Tech News

Yesterday, Microsoft announced in a statement, that their popular Bing search engine was banned in China. This would be Microsoft’s second setback since November 2017, after its Skype internet phone call and messaging service were discontinued from Apple and Android app stores. When users within China’s mainland tried performing a search on Bing’s China website--cn.bing.com--they were redirected to a page which read, the server cannot be reached. Chinese authorities have a firewall that blocks most of the US-based tech platforms including Facebook and Twitter. However, Microsoft has not reported if this outage could be because of the censorship or simply a technical problem. A Microsoft spokesperson said, "We've confirmed that Bing is currently inaccessible in China and are engaged to determine next steps.” Microsoft’s Bing was the only major foreign search engine accessible from within China-built Great Firewall. Bing’s biggest rival, Google shut down its search engine in China in 2010, after rows with the authorities over censorship and hacking. Google CEO Sundar Pichai, said that it has no plans to relaunch a search engine in China. Microsoft, however, has censored search results on sensitive topics, in accordance with government policy. Citing a source, The Financial Times, yesterday, reported that China Unicom, a major state-owned telecommunication company, had confirmed the government order to block the search engine. Also, Cyberspace Administration of China (CAC), a government watchdog, did not respond to faxed questions about Bing’s blocked website. CAC also said that it has also deleted more than 7 million pieces of online information and 9,382 mobile apps. “President Xi Jinping has accelerated control of the internet in China since 2016, as the ruling Communist Party seeks to crack down on dissent in the social media landscape”, the Reuters reported. China Telecom misdirected internet traffic, says Oracle report Bo Weaver on Cloud security, skills gap, and software development in 2019 Microsoft Edge mobile browser now shows warnings against fake news using NewsGuard

Yesterday, the team at NVIDIA released CUDA 10.1 with a new lightweight GEMM library, new functionalities and performance updates to existing libraries, and improvements to the CUDA Graphs APIs. What’s new in CUDA 10.1? Now there are new encoding and batched decoding functionalities in nvJPEG. This release also features faster performance for a broad set of random number generators in cuRAND. In this release, there is improved performance and support for fork/join kernels in CUDA Graphs APIs. Compiler In this release, the CUDA-C and CUDA-C++ compiler, nvcc, are found in the bin/ directory. They are built on top of the NVVM optimizer, which itself is built on top of the LLVM compiler infrastructure. Developers who are willing to target NVVM directly can do so by using the Compiler SDK, which is available in the nvvm/directory. Tools There are new development tools available in the bin/ directory including, few IDEs like nsight (Linux, Mac), Nsight VSE (Windows) and debuggers like cuda-memcheck, cuda-gdb (Linux), Nsight VSE (Windows). The tools also include a few profilers and utilities. Libraries This release comes with cuBLASLt, a new lightweight GEMM library with a flexible API and tensor core support for INT8 inputs and FP16 CGEMM split-complex matrix multiplication. CUDA 10.1 also features selective eigensolvers SYEVDX and SYGVDX in cuSOLVER. Few of the available utility libraries in the lib/ directory (DLLs on Windows are in bin/) are cublas (BLAS), cublas_device (BLAS Kernel Interface), cuda_occupancy (Kernel Occupancy Calculation [header file implementation]), etc. To know more about this news in detail, check out the post by Nvidia. Implementing color and shape-based object detection and tracking with OpenCV and CUDA [Tutorial] ClojureCUDA 0.6.0 now supports CUDA 10 Stable release of CUDA 10.0 out, with Turing support, tools and library changes

The Google AI team in collaboration with DeepMind announced a new and open source “Deep Planning” Network, called PlaNet, last week. PlaNet is an AI agent that learns a world model using only image inputs and further plans with these models to gain experiences. PlaNet can easily solve a variety of image-based control tasks as well as compete with the advanced model-free agents. The Google AI team is also releasing the source code for the research community to further explore and build upon PlaNet. How does PlaNet work? PlaNet depends on a compact sequence of hidden or latent states. This is known called a latent dynamics model where instead of predicting directly from one image to the next image, the latent state forward is first predicted. “By compressing the images in this way, the agent can automatically learn more abstract representations, such as positions and velocities of objects, making it easier to predict forward without having to generate images along the way”, states the Google AI team. In a latent dynamics model, the information of the input images gets integrated into the hidden states with the help of an encoder network. The hidden state then gets further projected forward to predict future images and rewards. For planning, past images are encoded into the current hidden state, and then the future rewards for multiple action sequences are predicted.  PlaNet agents trained on different image-based control tasks PlaNet agents are trained across a variety of image-based control tasks. These tasks pose different challenges such as partial observability, sparse rewards for catching a ball, etc. Moreover, a single PlaNet agent is trained to solve all six tasks. Without any changes to the hyperparameters, this multi-task agent is able to achieve the same mean performance as individual agents. “We advocate for further research that focuses on learning accurate dynamics models on tasks of even higher difficulty, such as 3D environments and real-world robotics tasks. We are excited about the possibilities that model-based reinforcement learning opens up”, states the Google AI team. For more information, check out the official Google AI PlaNet announcement. Google News Initiative partners with Google AI to help ‘deep fake’ audio detection research Google AI releases Cirq and Open Fermion-Cirq to boost Quantum computation Google announces the general availability of a new API for Google Docs

Last month, the Intercept informed that Google is reportedly building a prototype search engine for China called 'Dragonfly' which lead to Google employees pressuring Google to abandon the project on ethical grounds. Google has then appeased their employees stating that the project was simply an exploration and nowhere near completion. Now, there are fresh reports from the Intercept that Google’s custom search engine would link Chinese users’ search queries to their personal phone numbers, thus making it easier for the government to track their searches. This means those who search for banned information could be interrogated or detained if security agencies got hold of Google's search records. According to The intercept, Dragonfly will be designed for Android devices, and would remove content considered to be sensitive by China’s authoritarian Communist Party regime- which includes information about freedom of speech, dissidents, peaceful protest and human rights. Citing anonymous sources familiar with the plan—including a Google whistleblower having "moral and ethical concerns" about Google’s role in censorship, the Intercept revealed that "programmers and engineers at Google have created a custom Android app" which has already been demonstrated to the Chinese government. The finalized version could be launched in the next six to nine months,  after the approval from Chinese officials. What this means to other nations and to Google China has strict cyber surveillance, and the fact that this tech giant is bending to China’s demands is a topic of concern for US legislators as well as citizens of other countries. Last week, in an Open letter to Google CEO Sundar Pichai, the US Senator for Florida Marco Rubio led by a bipartisan group of senators, expresses his concerns over the project being   "deeply troubling" and risks making “Google complicit in human rights abuses related to China’s rigorous censorship regime”. He also requests answers for several unanswered doubts. For instance, what changed since Google’s 2010 withdrawal from China to make the tech giant comfortable in cooperating with China’s rigorous censorship regime. This project is also driving attention from users all over the Globe. Source: Reddit   Google has not yet confirmed the existence of Dragonfly, and has publicly declined to comment on reports about the project. The only comment released to Fox News from a Google spokesperson on Sunday was that it is just doing 'exploratory' work on a search service in China and that it is 'not close to launching a search product.' In protest to this project last month, more than 1,000 employees had signed an open letter asking the company to be transparent. Now, some employees have taken the next step by resigning from the company altogether.  This is not the first time that Google employees have resigned in protest over one of the company's projects. Earlier this year, Project Maven, a drone initiative for the US government that could weaponize their AI research caused a stir among at least a dozen employees who reportedly quit over the initiative. The scrutiny on Google’s take on privacy has continued to intensify. It is about time the company starts  taking into consideration all aspects of a user’s internet privacy. To know more about Project 'Dragonfly', head over to The intercept. Google’s ‘mistakenly deployed experiment’ covertly activated battery saving mode on multiple phones today Did you know your idle Android device sends data to Google 10 times more often than an iOS device does to Apple? Bloomberg says Google, Mastercard covertly track customers’ offline retail habits via a secret million dollar ad deal      

Last week, Google released the latest Android Q security release notes published to the Android Open Source Project (AOSP) security bulletin update. Per this update, there are 193 Android security vulnerabilities in the latest version of Android. These include elevation of privilege, remote code execution, information disclosure and denial of service categories. Two are in the Android runtime, two in the library and 24 in the framework. The Android media framework has 68 vulnerabilities and the Android system has 97. All have been defined with "moderate" severity. These issues Google says are fixed in the default Android 10 patch level of 2019-09-01 on the release of the new OS. “Android Q, as released on AOSP, has a default security patch level of 2019-09-01. Android devices running Android Q and with a security patch level of 2019-09-01 or later address all issues contained in these security release notes,” reads the update. The update specifies that "Google has had no reports of active customer exploitation or abuse of these newly reported issues." At the Google I/O in May, Google had released Android Q beta 3. With this new release, Google announced that Android Q will double down on security and privacy features, such as a Maps incognito mode, reminders for location usage and sharing (such as only when a delivery app is in use), and TLSV3 encryption for low-end devices. Security updates will also roll out faster, updating over the air without a reboot needed for the device. The last Beta update for Android Q was rolled out in August as Beta 6. Other privacy announcements announced for Android Q so far by Google include: Scoped storage: There are new limits on access to files in shared external storage. Device Location: Android Q has a new user option to allow access to device location only while using your app in the foreground Background App Starts: There are new restrictions on launching activities from the background without user interaction Hardware Identifiers: Restrictions on access to device hardware identifiers such as IMEI, serial number, MAC, and similar data Camera And Connectivity: Android 10 has restrictions on access to full camera metadata, and FINE location permission now required for many connectivity workflows. Android has been the target of hackers for a long time. Recently, in July, Check Point researchers reported a new mobile malware attack called ‘Agent Smith’ which infected around 25 million Android devices. This malware is being used for financial gains through the use of malicious advertisements. The malware, concealed under the identity of a Google-related app, exploited known Android vulnerabilities and automatically replaced installed apps with their malicious versions, without any consent of the user. Android Studio 3.4 releases with Android Q Beta emulator, a new resource manager and more. Android Q Beta is now available for developers on all Google Pixel devices Android Q will reportedly give network carriers more control over network devices

If you thought everyone loved cryptocurrency, think again: Apple has banned cryptocurrency mining on iOS devices. In a change that has coincided with WWDC, Apple has quietly updated the terms on the app store. Guidelines now state that "Apps, including any third party advertisements displayed within them, may not run unrelated background processes, such as cryptocurrency mining." First spotted by Apple Insider, this move by Apple actually follows a series of actions by tech companies to tackle a number of issues in the cryptocurrency world. Earlier this year both Google and Facebook banned cryptocurrency ads too - all of which suggests the cryptocurrency bubble might be slowly bursting. There is a slight loophole - Apple's guidelines allow you to mine cryptocurrency provided it's done externally to the device. Cloud mining would be fine. Of course, why you'd want to mine, say, Bitcoim, with your IPhone does seema bit strange. Given users complain about battery life now, the processing power required to mine cryptocurrency will sap your device's life incredibly quickly. Here is the main section of cryptocurrencies in the app store guidelines: 3.1.5 (b) Cryptocurrencies: (i) Wallets: Apps may facilitate virtual currency storage, provided they are offered by developers enrolled as an organization. (ii) Mining: Apps may not mine for cryptocurrencies unless the processing is performed off device (e.g. cloud-based mining). (iii) Exchanges: Apps may facilitate transactions or transmissions of cryptocurrency on an approved exchange, provided they are offered by the exchange itself. (iv) Initial Coin Offerings: Apps facilitating Initial Coin Offerings (“ICOs”), cryptocurrency futures trading, and other crypto-securities or quasi-securities trading must come from established banks, securities firms, futures commission merchants (“FCM”), or other approved financial institutions and must comply with all applicable law. (v) Cryptocurrency apps may not offer currency for completing tasks, such as downloading other apps, encouraging other users to download, posting to social networks, etc.

Today, in a blog post, Dropbox explained how the Prilo system used by the team has automated most of the processes of the company, that were previously manually attended to by Dropbox personnel. Pirlo is used by Dropbox in two main areas- validate and configure network switches and ensure the reliability of servers before entering production. This has, in turn, helped Dropbox to safely manage their physical infrastructure operations with ease. Pirlo consists of a distributed MySQL-backed job queue built by Dropbox itself, using primitives like gRPC, service discovery, and our managed MySQL clusters. Switch provisioning at Dropbox is handled by the TOR STarter which is a Pirlo component. The TOR Starter validates and configures switches in Dropbox datacenter server racks, PoP server racks, and at the different layers of the data center fabric; responsible to connect racks in the same facility together. Server provisioning and repair validation is handled by Pirlo Server Validation. All new servers arriving at the company are validated using this component. Repaired servers are also validated before they are transitioned back into production. Pirlo has automated these manual processes at Dropbox and has led to a reduction in downtime, outages, and inefficiencies associated with the incomplete or erroneous fixing of the systems. By reducing manual work, employees can now focus their attention to more value adding jobs. Before using Pirlo, the above tasks had to be performed by operations engineers and subject matter experts who used various server error logs to take appropriate actions to fix failed hardware. After applying the remediation actions, the engineer would send the machine back into production by sending the server to Dropbox re-imaging system. If the remediation actions didn’t fix the system or properly prepare it for re-imaging, the server would be sent back to the operations engineer for additional fixing. This would end up consuming a lot of the operation engineer's time as well as company resources. Operating engineers who used Pirlo system steadily increased their output by 40+%. The automation of manual tasks allowed engineers to address more issues in the same amount of time. You can head over to Dropbox’s official blog to explore the workings of Pirlo and how it benefited the organization. How to navigate files in a Vue app using the Dropbox API Tech jobs dominate LinkedIn’s most promising jobs in 2019 NGINX Hybrid Application Delivery Controller Platform improves API management, manages microservices and much more!

Imagine a visit to a store when you end up forgetting what you wanted to purchase, but the screen at front tells you all about your preferences. Before you struggle to pick a selection, the system tells you what you could possibly try and which items you could end up purchasing. All this is happening in the malls of China, thanks to artificial intelligence. Thanks to the Alibaba Group which is on a mission to revive the offline retail market. Ever since inception in 2009, the Singles Day festival has been considered the biggest opportunity for shopping in China. There was no better occasion for Alibaba to test its artificial intelligence product FashionAI. And this year, the sales zoomed. Alibaba’s gross merchandise volume gained a mammoth 25 billion dollars on Nov. 11, breaking its last year’s gross figure of $17.8 billion by a considerable margin. Majorly attributed to the FashionAI initiative. At a time when offline retail is in decline all across the world, Alibaba’s Fashion AI could single-handedly reinvent the market. It will drag you back to the malls. When the tiny sensors embedded in the cloth you just tried suggest you all ‘matching’ items, you do not mind visiting the stores with an easy to use recognizable interface at front that saves you from all the old drudgeries in retail shopping! The FashionAI screen interface uses machine learning for its suggestions, based on the items being tried on. It extends the information stored into the product tag to generate recommendations. Using the system, a customer can try clothes on, get related ‘smart’ suggestions from the AI, and then finalize a selection on the screen. But most importantly, the AI assistant doesn’t intend to replace humans with robots. It instead integrates them together to deliver better service. So when you want to try something different, you can click a button and a store attendant will be right there. Why are we cutting down on human intervention then? The point is, it is nearly impossible for a human staff to remember the shopping preferences of each customer, whereas an artificial intelligence system can do it with scale. This is why researchers thought to apply deep learning for real world scenarios like these. Unlike the human store attendant who gets irked with your massive shopping tantrums in terms of choices, the AI system has been programmed to rather leverage the big data for making ‘smart’ decisions. What more, it gets better with time, and learns more and more based on the suggested inputs from surroundings. We could say FashionAI is still an experiment. But Alibaba is on course to create history, if the Chinese e-commerce giant succedes in fueling life back into retail. As CEO Daniel Zhang reiterated, Alibaba is going to “digitize the offline retail world.” This is quite a first for such a line of thinking. But then, customers don’t recognize offline and online till it serves their interest.

Google Cloud launched new Blockchain tools for developers on Monday, as a result of a collaboration with a DLT (distributed ledger technology) startup to help developers easily build apps. Google seems to take inspiration from AWS. Amazon’s cloud company partnered with Hyperledger Fabric earlier this year, introducing Blockchain templates which, allow developers to launch Ethereum apps without needing to write all the code required to create a smart contract. Digital Asset, a Blockchain platform services run by former JPMorgan executive Blythe Masters, will provide a software development kit to developers working on Google cloud. Along with this, Digital Asset Modeling Language (DAML), a platform-as-a-service (PaaS) program will also be made available to developers. The DAML PaaS program is now available through Google Cloud’s Orbitera Application Marketplace. According to Leonard Law, Head of Financial Services Platform at Google Cloud, “DLT has great potential to benefit customers not just in the financial services industry, but across many industries, and we’re excited to bring these developer tools to Google Cloud.” Blythe Masters, CEO of Digital Asset, also mentioned that the company is partnering with Google Cloud to provide a full stack solution to developers “so they can unleash the potential for web-paced innovation in Blockchain”. This, in turn, will help developers and organizations overcome some of the most common technical barriers to DLT application development today. With the arrival of the new Blockchain toolkit, developers will be able to easily manage the distributed systems for financial applications, games, etc. Oracle makes its Blockchain cloud service generally available Blockchain can solve tech’s trust issues – Imran Bashir  

Last week Microsoft announced the release of Python Language Server which is a part of the July release for Python Extension for Visual Studio Code and will be released as a standalone product in the near future. Intellisense, Microsoft’s code analysis, and suggestion tool have been supporting Python since 2011, but this language support can now be extended to other tools using the Microsoft Language Server. Intellisense and Language Server Demystified IntelliSense is the general term for a number of features like List Members, Parameter Info, Quick Info, and Complete Word. These features help developers to learn more about the code they are using and to keep track of the parameters. With Intellisense, Microsoft has long featured the completion feature that makes writing code faster and less error-prone. Many aspects of IntelliSense are language-specific and many of its features are powered by a language server. Adding all these smart features in IntelliSense takes massive efforts and traditionally this effort is repeated for each development tool, as each tool provides different APIs for implementing the same feature. This effort can be significantly reduced with the help of a language server, as they provide these language-specific features to different tools with the help of a standard protocol known as Language Server Protocol (LSP). This way, a single Language Server can be re-used in multiple development tools, which in turn can support multiple languages with minimal effort. Benefits of the Python Language Server Python IntelliSense has been supported in Visual Studio since 2011 and is one of the most downloaded extensions, but only limited to Visual Studio developers. The Visual Studio team at Microsoft plan to separate the Python IntelliSense from Visual Studio and make it available as a standalone program using the language server protocol. Steve Dower, a developer at Microsoft, wrote in his blog that “Having a standalone, cross-platform language server means that we can continue to innovate and improve on our IntelliSense experience for Python developers in both Visual Studio and Visual Studio Code at the same time”. The July release of Visual Studio Codes Python extension includes features such as: Syntax errors will appear as the code is typed Improved performance for analyzing workspaces and presenting completions The ability to detect syntax errors within the entire workspace Faster startup times and imports Better handling for several language constructs The standalone release of the Python Language Server will be released in a few months, till then you can check out VS Code release announcement for more information. Microsoft’s GitHub acquisition is good for the open source community Microsoft launches a free version of its Teams app to take Slack head on Microsoft’s Brad Smith calls for facial recognition technology to be regulated

Later last week, Red Hat announced that RHEL has deprecated KDE (K Desktop Environment) support. KDE Plasma Workspaces (KDE) is an alternative to the default GNOME desktop environment for RHEL. Major future release of Red Hat Enterprise Linux will no longer support using KDE instead of the default GNOME desktop environment. In the 90’s, the Red Hat team was entirely against KDE and had put lots of effort into Gnome. Since Qt was under a not-quite-free license that time, the Red Hat team was firmly behind Gnome. Steve Almy, principal product manager of Red Hat Enterprise Linux, told the Register, “Based on trends in the Red Hat Enterprise Linux customer base, there is overwhelming interest in desktop technologies such as Gnome and Wayland, while interest in KDE has been waning in our installed base.” Red Hat heavily backs the Linux desktop environment GNOME, which is developed as an independent open-source project. Also, it is used by a large bunch of other distros. Although Red Hat is indicating the end of KDE support in RHEL, KDE is very much its own independent project that will continue on its own, with or without support from future RHEL editions. Almy said, “While Red Hat made the deprecation note in the RHEL 7.6 notes, KDE has quite a few years to go in RHEL's roadmap.” This is simply a warning that certain functionality may be removed or replaced from RHEL in the future with functionality similar or more advanced to the one deprecated. Though KDE, as well as anything listed in Chapter 51 of the Red Hat Enterprise Linux 7.6 release notes,  will continue to be supported for the life of Red Hat Enterprise Linux 7. Read more about this news on the official website of Red Hat. Red Hat released RHEL 7.6 Red Hat Enterprise Linux 7.5 (RHEL 7.5) now generally available Red Hat Enterprise Linux 7.6 Beta released with focus on security, cloud, and automation

Tableau team has announced a new version of Tableau Prep 2018.2.1 with lots of new features for easy enterprise deployments and more data connector options made available to the customers. This update has lots of user experience improvements focused on helping you manage data more efficiently and easily. Tableau Prep is a brand-new product introduced in April 2018, which is specifically designed to quickly combine, shape and clean data to perform easy-to-complex data analysis tasks. This product helps everyone with an appealing visual experience which makes data prep easier. It is seamlessly integrated with the existing Tableau analytical workflow, thus delivering a smart experience to go from data prep to analysis. Some of the noteworthy changes available in Tableau Prep 2018.2.1 are: Activate and deactivate Tableau Prep from the command line Now you have new command line options to deploy Tableau Prep easily on the hundreds to thousands of machines in an enterprise environment. You can use a key management system and use silent activation and deactivation capabilities just like in Tableau Desktop. In order to learn more about the feature, you can refer to the Deploy Tableau Desktop blog post.   Virtual desktop support to optimize Tableau Prep installations Just like Tableau Desktop, the new virtual desktop support allows you to optimize Tableau prep installations for non-persistent virtual desktops. Tableau-hosted Authorization to Run (ATR) service can be used to automatically deactivate Tableau Prep licenses after a predetermined period of time. In order to configure this option, refer to Configure Virtual Desktop Support blog post. Union summary to quickly align fields You often need to align or merge fields that represent the same data with different names especially while combining data from multiple sources. With this new feature, Tableau Prep offers a summary of the mismatched fields. It automatically recommends potential matches based on attributes such as similar data types and field names, thus making it quicker to align your data. Use ISO-8601 date parts As per the Idea forums, many customers, particularly those in Europe, wanted an easier approach to extract ISO-8601 date parts, especially for week numbers. With a new native support in the calculation language, you can get the required data you want by creating a simple calculation - [Week Number] = DATEPART("iso-week",[Order Date]. Thus you don’t need to write any complex date calculations which were previously involved. Group and filter data values New techniques to easily group and filter your data are now available. You just need to select the field values and right-click to group them or select a group and right-click to ungroup the field values. You can create new filters based on wildcard matches without writing a calculation. New data connectors New support has been added to the following connectors to help you connect to various cloud data sources and Hadoop Hive: MapR Hadoop Hive Apache Drill SparkSQL Snowflake Amazon EMR Hadoop Hive Cloudera Hadoop (Hive and Impala) Hortonworks Hadoop Hive In order to learn more about how to connect Tableau Prep to your data, see the Supported Connectors blog post. These are the key new features offered by the Tableau Prep 2018.2.1 version. However, there are more updates and user experience improvements. To find a complete list of new features, please refer to the what’s new blog post. A tale of two tools: Tableau and Power BI “Tableau is the most powerful and secure end-to-end analytics platform”: An interview with Joshua Milligan Visualizing BigQuery Data with Tableau

Fedora has always been shipping Mono 4.8, the open source development platform for building cross-platform applications, with each Fedora release. Even after shipping Mono 5.0 in May 2017, the company still continued with Mono 4.8. But it seems the idea will be changing now with the release of Fedora 31. With Fedora 31, the team at Fedora is finally planning to switch to Mono 5.20 which is expected to release later this year. An effort was made in the past few months by the Fedora team to build Mono from source. The build was also done for Debian using msc instead of csc and the reference assemblies were rebuilt from source. In case of Mono, it requires itself to build. The Mono version 4.8 which is included in Fedora currently, is too old to build version 5.20. Currently, the team has been using monolite and a little version of mono compiler, .NET 4.7.1 reference assemblies for first build time. The sources for the required patch files are maintained on Github. The transition from Mono 4 to Mono 5 was on halt because of the changes required in their compiler stack and its dependency upon some binary references. These binaries are available as a source but treated as pre-compiled binaries for simplification and speed. The Fedora developers are now working towards getting Mono 5 into Fedora 31. This will also let the cross-platform applications that are relying upon Microsoft's .NET framework 4.7 and later to now work. Mono 4.8 is also not compatible for PowerPC 64-bit but it is expected that Mono 5 will be. To know more about this news, check out the change proposal. Fedora 29 released with Modularity, Silverblue, and more Swift is now available on Fedora 28 Fedora 29 beta brings Modularity, GNOME 3.30 support and other changes

IPython team released version 7.0 of IPython, yesterday. IPython is a powerful Python interactive shell with features such as advanced tab completion, syntactic coloration, and more. IPython 7.0 explores new features such as AsyncIO integration, new Async libraries, and Async support in Notebooks. IPython (Interactive Python) provides a rich toolkit for interactive computing in multiple programming languages. It’s the Jupyter kernel for Python used by millions of users. Let’s discuss the key features in IPython 7.0 release. AsyncIO Integration IPython 7.0 comes with the integration of IPython and AsyncIO. This means that you don’t have to import or learn about asyncIO anymore. AsyncIO is a library which lets you write concurrent code using the async/await syntax. The asyncIO library is used as a foundation for multiple Python asynchronous frameworks providing high-performance network, web-servers, database connection libraries, distributed task queues, etc. Just remember that asyncIO is an async function, it won’t magically make your code faster but will make it easier to write. New Async Libraries (Curio and Trio integration) Python consists of keywords async and await. This helps simplify the use of asynchronous programming and the standardization around asyncIO. It also allows experimentation with the new paradigms for asynchronous libraries. Now, two new Async Libraries namely Curio and Trio, have been added in IPython 7.0. Both of these libraries explore ways to write asynchronous programs. They also explore how to use async, awaits, and coroutines when starting from a blank slate. Curio is a library which helps perform concurrent I/O and common system programming tasks. It makes use of the Python coroutines and the explicit async/await syntax. Trio is an async/await-native I/O library for Python. It lets you write programs that do multiple things at the same time with parallelized I/O. Async support in Notebooks Async code will now work in a notebook when using ipykernel for Jupyter users. With IPython 7.0, async will work with all the frontends that support the Jupyter Protocol, including the classic Notebook, JupyterLab, Hydrogen, nteract desktop, and nteract web. The default code will run in the existing asyncIO/tornado loop that runs the kernel. For more information, check out the official release notes. Make Your Presentation with IPython How to connect your Vim editor to IPython Increase your productivity with IPython

GitLab 11.3 was released on Saturday with support for Maven repositories, Code Owners, Protected Environments and other changes. These new added features help in automation of controls around environments and code while also providing further efficiencies for Java developers. Maven repositories in GitLab 11.3 Maven repositories are now directly available in GitLab. This gives Java developers a secure, standardized way to share version control in Maven libraries. It also saves time by reusing these libraries across projects but it is available only on GitLab premium. Lower-level services can now have their packaged libraries published to their project’s Maven repository. They can share a simple XML snippet with other teams to utilize the library while Maven and GitLab do the rest. Code owners and protected environments GitLab Starter now supports assignment of Code Owners to files indicating the appropriate team members contributing to the code. This is a primer for future releases, which will enforce internal controls at the code level. Operators can also use Protected Environments for setting permissions to determine which users can deploy code to production environments. This significantly reduces the risk of an unintended commit. This feature is also available only on premium. Epic forecasting with integrated milestone dates The new Portfolio Management feature in GitLab Ultimate forecasts an epic's start and end dates automatically based on the milestone dates of its issues. Portfolio managers will be able to compare their planned start and end dates against the scheduled work enabling faster decisions on delivery and plan adjustments. In older versions, fixed values could be set for the planned start and end dates of an epic. This was useful for high-level planning of epics. However, as issues are attached to the epic and the issues are scheduled for work with actual milestones, it is useful to have epic dates reflecting those milestones. In this version, the static values for the dates can be changed to a dynamic value called ‘From milestones’. The dynamic version of epic planned end dates are analogous. This is a useful feature to have if you want seamless transition from high-level, top-down planning to micro-level, and bottom-up planning. For more information, visit the GitLab website. GitLab raises $100 million, Alphabet backs it to surpass Microsoft’s GitHub Gitlab 11.2 releases with preview changes in Web IDE, Android Project Import and more GitLab is moving from Azure to Google Cloud in July