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Elon Musk revealed yesterday that Tesla is developing its own hardware in its bid to bring self-driving cars to the public. Up to now Tesla has used Nvidia's Drive Platform, but this will be replaced by 'Hardware 3,' which will be, according to Tesla at least, the 'world’s most advanced computer for autonomous driving.’ The Hardware 3 chip has been in the works for a few years now, with Jim Keller joining Tesla from chip manufacturer AMD back in 2016, and Musk confirming the project in December 2017. Keller has since left Tesla, and the Autopilot project - Tesla's self-driving car initiative - is now being led by Pete Bannon. "The chips are up and working, and we have drop-in replacements for S, X and 3, all have been driven in the field," Bannon said. "They support the current networks running today in the car at full frame rates with a lot of idle cycles to spare." Why has Tesla developed its own AI hardware? By developing its own AI hardware, Tesla is able to build the solutions tailored to its needs. It means it isn't relying on others - like Nvidia, say - to build what they need. Bannon explained "nobody was doing a bottoms-up design from scratch." By bringing hardware in-house, Tesla will not only be able to develop chips according to its needs, it will also make it easier to plan and move at its own pace. Essentially, it allows Tesla to take control of its own destiny. In the context of safety concerns around safe-driving cars, taking on responsibility for developing the hardware on which your machine intelligence will sit makes a lot of sense. It means you can assume responsibility for solving your own problems. How does Tesla's Hardware 3 compare with other chips? The hardware 3 chips are, according to Musk, 10x better than the current Nvidia GPUs. The current GPUs in Tesla's Autopilot system can analyze 200 frames per second. Tesla's new hardware can run on 2000 frames per second. This significant performance boost should, in theory, bring significant gains in terms of safety. What's particularly remarkable is that the new chip isn't actually costing Tesla any more than its current solution. Musk explained how the team was able to find such significant performance gains. "The key is to be able to run the neural network at a fundamental, bare metal level. You have to do these calculations in the circuit itself, not in some sort of emulation mode, which is how a GPU or CPU would operate. You want to do a massive amount of [calculations] with the memory right there.” The hardware is expected to roll out in 2019 and offered as a hardware upgrade to all owners of Autopilot 2.0 cars and up. Read next Nvidia Tesla V100 GPUs publicly available in beta on Google Compute Engine and Kubernetes Engine DeepMind, Elon Musk, and others pledge not to build lethal AI Elon Musk’s tiny submarine is a lesson in how not to solve problems in tech

GnuPG developers have recently begun working on Sequoia, a new OpenPGP implementation in Rust. OpenPGP is an open, free version of the Pretty Good Privacy (PGP) standard. It defines standard formats for emails and other message encryption and is based on the original PGP (Pretty Good Privacy) software. Sequoia is an OpenPGP library that provides easy-to-use cryptography for applications. It helps you protect the privacy of your users and is easy to incorporate into your application, no matter what language you use. It helps you manage your keys better as its keystore stores keys and updates them so that new keys or revocations are discovered in a timely manner. It is currently in development led by three former GnuPG developers, Neal H. Walfield, Justus Winter, and Kai. The project is funded by the  p≡p foundation, where each of the aforementioned developers has been working since fall 2017. What motivated the developers for this new implementation was their experience with GnuPG, a free software replacement for Symantec's PGP cryptographic software. PGP or Pretty Good Privacy is a program which is used to encrypt and decrypt texts, emails, files, directories, etc. to increase the security of data communications. According to Neal H. Walfield, GnuPG posed several problems as “it is hard to modify due to lack of unit tests and tight component coupling”. He also mentioned other reasons like how a lot of developers are unsatisfied with GnuPG’s API and that GnuPG can’t be used on iOS due to GPL. The developers also have major social and technical goals in mind for Sequoia. “The social goals are -- to create an inclusive environment in our project, it should be free software and -- community-centered,” says Neal. Here’s the video of Neal introducing the new OpenPGP library:  Sequoia  On the technical side, the team is taking a different approach. They are putting the library API first, and a command-line interface tool, second. Neal says that the team “encourages” the users to use the library. They also aim to create an API which is friendly, easy to use and supports all modern platforms such as Android, iOS, Mac, etc. Let’s have a look at how Sequoia is built. Starting at the bottom level, we have the OpenPGP library which provides the low-level interface. There are two services built on top of this library, namely, Sequoia network service ( helps with accessing keyservers) and Sequoia-store which is used for accessing and storing the public keys along with the private keys.    Architecture of Sequoia On top of these three, there is a Sequoia library, a high-level API. If it’s a rust application, then it can use this library directly or else it can access the library via FFI ( foreign function interface). Apart from this, the vision for Sequoia is “a nice OpenPGP implementation -- with focus on user development, and its community” says Neal. For more information on Sequoia, check out the official Sequoia documentation. Will Rust Replace C++? Mozilla is building a bridge between Rust and JavaScript Perform Advanced Programming with Rust

Plotly released Dash DAQ, a modern UI component library, which helps with data acquisition in Python, earlier this week. A data acquisition system (DAQ) helps collect, store, and distribute information. Dash DAQ is built on top of Plotly’s Dash (a Python framework used for building analytical web applications without requiring the use of JavaScript). Dash DAQ consists of 16 components. These components are used for building user interfaces that are capable of controlling and reading scientific instruments. To know more about each of their usage and configuration options, check out the official Dash DAQ components page. You can use Dash DAQ with Python drivers which are provided by instrument vendors. Alternatively, you can also write your own drivers with PySerial, PyUSB, or PyVISA. Dash DAQ is priced at $1980 as it is built with research labs in mind and is not suited currently for general python users. To install Dash DAQ, you have to purchase it first. After you make the purchase, a download page will automatically appear via which you can download it. Only one Dash DAQ library is allotted per developer. Here are the installation steps as mentioned in the official Dash DAQ installation page. Multiple apps of different variety have already been made using Dash DAQ. Here are some of the examples: Wireless Arduino Robot in Python, an app that wirelessly controls Sparki, an Arduino-based robot. Dash DAQ. Using Dash DAQ  for this app gives it clean, intuitive and virtual controls to build GUIs for your hardware. Robotic Arm in Python, an app that allows you to operate Robotic Arm Edge. Dash DAQ’s GUI components allow you to interface with all the robot’s motors and LED. Users can even do it via their mobile device, thereby enjoying the experience of a real remote control! Ocean Optics Spectrometer in Python, an app which allows users to interface with an Ocean Optics spectrometer. Here Dash DAQ offers interactive UI components which are written in Python allowing you to read and control the instrument in real-time. Apart from these few examples, there are a lot more applications that the developers at Plotly have built using Dash DAQ. plotly.py 3.0 releases 15 Useful Python Libraries to make your Data Science tasks Easier  

Uber has revamped their monolithic framework design with a brand new Fusion.js web framework. This plugin-based open source web framework makes it easy to develop lightweight and high-performing apps. Fusion.js is a JavaScript framework that comes with modern features like hot module reloading, data-aware server-side rendering, and bundle splitting support. It also supports popular libraries like React and Redux. Why was Fusion.js required? Uber has been in the app-development business for quite some time now. With the quickly changing web technologies, they wanted to build a high quality framework with modern features which also kept up with the dynamic nature of their web platform. Specifically, they wanted their new framework to address the following pain points: Complex configuration and required boilerplate of multiple tools needed for server-side rendering, code splitting, and hot module reloading Lack of good abstractions for implementing and sharing features that involve different aspects of server-rendered React applications Tight coupling of code located in different places Testing difficulties arising from side effects and singletons Lack of flexibility of a monolithic framework Fusion.js addresses all of these problems. What are the benefits of using Fusion.js? On top of the benefits of a pre-configured, optimized boilerplate, Fusion.js also provides a flexible plugin-based architecture which makes it appropriate for building single-page applications and web apps that depend on complex service layers to meet quality requirements. Fusion.js applications allow apps to have a single entry point file and it’s possible to reuse code on both the server and browser. The single entry point architecture allows plugin developers to co-locate snippets of code based on the library the code pertains to. Plugins use dependency injection so they can expose well-defined APIs as services to other plugins, and a plugin’s dependencies can easily be mocked during tests. For middleware management, Uber uses Koa, which provides a unit-test friendly context-based API. It has a lightweight abstraction for request lifetime management based on the concept of downstreams and upstreams. Fusion.js provides testing tools for developers to test plugins in addition to supporting modern testing tools such as Jest, Enzyme, and Puppeter. The fusion-test-utils package allows mocking the server itself, making it possible to quickly run integration tests between plugins and mocks. For now, over 60 repositories of Uber are using Fusion.js since its internal release. Next in the roadmap are the addition of more performance optimizations, test-oriented tooling, and better flow support. You can checkout the documentation and Github for further information. Masonite 2.0 released, a Python web development framework Is web development dying? Meet Sapper, a military grade PWA framework inspired by Next.js

Apple announced its financial earnings for fiscal 2018 third quarter yesterday. The Company hit revenue of $53.3 billion, seeing an increase of 17 percent from the last year’s quarter revenue of $45.4 billion, making it company’s seventh consecutive quarter of “accelerating growth”. Also, Apple generated earnings per share of $2.34, which is an increase of 40% from last year. These values beat analysts expectations of $52.31 billion in revenue and $2.18 earnings per share. As you can tell from the chart below, its stock has reached an all-time high of $190.29.  Apple stock report Over the earnings call, Tim Cook, CEO of Apple said: “We’re thrilled to report Apple’s best June quarter ever, and our fourth consecutive quarter of double-digit revenue growth”. He also added that “Our Q3 results were driven by continued strong sales of iPhone, Services, and Wearables, and we are very excited about the products and services in our pipeline.” Apple’s other financial highlights of Q3 2018: Here are the figures, as reported by Luca Maestri: Net income is $11.5 billion this quarter which is up $2.8 billion, or 32% up as compared to last year. Diluted earnings per share were $2.34 which is up 40% from last year’s $1.67. Cash flow from operations is $14.5 billion, a high from last year’s $12.5 billion. iPhone revenue was 20% year over year, with iPhone ASP increasing to $724 from $606 a year ago. During the quarter, 41.3 million iPhones got sold with double-digit unit growth in several mature markets. Services hit an all-time record revenue of $9.5 billion dollars, a 31% higher than last year’s $7 billion. Macs hit double-digit year over year growth with nearly 60% of purchases during the quarter coming from customers who are new to Mac. Wearables ( Apple Watch, AirPods, Beats ) revenue exceeded $10 billion over the last four quarters, a remarkable accomplishment for a set of products that have only recently entered the market.   Apple data summary Among these, the top three highlights for the company this quarter, as mentioned by Tim, are iPhone revenue, services revenue, and Wearables revenue. According to Tim,“iPhone had a very strong quarter --  active installed base grew by double digits, driven by switchers, first-time smartphone buyers, and our existing customers -- iPhone X was the most popular iPhone in the quarter -- with a customer satisfaction score of 98%”. Now, talking about services, paid subscriptions from Apple and third parties have surpassed 300 million which is an increase of more than 60% last year. Revenue from subscriptions played an important role in increasing the percentage of its overall services business. With almost 30,000 apps available in the app store now, the number of apps offering subscriptions also continue to grow. Other services such as Apple music, cloud services, Apple pay, Apple News, Facetime calls are also going strong these days. And third highlight, Wearables revenue “was up over 60% year over year with growth accelerating from the March quarter”. Other than that, Apple had organized a developers conference in June which included a preview of major advances coming soon to Apple’s four operating systems: iOS, macOS, watchOS, and tvOS. Tim said over the call that “Developer and customer reaction has been very positive -- we have over 4 million users participating in our new OS beta programs”. Apple’s guidance for its upcoming September Q4 2018 Following are the guidance figures as reported by Luca Maestri: Expected Revenue: Between $60 billion to $62 billion Expected gross margin: between 38 percent and 38.5 percent Expected operating expenses: between $7.95 billion and $8.05 billion Other income/(expense): $300 million Tax rate: approximately 15% before discrete items Apple is constantly taking initiatives to make sure that its current state continues to grow. In fact, major additions are being made to iOS 12 as Tim mentioned that “Siri will take a major step forward with Siri shortcuts which will deliver a new, much faster way to get things done and allow any app to work with Siri”. Other features like Activity Reports and Screen Time in iOS 12 will enable parents to monitor their children's activity from their own iOS devices. With macOS Mojave, new apps like News, Stocks, Voice Memos, and Home will be available on the Mac for the first time. He also added that “Developers will be able to build even more intelligent apps -- using the power of machine learning with CoreML and CreateML. With ARKit 2, iOS 12 will provide an even more powerful platform to make dynamic AR apps, integrating shared and persistent AR experiences, object detection, and image tracking”. The earnings result speaks highly of how Apple’s strategies and efforts have worked for them. Apple’s current market capitalization is worth $956.48 billion, which makes the tech giant only 7% away from becoming the first trillion-dollar company. Apple changes app store guidelines on cryptocurrency mining Apple USB Restricted Mode: Here’s Everything You Need to Know  

Security over the web via passwords can be crucial as passwords are hard to memorize, easy to forget and can be easily phished or cracked. However, Microsoft Edge has recently made dealing with passwords a lot easier by introducing the Web Authentication specification. This new feature allows an improved and a more secure user experience along with a passwordless experience on the web. Using Web Authentication, Edge users can now sign in with their face, fingerprint, PIN, or portable FIDO2 devices. These methods leverage strong public-key credentials instead of passwords. Why go passwordless? Many users might still be skeptical of moving onto these methods. On the other hand, we allow most of the online websites (shopping, food ordering websites, and so on) to store our credit card numbers, our other sensitive information without any investigation. These credentials are hidden using just passwords; an outdated security model which can be easily hacked. Microsoft aims for a secure and passwordless experience on the web via advanced methods such as Windows Hello biometrics and creation of Web Authentication, an open standard for passwordless authentication. How does Web authentication work? Windows Hello allows users to authenticate without a password on any Windows 10 device. They can make use of biometrics like face and fingerprint recognition to log in to websites by a simple glance or use a PIN number to sign in. External FIDO2 security keys also work for authentication with a removable device and the user’s biometrics or PIN. There are still some websites which do not offer a complete passwordless model yet. For such websites, backward compatibility with FIDO U2F devices can act as a strong enough secondary security besides the password. At the RSA 2018 conference, Microsoft discussed how APIs shall be used to approve a payment on the web via one’s facial identity. To get started with Web Authentication in Microsoft Edge, one can install Windows Insider Preview build 17723 or higher to try out the updated feature. Read more about this feature on the Microsoft Web Authentication guide. Web Security Update: CASL 2.0 releases! Amazon Cognito for secure mobile and web user authentication [Tutorial] Oracle Web Services Manager: Authentication and Authorization

After Kotlin had released 1.2 nearly six months ago, the official language for Android finally announced the first release candidate of their next major version: ‘1.3 M1’. This new release comes with plenty of features most notably coroutines, new experimental unsigned arithmetic, and much more. Coroutines are no longer a pipe dream Coroutines have been in the experimental stage since they were first introduced in Kotlin 1.1. Kotlin 1.3 brings Coroutines to limelight as JetBrains’ Ilya Gorbunov explained in his blog post announcing Kotlin 1.3-M1. Coroutines simplify asynchronous programming by putting the complications into libraries. With coroutines, developers can express the logic of the program sequentially and the library will take care of the asynchrony. What’s more interesting is that with Kotlin coroutines developers can call asynchronous mechanisms from different programming languages into their own program as libraries. For example  async/await from C# and ECMAScript, channels and select from Go, and generators/yield from C# and Python. The Kotlin team has significantly improved the features surrounding coroutines since they were first introduced, some notable features include support callable references to suspending functions and serializability of all coroutine-related classes. But that’s not it, the Kotlin team plan to add a multiplatform version of the coroutine API which will support iOS through Kotlin/Native. Experimental New Features The technique of introducing experimental features in a release version helps the development team to gather valuable feedback from the community and implement their suggestions in the future releases, and that is exactly what the the Kotlin team has been doing. We saw a similar implementation with Coroutines in the previous version, and now the team is introducing a plethora of new experimental features such as inline classes, unsigned integer types, and annotations for marking an experimental API and opting-in for it. In order to use these experimental features in projects, developers would have to explicitly choose an opt-in flag. Without an opt-in, usage of a feature will be marked as warnings or errors. Other small features in Kotlin 1.3 While the bigger new features have been released as experimental there are numerous small features which have been released in this new version. Capturing when subject in a variable Capturing the subject of when expression in a variable is now supported in Kotlin language. This feature was one of the most popular feature request in Kotlin team’s issue tracker. This addition makes the response variable properly scoped within when expression, and makes the logic flow in a more expression-like fashion. @JvmStatic and @JvmField in companions of interfaces Kotlin interfaces can now expose static members (fields and methods) to Java clients. However, to use @JvmField on interface companion properties, all of them must be public final vals and all of them must be annotated. Also, the -jvm-target compiler option must be at least 1.8. Nested declarations in annotation classes Before 1.3, annotation classes could not have bodies in Kotlin. This release relaxes this limitation, allowing them to have nested classes, interfaces, and objects including a companion object. These are just a select set of features that make up for the entire release announcement of Kotlin 1.3. If you are interested in checking out the entire feature-set including the new APIs that are available in the standard library and the breaking changes, you can refer to JetBrain’s official blog. Kotlin/Native 0.8 recently released with safer concurrent programming 4 operator overloading techniques in Kotlin you need to know Building RESTful web services with Kotlin

After the recent release to TensorFlow 1.10.0 release candidate family, rc-0, the new release candidate rc-1 is out and available. Key highlights of this new version include major features and improvements to model training and evaluation, along with lots of bug fixes to the existing ecosystem. What’s new in TensorFlow 1.10.0 RC1? Modular changes The tf.lite runtime module now supports complex64 type Bigtable is a high-performance storage system which can help you store and serve training data. This new version will support the initial bigtable integration for tf.data With improved local run behavior in tf.estimator.train_and_evaluate function, there is no need to reload checkpoints for evaluation Now you can restrict the way workers and PS interact by setting device_filters in RunConfig class. Thus speeding up the training process and ensuring clean shutdowns in specific situations. However, if you want the workers and PS to communicate in order to complete the jobs, you will have to set customized session_options in RunConfig class. Feature additions and improvements Now you can find Distributions and Bijectors in TensorFlow Probability, which was initially found at tf.contrib.distributions. By the end of 2018 tf.contrib.distributions will be removed. New endpoints are added for existing TensorFlow symbols. Going forward these new endpoints are expected to be the preferred endpoints and may replace some of the existing endpoints in the future. You can find the new symbols added to the following modules: tf.debugging, tf.dtypes, tf.image, tf.io, tf.linalg, tf.manip, tf.math, tf.quantization, tf.strings. Breaking changes done to the ecosystem All the new prebuilt libraries are built against NCCL 2.2. They no longer include NCCL in the binary install. If you want to bring the complete usage of TensorFlow with multiple GPUs and NCCL you will need to upgrade it to NCCL 2.2. You can find the updated installation guide on Installing TensorFlow on Ubuntu and Install TensorFlow from Sources. From TensorFlow 1.11 release onwards, Windows builds will use Bazel. Hence this change will drop the official support for cmake. To get full details on the features list and bug fixes done in this release candidate, you can check out Tensorflow’s official release page on Github. Read more Why Twitter (finally!) migrated to Tensorflow How TFLearn makes building TensorFlow models easier Distributed TensorFlow: Working with multiple GPUs and servers

Istio, an open-source platform that connects, manages and secures microservices announced its version 1.0. Istio provides service mesh for microservices from Google, IBM, Lyft, Red Hat, and other collaborators from the open-source community. What’s Istio? Popularly known as a service mesh, Istio collects logs, traces, and telemetry and then adds security and policy without embedding client libraries. Istio also acts as a platform which provides APIs that allows integration with systems for logging, telemetry, and policy. Istio also helps in measuring the actual traffic between services including requests per second, error rates, and latency. It also generates a dependency graph to know how services affect one another. Istio offers a helping hand to one’s DevOps team by providing them with tools to run distributed apps smoothly. Here’s a list of what Istio does for your team: Performs Canary rollouts for allowing the DevOps team to smoke-test any new build and ensure a good build performance. Offers fault-injection, retry logic and circuit breaking so that DevOps teams can perform more testing and change network behavior at runtime to keep applications up and running. Istio adds security. It can be used to layer mTLS on every call, adding encryption-in-flight with an ability to authorize every single call on one’s cluster and mesh. What’s new in Istio 1.0? Multi-cluster support for Kubernetes Multiple Kubernetes clusters can now be added to a single mesh, enabling cross-cluster communication and consistent policy enforcement. The multi-cluster support is now in beta. Networking APIs now in beta Networking APIs that enable fine-grained control over the flow of traffic through a mesh are now in Beta. Explicitly modeling ingress and egress concerns using Gateways allows operators to control the network topology and meet access security requirements at the edge. Mutual TLS can be easily rolled out incrementally without updating all clients Mutual TLS can now be rolled out incrementally without requiring all clients of a service to be updated. This is a critical feature that unblocks adoption in-place by existing production deployments. Istio’s mixer configuration has a support to develop out-of-process adapters Mixer now has support for developing out-of-process adapters. This will become the default way to extend Mixer over the coming releases and makes building adapters much simpler. Updated authorization policies Authorization policies which control access to services are now entirely evaluated locally in Envoy increasing their performance and reliability. Recommended Install method Helm chart installation is now the recommended install method offering rich customization options to adopt Istio on your terms. Istio 1.0 also includes performance improvement parameters such as continuous regression testing, large-scale environment simulation, and targeted fixes. Read more in detail about Istio 1.0 in its official release notes. 6 Ways to blow up your Microservices! How to build Dockers with microservices How to build and deploy Microservices using Payara Micro  

Recently, Cisco and Huawei had faced a major breach in their routers where attackers used two different bypass methods. Hackers managed to compromise Cisco routers through a backdoor attack while Huawei was a victim of botnets. This year has been crucial for big players targeted with modern cyber attacks like Meltdown and Spectre. Who would have ever imagined a CD being the cause of a security breach in the year 2018. However, this time hackers have taken an old school approach or must I say one of the most unexpected methods of opening a backdoor to sensitive information. Packages with China postmarks had ended-up at several local and state government offices. The envelope contained a rambling letter and a small CD. The letter included lengthy paragraphs about fireworks, parades, and film industry but nothing in particular. While the CD contained a set of Word files that consisted of script-based malware. These scripts were supposed to run when the government officials would access them on their computers, eventually compromising that system. Well, people usually end up with blunders when they are confused or curious. The hackers knew exactly how to kick the victims curiosity and confusion into high gear. Until now, State Department of Cultural Affairs, State Historical Societies, and State Archives have received these packages addressed specifically to them. The MS-ISAC claim that these CDs included Mandarin language Microsoft Word (.doc) files from which a few include malicious Visual Basic scripts. It’s not very clear if anyone was tricked into inserting the disk in government systems. Well, it's common sense that you don’t insert a random disk into your system, but that’s not always the case. In 2016, a study found 50% of people plugging-in random USB devices into their system found at public places. The government agencies receiving these packages look quite strange but may be the hackers are looking at breaching a system where they won’t be detected easily; the perfect spot to quickly attack a bigger target. Human curiosity can lead to an invention or a disaster, but, in the security chain, humans are considered as the most delicate link. It’s quite obvious to not insert a random storage device into your systems, but here the hackers have shelled a little cash to target victims still using CD-ROMs in this modern age.  Now the only thing state agencies can hope for is that no one accidentally or out of curiosity inserts disks or USB devices of unknown origin into government systems. Related Links Top 5 cybersecurity trends you should be aware of in 2018 Intel’s Spectre variant 4 patch impacts CPU performance NetSpectre attack exploits data from CPU memory

Google Summer of Code (GSoC), a global program focused on introducing students to open source software development is nearing the end of its competition for this year. A student developer named Victor Perevertkin has been successful in his GSoC 2018 project on Btrfs file-system support for ReactOS. He has been able to boot the Windows API/ABI compatible OS off Btrfs. [box type="shadow" align="" class="" width=""]ReactOS is a free and open Source operating system and is compatible with applications and drivers written for the Microsoft Windows NT family of operating systems (NT4, 2000, XP, 2003, Vista, Seven). [/box] For his GSoC 2018 project, Perevertkin has been working on Btrfs support within the ReactOS boot-loader as well as other fixes needed to allow for ReactOS to be installed on and boot from a Btrfs file-system. BTRFS is case-sensitive file system, so paths like /ReactOS/System32, /reactos/system32, /ReactOS/system32 are different here. However, Windows is written assuming that case does not matter during path lookup. This issue is solved in WinBtrfs driver, but for Freeloader it can be a bit tricky. After Perevertkin was done with the Freeloader development and had fixed the VirtualBox bug, he was able to get to first error message from btrfs-booted ReactOS. He later found out that this was due to a bug in WinBtrfs driver. A pull-request to the upstream repository with a bugfix is provided on GitHub repository. At present, ReactOS is able to boot from BTRFS partition and also is in quite stable state. However, some problems are yet to be addressed. Read about this news in detail on ReactOS Blog. ReactOS version 0.4.9 released with Self-hosting and FastFAT crash fixes Google’s App Maker, a low-code tool for building business apps, is now generally available 5 reasons you should learn to code

A good news for Android developers; Android Studio 3.2 Beta 5 is now available and ready for download. This is the latest beta channel preview version. Android Studio is Android’s official IDE, which is purpose-built for Android. It helps in accelerating application development and helps in building the highest-quality apps for every Android device. Read Also:  What is Android Studio and how does it differ from other IDEs? The last major release was Android 3.1.0 released in March 2018. The current stable version available is Android Studio 3.1.3. The latest Canary channel preview version is Android Studio 3.3 Canary 3. The Canary build allows one to experience latest features which are lightly tested as compared to the apps in the Beta build. What’s new in the Android Studio 3.2 Beta 5 update? Change in behaviour This version update now includes a minimum Protobuf Gradle plugin version of 0.8.6. This Gradle plugin compiles Protocol Buffer (aka. Protobuf) definition files (*.proto) in any project. Apart from this change, some bug fixes in this Beta 5 version include: Translations Editor rows are now aligned properly after scrolling Running another project using GradleBuild was causing java.lang.IllegalMonitorStateException: attempt to unlock read lock, not locked by current thread. This bug has now been fixed. Layout styles which were improperly requiring API level 17 instead of 15 are now fixed. Android Studio that was not properly navigating to certain styles with error message: "Cannot find declaration to go to" has been fixed. Linter is now properly resolving some API level values. Read more this update on Android Studio Developer page 9 Most Important features in Android Studio 3.2 The Art of Android Development Using Android Studio Build your first Android app with Kotlin

Researchers at OpenAI have developed a system trained with reinforcement learning algorithms which is dexterous in-hand manipulation. Termed as Dactyl, this system can solve object orientation tasks entirely in a simulation without any human input. After the system’s training phase, it was able to work on a real robot without any fine-tuning. Using humanoid hand systems to manipulate objects has been a long-standing challenge in robotic control. Current techniques remain limited in their ability to manipulate objects in the real world. Although robotic hands have been available for quite some time, they were largely unable to utilize complex end-effectors to perform dexterous manipulation tasks. The Shadow Dexterous Hand, for instance, has been available since 2005 with five fingers and 24 degrees of freedom. However, it did not see large-scale adoption because of the difficulty of controlling such complex systems. Now OpenAI researchers have developed a system that trained control policies allowing a robot hand to perform complex in-hand manipulations. This systems shows unprecedented levels of dexterity and discovers different hand grasp types found in humans, such as the tripod, prismatic, and tip pinch grasps. It is also able to display dynamic behaviors such as finger gaiting, multi-finger coordination, the controlled use of gravity, and application of translational and torsional forces to the object. How does the OpenAI system work? First, they used a large distribution of simulations with randomized parameters to collect data for the control policy and vision-based pose estimator. The control policy receives observed robot states and rewards from the distributed simulations. It then learns to map observations to actions using RNN and reinforcement learning. The vision-based pose estimator renders scenes collected from the distributed simulations. It then learns to predict the pose of the object from images using a CNN, trained from the control policy. The object pose is predicted from 3 camera feeds with the CNN. These cameras measure the robot fingertip locations using a 3D motion capture system and give them to the control policy to produce an action for the robot. OpenAI blog You can place a block in the palm of the Shadow Dexterous hand and the Dactyl can reposition it into different orientations. For example, it can rotate the block to put a new face on top. OpenAI blog According to OpenAI, this project completes a full cycle of AI development that OpenAI has been pursuing for the past two years. “We’ve developed a new learning algorithm, scaled it massively to solve hard simulated tasks, and then applied the resulting system to the real world.” You can read more about Dactyl on OpenAI blog. You can also read the research paper for further analysis. AI beats human again – this time in a team-based strategy game OpenAI charter puts safety, standards, and transparency first Introducing Open AI’s Reptile: The latest scalable meta-learning Algorithm on the block

With Android P expected to release sometime in August, Google released the beta 4 version of Android P. Android P Beta 4 is the last preview before the official launch of Android P platform. Android P beta 4 consists of a release candidate build with updates to final system behaviors and official Android P APIs. Android P is the next major release after Android Oreo released last year in August. With the Beta 4 version of Android P,  Google has provided “everything” that developers need to wrap up their testing. There are different devices such as Google Pixel phones, Oppo R15 pro, Sony Xperia XZ2, etc, on which you can run the latest Android P beta 4.  Here’s a look at key features that Android P will include. The latest release is available for users who have enrolled themselves in the Android Beta program. Android Beta program is necessary to start testing the update which delivers over-the-air (OTA) updates to its user base. With all the APIs and system behavior updates in Beta 4, we might not see any major changes between Android P beta 4 version and the official stable version of Android P. Android P Beta 4, for now, is available for Google Pixel devices, namely, Pixel, Pixel XL, Pixel 2, and Pixel 2 XL. Pixel users can get Beta 4 on their phone by using the Factory images and OTA files carrying build PPP5.180610.010 to install the latest Android P Beta on their devices instantly. If you don't want to install the latest beta version on your Pixel device then there is a new version provided by Google for Android Emulator where you can test out Beta 4. For more information on Android P Beta 4, check out the official documentation. Top 5 Google I/O 2018 conference Day 1 Highlights: Android P, Android Things, ARCore, ML kit and Lighthouse Google updates biometric authentication for Android P, introduces BiometricPrompt API

It’s finally here! Microsoft just released the latest version of their favorite web development language almost 2 years after TypeScript 2.0 was announced. TypeScript 3.0 comes with plenty of new features and a few breaking changes. The release candidate for 3.0 which was released a few weeks before introduced some new features such as project reference functionality, extracting and spreading parameter lists using tuples, and support for the JSX defaultProps. This final release brought in some new features which makes this new version much more exciting, especially when we consider the changes brought in to improve the developer experience. Improved errors and UX When you see ‘improved errors’ in a feature set, you might think, “Okay, so even now we get errors, but in a better way”. But that is not exactly what the TypeScript team meant. Improving the error messages had been one of the biggest agenda for this release and the TypeScript team has not let us down by introducing related error spans and improved messages and collaboration. Related error spans and elaborated error messages An error can occur due to multiple reasons. One of those reasons and a pretty important one is error coming from different parts of the code. Related error spans are a new way to surface that information to users. In TypeScript 3.0, error messages can provide messages on other locations so that users can reason about cause-and-effect of an error. Since TypeScript 2.9, the team at Microsoft has been investing more resources on how to provide better and accurate error messages, which would lead to faster resolution time. By taking a core set of cases that give a smarter, cleaner, and more accurate error experience. Editor Productivity Writing code with TypeScript has always been a pleasant experience since the language leverages its syntactic and semantic knowledge, which allows one to write code more easily. Editors like Visual Studio, Visual Studio Code, and others with a TypeScript plugin to provide things users love like code completion, Go to Definition, and even quick fixes and refactorings. Building on this same trend, TypeScript 3.0 brings in new features like Named import refactorings, productivity features around JSX like providing completions for JSX closing tags, and Quick fixes for unreachable code and unused labels. Over the last few years, TypeScript has been slowly creeping into more and more web development projects with a clear advantage over JavaScript. Big time frameworks like Angular, React, and now Vue.js have started providing support for this next generation programming language. With this new release, TypeScript looks all set for total domination on the web frontier. For more information on the final TypeScript 3.0 release, check out the official Microsoft Blog. Read Next: TypeScript 3.0 release candidate is here How to work with classes in Typescript Microsoft launches Quantum Katas, a programming project to learn Q#, its Quantum programming language