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GitHub for Unity is now available in version 1. GitHub for Unity 1.0 is a free and open source Unity editor extension that brings Git into Unity 5.6, 2017.x, and 2018.x. GitHub for Unity was announced as an alpha version in March 2017.  The beta version was released earlier this year. Now the full release GitHub for Unity 1.0 is available just in time for Unite Berlin 2018, scheduled to happen on June 19-21. GitHub for Unity 1.0 allows you to stay in sync with your team as you can now collaborate with other developers, pull down recent changes, and lock files to avoid troublesome merge conflicts. It also introduces two key features for game developers and their teams for managing large assets and critical scene files using Git, with the same ease of managing code files. Updates to Git LFS GitHub for Unity 1.0 has improved Git and Git LFS support for Mac. Git Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git. Previously, the package included full portable installations of the Git and Git LFS. Now, these are downloaded when needed, reducing the package size to 1.6MB. Critical Git and Git LFS updates and patches are distributed faster and in a more flexible way now. File locking File locking management is now a top-level view within the GitHub window. With this new feature developers can now lock or unlock multiple files. Other features include: Diffing support to visualize changes to files. The diffing program can be customized (set in the “Unity Preferences” area) directly from the “Changes” view in the GitHub window. No hassles of command line, as developers can now view project history, experiment in branches, craft a commit from their changes and push their code to GitHub without leaving Unity. A Git action bar for essential operations. Game developers will now get a notification within Unity whenever a new version is available. They can choose to download or skip the current update. Easy email sign in. Developers can sign in to their GitHub account with their GitHub username or the email address associated with their account. GitHub for Unity 1.0 is available for download at unity.github.com and from the Unity Asset Store. Lead developer at Unity, Andreia Gaita will conduct a GitHub for Unity talk on June 19 at Unite Berlin to explain how to incorporate Git into your game development workflow. Put your game face on! Unity 2018.1 is now available Unity announces a new automotive division and two-day Unity AutoTech Summit AI for Unity game developers: How to emulate real-world senses in your NPC agent behavior

For all those who wish to run their SAP solutions on the cloud, Alibaba has granted this wish for you! At the SAPPHIRE NOW 2018, Alibaba showcased SAP products and solutions on its Cloud platform. Now one can run their SAP solutions on Alibaba Cloud with their choice of Operating System. Alibaba Cloud is among the world's top three IaaS providers according to Gartner. It is also the largest provider of public cloud services in China, according to IDC. It provides a comprehensive suite of cloud computing services to businesses all over the world. This includes merchants with businesses located within Alibaba Group marketplaces, startups, corporations and government organizations. Using Alibaba Cloud’s global infrastructure, enterprises can leverage its robust infrastructure and computing power to achieve greater business value. It has expanded its support to SAP systems by providing: Linux support – SAP HANA, SAP MaxDB, and SAP ASE Windows support – SAP MaxDB, SQL Server to run SAP Business Suite, and other applications on the SAP Application Server ABAP Alibaba has also passed the certification to run SAP Business One HANA on its cloud. The partnership of SAP and Alibaba brings a versatile, one-stop cloud computing environment by Alibaba Cloud's reliable, high-performance and secure infrastructure, interoperating with enterprise-level business application solutions from SAP." With SAP, Alibaba Cloud platform gets an added robust global IT infrastructure and computing strengths. It also delivers enhanced ERP services in cloud environments, which in turn aids enterprises in driving their digital transformation. Read more about this on SAP on Alibaba's cloud official website.

Machine learning is gradually transforming the development landscape. Being the hottest technology in the software industry currently, everyone from professionals to beginners, are hopping on the machine learning bandwagon. Machine learning is filled with immense potential, paving the way for people to build cutting-edge applications across different domains. This is why application developers have started to incorporate parts of machine learning into their development process to make it more effective. A web or an app developer who knows ML has a competitive edge over the one who doesn’t. In this year’s Skill Up 2018 Survey, we asked developers about the most valuable skill they would want to adopt and the answer was: Machine Learning.   Source: Packt Skill Up Survey 2018 But, how does machine learning help with the web and app development process? Impact of Machine Learning on web & app development Self-driving cars, robots, face detectors, etc, all have a common denominator: Machine Learning. These are some popular areas, we have seen ML models create wonders by identifying the best and the worst of the user-generated content to make it highly valuable experience on the web. But machine learning is everywhere. How can we not remember Machine learning to help us find out and eradicate web spam which used to damage user experience?  Google’s artificial neural network helped in email spam filtering which has blocked almost 99% of spam emails from reaching our inboxes. Companies like Pinterest and Instagram use ML to show ever interesting and engaging content on their apps. Another example is of Uber app which uses Machine Learning to create a seamless and reliable experience for customers.  With advanced technologies like ML & AI used for designing the Uber app, helps estimate the time of arrival and cost of travel. It also helps in providing real-time information about the driver’s location to the customers. Among other areas, Uber uses ML to enable an efficient ride-sharing marketplace, identify suspicious or fraudulent accounts, suggest optimal pickup and dropoff points and even facilitate UberEATS delivery. Machine learning has the potential to take development skills to the next level. So if you want to be a versatile developer, ML, no longer has to be a skill that you put on the back-burner. However, that's not all to the story, there are plenty of such examples where companies use ML to build their products. And there are plenty of reasons and opportunities for web developers to dive into machine learning. Let us take a look at each one by one: Machine learning for data mining Organizations across the globe use different data mining techniques to examine their large database in order to discover new information. ML can be used for data mining since it is quite effective in detecting new patterns based on huge amounts of data. It uses pattern recognition techniques and computational learning for data prediction. Web developers can leverage web mining technique which is a subset of data mining. It uncovers distinct usage patterns from web data to understand and better serve the needs of Web-based applications. It helps developers discover useful data such as users’ browsing history and the origin of the web users. Web structure mining can further help developers to analyze nodes and connection structure of a website to describe HTML or XML tags usage. Comprehending customer behavior Web apps and other mobile apps make use of supervised machine learning algorithms to address issues faced by the user. This, in turn, helps ameliorate the entire customer service process. For instance, contact us forms are quite prevalent on websites these days. Contact us forms eliminate the need for the users to self-select an issue and fill out ceaseless form fields to get in touch with the customer care executive. All you have to do now is fill in the contact us form and you’ll hear back from the respective customer care center. This helps streamline the customer service process. Another great example is Chatbots. Chatbots helps website or an app to better understand the patterns in customer behavior. What do customers search for the most? What is customer’s buying tendency? What problems are they facing? These questions can be easily answered by a chatbot which is built on machine learning algorithms. As a developer, you will feel overwhelmed by having developed such innovative solutions and enhance the whole process. Personalizing content The number one example of machine learning helping developers personalize the content within their sites is Facebook. In fact, several social media applications are heavily leveraging the potential of machine learning to provide users with more personalized and relevant content. Facebook uses ML in the form of automatic friend tagging suggestions, mutual friend analysis,  personalized news feed, and video recommendations as per user’s choice. It uses a combination of predictive analytics and statistical analysis to detect patterns based on the user’s data to create an ever engaging content. Recently, Twitter also started implementing machine learning algorithms to value user’s time by providing them with a deeply personalized feed which is custom-tailored as per the user’s choice and liking. Machine learning has become game-changer for the social media websites and developers should grab this opportunity with both their hands. Dealing with security threats Machine learning technique such as logistic regression can help developers find and evaluate websites that are malicious in nature. Another such machine learning algorithm is called classification algorithm. It can help detect and predict phishing websites. Detection of phishing websites depends on factors such as security features, domain identity, and data encryption technique. Some of the examples of prevalent applications that are making use of ML for web and app development are Snapchat, Tinder, Netflix, etc. For instance, Snapchat uses machine learning which helps in perceiving people’s facial components. Similarly, Netflix uses Linear relapse, Logistic relapse, and other machine learning calculations. These calculations at Netflix track users’ activities to provide personalized content for the viewers. Hence, we speculate Machine learning to completely transform the development process and help web developers take a bigger leap in their career. The Skill up survey also revealed another skill that the developers are keen to learn in the next 12 months; that is Python. Python: the go-to language for both machine learning and web development Python is one of the top languages for both web development as well as machine learning. It has an easy syntax and faster development time which makes it a good choice for the developers. Python contains a vast number of ML libraries such as scikit learn, Keras, Tensorflow, SciPy, and boasts a rich and vibrant machine learning community. The versatile features of the Python language have helped build some of the most robust and popular websites like Instagram, Quora, Youtube, etc. Likewise, the powerful capabilities of machine learning have made our life simpler. It has introduced us to the world of virtual assistants like Siri, Cortana, and face detection technology among others. Machine learning is an incredible breakthrough for businesses and consumers alike. So, if you’re interested in getting counted among the upper echelons of the development world then be ready to expand your toolbelt. Dive into the machine learning world and brace yourself for the opportunities that are to find your way. Asking if good developers can be great entrepreneurs is like asking if moms can excel at work and motherhood What are web developers favorite front-end tools? Packt’s Skill Up report reveals all Developers think managers don’t know enough about technology. And that’s hurting business.

Staying up to date with software is hard. This year's Skill Up report confirms that, with 43% of developers claiming that a lack of quality learning resources were a big barrier to their organization reaching its goals. It's not that there aren't learning resources are out there - there are. It's just that finding the right ones can take some time. And when you've got a million things to do and deadlines yesterday, searching for the best deal feels like a bit of a waste of your day. 2 subscription services. 3 months. $30. Luckily, 2 subscription services have teamed up to help you stay up to date in tech and prepared for future trends and tools as they emerge. Mapt and SitePoint both offer an impressive range of software learning resources - combined, they provide anyone in software with the reassurance that they have immediate access to insight and guidance on the latest tools in their field. From Monday 18 June to Sunday 24 June, you'll be able to get 3 months of Mapt - that's full access to all Packt's eBooks and videos - and SitePoint Premium for just $30. That's a whole lot of content for the price of just one book. Learn more about the offer. What you get with Mapt... Access to over 6,000 eBooks & Videos Over 100 new courses added each month Over 1,000 technologies and tools covered Curated Skill Plans to help direct your learning Skill Assessments to reinforce your learning Up to 2 FREE eBooks & Video tokens a month to keep forever Sounds good, right? But there's a whole lot more thanks to SitePoint... What you get with SitePoint Premium... 123+ DRM Free eBooks and Courses Access to over 123 courses New and trending web development content released monthly Join a community of 35,000+ members Course helpers assistance The offer ends on Sunday 24 June, so don't waste time - a comprehensive duo of software learning subscriptions are just a few clicks away...  Get your 3 month subscription to Mapt and SitePoint Premium

GitLab Web IDE, aimed to simplify the workflow of accepting merge requests, is now available in GitLab 10.7, along with other features focused on improving C++ and Go code security and improving Kubernetes integration. The GitLab Web IDE was initially released as a beta in GitLab 10.4 Ultimate. The primary goal of this IDE was to streamline the workflow to contribute small fixes and to resolve merge requests. This allowed developers to contribute without storing their changes and switching to a new branch locally, then back.This could be of particular interest to developers who have a significant number of PRs to review, as well as to developers starting their journey with Git. GitLab Web IDE has the familiar layout of many graphical IDEs, with a left panel showing the files belonging to the project and a middle panel allowing to view and edit their contents. The right panel in GitLab Web IDE is the commit panel, which allows developers to keep track of which files make up the commit, to review changes, edit the commit message, and control whether the commit goes to the master branch or to a new branch, and whether to merge the changes or not. GitLab Web IDE GitLab has made its Web IDE open source with the goal of letting the developer community contribute to its development and improvement. Setting up a local development environment, or needing to stash changes and switch branches locally, can add friction to the development process. Using the Web IDE you can change multiple files, preview Markdown, review the changes and commit directly all from a browser. You can even open the diff from a merge request and get a side by side view of the changes. The latest release of Web IDE comes with performance improvements and the ability to contribute small fixes and resolve merge request feedback. GitLab 10.7 also released SAST for Go and C/C++ Additionally, GitLab 10.7 also extends GitLab Static Application Security Testing (SAST) by adding support for C/C++ and Go. GitLab SAST is a set of security tools aimed to analyze source code to detect known vulnerabilities. In addition to C/C++ and Go, SAST supports Python, Java, and Ruby on Rails. GitLab SAST is somewhat reminiscent of GitHub Security Alerts, which support Ruby and JavaScript, while Python support is in the workings. Another interesting change brought by GitLab 10.7 is Deploy Tokens, which provides support for long-lived read-only authenticated sessions for specific needs, such as when using Kubernetes to orchestrate a bunch of containers in a CI pipeline. Indeed, to flawlessly handle pod restarts and auto scaling, Kubernetes requires ongoing access to the container registry. This was previously available through CI job token, which had the downside of expiring once Kubernetes completed its task. As a workaround, personal access tokens could be used, but this could be undesirable when the access token had to be shared among multiple users. Deploy token provides a solution to both limitations. GitLab 10.7 includes many more new features, such as parametrized CI/CD control flow, project badges, subgroup issues boards, and others. Do not miss the official announcement for full details. The Microsoft-GitHub deal has set into motion an exodus of GitHub projects to GitLab GitLab’s new DevOps solution Microsoft is going to acquire GitHub

Masonite, the popular Python web development framework, has released a new version. Masonite 2.0 comes with several new features to Masonite including new status codes, database seeding, built in cron scheduling, controller constructor resolving, speed improvements and much more. A new ‘Tinker’ Command Masonite 2.0 adds a new Tinker command that starts a Python shell and imports the container. It works as a great debugging tool and can be used for verifying that objects are loaded into the container correctly. A new Task Scheduler Masonite 2.0 adds a task scheduler,  a new default package that allows scheduling recurring tasks. You can read about the Masonite Scheduler under the Task Scheduling documentation. Automatic Server Reloading A huge update to Masonite is the new --reload flag on the serve command. Now the server will automatically restart when it detects a file change. You can use the -r flag as a shorthand. Autoloading With the new autoloading feature, you can list directories in the AUTOLOAD constant in the config/application.py file and it will automatically load all classes into the container. Autoloading is great for loading command and models into the container when the server starts up. Database Seeding Support Masonite 2.0 adds the ability to seed the database with dummy data. Seeding the database helps in populating the database with data that would be needed for future development. Explicitly Imported Providers Providers are now explicitly imported at the top of the file and added to the PROVIDERS list, located in config/providers.py. This completely removes the need for string providers and boosts the performance of the application substantially. Status Code Provider Masonite 2 removes the bland error codes such as 404 and 500 errors and replaces them with a cleaner view. It also allows adding of custom error pages. Upgrading from Masonite 1.6 to Masonite 2.0 Masonite 1.6 to Masonite 2.0 has quite a large number of changes and updates in a single release. However, upgrading takes only around 30 mins for an average sized project. Read the Masonite upgrade guide for a step-by-step guide to upgrading. You can read the release notes, for the full list of features. Python web development: Django vs Flask in 2018 What the Python Software Foundation & Jetbrains 2017 Python Developer Survey had to reveal Should you move to Python 3? 7 Python experts’ opinions

The Qt team, in their blog, announced the official release of Qt with Python support. This is the first official of Qt framework with the support for Python and this release is tagged as Qt for Python 5.11. Previously  Python support for Qt developers was provided through the development of PySide module and now the work is said to have been done on PySide 2 to provide Qt for Python. However, Qt team has been working on the core Qt framework for quite some time to incorporate Python support and this is the first breakthrough in that direction. Adding to this, the Qt team has also informed that r version of Qt earlier than v5.11 will not support Python. In the release notes, the team has mentioned that the following versions of Qt will continue supporting this project and make the support for Python, stable going ahead. This is said to be the preview release, with a list of known issues for early adopters. The team is hoping to receive the feedback from the users so that it can make the binding more smooth and rectify the bugs. A lot of work has also gone into keeping the Qt syntax unchanged for flexible migration from C++, the de facto language for developing UI with Qt, to Python and the other way round. It mentions in the release blog, that the major roadblock in providing the Python binding for the C++ based Qt was the size of packages. This made the team to work on using external tools for Qt scripting with Python, which had resulted in the development of PySide in 2009. To extend the support for Python, the work has been done on C++ headers in Qt framework, so that the developers can write modules in Python. These efforts resulted in the latest PySide 2 which has very less overhead for using Python and Qt for GUI development. The Qt team has worked on developing the documentation for this and has provided examples enables you to understand the binding. Along with the Python binding for the core Qt framework, the team has also extended support for various Qt toolkits like Qtwidgets and QML to build interactive GUI with Qt and Python. For the early adopters of Qt for Python, to report a bug you use the Qt for Python project on bugreports.qt.io. The team can be reached on Freenode with #qt-pyside. Read more Qt 5.11 has arrived! WebAssembly comes to Qt. Now you can deploy your next Qt app in browser

If Vue.js is the Javascript framework of your choice, you will definitely enjoy Vue Native. Developed by Geeky Ants, Vue Native can help you build powerful native mobile apps using JavaScript and Vue.js. It is designed to connect React Native and Vue.js, making app development simpler. It has gained quite some popularity with already 2500+ starts on Github, since its official announcement, a few days ago. It was originally started by SmallComfort as react-vue which transpiles Vue files to React & React Native Components. GeekyAnts forked it later. It brings the goodness of Vue ecosystem to native mobile app development such as support for templating, styling, state-management with Vuex and router makes. More distinctive features include declarative rendering, two-way binding, and completeness of React Native ecosystem. How does it work Vue Native transpiles to React Native. React Native is a framework to build native Android and iOS apps using JavaScript. The Vue Native CLI is used to generate a Vue Native app, which is a React Native API wrapper. It generates a simple single page application (SPA) using create-react-native-app and vue-native-core. React Native is a direct dependency of Vue Native. Once you initialize a new app using vue-native-cli, the entry script is App.vue. You can use: the reactivity system of Vue to observe React component the react-vue-loader to run Vue component in React application the vue-native-scripts to run Vue component in React Native How to get started The first step is to install React Native on your system. Next, install the Vue Native CLI on your system using npm. $ npm install -g vue-native-cli Now all you need to do is initialize a new Vue-Native project directory in your system. You can now run this app on an iOS or Android Simulator using the npm run command. The installation guide contains the full list of features along with details to get started and code examples. Vue Native is open source; you can find it on GitHub. Why has Vue.js become so popular? Vue.js developer special: What we learnt from VUECONF.US 2018 Testing Single Page Applications (SPAs) using Vue.js developer tools

Google's App Maker, has come out of its private preview mode and is now generally available. App Maker is an online tool for building and deploying business apps on the web. The company has remained quiet about its development ever since the launch one and a half years ago. However, now seeing the surge in making everything open source, App Maker remains open and free for all developers who want to give it a shot. App Maker is available to all G Suite Business and Enterprise customers, as well as G Suite for Education customers. If App Maker isn’t yet enabled for your eligible domain, you can turn it on in the Apps > Additional Google services section of the Admin console. The newly designed App Maker also has multiple features added to its basket. Most prominently, it now offers built-in support for Cloud SQL bringing high performance, scalability, and convenience. It has a Bring Your Own Database (BYODB) model, letting you connect it to the database of your choice using JDBC or a REST API. The new Google App Maker is fast and highly automated for app designing and development with Responsive templates, samples, a drag-and-drop UI design and declarative data modeling. It can be easily connected with data and services such as Gmail, Calendar, or Sheets. You can also use Apps Script to access over 40 Google services, Google Cloud Platform and other third-party services that support JDBC and REST. In fact, G Suite administrators will have complete visibility over the apps running in their organization. They can now view owners, usage metrics, and OAuth permissions. For example, you can view the activity of users using the Drive audit logs, or view the activity of end users in the OAuth Token audit logs. Administrators will also be able to prevent apps from running without their approval with expanded OAuth Whitelisting controls. You can read the details on Google Blog. For more information on App Maker, check out the Help Center. Google’s translation tool is now offline – and more powerful than ever thanks to AI Top reasons to use Google Lighthouse 3.0 Google Kubernetes Engine 1.10 is now generally available and ready for enterprise use

Juniper networks, one of industry leads in automated, scalable and secure networks, today announced fifth generation of it’s MX Series 5G Universal Routing Platform. This series has more offerings for cutting-edge infrastructure and technology like cloud and IoT, enabling high-level network programmability. It has improved the programmability, performance and flexibility, for rapid cloud deployment by introducing a new set of software. This platform supports complex networks and service-intensive applications such as secured SD-WAN-based services and so on. Executive vice president and chief product officer at Juniper Networks, Manoj Leelanivas, said “ Cloud is eating the world, 5G is ramping up, IoT is presenting a host of new challenges, and security teams simply can’t keep up with the sheer volume of cyber attacks on today’s network. One thing service providers should not have to worry about among all this is the unknown of what lies ahead.” Few highlights of this release are as follows: Juniper Penta Silicon Penta silicon is considered the heart of the 5G platform which is next-generation 16 nm service-optimized, having a packet-forwarding engine that delivers upto 50% power efficiency over existing Junos trio chipset. Pena silicon has native support to MACsec and IPsec crypto engine that enables end to end secure connectivity at scale. In addition to this, Penta silicon also supports flexible native Ethernet (FlexE). MX 5G Control User-Plane Separation (CUPS) The 3GPP CUPS standard allows the customer to separate the evolved packet core user plane (GTP-U), and control plane (GTP-C) with standard interface to help service providers scale each independently as needed. The MX Series 5G platform is the first networking platform to support a standard-based hardware accelerated 5G user-plane in both existing and future MX routers. It enables converged services (wireless and wireline) on the same platform while also allowing integration with third-party 5G control planes. MX10008 and MX10016 Universal Chassis MX series continues to do innovations in the area of cloud, enterprise networking, and previously announced PTX and QFX Universal Chassis gains two new MX variants with today’s announcement: MX10008 and MX10016. A variety of line cards and software are available to satisfy specific networking use cases across the data center, enterprise and WAN. Refer to the official Juniper website for details on MX Series 5G. Five developer centric sessions at IoT World 2018 Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT Windows 10 IoT Core: What you need to know  

Microsoft recently announced few innovations to their AI platform powered by Microsoft Azure. These updates are well aligned to their Digital Transformation strategy of helping organizations augment their machine learning capabilities for better performance. Cognitive Search Cognitive Search is a new feature in Azure portal which leverages the power of AI to understand the content and append the information into Azure Search. It also has support for different file-readers like PDF, office documents.It also enables OCR capabilities like key phrase extraction, language detection, image analysis and even facial recognition. So the initial search will pull all the data from various resources and then apply cognitive skills to store data in the optimized index. Azure ML SDK for Python In the Azure Machine Learning ecosystem, this additional SDK facilitates the developers and the data scientists to execute key AML workflows, Model training, Model deployment, and scoring directly using a single control plane API within Python Azure ML Packages Microsoft now offers Azure ML packages that represent a rich set of pip- installable extensions to Azure ML. This makes the process of building efficient ML models more streamlined by building on deep learning capabilities of Azure AI platform. ML.NET This cross-platform open source framework is meant for .NET developers and provides enterprise-grade software libraries of latest innovations in Machine Learning and platforms that includes Bing, Office, and Windows. This service is available in the AI platform for preview. Project Brainware This service is also available on Azure ML portal for preview. This architecture is essentially built to process deep neural networks; it uses hardware acceleration to enable fast AI. You can have a look at the Azure AI portal for more details. New updates to Microsoft Azure services for SQL Server, MySQL, and PostgreSQL Epicor partners with Microsoft Azure to adopt Cloud ERP SAP Cloud Platform is now generally available on Microsoft Azure  

Google has today rolled out its super fast translation package in offline mode. This will deliver accurate and natural machine translations to users without a live connection to the internet. The team at Google worked for almost more than 2 years to deliver the powerful neural machine translation (NMT) technology to Google’s native Translate applications on smartphones. Using neural nets, the package should provide instant and accurate human-sounding translations for both Android and iOS users. Previously, the offline translation tool worked by breaking down sentences and then translating every individual phrase. However, with AI-powered NMT technology, the app translates the whole sentence swiftly in one. NMT uses millions of translated examples collected from different sources including books, documents, articles, and search engine results. This information is then used to understand how a given sentence can be formulated in a natural way that remains true to its intended context. In addition, this offline feature is surprisingly compact. Each language package is just 35 MB. That means you’ll be able to download it to your phone without using up all of your precious storage. Google says that the package would be very soon rolled out in over 59 languages in next couple of days. It should include European, Indian and several other languages. At present, you will be able to translate the following languages offline: Afrikaans, Albanian, Arabic, Belarusian, Bengali, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian, Creole, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Kannada, Korean, Latvian, Lithuanian, Macedonian, Malay, Maltese, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese and Welsh. To use offline translations in your Google Translate app, browse to Offline Translation settings, tap the symbol next to the language name and the package gets downloaded. To learn more, check out the official announcement at the Google Blog page. FAE (Fast Adaptation Engine): iOlite’s tool to write Smart Contracts using machine translation How to auto-generate texts from Shakespeare writing using deep recurrent neural networks Implement Named Entity Recognition (NER) using OpenNLP and Java

Meteor 1.7 released to support both modern as well as legacy browsers that will keep themselves updated as and when new features arrive. It also provides options for developers to selectively compile their node-modules before publishing it on npm. When Meteor was initially released in 2012, it was considered as a game-changing framework for developers to use JavaScript on the client as well as on the server-side. This gave birth to the “Isomorphic” revolution that allows developers to break from the shackles of rendering pages on the server-side. Over the years Meteor has followed an incremental update plan without drastically changing the framework, a move which developers have really appreciated, and the trend follows for the 1.7 release as well. Notable features in Meteor 1.7 Differential modern/legacy browser support When a new version for a browser is shipped, it comes with all the latest features of ECMAScript packed inside it. This has a very significant impact on web development as developers can start using the latest standards to ease the process and write more fluid and performant code. But it is not always possible, using these new features means leaving behind the users who are still using the old browser, posing a dilemma for developers. Well, Meteor 1.7 brings the best of both worlds by automatically building two sets of client-side assets, one tailored to the capabilities of the modern browser, and the other designed to work equally well on all supported browsers. This will help legacy browsers continue to work exactly the same as they were before. Selective compilation of node modules The npm packages are precompiled before they are published, so users of these packages have to settle for the compilation strategy decided by the author. Meteor 1.7, allows you to control how an npm package is compiled, or compile the package for modern/legacy browsers and Node. This lets application developers take complete control of the npm modules they are using. Most of the new features in Meteor 1.7 can be applied in a backward compatible manner or they can be opted out, it is completely a developer’s choice. For a complete breakdown of the new Meteor features, refer to this Official Meteor blog page.  

Oracle recently released Oracle Soar, a brand new tools, and services package to help customers migrate their applications on the cloud. Oracle Soar comprises a set of automated migration tools along with professional services i.e. a complete solution for migration. It is a semi-automated solution that fits in with Oracle's recent efforts to stand apart from other cloud providers which offer advanced automated services. Tools available within the Oracle Soar package are: Discovery assessment tool Process analyzer tool Automated data and configuration migration utilities tool Rapid integration tool The automated process is powered by True Cloud Method, which is Oracle’s proprietary approach to support customers throughout their cloud journey. Customers are also guided by a dedicated Oracle concierge service that ensures the migration aligns with modern, industry best practices. Customers can monitor the status of their cloud transition via an intuitive mobile application, which allows them to follow a step-by-step implementation guide for what needs to be done on each day. With Soar, customers can save up to 30% on cost and time as it offers simple migrations taking as little as 20 weeks for completion of the process. Oracle Soar is currently available for customers from the Oracle E-Business Suite, Oracle PeopleSoft and Oracle Hyperion Planning who will move to Oracle ERP Cloud, Oracle SCM Cloud and Oracle EPM Cloud. Read more about Oracle Soar, on Oracle’s official blog post. Oracle reveals issues in Object Serialization. Plans to drop it from core Java. Oracle Apex 18.1 is here! What Google, RedHat, Oracle, and others announced at KubeCon + CloudNativeCon 2018    

Julia 0.7  announced by the Julia developer team was speculated for a long time after v0.6 was released last year. This sets the gate open for Julia v1.0 since we had seen discussions and forecast about the v1.0 after v0.6 was out. Most features included in Julia v0.7 are the feature set for Julia v1.0 with few improvements and bug-fixes. Nothing is confirmed by the Julia team yet but the community is predicting Julia v1.0 to be next after Julia 0.7. Important features in Julia 0.7 are as follows: New macros defined for testing the local variable. Destructuring in function arguments so that the variable in a function expression gets unpacked into the local variable. Keyword argument containers will be named tuples. Keyword arguments will be required in the declaration to avoid the exceptions. To know the whole list of specifications, check out the release notes page here at Julia’s GitHub repository. Julia 0.7 will be released with exceptions in compatibility for functionalities of Julia 0.6. These exceptions will, however, discontinue in Julia 1.0. Apart from this, Julia 1.0 will add some functionality that will be prevented in Julia v0.7. It seems to be a bit complicated compatibility planning by Julia community, which is expected to get sorted with Julia 1.0. The community hinted Julia 0.7 and 1.0 to be identical. This means programs that are running on Julia 0.7, will work exactly the same on 1.0 without any warnings. The release time for Julia 1.0 is expected to be short after Julia 0.7. Julia 0.7 is said to be stable and production ready, but the community advised to keep the deprecations of features in mind. Additionally, when developers upgrade to Julia 1.0, they will have to run the tests and use cases all over again. Basics of Programming in Julia Computer vision is expanding quickly. Here’s why. Visual Studio 2019: New features you should expect to see