Tech News - Application Development

A Microsoft team named Bling (Beyond Language Understanding) announced a Finite State machine and regular expression manipulation library called Fire, yesterday. Fire has been developed to use in case of different linguistic operations inside Bing including Tokenization, Multi-word expression matching, Unknown word-guessing, and Stemming/Lemmatization among others. Under Fire comes a tokenizer, which has been designed for fast-speed and quality tokenization of Natural Language text. Fire tokenization uses the tokenization logic of NLTK (Natural Language Toolkit), with an exception that hyphenated words can be split and only a few errors can be fixed. Also, when compared with other popular NLP libraries, Bling Fire becomes 10X faster speed in tokenization task. The latest release of Bling Fire model is enabled to support most languages including East Asian (Chinese Simplified, Traditional, Japanese, Korean, Thai). The tokenizer’s high-level API is friendly to use from languages such as Python, Perl, C#, Java, etc. Also, the tokenizer has been designed in a way that it requires 0 zero configurations, or initialization, or additional files. The reason Tokenizer is very fast is because it makes use of deterministic finite state machines underneath. In order to use the Bling Fire Library and Finite State Machine manipulation tools, the project can be built on Windows/Linux using CMake, which allows you to create your own tokenization/segmentation, stemming, etc. To use the Bling Fire Library in Python, users can install the release with the help of using: pip install blingfire For more information, check out Bling Fire on GitHub. Microsoft reveals certain Outlook.com user accounts were hacked for months Microsoft makes the first preview builds of Chromium-based Edge available for testing Microsoft announces the general availability of Live Share and brings it to Visual Studio 2019

Yesterday, Red Hat announced that it will serve as a steward of OpenJDK 8 and OpenJDK 11, following the transition from Oracle. “With this transition”, says Red Hat, “we are affirming our support of the Java community and following a similar path that led to its leadership of both the OpenJDK 6 and OpenJDK 7 projects.” At the end of January 2019, Oracle officially ended free public updates to Oracle JDK for non-Oracle customer commercial users. These users will no longer be able to get updates without an Oracle support contract. Additionally, Oracle has changed the Oracle JDK license (BCPL) so that commercial use for JDK 11 and beyond will require an Oracle subscription. The leadership transfer actually happened mid-February, when Red Hat's Java technical lead, Andrew Haley was assigned as the Lead Maintainer in the JDK 8u Updates and JDK 11u Updates projects in OpenJDK. While Andrew is the lead whose job is to accept backports, the actual people who do the backporting work span multiple companies: Red Hat, SAP, Oracle, Amazon, Google, etc. These vendors share the backporting, reviewing, testing work. OpenJDK 8u, 11u Update Projects is where that collaboration happens, and Red Hat is leading that collaboration. Additionally, in December 2018, Red Hat announced commercial support for OpenJDK on Microsoft Windows. Red Hat plans to launch OpenJDK in a Microsoft installer in the coming weeks and distribute IcedTea-Web, the free software implementation of Java Web Start, as part of the Windows OpenJDK distribution. Mike Piech, vice president, and general manager, Middleware, Red Hat notes, " There is a developer hunger to bring Java into the next generation of development, and Red Hat is a leader in this movement through our involvement in the OpenJDK project. We are helping to lead the way in our efforts to enable users of JDK to have support and innovation in their existing environments. Red Hat remains committed to Java and is excited to have the opportunity to help steward the OpenJDK community." The OpenJDK Transition: Things to know and do Mark Reinhold on the evolution of Java platform and OpenJDK RedHat’s OperatorHub.io makes it easier for Kuberenetes developers and admins to find pre-tested ‘Operators’ for applications.

Yesterday, Google released Android Studio 3.4, the latest version of its integrated development environment (IDE). Version 3.3 was released earlier this year. This release is the continuation of 'Project Marble’, Google’s initiative to improve Android Studio features. Android Studio 3.4 has an updated Project Structure Dialog (PSD). It also replaces Proguard with R8 as the default code shrinker and obfuscator. This release also supports the Android Q Beta and Intellij 2018.3.4. New features in Android Studio 3.4 Project Structure Dialog: This is a new user interface front end to manage Gradle project files. PSD allows developers to see and add dependencies to their project at a module level. Additionally, it displays build variables, suggestions to improve build file configuration etc. New Resource Manager: The resource manager is a new tool to visualize the drawables, colors, and layouts across your app project in a consolidated view. In addition to visualization, the panel supports drag & drop bulk asset import, and bulk SVG to VectorDrawable conversion. R8 replaces Proguard: R8 is now used as the default code shinker for new projects created with Android Studio 3.4. R8 code shrinking helps reduce the size of your APK by getting rid of unused code and resources as well as making your actual code take less space. Additionally, in comparison to Proguard, R8 combines shrinking, desugaring and dexing operations into one step. Import Intentions: Android Studio 3.4 will now recognize common classes in Jetpack and Firebase libraries. It will also suggest, via code intentions, adding the required import statement and library dependency to your Gradle project files. Android Emulator Skin updates and Android Q Beta Emulator System Image: Users can now download Android Q Beta emulator system images for app testing on Android Q. Android Studio 3.4 also includes the latest Google Pixel 3 and Google Pixel 3 XL device skins. Read more about this release on the Android Developers Blog. You can download the latest version of Android Studio 3.4 from the Android download page. Android Studio 3.3 released with support for Navigation Editor, C++ code lint inspections, and more Google announces the stable release of Android Jetpack Navigation Android Q will reportedly give network carriers more control over network devices

Last month, the team at Ruby announced that support for Ruby 2.3 series has ended. Security and bug fixes from the recent Ruby versions won’t be backported to Ruby 2.3. As there won’t be any patches of 2.3, the Ruby team has recommended users to upgrade to Ruby 2.6 or 2.5 as soon as possible. Currently supported Ruby versions Ruby 2.6 series Ruby 2.6 series is currently in the normal maintenance phase. The team will backport bug fixes and will release an urgent fix for it in case of urgent security issue/bug. Ruby 2.5 series Ruby 2.5 series is currently in the normal maintenance phase. The team will backport bug fixes and will release an urgent fix for it in case of urgent security issue/bug. Ruby 2.4 series Ruby 2.4 series is currently in security maintenance phase. The team won’t backport any bug fixes to 2.4 except for security fixes. The team will release an urgent fix for it in case of urgent security issue/bug. The team is also planning to end the support for Ruby 2.4 series by March 31, 2020. To know more about this news, check out the post by Ruby. How Deliveroo migrated from Ruby to Rust without breaking production Ruby on Rails 6.0 Beta 1 brings new frameworks, multiple DBs, and parallel testing Ruby 2.6.0 released with a new JIT compiler

Yesterday, the team behind Qt released the latest version, Qt Creator 4.9.0, a cross-platform software development framework for embedded and desktop applications. This release comes with programming language support, changes to UI, QML support and much more. What’s new in Qt Creator 4.9.0? Language support Qt Creator 4.9 comes with added support for document outline, find usages, and also for code actions that allow the language server to suggest fixes at a specified place in the code. The team has changed the highlighter. It is now based on the KSyntaxHighlighting library, which is used in KDE for this purpose. Changes to UI In this release, the UI for diagnostics from the Clang analyzer tools have been improved as they now are grouped by file now. Diagnostics from the project’s header files are now also included. QML Support The team updated their QML parser to Qt 5.12 that added support for ECMAScript 7. Profiling This release comes with perf, which is a performance profiling tool for software that runs on a Linux system. The integration in Qt Creator is available for applications that run on a local Linux system, and for applications that run on a remote Linux system from a Linux or Windows host. Generic Projects Users can now add a QtCreatorDeployment.txt file to their generic project for specifying the necessary information about where to deploy and which files to deploy. Support for OS For Windows, the team has added support for MSVC (Microsoft Visual C++) 2019. For macOS, a Touch Bar has been added so that users can run Qt Creator on a MacBook. And for Linux, the team has added OpenSSH tools. To know more about this news, check out the Qt blog post. Qt Creator 4.9 Beta released with QML support, programming language support and more! Qt team releases Qt Creator 4.8.0 and Qt 5.12 LTS Qt creator 4.8 beta released, adds language server protocol  

Last month, the team at Eclipse announced that Eclipse now supports Java 12. What are the latest changes in support with Java 12 Updated project compliance and JRE Eclipse comes with project compliance and JRE updated to 12 that changes the current project to be compatible with Java 12. Preview features Users can enable preview features in Java 12 by selecting Preferences > Java > Compiler > Enable preview features option. Users can further configure the problem severity of these preview features. Set enable preview features The issue with the Enable preview features option in preferences has been resolved. Configure problem severity of preview features Configure problem severity is now provided to update the problem severity of preview features in Java 12. Default case has been added Add 'default' option is now available to add a default case to the enhanced switch statement in Java 12. Missing case statements An option to add missing case statements has been provided for the enhanced switch statement in Java 12. Java Editor In the Java > Editor > Code Mining preference, users can now enable the Show parameter names option which will show the parameter name in method or constructor calls. Java views and dialogs An option to control the comment generation while creating module-info.java or package-info.java is now available. To know more about this news, check out the post by Eclipse. Eclipse 4.10.0 released with major improvements to colors, fonts preference page and more Eclipse IDE’s Photon release will support Rust What can Blockchain developers learn from Eclipse Attacks in a Bitcoin network – Koshik Raj

Yesterday, the team at Netlify announced the new Netlify Dev for local testing and live stream preview capabilities. Web developers can now locally test serverless functions, API integrations, and CDN Logic; thus promoting instant progress sharing.. They can now have access to capabilities of the Netlify platform on their laptops which means they no longer have to wait for staging or production to test and get feedback on their websites and applications. Developers can live-stream their development server to a cloud URL and share updates as the code and content changes. In a statement to Business Wire, Kent C. Dodds, software engineer and educator, said, “Netlify has a knack for simplifying things that are hard so I can focus on building my web application, and Netlify Dev is another example of that. “I'm excited about being able to simply develop, test, and debug my Netlify web applications with one simple command.” Netlify has compiled its entire edge redirect engine into WebAssembly so developers can locally test before deploying to production. They can now write and validate AWS Lambda functions in the Netlify CLI using modern JavaScript and also deploy them as full API endpoints. Mathias Biilmann, CEO, said, “Netlify is obsessed with developer productivity for building modern sites on the JAMstack. The new local test and share capabilities of Netlify Dev provide a single, simplified workflow that brings everything together—from the earliest code to production global deployment. Netlify Dev can automatically detect common tools like Gatsby, Hugo, Jekyll, React Static, Eleventy and more. It also provides a single development server and workflow. New and existing users can use Netlify Dev by installing or updating the Netlify CLI for creating new sites, setting up continuous deployment and for pushing new deployments. The new features of Netlify Dev are tightly coupled with Netlify's git-based workflow for team collaboration. Netlify brings an instant CI/CD pipeline for the developers who work in Git so that every commit and pull request can build the site into a deploy preview. Developers can easily build and collaborate in the full production environment. To know more about this news, check out Netlify’s official page. Netlify raises $30 million for a new ‘Application Delivery Network’, aiming to replace servers and infrastructure management Introducing Gitpod, a one-click IDE for GitHub IPv6 support to be automatically rolled out for most Netify Application Delivery Network users  

Last week, Google announced that its Cloud Healthcare API is now available in beta. The API acts as a bridge between on-site healthcare systems and applications that are hosted on Google Cloud. This API is HIPAA compliant, ecosystem-ready and developer-friendly. The aim of the team at Google is to give hospitals and other healthcare facilities more analytical power with the help of Cloud Healthcare API. The official post reads, "From the beginning, our primary goal with Cloud Healthcare API has been to advance data interoperability by breaking down the data silos that exist within care systems. The API enables healthcare organizations to ingest and manage key data and better understand that data through the application of analytics and machine learning in real time, at scale." This API offers a managed solution for storing and accessing healthcare data in Google Cloud Platform (GCP). With the help of this API, users can now explore new capabilities for data analysis, machine learning, and application development for healthcare solutions. The  Cloud Healthcare API also simplifies app development and device integration to speed up the process. This API also supports standards-based data formats and protocols of existing healthcare tech. For instance, it will allow healthcare organizations to stream data processing with Cloud Dataflow, analyze data at scale with BigQuery, and tap into machine learning with the Cloud Machine Learning Engine. Features of Cloud Healthcare API Compliant and certified This API is HIPAA compliant and HITRUST CSF certified. Google is also planning ISO 27001, ISO 27017, and ISO 27018 certifications for Cloud Healthcare API. Explore your data This API allows users to explore their healthcare data by incorporating advanced analytics and machine learning solutions such as BigQuery, Cloud AutoML, and Cloud ML Engine. Managed scalability Google’s Cloud Healthcare API provides web-native, serverless scaling which is optimized by Google’s infrastructure. Users can simply activate the API to send requests as the initial capacity configuration is not required. Apigee Integration This API integrates with Apigee, which is recognized by Gartner as a leader in full lifecycle API management, for delivering app and service ecosystems around user data. Developer-friendly This API organizes users’ healthcare information into datasets with one or more modality-specific stores per set where each store exposes both a REST and RPC interface. Enhanced data liquidity The API also supports bulk import and export of FHIR data and DICOM data, which accelerates delivery for applications with dependencies on existing datasets. It further provides a convenient API for moving data between projects. The official post reads, “While our product and engineering teams are focused on building products to solve challenges across the healthcare and life sciences industries, our core mission embraces close collaboration with our partners and customers.” Google will highlight what its partners, including the American Cancer Society, CareCloud, Kaiser Permanente, and iDigital are doing with the API at the ongoing Google Cloud Next. To know more about this news, check out Google’s official announcement. Ian Goodfellow quits Google and joins Apple as a director of machine learning Google dissolves its Advanced Technology External Advisory Council in a week after repeat criticism on selection of members Google employees filed petition to remove anti-trans, anti-LGBTQ and anti-immigrant Kay Coles James from the AI council  

Facebook AI team announced a new tool, called Aroma, last week. Aroma is a code-to-code search and recommendation tool that makes use of machine learning (ML) to simplify the process of gaining insights from big codebases. Aroma allows engineers to find common coding patterns easily by making a search query without any need to manually browse through code snippets. This, in turn, helps save time in their development workflow. So, in case a developer has written code but wants to see how others have implemented the same code, he can run the search query to find similar code in related projects. After the search query is run, results for codes are returned as code ‘recommendations’. Each code recommendation is built from a cluster of similar code snippets that are found in the repository. Aroma is a more advanced tool in comparison to the other traditional code search tools. For instance, Aroma performs the search on syntax trees. Instead of looking for string-level or token-level matches, Aroma can find instances that are syntactically similar to the query code. It can then further highlight the matching code by cutting down the unrelated syntax structures. Aroma is very fast and creates recommendations within seconds for large codebases. Moreover, Aroma’s core algorithm is language-agnostic and can be deployed across codebases in Hack, JavaScript, Python, and Java. How does Aroma work? Aroma follows a three-step process to make code recommendations, namely, Feature-based search, re-ranking and clustering, and intersecting. For feature-based search, Aroma indexes the code corpus as a sparse matrix. It parses each method in the corpus and then creates its parse tree. It further extracts a set of structural features from the parse tree of each method. These features capture information about variable usage, method calls, and control structures. Finally, a sparse vector is created for each method according to its features and then the top 1,000 method bodies whose dot products are highest are retrieved as the candidate set for the recommendation. Aroma In the case of re-ranking and clustering, Aroma first reranks the candidate methods by their similarity to the query code snippet. Since the sparse vectors contain only abstract information about what features are present, the dot product score is an underestimate of the actual similarity of a code snippet to the query. To eliminate that, Aroma applies ‘pruning’ on the method syntax trees. This helps to discard the irrelevant parts of a method body and helps retain all the parts best match the query snippet. This is how it reranks the candidate code snippets by their actual similarities to the query. Further ahead, Aroma runs an iterative clustering algorithm to find clusters of code snippets similar to each other and consist of extra statements useful for making code recommendations. In the case of intersecting, a code snippet is taken first as the “base” code and then ‘pruning’ is applied iteratively on it with respect to every other method in the cluster. The remaining code after the pruning process is the code which is common among all methods, making it a code recommendation. “We believe that programming should become a semiautomated task in which humans express higher-level ideas and detailed implementation is done by the computers themselves”, states Facebook AI team. For more information, check out the official Facebook AI blog. How to make machine learning based recommendations using Julia [Tutorial] Facebook AI open-sources PyTorch-BigGraph for faster embeddings in large graphs Facebook AI research and NYU school of medicine announces new open-source AI models and MRI dataset

Last week, the team behind ML.NET announced the release of ML.NET 1.0 RC (Release Candidate), an open-source and cross-platform machine learning framework for .NET developers.  ML.NET 1.0 RC is the last preview release before releasing the final ML.NET 1.0 RTM (Requirements Traceability Matrix) this year. Developers can use ML.NET in sentiment analysis, product recommendation, spam detection, image classification, and much more. What’s new in ML.NET 1.0 RC? Preview packages According to the Microsoft blog, “Heading ML.NET 1.0, most of the functionality in ML.NET (around 95%) is going to be released as stable (version 1.0).” The packages that will be available for the preview state are TensorFlow, Onnx components, TimeSeries components, and recommendation components. IDataView moved to Microsoft.ML namespace In this release, IDataView has been moved back into Microsoft. ML namespace based on feedback the team received. Support for TensorFlow models This release comes with added support for TensorFlow models, an open source machine learning framework used for deep learning projects. The issues in ML.NET version 0.11 related to TensorFlow models have been fixed in this release. Major changes in ML.NET 1.0 RC The ‘Data’ namespace has been removed in this release with the help using Microsoft.Data.DataView. The Nuget package has been added for Microsoft.ML.FastTree. Also, PoissonRegression has been changed to LbfgsPoissonRegression. To know more about this release, check out the official announcement. .NET team announces ML.NET 0.6 Qml.Net: A new C# library for cross-platform .NET GUI development ML.NET 0.4 is here with support for SymSGD, F#, and word embeddings transform!A

Today, Sven Efftinge, the Technical Co-founder of Gitpod.io, announced the launch of Gitpod, a cloud IDE that tightly integrates with GitHub. Along with the launch, starting from today, the Gitpod app is also available on GitHub marketplace. What is Gitpod? While working on a project, a lot of time goes into switching contexts between projects and branches, setting up a development environment, or simply waiting for the build to complete. To reduce this time and effort, Gitpod provides developers disposable, ready-to-code development environments for their GitHub projects. What are its advantages? Automatically pre-builts every commit Gitpod, similar to continuous integration tools, automatically pre-builds every commit. So, when you open a Gitpod workspace you will not only find the code and tools ready but also that the build has already finished. Easily go back to previous releases A Gitpod workspace is configured through a .gitpod.yml file written in YAML. This file is versioned with your code, so if at some point, you need to go back to old releases, you can easily do that. Pre-installed VS Code extensions You will get several VS Code extensions pre-installed in Gitpod such as Go support from Microsoft’s own extension. The team plans to add more VS Code extensions in the near future and later developers will be allowed to define any extensions they want. Supports full-featured terminals In addition to supporting one of the best code editors, Gitpod comes with full-featured terminals that are backed by Linux container running in the cloud. So, you get the same command-line tools you would use locally. Better collaboration Gitpod supports two major features for collaboration: Sharing running workspaces: This feature allows you to share a workspace with a remote colleague. It comes handy when you want to hunt down a bug together or do some pair programming. Snapshots: With this feature, you can take an immutable copy of your dev environment at any point in time and share the link wherever you want. Users will receive an exact clone of the environment including all state and even UI layout. How you can use Gitpod? For creating a workspace you have two options: You can prefix any GitHub URL with gitpod.io/#. You can also use the Gitpod browser extension available for Chrome and Firefox users, which adds a button to GitHub that does the prefixing for you. You can watch the following video to know exactly how Gitpod works: https://www.youtube.com/watch?v=D41zSHJthZI Read more in detail on Gitpod’s official website. Introducing git/fs: A native git client for Plan 9 ‘Developers’ lives matter’: Chinese developers protest over the “996 work schedule” on GitHub Sublime Text 3.2 released with Git integration, improved themes, editor control and much more!

On Wednesday, Ori Bernstein, a software engineer at Google, shared details about the Git client he has implemented for Plan 9, a non-posix system. The client named git/fs is implemented in Plan 9 flavor C and comes with tools for writing repository contents. Why git/fs is being introduced? This is the first time someone has implemented a Git client for Plan 9. The upstream Git uses a large number of system calls that are not supported in Plan 9. Bernstein came up with this client to enable working with git repositories without having to clone the git interface directly. Git/fs structure Git/fs provides read-only access to scripts via a file system mounted on ‘/mnt/git’. You will find the following content in ‘/mnt/git’: /mnt/git/object: This includes the objects in the repo. /mnt/git/branch: This includes the branches in the repo. /mnt/git/ctl: This is a file showing the status of the repo. /mnt/git/HEAD: This is an alias for the currently checked out commit directory. You can directly access the repository from the shell using standard tools. The scripts and binaries will manipulate the repository contents directly and the changes done will be immediately mirrored in the filesystem. To improve user experience, the author has put more focus on building a consistent and minimalist interface that supports the necessary functionality. Git/fs does not have any concept of the staging area. There are only three states that files can be in namely, ‘untracked', 'dirty', and 'committed'. To do the tracking it uses empty files under .git/index9/{removed,tracked}/path/to/file. The client is currently hosted in Mercurial, a distributed revision-control tool, as it is the current native plan 9 version control system. To know more detail about Git/fs, head over to its Bitbucket repository. Chris Dickinson on how to implement Git in Rust ‘Developers’ lives matter’: Chinese developers protest over the “996 work schedule” on GitHub Sublime Text 3.2 released with Git integration, improved themes, editor control and much more!  

Pivotal Inc., a software and services firm, announced yesterday that it has teamed up with Heroku to create Cloud Native Buildpacks for Kubernetes and beyond. Cloud Native Buildpacks turn source code into production-ready Docker images that are OCI image compatible and is based around the popular Buildpack model. The new project is aimed at allowing developers to get more productive with Kubernetes. The Cloud Foundry Buildpacks team also released a selection of next-gen Cloud Foundry buildpacks that are compatible with the Cloud Native Buildpacks. This will allow users to try buildpacks out on Pivotal Container Service (PKS) and Pivotal Application Service (PAS). https://twitter.com/pivotalcf/status/1113426937685446657 “The project aims to deliver a consistent platform-to-buildpack contract for use in more places. The interface defined by this contract is informed by learnings from maintaining production-grade buildpacks for years at both Pivotal and Heroku” states the Pivotal team. With the new Cloud Native Buildpacks, you can create containers by just pushing the code without using any runtime dependencies. On “cf” pushing the custom code, buildpacks automatically add in the framework dependencies and create an application “droplet” that can be run on the platform. This droplet model allows Cloud Foundry to handle all the dependency updates. Application runtimes can also be updated by pulling in the latest buildpacks and rebuilding a droplet. Cloud Native Buildpacks expand on this idea and build an OCI (Open Container) image, capable of running on any platform.“We believe developers will love the simplicity of this single command to get a production quality container when they prefer not to author and maintain their own Dockerfile”, states the Pivotal team. Other reasons why Cloud Native Buildpacks are a step ahead than traditional buildpacks: Portability through OCI standard. Cloud Native Buildpacks can directly produce the OCI Images from source code. This makes Cloud Native Buildpacks much more portable, making them easy to use with  Kubernetes and Knative. Better modularity. Cloud Native Buildpacks are modular, offering platform operators more control over how developers can build their code during runtime. Speed. Cloud Native Buildpacks build faster because of advanced build caching, layer reuse, and data deduplication. Fast troubleshooting. Cloud Native Buildpacks helps troubleshoot production issues much faster as they can be used in a developer local environment. Reproducible builds. Cloud Native Buildpacks allow reproducible container image builds. What next? Pivotal team states that Cloud Native Buildpacks need some more work for it to be ready for enterprise scenarios. Pivotal is currently exploring adding three new features such as image promotion, operator control, and automated image patching. For image promotion, Pivotal is exploring a build service effective at image updating. This would allow the developers to promote images through environments, and cross PCF foundations. Also, Pivotal is exploring a declarative configuration model which will deliver new images to your registry whenever your configuration falls out of sync. “The best developers strive to eliminate toil from their lives. These engineers figure that if a task doesn’t add value, it should be automated..with Cloud Native Buildpacks, developers can happily remove.. toil from their jobs”, states Pivtol team. For more information, check out the official Pivotal Blog. CNCF accepts Cloud Native Buildpacks to the Cloud Native Sandbox CNCF Sandbox, the home for evolving cloud native projects, accepts Google’s OpenMetrics Project. Google Cloud hands over Kubernetes project operations to CNCF, grants $9M in GCP credits.

Just two days ago, the team at Fedora announced the release of Fedora 30 Beta to test its six variants including a workstation, server, silverblue, spins, Labs, and ARM. This release comes with GNOME 3.32, improved performance and much more. https://twitter.com/mattdm/status/1113079013696856065 What’s new in Fedora 30 Beta? Desktop environment options This release features two new options for the desktop environment, namely DeepinDE, a user-friendly domestic desktop by Deepin Technology Co. and Pantheon Desktop, mainly used in the elementary OS and is least customizable. Improved DNF performance This release features zchunk format which is a new compression format designed for highly efficient deltas. All the DNF (Dandified YUM) repository metadata is now compressed with the zchunk format in addition to xz or gzip. When Fedora’s metadata is compressed using zchunk, DNF downloads only the differences between earlier copies of the metadata and the current version. GNOME 3.32 This release comes with GNOME 3.32, which is the latest version of GNOME 3. It features updated visual style, improved user interface, icons, and much more. Testing needed Since it is a beta release, users might encounter bugs or experience that some of the features are missing. Users can report issues encountered during testing by contacting the Fedora QA team via the mailing list or in #fedora-qa on Freenode. Updated packages This release includes updated versions of many popular packages including Golang, GNU C Library, Bash shell, Python, and Perl. Major changes Binary support for deprecated and unsafe functions have been removed from libcrypt. Python 2 package has been removed from this release. In this release, language support groups in Comps file has been replaced by rich dependencies in the langpacks package. Obsolete scriplets have been removed from this release. Few users are excited about this release but others are still facing some bugs and dependency issues since it is the beta version. https://twitter.com/YanivKaul/status/1113132353096953857 To know more about this news, check out the official post by Fedora Magazine. GNOME 3.32 released with fractional scaling, improvements to desktop, web and much more Fedora 31 will now come with Mono 5 to offer open-source .NET support Fedora 29 released with Modularity, Silverblue, and more

Last week, Microsoft announced the general availability of F# 4.6 and F# tools for Visual Studio 2019. This release comes with a new record type called Anonymous Records and also few updates in the F# Core library. F# 4.6 and F# tools for Visual Studio 2019 For the updates and development of new features in F# 4.6, the team followed an open RFC process. Writing named record types in F# was not really easy in previous versions and to address exactly that a new type is introduced called Anonymous Records. These F# record types do not have any explicit name and can be declared in an ad-hoc fashion. Updates in F# Core library In the F# Core library, updates are made to the 'ValueOption' type. With this release, a new attribute is added called DebuggerDisplay that helps in debugging. The IsNone, IsSome, None, Some, op_Implicit, and ToString members are added. In addition to these updates, there is now a 'ValueOption' module, which has the same functions the Option module has. F# tools for Visual Studio 2019 A lot of focus has been put on improving the performance of F# tools for Visual Studio, especially for larger solutions. Previously, F# compiler and tools struggled when used for larger solutions and caused a lot of memory and CPU usage. To address this problem the team has done few updates in the F# parser, reduced the cache sizes, significantly reduced the allocations when processing format strings, and more. This release also comes with a new feature that intelligently idents pasted code based on where your cursor is. You can use this feature by turning on Smart Indent via Tools > Options > Text Editor > F# > Tabs > Smart this will be on automatically. Read the entire list of updates in F# 4.6 and F# tools for Visual Studio 2019 on Microsoft’s blog. Microsoft releases TypeScript 3.4 with an update for faster subsequent builds, and more Microsoft, Adobe, and SAP share new details about the Open Data Initiative Microsoft introduces Pyright, a static type checker for the Python language written in TypeScript