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Last week, the Nuxt.js community announced the release of Nuxt.js 2.0 with major improvements. This release comes with a scaffolding tool, create-nuxt-app to quickly get you started with Nuxt.js development. To provide a faster boot-up and re-compilation, this release is upgraded to Webpack 4 (Legato) and Babel 7. Nuxt.js is an open source web application framework for creating Vue.js applications. You can choose between universal, static generated or single page application. What is new in Nuxt.js 2.0? Introduced features and upgradations create-nuxt-app To get you quickly started with Nuxt.js development, you can use the newly introduced create-nuxt-app tool. This tool includes all the Nuxt templates such as starter template, express templates, and so on. With create-nuxt-app you can choose between integrated server-side framework, UI frameworks, and add axios module. Introducing nuxt-start and nuxt-legacy To start Nuxt.js application in production mode nuxt-start is introduced. To support legacy build of Nuxt.js for Node.js < 8.0.0,  nuxt-legacy is added. Upgraded to Webpack 4 and Babel 7 To provide faster boot-up time and faster re-compilation, this release uses Webpack 4 (Legato) and Babel 7. ESM supported everywhere In this release, ESM is supported everywhere. You can now use export/import syntax in nuxt.config.js, serverMiddleware, and modules. Replace postcss-next with postcss-preset-env Due to the deprecation of cssnext, you have to use postcss-preset-env instead of postcss-cssnext. Use ~assets instead of ~/assets Due to css-loader upgradation, use ~assets instead of ~/assets for alias in <url> CSS data type, for example, background: url("~assets/banner.svg"). Improvements The HTML script tag in core/renderer.js is fixed to pass W3C validation. The background-color property is now replaced with background in loadingIndicator, to allow the use of images and gradients for your background in SPA mode. Due to server/client artifact isolation, external build.publicPath need to upload built content to .nuxt/dist/client directory instead of .nuxt/dist. webpackbar and consola provide a improved CLI experience and better CI compatibility. Template literals in lodash templates are disabled. Better error handling if the specified plugin isn't found. Deprecated features The vendor array isn't supported anymore. DLL support is removed because it was not stable enough. AggressiveSplittingPlugin is obsoleted, users can use optimization.splitChunks.maxSize instead. The render.gzip option is deprecated. Users can use render.compressor instead. To read more about the updates, check out Nuxt’s official announcement on Medium and also see the release notes on its GitHub repository. Next.js 7, a framework for server-rendered React applications, releases with support for React context API and Webassembly low.js, a Node.js port for embedded systems Welcome Express Gateway 1.11.0, a microservices API Gateway on Express.js

AI researchers have always been super thrilled about building Artificial Intelligence bots that can play games as smartly as a human. Back in June, OpenAI Five, the artificial intelligence bot team, had smashed amateur humans in the video game Dota 2. In August, OpenAI Five bots beat a team of former pros at Dota 2. Now, researchers from Chinese tech giant Tencent have recently developed a pair of AI agents that were successful in beating the Cheating Level Built-in AI in StarCraft II in the full game. Starcraft II is widely considered as the most challenging Real Time Strategy game, due to large observation space, huge action space, partial observation, multi-player simultaneous game model, and long time horizon decision. The two AI Agents Their research paper describes two AI agents - TSTARBOT 1 and TSTARBOT 2. The first is a macro-level controller agent based on deep reinforcement learning over flat action structure. It oversees several specific algorithms designed to handle lower level functions. TSTARBOT 1: Overview of macro actions and reinforcement learning TSTARBOT2, the more robust of the two, is a macro-micro controller consisting of several modules that handle entire facets of the gameplay independently. TSTARBOT 2: Overview of macro-micro hierarchical actions The gameplay Tencent's AI played the game using methods similar to a mouse click and macros and played exactly the same way as a human player would. The AI saw the game by interpreting video output in a frame-to-frame basis, and translating the information into data it can work with. Tencent's AI played StarCraft II with the "fog-of-war" turned on. This means that the AI can’t see the enemy AI's units and base until it scouts the map. The TSTARBOTs were designed to imitate the human thought process.   The agents were tested in a 1v1 Zerg-vs-Zerg full game. They played against built-in AI ranging from level 1 (the easiest) to level 10 (the hardest). The training used the Abyssal Reef, a map known to have thwarted neural network AIs from winning against StarCraft II's built-in AIs. Interestingly, Tencent trained the agents using only a single CPU. This, however, took a large number of processors to process the data it takes to train the bots on billions of frames of video. The researchers took 1920 parallel actors (with 3840 CPUs across 80 machines) to generate the replay transitions, at the speed of about 16,000 frames per second. Results Win-rate (in %) of TSTARBOT 1 and TSTARBOT 2 agents, against built-in AIs of various difficulty levels. Each reported win-rate is obtained by taking the mean of 200 games with different random seeds, where a tie is counted as 0.5 when calculating the win-rate. TSTARBOT1 and TSTARBOT2 also played against several human players ranging from Platinum to Diamond level players in the ranking system of SCII Battle.net League. TSTARBOTs vs. Human Players Each entry means how many games TStarBot1/TStarBot2 wins and loses. The agent is able to consistently defeat built-in AIs in all levels, showing the effectiveness of the hierarchical action modeling. In another informal test, the researchers also let the two TSTARBOT play against each other. TSTARBOT 1 always defeated TSTARBOT 2. This is because TSTARBOT 1 tends to use the Zergling Rush strategy. In StarCraft, a Zerg rush is a strategy where a player using the Zerg race tries to overwhelm the opponent through large numbers of smaller units before the enemy is fully prepared for battle. TSTARBOT 2 lacks anti-rush strategy and henceforth always loses. In the future, the team plans to build a more carefully hand-tuned action hierarchy to enable the reinforcement learning algorithms to develop better strategies for full StarCraft II games. If you want to dive a little deeper into how the bots work, you can read the research paper. AI beats human again – this time in a team-based strategy game. OpenAI Five loses against humans in Dota 2 at The International 2018. OpenAI set their eyes to beat Professional Dota 2 team at The International.

Last week, the Ubuntu community announced the release of Mir 1.0.0, a fast, open and secure display server. The important highlights of this release are support for the Wayland xdg-shell (stable) extension and improved facilities for customizing display layouts. Mir is a system-level component that can be used to unlock next-generation user experiences. It runs on a range of Linux powered devices including traditional desktops, IoT and embedded products. Highlights in Mir 1.0.0 Wayland extension protocols At present, there are many Wayland “extension protocols” to provide specialized support for specific needs. Mir will continue to implement those protocols that are important for the projects that it supports. With the Mir 1.0.0 release, the list of supported extension protocols is: protocol name=“wayland” protocol name=“xdg_shell_unstable_v6” protocol name=“xdg_shell” These are sufficient for the vast majority of desktop and IoT applications. Display layout Mir has a new .display configuration file that tells it how to organize multiple outputs. This is described in Display Configuration for mir-kiosk and Egmde snap: update 0.2 As Mir is designed to handle a wide range of platforms, Mir can be used to create a Wayland based “Desktop Environment” or “Shell”. A couple of examples that use Mir are: Unity8 Egmde Developers using Mir will find it packaged and available on Ubuntu, Fedora and Arch; and soon on Debian. The latest Mir release is available for all supported Ubuntu series from the Mir team’s ‘Release PPA’. To know more about Mir 1.0.0 in detail, visit Ubuntu community blog. Ubuntu free Linux Mint Project, LMDE 3 ‘Cindy’ Cinnamon, released Bodhi Linux 5.0.0 released with updated Ubuntu core 18.04 and a modern look What to expect from upcoming Ubuntu 18.04 release  

The PostGIS development team released version 2.5.0 of PostGIS, a spatial database extender for PostgreSQL, yesterday. PostGIS 2.5.0 explores new features, breaking changes, improvements, and fixes.  PostGIS is an open source software program which provides support for geographic objects to the PostgreSQL object-relational database. PostGIS comprises simple features for SQL specification from the Open Geospatial Consortium (OGC). New Features ST_OrientedEnvelope (Returns a minimum rotated rectangle enclosing a geometry) and ST_QuantizeCoordinates  (Sets least significant bits of coordinates to zero) have been added in PostGIS 2.5.0. Apart from that, other new features such as ST_FilterByM (Filters vertex points based on their m-value) and ST_ChaikinSmoothing (Returns a "smoothed" version of the given geometry with the help of Chaikin algorithm) have also been added in PostGIS 2.5.0. Breaking Changes The Version number has been removed from address_standardize lib file in PostGIS 2.5.0. The raster support functions can be loaded only in the same schema with core PostGIS functions. The dummy pgis_abs type has been removed from aggregate/collect routines. Support has been removed for drop support GEOS < 3.5 and PostgreSQL < 9.4. Improvements and bug Fixes There’s been performance improvement for sorting POINT geometries in PostGIS 2.5.0. An external raster band index has been added to ST_BandMetaData. Also, there’s an added Raster Tips section in Documentation for information about Raster behavior (e.g. Out-DB performance, maximum open files). The use of GEOS in topology implementation has been reduced in PostGIS 2.5.0. A bug that created MVTs with incorrect property values under parallel plans has been fixed. Geometry in PostGIS 2.5.0 has been simplified using map grid cell size before generating MVT.  BTree sort order has now been defined on collections of EMPTY and same-prefix geometries in PostGIS 2.5.0. Hashable geometry feature enables direct use in CTE signatures in PostGIS 2.5.0. PostGIS 2.5.0 will not be accepting EMPTY points as topology nodes. ST_GeometricMedian now provides support for point weights in PostGIS 2.5.0. Duplicated code in lwgeom_geos has been removed. For more information on other updates and fixes in PostGIS 2.5.0, check out the official release notes. Writing PostGIS functions in Python language [Tutorial] Adding PostGIS layers using QGIS [Tutorial] PostGIS extension: pgRouting for calculating driving distance [Tutorial]

Earlier last week, a team of US scientists, from Florida Atlantic University (FAU) and the US Office of Naval Research published a paper on five jellyfish robots that they have manufactured. The paper is titled Thrust force characterization of free-swimming soft robotic jellyfish. The prime motive of the scientists to build such robotic jellyfish is to track and monitor fragile marine ecosystems without causing unintentional damage to them. These soft robots can swim through openings narrower than their bodies and are powered by hydraulic silicon tentacles. These so-called ‘jelly-bots’ have the ability to squeeze through narrow openings using circular holes cut in a plexiglass plate. The design structure of ‘Jelly-bots’ Jelly-bots have a similar design to that of a moon jellyfish (Aurelia aurita) during the ephyra stage of its life cycle before they becoming a fully grown medusa. To avoid the damage to fragile biological systems by the robots, soft hydraulic network actuators are chosen. To allow the jellyfish to steer, the team uses two impeller pumps to inflate the eight tentacles. The mold models for the jellyfish robot were designed in SolidWorks and subsequently, 3D printed with an Ultimaker 2 out of PLA (polylactic acid). Each jellyfish has varying rubber hardness to test the effect it has on the propulsion efficiency. Source: IOPScience What this study was about? These jelly robots will help the scientists in determining the impact of the following factors on the measured thrust force: Actuator material Shore hardness Actuation frequency Tentacle stroke actuation amplitude The scientists found that all three of these factors significantly impact mean thrust force generation, which peaks with a half-stroke actuation amplitude at a frequency of 0.8 Hz. Results The material composition of the actuators significantly impacted the measured force produced by the jellyfish, as did the actuation frequency and stroke amplitude. The greatest forces were measured with a half-stroke amplitude at 0.8 Hz and a tentacle actuator-flap material Shore hardness composition of 30–30. In the test, the jellyfish was able to swim through the narrow openings than the nominal diameter of the robot and demonstrated the ability to swim directionally. The jellyfish robots were tested in the ocean and have the potential to monitor and explore delicate ecosystems without inadvertently damaging them. One of the scientists, Dr. Engeberg said to Tech Xplore: "In the future, we plan to incorporate environmental sensors like sonar into the robot's control algorithm, along with a navigational algorithm. This will enable it to find gaps and determine if it can swim through them." To know more in detail about the jellybots, read the research paper published by these scientists. You may also go through a  video showing jellybots functioning in deep waters. Sex robots, artificial intelligence, and ethics: How desire shapes and is shaped by algorithms MEPs pass a resolution to ban “Killer robots” 6 powerful microbots developed by researchers around the world

Eric Schmidt, former Google CEO, has said that he believes the internet will split in two in the next decade. Speaking at a private event hosted by Silicon Valley investment firm Village Global VC, he warned that at the heart of this split would be China and the U.S., with two different 'internets' taking shape. Asked a question about the possible fragmentation of the internet, Schmidt said "I think the most likely scenario now is not a splintering, but rather a bifurcation into a Chinese-led internet and a non-Chinese internet led by America." For Schmidt, what's distinctive about the 'Chinese internet' is the amount of wealth that's invested into it, and how it plays a part in the Chinese Government's wider economic strategy. He cites the fact that the Chinese internet is an even larger proportion of GDP than the U.S. internet as evidence of the importance its playing within China's wider economic - and maybe political - plans. China's Belt and Road Initiative Schmidt also mentions China's Belt and Road Initiative in the context of the internet. The Belt and Road Initiative is essentially China's attempt to increase its economic growth by building better relationships and links with countries all around the world, particularly in Europe, Africa, and the Middle East. Precisely because so much trade happens digitally, the internet is necessarily a crucial component of the initiative. The implications of this, according to Schmidt at least, are that we may begin to see some of the more closed and controlled aspects of the internet in China replicated around the world. The differences between the Chinese internet and the U.S. one have been playing out in some recent controversies - such as Google's own Project Dragonfly, a search engine that is censored in a way that's amenable to the Chinese authorities. However, while the U.S v. China narrative offers an easily digestible view of digital geopolitics, it would be wrong to ignore the attitudes of lawmakers in other parts of the world - like Europe - that are going to shape the future of the internet.  

Today, MariaDB Corp. announced that it has acquired a San Francisco-based database company, Clustrix. With this acquisition, MariaDB plans to add a scale-out capability that runs on-premises with commodity hardware or in any cloud environment. This, in turn, will provide greater scalability and higher availability than other traditional distributed options such as Oracle RAC. MariaDB is an adaptable solution because of its architecture that supports pluggable and purpose-built storage engines. Clustrix is a provider of relational database engineered for cloud and datacenter. Known for the scalability it provides, the ClustrixDB is an ideal database solution for high-transaction, high-value applications. How will Clustrix acquisition benefit MariaDB? Michael Howard, CEO, MariaDB Corporation, says, “With Clustrix, MariaDB can provide a better solution for our customers that have challenging scale-out enterprise environments. Our distributed solution will satisfy the most extreme requirements of our largest customers and gives them the freedom to break from Oracle’s lock-in.” The company believes that the acquisition of Clustrix will allow MariaDB Labs to tackle the challenges in the database field. These challenges are extreme in distributed computing, machine learning, next-generation chips, and memory and storage environments. Both Clustrix and MariaDB share the same vocabulary and work on similar problems. Also, both aim to be compatible with MySQL. This makes Clustrix a good fit for MariaDB. Howard says, “the database was always built to accommodate external database storage engines. MariaDB will have to make some changes to its APIs to be ready for the clustering features of Clustrix. It’s not going to be a 1-2-3 effort, it’s going to be a heavy-duty effort for us to do this right. But everyone on the team wants to do it because it’s good for the company and our customers.” This simply means integrating the Clustrix database technology into MariaDB won’t be trivial. To know more about this acquisition in detail, visit MariaDB blog MariaDB 10.3.7 releases Building a Web Application with PHP and MariaDB – Introduction to caching Installing MariaDB on Windows and Mac OS X

Yesterday, Google launched the beta version of its Cloud Source Repositories. Claiming to provide its users with a better search experience, Google Cloud Source Repositories is a Git-based source code repository built on Google Cloud. The Cloud Source Repositories introduce a powerful code search feature, which uses the document index and retrieval methods similar to Google Search. These Cloud source repositories can pose a major comeback for Google after Google Code, began shutting down in 2015. This could be a very strategic move for Google, as many coders have been looking for an alternative to GitHub, after its acquisition by Microsoft. How does Google code search work? Code search in Cloud Source Repositories optimizes the indexing, algorithms, and result types for searching code. On submitting a query, the query is sent to a root machine and sharded to hundreds of secondary machines. The machines look for matches by file names, classes, functions and other symbols, and matches the context and namespace of the symbols. A single query can search across thousands of different repositories. Cloud Source Repositories also has a semantic understanding of the code. For Java, JavaScript, Go, C++, Python, TypeScript and Proto files, the tools will also return information on whether the match is a class, method, enum or field. Solution to common code search challenges #1 To execute searches across all the code at ones’ company If a company has repositories storing different versions of the code, executing searches across all the code is exhaustive and ttime-consuming While using Cloud Source Repositories, the default branches of all repositories are always indexed and up-to-date. Hence, searching across all the code is faster. #2 To search for code that performs a common operation Cloud Source Repositories enables users to perform quick searches. Users can also save time by discovering and using the existing solution while avoiding bugs in their code. #3 If a developer cannot remember the right way to use a common code component Developers can enter a query and search across all of their company’s code for examples of how the common piece of code has been used successfully by other developers. #4 Issues with production application  If a developer encounters a specific error message to the server logs that reads ‘User ID 123 not found in PaymentDatabase’, they can perform a regular expression search for ‘User ID .* not found in PaymentDatabase’ and instantly find the location in the code where this error was triggered. All repositories that are either mirrored or added to Cloud Source Repositories can be searched in a single query. Cloud Source Repositories has a limited free tier that supports projects up to 50GB with a maximum of 5 users. You can read more about Cloud Source Repositories in the official documentation. Google announces Flutter Release Preview 2 with extended support for Cupertino themed controls and more! Google to allegedly launch a new Smart home device Google’s prototype Chinese search engine ‘Dragonfly’ reportedly links searches to phone numbers

Facebook researchers have collected and compiled a new dataset providing 5 million personas and 700 million persona-based dialogues. The aim with this dataset is to enhance the performance of their end-to-end dialogue systems by training them using personas. This will result in increased and improved engagement between human beings and computer agents. Generally, end-to-end dialogue systems are mostly based on neural architectures like bidirectional LSTMs or Memory Networks. These are trained directly by gradient descent on dialogue logs and have been showing promising performance in multiple contexts. One of their major advantages lies in the fact that they rely on large data sources of existing dialogues to learn various domains without any expert knowledge. However, these dialogue systems show limited engagement and lack of consistency. To solve this issue, a team of researchers at the Montreal Institute for Learning Algorithms (MILA) and Facebook AI introduced the PERSONACHAT dataset. This dataset comprises dialogues between different pairs of agents with text profiles, or personas, attached to each of them. This leads the path for end-to-end personalized chatbots as the personas of the bots are short texts that could be easily edited by most users. “However, the PERSONA-CHAT dataset was created using an artificial data collection mechanism based on Mechanical Turk. As a result, neither dialogs nor personas can be fully representative of real user-bot interactions and the dataset coverage remains limited, containing a bit more than 1k different personas” reads the research paper. So, the researchers have built another large-scale persona-based dialogue dataset using conversations that were previously extracted from the REDDIT dataset. ”With simple heuristics, we create a corpus of over 5 million personas spanning more than 700 million conversations. We train persona-based end-to-end dialogue models on this dataset” mentions researchers in the paper. Read also: Best Machine Learning Datasets for beginners The goal is to learn to predict responses based on a persona for a wide range of personas. Researchers have built a dataset using the data from Reddit of following examples: Persona: [“I like sport”, “I work a lot”] Context: “I love running.” Response: “Me too! But only on weekends.”                                            Persona-based Network Architecture The persona consists of a set of sentences which represent the personality of the responding agent, the context refers to the utterance that it responds to, and the response is an answer which is to be predicted. The researchers then went ahead and trained the persona-based end-to-end dialogue systems using their newly developed dataset. Systems that were trained on this dataset outperformed other conversational agents (which were not trained using personas) and held far more engaging conversations. “As pretraining leads to a considerable improvement in performance, future work could be done fine-tuning this model for various dialog systems. Future work may also entail building more advanced strategies to select a limited number of personas for each user while maximizing the prediction performance,” say researchers in the paper. For more details, check out the official research paper. Google launches a Dataset Search Engine for finding Datasets on the Internet How to create and prepare your first dataset in Salesforce Einstein 25 Datasets for Deep Learning in IoT

With more than 3 years from the time of previous version release OpenCV 3.0, the team happily announced the alpha release of the most awaited OpenCV 4.0. The new version is set to encompass exclusive features such as 3D dense reconstruction algorithm, newest improvements and bug fixes to recent maintenance release of OpenCV 3.4. Key Features: OpenCV is a C++11 library now Default features include lambda functions, convenient iteration, and initialization of cv::Mat Added new chessboard detector Added exclusive HPX parallel backend and basic FP16 support Standard std::string and std::shared_ptr replaced hand-crafted cv::String and cv::Ptr parallel_for can now use the pool of std::threads as backend Major improvements and bug fixes: ONNX parser added to existing OpenCV DNN module Support to various classification networks - AlexNet, Inception v2, Resnet, VGG Partial support to YOLO v2 object detection network Faster object detection using Intel Inference Engine, part of Intel OpenVINO Added stability improvements in the OpenCL backend Fast QR code detector with support to add QR code decoder soon SSE4-, AVX2- and NEON-optimized kernels expanded Legacy C API from OpenCV 1.x partially excluded This alpha release appears to be a massive version with 85 patches including 28 merge requests. This release is assumed to be quite stable although few changes in OpenCV API and implementation are expected before 4.0 final release. For more information on the detailed list of features and improvements, please read official documentation. Image filtering techniques in OpenCV 3 ways to deploy a QT and OpenCV application OpenCV and Android: Making Your Apps See

Yesterday, Adobe Systems confirmed their plans to acquire Marketo Inc for $4.75 billion from Vista Equity Partners Management. This deal is expected to close in the fourth quarter of Adobe’s Fiscal Year 2018 in December. Adobe, with this acquisition, aims to combine Adobe Experience Cloud and Marketo Commerce Cloud to provide a unified platform for all marketers. Marketo is a US-based software company, which develops marketing software that provides inbound marketing, social marketing, CRM, and other related services. The industries it serves include healthcare, technology, financial services, manufacturing, and media, among others. What acquiring Marketo means to Adobe? A single platform to serve both B2B and B2C customers The integration of Marketo Commerce Cloud into the Adobe Experience Cloud will help Adobe deliver a single platform that serves both B2B and B2C customers globally. This acquisition will bring together Marketo’s lead account-based marketing technology and Adobe’s Experience Cloud analytics, advertising, and commerce capabilities. This will enable B2B companies to create, manage, and execute marketing engagements at scale. Access to Marketo’s huge customer base Enterprises from various industries are using Marketo’s marketing applications to drive engagement and customer loyalty. Marketo will bring its huge ecosystem, which consists of nearly 5000 customers and over 500 partners to Adobe. Brad Rencher, Executive Vice President and General Manager, Digital Experience at Adobe said: “The acquisition of Marketo widens Adobe’s lead in customer experience across B2C and B2B and puts Adobe Experience Cloud at the heart of all marketing.” What’s in it for Marketo? Signaling the next phase of Marketo's growth, its acquisition by Adobe will further accelerate its product roadmap and go-to-market execution. With Adobe, Marketo's products will get a new level of global operational scale and the ability to penetrate new verticals and geographies. The CEO of Marketo, Steve Lucas, believes that with Adobe they will be able to rapidly innovate and provide their customers a definitive system of engagement: “Adobe and Marketo both share an unwavering belief in the power of content and data to drive business results. Marketo delivers the leading B2B marketing engagement platform for the modern marketer, and there is no better home for Marketo to continue to rapidly innovate than Adobe.” To know more about Adobe acquiring Marketo, read their official announcement on Adobe’s  website. Adobe to spot fake images using Artificial Intelligence Adobe is going to acquire Magento for $1.68 Billion Adobe glides into Augmented Reality with Adobe Aero

Yesterday, Cloud Native Computing Foundation (CNCF) accepted Cortex as a CNCF Sandbox project. Cortex is an open source, horizontally scalable, multi-tenant Prometheus-as-a-service. It provides long-term storage for Prometheus metrics when used as a remote write destination. It also comes with a horizontally scalable, Prometheus-compatible query API. It provides uses cases for: Service providers to enable them to manage a large number of Prometheus instances and provide long-term storage. Enterprises to centralize management of large-scale Prometheus deployments and ensure long-term durability of Prometheus data. Originally developed by Weaveworks, it is now being used in production by organizations like Grafana Labs, FreshTracks, and EA. How does it work? The following diagram shows its architecture: Source: CNCF 1. Scraping samples: First, a Prometheus instance scraps all of the users’ services and then forwards them to a Cortex deployment. It does this using the remote_write API, which was added to Prometheus to support Cortex and other integrations. 2. Distributor distributes the samples: The instance then sends all these samples to distributor, which is a stateless service that consults the ring to figure out which ingesters should ingest the sample. The ingesters are arranged using a consistent hash ring, keyed on the fingerprint of the time series, and stored in a consistent data store, such as Consul. Distributor finds the owner ingester and forwards the sample to it and also to two ingesters after it in the ring. This means if an ingester goes down, we have two others that have its data. 3. Ingesters make chunks of samples: Ingesters continuously receive a stream of samples and group them together in chunks. These chunks are then stored in a backend database, such as DynamoDB, BigTable, or Cassandra. Ingesters facilitate this chunking process so that Cortex isn’t constantly writing to its backend database. Alexis Richardson, CEO of Weaveworks believes that being a CNCF Sandbox project will help grow the Prometheus ecosystem: “By joining CNCF, Cortex will have a neutral home for collaboration between contributor companies, while allowing the Prometheus ecosystem to grow a more robust set of integrations and solutions. Cortex already has a strong affinity with several CNCF technologies, including Kubernetes, gRPC, OpenTracing and Jaeger, so it’s a natural fit for us to continue building on these interoperabilities as part of CNCF.” To know more in detail, check out the official announcement by CNCF and also read What is Cortex?, a blog post published on Weaveworks Blog. Google Cloud hands over Kubernetes project operations to CNCF, grants $9M in GCP credits CNCF Sandbox, the home for evolving cloud native projects, accepts Google’s OpenMetrics Project Modern Cloud Native architectures: Microservices, Containers, and Serverless – Part 1

GitLab announced it had raised a $100 million Series D funding after being backed by one of its chief financial investors Alphabet Inc., who believe the code sharing and collaboration platform has what it takes to surpass GitHub. GitHub is currently the world’s most popular platform for distributed version control and source code management. Alphabet’s involvement in GitLab began after it lost out to Microsoft in the acquisition of GitHub for $7.5 billion back in June. They were quick to respond with an investment in GitLab’s $20 million funding round held in October 2017 through their GV, their capital venture arm. It is also worth remembering that GitLab, whose primary customers include major companies such as Intel, NASDAQ and Alibaba, saw an uptick in their user-base as thousands of developers shifted their codebase from GitHub to GitLab, citing uncertainty over the way Microsoft has historically treated open source projects. GitLab CEO Sid Sijbrandij was not too keen to conduct the next round of fundraising, but said Microsoft’s acquisition of GitHub accelerated the process. “We weren’t planning on fundraising actually. Our plan is to IPO in November of 2020 and we anticipated one more fundraise. I think in the current climate, people are seeing that there’s one independent company, one startup left basically in this space. And we saw an opportunity to become best in class in a lot of categories.” According to Sijbrandij, while many people still see GitLab as a competitor to GitHub and BitBucket, GitLab wants to be much more than that. With a 200% faster DevOps cycle, Sijbrandij envisions GitLab will rival the likes of Jira, Jenkins, BlackDuck and Puppet. “The biggest misunderstanding we’re seeing is that GitLab is an alternative to GitHub and we’ve grown beyond that,” he said. “We now offer services in nine categories all the way from planning to monitoring.” Alphabet’s investment in GitLab is an indication of their belief that GitLab can become a bigger and much better platform than GitHub, with the former having plans in place to integrate newer features such as tracing and log aggregation as well. While GitLab has already signaled that it plans to stay independent, Sijbrandij admitted that “there’s always a price”, if a major player wanted to come along and acquire GitLab. Alphabet Inc. without a doubt will be a front-runner if that’s the case. Read more Gitlab 11.2 releases with preview changes in Web IDE, Android Project Import and more The Microsoft-GitHub deal has set into motion an exodus of GitHub projects to GitLab GitHub introduces ‘Experiments’, a platform to share live demos of their research projects

The Kotlin team has come out with a release candidate 1.3 of the Kotlin Language. Kotlin 1.3 RC1 comes with improvements and changes to its compiler and the IDE, IntelliJ IDEA. Let’s discuss key updates in Kotlin 1.3 RC1. Compiler Changes Improvements Support has been added for main entry point without arguments in the frontend, IDE and JVM in Kotlin 1.3 RC1. Other than that, there is added support for suspend fun main function in JVM. The boxing technique has been changed. Now, instead of calling valueOf, a new wrapper type will be allocated. Bug Fixes The invoke function that kept getting called with lambda parameter on a field named suspend has been fixed. With Kotlin 1.3 RC1, correct WhenMappings code is generated in case of mixed enum classes in when conditions. The use of  KSuspendFunctionN and SuspendFunctionN as supertypes has been forbidden. Also, the suspend functions are annotated with @kotlin.test.Test have been forbidden. Use of kotlin.Result as a return type and with special operators has been prohibited. The constructors containing inline classes as parameter types will be now generated as private with synthetic accessors. An inline class that was missing unboxing when using indexer into an ArrayList has been fixed. IDE Changes Support has been added for type parameters in where clause (multiple type constraints). Bug Fixes The issue where @Language prefix and suffix were getting ignored for function arguments has been fixed. Coroutine migrator has been renamed to buildSequence/buildIterator to their new names. Deadlock in databinding with AndroidX which led to Android Studio hanging has been fixed. The issue of Android module in a multiplatform project not being recognized earlier as a multiplatform module has been fixed. Multiplatform projects without Android target were not being imported properly into Android Studio, this has been fixed with Kotlin 1.3 RC1. IDEA used to hang when Kotlin bytecode tool window remained open while editing a class with a secondary constructor. This is fixed now. IDE Multi-Platform: Old multi-platform modules templates have been removed from New Project/New Module wizard. ConcurrentModificationException, an actual type alias has been introduced in the JVM library. There are more changes and improvements in Kotlin 1.3RC1. Check out Kotlin 1.3RC official release notes for the complete list. Building RESTful web services with Kotlin Kotlin 1.3 M1 arrives with coroutines and new experimental features like unsigned integer types IntelliJ IDEA 2018.3 Early Access Program is now open!

Amazon has unveiled a range of Echo devices at the Amazon Devices Event hosted in their Seattle headquarters, yesterday. The products announced included a revamped selection of Amazon’s smart speakers ( Echo Sub, Echo Dot, and Echo Plus), smart displays (the Echo Show and Echo Spot), and other smart devices. Also released, was a smart microwave (AmazonBasics Microwave), Echo Wall Clock, Fire TV Recast, and Amazon Smart Plug This event marks the largest number of devices and features (over 30) that Amazon has ever launched in a day. Alexa Presentation Language For developers, Amazon introduced the Alexa Presentation Language, to easily create Alexa skills for Alexa devices with screens. The Alexa Presentation Language (APL) is in preview and allows developers to build voice experiences with graphics, images, slideshows and video. Developers will be able to control how graphics flow with voice, customize visuals and adapt them to Alexa devices and skills.  Supported devices will include Echo Show, Echo Spot, Fire TV, and select Fire Tablet devices. Now let’s take a broad look at the key device announcements. Amazon Smart Speakers Echo Dot: The new version of the Smart speaker now offers 70 percent louder sound as compared to its predecessor. It is a voice-controlled smart speaker with Alexa integration. It can sort music, news, information, and more. The driver is now much larger from 1.1” to 1.6” for better sound clarity and improved bass. It is Bluetooth enabled so you can connect to another speaker or use it all by itself. Echo Input:  If you already have speakers, this device can add Alexa voice control to them via a 3.5mm audio cable or Bluetooth. It has a four-microphone array. Echo Input is just 12.5mm tall and thin enough to disappear into the room. It will be available later this year for $34.99. Echo Plus: Echo Plus combines Amazon’s cloud-based Natural Language Understanding and Automatic Speech Recognition along with built-in Zigbee hub to make it one of the premier smart speakers. It also has a new fabric casing, and built-in temperature sensor. This model's pre-orders begin today for $149.99. Echo Link: The Echo Link device can connect to a receiver or amplifier, with multiple digital and analog inputs and outputs for compatibility with your existing stereo equipment. It can control music selection, volume, and multi-room playback on your stereo with your Echo or the Alexa app. Echo Link will be available to customers soon. Echo Sub: This 100-watt subwoofer can connect to other speakers and create a 2.1-sound solution. The $129.99 Echo Sub will launch later this month with pre-orders beginning today. Amazon Smart Displays Echo Show: The new Echo Show is completely redesigned with a larger screen, smart home hub, and improved sound quality. Amazon is also introducing Doorbell Chime Announcements, so users will hear a chime on all Echo devices when someone presses your smart doorbell. Echo Show includes a high resolution 10-inch HD display and an 8-mic array. The new Echo Show will be available to customers for $229.99. Shipping starts next month. Other Smart devices Echo Wall Clock: It is a $30 Echo companion device, an analog clock with Alexa-powered voice recognition. It is 10-inch, battery-powered and features a ring of 60 LEDs around the rim that show ongoing Alexa timers. It also has automatic time syncing and Daylight Savings Time adjustment. AmazonBasics Microwave: It’s a $59.99 voice-activated microwave. It features Dash Replenishment and an array of Alexa features including integration with connected ovens, door locks, and other smart fixtures, reminders, and access to more than 50,000 third-party skills. Fire TV Recast: This is a companion DVR that lets users watch, record, and replay free over-the-air programming to any Fire TV, Echo Show, and on compatible Fire tablet and mobile devices. Users can also record up to two or four shows at once, and stream on any two devices at a time. It can also be paired with Alexa. Amazon Smart Plug: The Amazon Smart Plug works with Alexa to add voice control to any outlet. You can schedule lights, fans, and appliances to turn on and off automatically, or control them remotely when you’re away. Follow along the live blog of the event for a minute to minute update. Google to allegedly launch a new Smart home device. Cortana and Alexa become best friends: Microsoft and Amazon release a preview of this integration. The iRobot Roomba i7+ is a cleaning robot that maps and stores your house and also empties the trash automatically.