the developers, to try them out and provide feedback on the official Github repo. Let’s take a look at the most likely new C# 8 features: Nullable Reference Types The name of this particular feature might confuse a lot of developers wondering “Isn’t nullable reference a bad idea?” or “Shouldn’t it be called non nullable reference types?”. Sir Tony Hoare, a British computer scientist invented null references and famously called them the “Billion Dollar Mistake” as the biggest problem is, of course, the risk of getting the infamous null-reference exception. Since all reference types in C# can be null, you always run the risk of getting an exception when you try to access some member of the object. Functional languages try to deal with this problem by having a type that represents the concept of potential absent value. Instead of introducing non-nullable reference types in C#, Microsoft has chosen to consider reference types as non-nullable by default and provide mechanisms to deal with nullable types. Since the premise of a reference is often considered to be non-nullable and to be dereferenced. Asynchronous Streams Asynchronous streams provide the ability to use async/await inside an iterator. In most cases, an iterator is implemented synchronously. There are some cases, however, where it might need to await a call on every iteration to fetch the next item. In such cases, asynchronous streams can come in handy. To support this feature, a couple of things need to be taken care of: New types, the async equivalents of IEnumerable and IEnumerator Ability to specify an await on an iterator construct such as foreach. Default interface implementations: The primary use case for default interface methods is to enable the developer to safely evolve an interface. You could add new methods to it and, as long as you provide a default implementation, existing clients of the interface wouldn’t be forced to implement it. Another important value proposition of default implementation on interfaces relates to Android and iOS. Since both Java and Swift offer this feature, it’s tricky to use C# to wrap Android/iOS APIs that make use of default interface implementations. C# 8.0 will make it possible to wrap those APIs more faithfully. There are plenty of other features proposed to be implemented in C# 8 such as target-typed new expressions, covariant return types, and extension everything. All these features are in different stages of development and a lot can (and probably will) change from now until C# 8.0’s release Till then you can closely follow the official Github repo for C#.   Stay up to date with the latest features, release dates, and general discussions on key programming tools and tech by subscribing to Packt Hub. Check out other latest news: Red Programming Language for Android development! What’s new in Visual Studio 1.22 OpenCV 4.0 is on schedule for July release

Red has been around for a while now and has been extending its purpose from systems programming to various fields like Blockchain, Android development, among others. Recently, Red introduced some modern implementation support for Android programming. The way Red can be used in Android programming is using a specific bridge, that allows the Red code to call the whole Android API in Java. This allows you to write Android applications in Red language without having to write any Java code. Red, in its latest release, has introduced a new and improved Java bridge for Android. Java bridge was first introduced in Red's version 0.3. With this bridge, the Red programs are able to interface Java Virtual Machine through JNI (Java Native Interface). The new Java bridge now replaces the old implementation and is now optimized for efficient bindings of Red programs to the Android API in Java. This optimization greatly improves the performance of the Android application. To explain this, Red has published a short overview of Red/View backend on Android, the source code for which can be found on this GitHub repo. Red his also been working on implementing several features in its v0.7 release. The community has released a short list of features on its site, though all the work on these features is done in a private repository. The private repo will be opened to selected developers soon for testing and fine-tuning. Some of the features that are set to come in the new release are as follows: Full set of Red/View widgets support Draw & Shape dialects full support Compatibility with Android GUI themes Device-independent coordinate system Updated APK building toolchain for targeting API 23+ Android systems Improved ARM backend, support for ARMv7+ Android support Red has also released a roadmap for the upcoming releases which shows that v0.7 is going to be an important milestone for Android development with Red. Apart from the work in Android, Red has been concentrating on the Blockchain-related wave of tasks, Full I/O, among other high-priority milestones. As of now, Red v0.6.4 has been released on the GitHub repository. The community is still considering a 0.6.5 release, to insert a small extra milestone before going into the 0.7 release. Build your first Android app with Kotlin Why are Android developers switching from Java to Kotlin?

GitHub celebrated its tenth birthday on Tuesday. Since its launch in April 2008, the version control platform has come to define the way we build software. It's difficult to see open source software culture evolving to the extent it has without GitHub. Its impact can be felt even beyond software. It has changed the ways users experience the web, and it has made artificial intelligence more pervasive than ever. For that reason, we should all pay tribute to GitHub - developers and non-developers alike. You can find Packt on GitHub here. We have more than 1,400 code repositories for our products that you can use as you learn. 27 million developers have 'contributed' to GitHub's success GitHub now has more than 27 million developers on its platform and is home to more than 80 million projects. To say thank you to everyone who has been a part of the last 10 years - everyone who has quite literally contributed to its success - the team put together this short video: https://www.youtube.com/watch?v=hQXV70Z4cFI Key GitHub milestones Let's take a look at some of the most important milestones in GitHub's first decade. Find out more here. April 12, 2008 - GitHub officially launches. Read the post from 2008 to take a trip back in time... May 21, 2009 - Node.js launches and its saga begins. From io.js forking in 2014, to reunification a year later, the JavaScript library is today one of the most popular tools on GitHub. There are almost 2,000 contributors to Node.js Core, the central Node.js project. January 1, 2012 - JavaScript begins 2012 as the most popular language on GitHub. More than 6 years later that still remains the case. January 16, 2013 - GitHub reaches 3 million users. In the space of just 5 year,s the platform was truly embedded in the software landscape. October 23, 2014 - Microsoft takes the significant step of making .NET open source. This was the start of a cultural shift at Microsoft that's still happening today. Perhaps more than any other moment in the history of open source, this underlined the fact that it was no longer a niche stream of the software landscape. It had become part of the mainstream. March 2, 2015 - Unreal Engine 4 makes its source code available for free. This gave game developers access to an incredibly powerful tool for no cost whatsoever. The impact on the growth of game development is important to note. 'Games' was one of the most popular topics on the platform in 2017. December 3, 2015 - Mirroring the move made by Microsoft in 2014, Apple makes Swift open source. Again, this was a huge tech company - with a similar reputation for isolationism - embracing open source. February 15, 2017 - The launch of TensorFlow 1.0 marks the start of the boom in artificial intelligence. Or more specifically, it marks a point at which artificial intelligence and machine learning became accessible to millions more people than ever before. The range of projects that TensorFlow has been a part of is astonishing - from research to medicine to marketing, its accessibility has had an impact on the world in ways that many people don't realise. May 31, 2017 - GitHub's 100 millionth pull request is merged. Thank you, GitHub for 10 years supporting software developers. It really wouldn't have been the same without you. Here's to another 10 years... Is Comet the new Github for Artificial Intelligence? Mine Popular Trends on GitHub using Python – Part 1 Github’s plan for coding automation, TensorFlow releases Tensorflow Lattice – 11th Oct.’ 17 Headlines Collaboration Using the GitHub Workflow Using Gerrit with GitHub