Tech News

Yesterday, the Facebook Open Source team announced that they will no longer be able to contribute to the open source development of the Nuclide extension, Atom IDE, and other associated repos. https://twitter.com/fbOpenSource/status/1072928679695548416 Nuclide is a code editor built as a suite of features on top of the Atom text editor to provide hackability and the support of an active community. Facebook developed Nuclide to provide a first-class unified development environment for React Native, Hack, and Flow projects. Nuclide was first created for Facebook’s internal engineers and then was later open sourced in the hopes that others could also benefit from it too. In their announcement, Facebook told that this decision was made because they were not able to pay much attention to the project. They added, “However, our team has not been able to give this project the amount of attention and responsiveness it deserves and as a result, we’ve made the difficult decision to retire Nuclide and associated repos, such as the Atom-IDE packages.” Though they are not going to contribute to the Nuclide open source project, Facebook will continue to use it internally: https://twitter.com/amasad/status/1072930703065501696 The latest release, that is, Nuclide 0.366 will be the last release by Facebook. They have made its source code available in the Facebook Open Source Archive. The language and debugging services will still be supported in Atom and other compatible IDEs such as Microsoft Visual Studio Code or the clients listed on Langserver.org. Users on Hacker News are speculating that maybe this is the time to adopt VSCode and the main reason is that it provides good integration with TypeScript. Here’s what a user said, “A shame, in an ideal world there would be the benefit of outside contributions that made less internal work needed, so overall would be a win for Facebook. But probably this is related to Atom itself being taken over by VSCode, the number of users (and maybe contributors) appears to be going down.” Read the official announcement by Facebook on Nuclide’s website. Facebook’s artificial intelligence research team, FAIR, turns five. But what are its biggest accomplishments? Facebook AI research and NYU school of medicine announces new open-source AI models and MRI dataset as part of their FastMRI project Facebook plans to change its algorithm to demote “borderline content” that promotes misinformation, and hate speech on the platform

Introducing a new library called Deep Graph Library (DGL) developed by the NYU & AWS teams, Shanghai. DGL is a package built on Python to simplify deep learning on graph, atop of existing deep learning frameworks. DGL is essentially a Python package which serves as an interface between any existing tensor libraries and data that is expressed as graphs. It helps in easy implementation of graph neural networks such as Graph Convolution Networks, TreeLSTM and others. It also maintains high computation efficiency while doing this. This new Python library is made in an effort to make graph implementations in deep learning simpler. According to the results they state, the improvement on some models is as high as 10 times and has better accuracy in some cases. Check out the results on GitHub. Their website states: “We are keen to bring graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible.” As of now, DGL supports PyTorch v1.0. The autobatching is up to 4x faster than DyNet. DGL is tested on Ubuntu 16.04, macOS X, and Windows 10 and will work on any newer versions of these OSes. Python 3.5 or later is required while Python 3.4 or older is not tested. Support for Python 2 is in the works. Installing it is as same as any other Python package. With pip: pip install dgl And with conda: conda install -c dglteam dgl https://twitter.com/aussetg/status/1072897828677144582 DGL is currently in the beta stage, licensed under Apache 2.0, and they have a Twitter page. You can check out DGL at their website. UK researchers have developed a new PyTorch framework for preserving privacy in deep learning OpenAI launches Spinning Up, a learning resource for potential deep learning practitioners Deep Learning Indaba presents the state of Natural Language Processing in 2018

Yesterday, Instaclustr released three open source projects easing Cassandra-Kubernetes Integration and LDAP/Kerberos Authentication. These three projects include a Cassandra operator for Kubernetes, LDAP authenticator, and Kerberos authenticator plugins. Cassandra on Kubernetes The Cassandra operator functions as a Cassandra-as-a-Service on Kubernetes, making it easier for developers to combine these technologies. The Cassandra operator provides a consistent environment and set of operations that are reproducible across production clusters and development, staging, and QA environments. The Cassandra operator is now ready to use in development environments through GitHub. Enterprise support for the Cassandra operator will start next year. LDAP authenticator The LDAP authenticator plug-in enables developers to benefit from the secure LDAP authentication without any need to write their own solutions and to transition to using the authenticator with zero downtime. The LDAP authenticator is freely available on GitHub, along with setup and usage instructions. Kerberos authenticator The Kerberos authenticator makes Kerberos’ secure authentication and true single sign-on capabilities available to developers using Apache Cassandra. This project also includes a Kerberos authenticator plugin for the Cassandra Java driver. “With these open source projects, we’ve set out to empower any developer who wishes to pair Cassandra with Kubernetes, or take advantage of LDAP or Kerberos authentication within their Cassandra deployments. We invite anyone interested to join our community of contributors, and suggest or offer improvements to these open source projects.” said Ben Bromhead, CTO. ScyllaDB announces Scylla 3.0, a NoSQL database surpassing Apache Cassandra in features. cstar: Spotify’s Cassandra orchestration tool is now open source! Twitter adopts Apache Kafka as their Pub/Sub System

Yesterday, at the ongoing KubeCon + CloudNativeCon North America 2018, RedHat announced its contribution towards etcd, an open source project and its acceptance into the Cloud Native Computing Foundation (CNCF). Red Hat is participating in developing etcd, as a part of the enterprise Kubernetes product, Red Hat OpenShift. https://twitter.com/coreos/status/1072562301864161281 etcd is an open source, distributed, consistent key-value store for service discovery, shared configuration, and scheduler coordination. It is a core component of software that comes with safer automatic updates and it also sets up overlay networking for containers. The CoreOS team created etcd in 2013 and the Red Hat engineers maintained it by working alongside a team of professionals from across the industry. The etcd project focuses on safely storing critical data of a distributed system and demonstrating its quality. It is also the primary data store for Kubernetes. It uses the Raft consensus algorithm for replicated logs. With etcd, applications can maintain more consistent uptime and work smoothly even when the individual servers are failing. Etcd is progressing and it already has 157 releases with etcd v3.3.10 being the latest one that got released just two month ago. etcd is designed as a consistency store across environments including public cloud, hybrid cloud and bare metal. Where is etcd used? Kubernetes clusters use etcd as their primary data store. Red Hat OpenShift customers and Kubernetes users benefit from the community work on the etcd project. It is also used by communities and users like Uber, Alibaba Cloud, Google Cloud, Amazon Web Services, and Red Hat. etcd will be under Linux Foundation and the domains and accounts will be managed by CNCF. The community of etcd maintainers, including Red Hat, Alibaba Cloud, Google Cloud, Amazon, etc, won’t be changed. The project will continue to focus on the communities that depend on it. Red Hat will continue extending etcd with the etcd Operator in order to bring more security and operational ease. It will enable users to easily configure and manage etcd by using a declarative configuration that creates, configures, and manages etcd clusters. Read more about this news on RedHat’s official blog. RedHat shares what to expect from next week’s first-ever DNSSEC root key rollover Google, IBM, RedHat and others launch Istio 1.0 service mesh for microservices What Google, RedHat, Oracle, and others announced at KubeCon + CloudNativeCon 2018

Yesterday, at the ongoing KubeCon + CloudNativeCon North America 2018, the Oracle team introduced the Oracle Cloud Native Framework. This framework provides developers with a cloud native solution for public cloud, on premises and hybrid cloud deployments. The Oracle Cloud Native Framework supports modern cloud native and traditional applications like, WebLogic, Java, and database. It comprises of the recently announced Oracle Linux Cloud Native Environment and Oracle cloud infrastructure native services. The Oracle Cloud Native Framework supports both dev and ops so it can be used by startups and enterprises. What’s new in Oracle Cloud Native Framework? Application definition & development Oracle Functions: It is a serverless cloud service based on the open source Fn Project that can run on-premises, in a data center, or on any cloud. With Oracle Functions, developers can seamlessly deploy and execute function-based applications without the hassle of managing compute infrastructure. It is Docker container-based and follows the pay-per-use method. Streaming: It is a highly scalable and multi-tenant streaming platform that makes the process of collecting and managing streaming data easy. It also enables applications like security, supply chain and IoT, where large amounts of data gets collected from various sources and is processed in real time. Provisioning Resource Manager: It is a managed service that provisions Oracle Cloud Infrastructure resources and services. It reduces configuration errors while increasing productivity by managing infrastructure as code. Observability & Analysis Monitoring: It is an integrated service that helps in reporting metrics from all resources and services in Oracle Cloud Infrastructure. It uses predefined metrics and dashboards, or service API for getting a holistic view of the performance, health, and capacity of the system. This monitoring service uses alarms for tracking metrics and takes action when they vary or exceed defined thresholds. Notification Service: It is a scalable service that broadcasts messages to distributed components like, PagerDuty and email. The notification service helps users to deliver messages about Oracle Cloud Infrastructure to a large numbers of subscribers. Events: It can store information to object storage and trigger functions to take actions. It also enables users to react to changes in the state of Oracle Cloud Infrastructure resources. The Oracle Cloud Native Framework provides cloud-native capabilities and offerings to the customers by using the open standards established by CNFC. Don Johnson, executive vice president, product development, Oracle Cloud Infrastructure said, “With the growing popularity of the CNCF as a unifying and organizing force in the cloud native ecosystem and organizations increasingly embracing multi cloud and hybrid cloud models, developers should have the flexibility to build and deploy their applications anywhere they choose without the threat of cloud vendor lock-in. Oracle is making this a reality,” To know more about this news, check out the press release. Introducing ‘Pivotal Function Service’ (alpha): an open, Kubernetes based, multi-cloud serverless framework for developer workloads Red Hat acquires Israeli multi-cloud storage software company, NooBaa Cloud Native Application Bundle (CNAB): Docker, Microsoft partner on an open source cloud-agnostic all-in-one packaging format

Yesterday, GitLab and TriggerMesh introduced GitLab Serverless which helps enterprises run serverless workloads on any cloud with the help of Google’s Kubernetes-based platform Knative, which is used to build, deploy, and manage serverless workloads. GitLab Serverless enables businesses in deploying serverless functions and applications on any cloud or infrastructure from GitLab UI by using Knative. GitLab Serverless is scheduled for public release on 22 December 2018 and will be available in GitLab 11.6. It involves a technology developed by TriggerMesh, a multi cloud serverless platform, for enabling businesses to run serverless workloads on Kubernetes. Sid Sijbrandij, co-founder and CEO of GitLab said, “We’re pleased to offer cloud-agnostic serverless as a built-in part of GitLab’s end-to-end DevOps experience, allowing organizations to go from planning to monitoring in a single application.” Functions as a service (Faas) With GitLab Serverless, users can run their own Function-as-a-Service (FaaS) on any infrastructure without worrying about vendor lock-in. FaaS allows users to write small and discrete units of code with event-based execution. While deploying the code, developers need not worry about the infrastructure it will run on. It saves resources as the code executes only when needed, so resources don’t get used while the app is idle. Kubernetes and Knative Flexibility and portability can be achieved by running serverless workloads on Kubernetes. The Serverless uses Knative for creating a seamless experience for the entire DevOps lifecycle. Deploy on any infrastructure With Serverless, users can deploy to any cloud or on-premises infrastructure. GitLab can connect to any Kubernetes cluster so users can choose to run their serverless workloads anywhere Kubernetes runs. Auto-scaling with ‘scale to zero’ The Kubernetes cluster automatically scales up and down based on the load. The "Scale to zero" is used for stopping consumption of resources when there are no requests. To know more about this news, check out the official announcement. Haskell is moving to GitLab due to issues with Phabricator GitLab 11.5 released with group security and operations-focused dashboard, control access to GitLab pages GitLab 11.4 is here with merge request reviews and many more features

Yesterday, Google announced in a support page that they will ‘turn down’ Google Fusion tables. Google Fusion tables users got an email yesterday saying that the service will be retired on December 3, 2019. With Fusion tables, users can plot data on a Google Map without any coding. Fusion Tables is popular among journalists, scientists and other non-technical groups that use data visualization regularly. Google encouraged users to switch to other products, like its BigQuery cloud data warehouse system, its Google Data Studio business intelligence tool, or simply Google Sheets. The company says it’s also working to make other mapping tools, currently used internally, available. In their blog post, Google has mentioned alternatives to Fusion tables. They are: Google BigQuery Google Cloud SQL Google Sheets Google Data Studio Other tools that will be available in the coming months The email from Google, reads: “Google Fusion Tables was launched almost nine years ago as a research project in Google Labs, later evolving into an experimental product. For a long time, it was one of the few free tools for easily visualizing large datasets, especially on a map. Since then, Google has developed several alternatives, providing deeper experiences in more specialized domains.” Any maps using a Fusion Tables Layer in the Maps JavaScript API v3.37 will be met with errors from August 2019. You can download your data in various formats before Fusion tables ‘turns down’. The available formats are CSV, KML, and KML Network Link. A comment from Hacker news reads: “I stopped teaching FT because several years ago because it seemed clear, in an implicit way, that it wasn't getting the traction. I hardly ever heard anyone inside or outside of Google talk/tweet/etc about it, in the same way people do for Sheets or BigQuery. I missed the easy data-to-interactive-map workflow for teaching, but for production work, FT was just too clunky (and merge far too limited compared to a SQL join) to justify using as a data store.” https://twitter.com/Vince_Dixon_/status/1072556568665866241 https://twitter.com/jackserle/status/1072800316289179648 Seems like Fusion table never got enough traction, a story we have seem played with other recently retiring Google products like G+ and Allo. For more details, visit the Google support page. Google to discontinue Allo; plans to power ‘Messages’ with Rich Communication Services (RCS) Chat Google+ affected by another bug, 52M users compromised, shut down within 90 days Google hints shutting down Google News over EU’s implementation of Article 11 or the “link tax”

After releasing Firefox 63 in October this year, Mozilla announced the availability of Firefox 64, yesterday. This release comes with support for multiple tab selection, WebVR 1.1 on macOS, and improvements in privacy features. Mozilla has also released Firefox 65 beta with support for WebP, Flexbox Inspector tool, and more. Following are some of the updates Firefox 64 comes with: Support for multiple tab selection Firefox 64 introduces multiple tab selection to make managing windows with many open tabs easier. You can use this feature by simply holding Control (Windows, Linux) or Command (macOS) and click on tabs to select them. After selecting the tab, click and drag to move the tabs as a group, either within a given window or out into a new window. Developer Tools improvements When you hover over text, the Accessibility Inspector will now display the text contrast ratios in the pop-up infobar. Once an element is selected by the Accessibility Inspector, the highlighters show the AA contrast ratio. The infobar will also indicate whether or not the text meets WCAG 2.0 Level AA or AAA accessibility guidelines for minimum contrast. Responsive Design Mode now supports saving device selection between session. JavaScript improvements The well-formed JSON.stringify proposal by TC39 (Technical Committee 39) has been implemented. This will ensure that JSON.stringify does not returns ill-formed Unicode strings. Another improvement is that now the proxied functions can be passed to Function.prototype.toString .call(). New Web API highlights The prefix has been removed from the mozRequestFullScreen API and it is now just the FullScreen API. Also, the requestFullscreen and exitFullscreen APIs now return promises that resolve once the browser finishes transitioning between states. Firefox 64 now supports WebVR 1.1 on macOS. To receive queued worker messages even before page loading has completed, pages with Service Workers can now use the startMessages() API. Better protection with new and updated privacy features Firefox 64 will not trust TLS certificates issued by Symantec. Referrer-Policy now applies to requests initiated by CSS. The non-standard navigator.buidID property will now return a fixed timestamp, 20181001000000, to prevent its potential abuse for fingerprinting Firefox 65 beta release Along with Firefox 64, Mozilla has also released Firefox 65 beta, the stable version of which is planned to be released on Jan 29, 2019. Following are few of the updates introduced in this release: You can now change the display language for the Firefox application in the Options page. WebP, an image format that provides both lossy and lossless compression, is supported. Users can now install Firefox on Windows using an MSI installer. Handoff is supported in Firefox for macOS to continue browsing from your iOS device to your Mac. Tabs can be switched by scrolling in the tab bar in Linux. A new Flexbox inspector tool is added to detect and highlight Flexbox containers and debug Flex items' sizes. Support is added for the Storage Access API on desktop platforms. See the full list of updates on Mozilla’s website. Firefox 63.0 is released for desktop and Android, aiming to give users “greater control over technology that can track them on the web” Mozilla introduces new Firefox Test Pilot experiments: Price Wise and Email tabs

At KubeCon+CloudNativeCon this week, DigitalOcean announced the launch of its Kubernetes-as-a-Service offering to all developers. This is a limited release with full general availability planned for early 2019. DigitalOcean had first announced its container offering through an early access program in May this year followed by its limited availability in October. Building up on the simplicity factor that was appreciated the most by customers, DigitalOcean Kubernetes (DOK8s) claims to be a powerfully simple managed Kubernetes service. Once customers define the size and location of their worker nodes, DigitalOcean will provision, manage, and optimize the services needed to run a Kubernetes cluster. The DOK8s is easy to setup as well. During the announcement, DigitalOcean VP of Product Shiven Ramji said that “Kubernetes promises to be one of the leading technologies in a developer’s arsenal to gain the scalability, portability and availability needed to build modern apps. Unfortunately, for many, it’s extremely complex to manage and deploy. With DigitalOcean Kubernetes, we make running containerized apps consumable for any developer, regardless of their skills or resources.” The new release builds on the early access release of the service including capabilities like node provisioning, handling durable storage, firewall, load balancing and similar tools. Further, the new features added now include: Guided configuration experiences to assist users in provisioning, configuring and deploying clusters Open APIs to enable easy integrations with developer tools Ability to programmatically create and update cluster and nodes settings Expanded version support including Kubernetes version 1.12.1 with support for 1.13.1 shortly. Support released for DOK8s in the DigitalOcean API, making it easy for users to create and manage their clusters through DigitalOcean’s API. Effective pricing for DigitalOcean Kubernetes- Customers pay only for the underlying resources they use (Droplets, Block Storage, and Load Balancers) Head over to DigitalOcean’s blog to know more about this announcement. ‘AWS Service Operator’ for Kubernetes now available allowing the creation of AWS resources using kubectl Introducing ‘Pivotal Function Service’ (alpha): an open, Kubernetes based, multi-cloud serverless framework for developer workloads Stripe open sources ‘Skycfg’, a configuration builder for Kubernetes

Yesterday, Microsoft announced that the OpenSSH client and server are available as a supported feature-on-Demand in Windows Server 2019 and Windows 10 1809. OpenSSH is a collection of client/server utilities allowing secure login, remote file transfer, and public/private key pair management. It originated as a part of the OpenBSD project and has been used across the BSD, Linux, macOS, and Unix ecosystems, for years. In 2015, Microsoft said they would build OpenSSH into Windows, while also making contributions to its development. The Win32 port of OpenSSH was first included in the Windows 10 Fall Creators Update and Windows Server 1709 as a pre-release feature. With OpenSSH in the Windows Server 2019, organizations can work across a broad range of operating systems and also utilize a consistent set of tools for remote server administration. The community welcomes OpenSSH on Windows Server 2019 According to some on HackerNews, “Having used DSC and PowerShell remoting extensively, these create as many problems as they solve. Nothing works smoothly. Not a thing. The saving grace here will be SSH because then at least we can drive all our kit across both platforms from Ansible and be done with the entire MSFT management stack.” Another review says, “Mounting requires other ports to be opened, which no sysadmin will do on the internet. Ssh, on the other hand, can be started on a non-standard port.” “SSH is an awesome tool & capability as a relatively high-level network channel. The defacto “shell” approach leads to a lot of problems when used as a management device. It encourages ad-hoc, unstructured, and opaque changes. Managing your hosts via Secure Shell simply leads to bespoke, unrepeatable, outcomes and crushing debt.” To know more about this news in detail, visit the Windows official blog. Microsoft fixes 62 security flaws on Patch Tuesday and re-releases Windows 10 version 1809 and Windows Server 2019 Microsoft releases first test build of Windows Server 1803 How to use PowerShell Web Access to manage Windows Server

At the KubeCon+CloudNativeCon happening at Seattle this week, Elastic N.V., the pioneer behind Elasticsearch and the Elastic Stack, announced the alpha availability of Helm Charts for Elasticsearch on Kubernetes. Helm Charts will make it possible to deploy Elasticsearch and Kibana to Kubernetes almost instantly. Developers use Helm charts for its flexibility in creating, publishing and sharing Kubernetes applications. The ease of using Kubernetes to manage containerized workloads has also lead to Elastic users deploying their ElasticSearch workloads to Kubernetes. Now, with the Helm chart support provided for Elasticsearch on Kubernetes, developers can harness the benefits of both, Helm charts and Kubernetes, to instal, configure, upgrade and run their applications on Kubernetes. With this new functionality in place, users can now take advantage of the best practices and templates to deploy Elasticsearch and Kibana. They will obtain access to some basic free features like monitoring, Kibana Canvas and spaces. According to the blog post, Helm charts will serve as a “ way to help enable Elastic users to run the Elastic Stack using modern, cloud-native deployment models and technologies.” Why should developers consider Helm charts? Helm charts have been known to provide users with the ability to leverage Kubernetes packages through the click of a button or single CLI command. Kubernetes is sometimes complex to use, thus impairing developer productivity. Helm charts improve their productivity as follows: With helm charts, developers can focus on developing applications rather than  deploying dev-test environments. They can author their own chart, which in turn automates deployment of their dev-test environment It comes with a “push button” deployment and deletion of apps, making adoption and development of Kubernetes apps easier for those with little container or microservices experience. Combating the complexity related of deploying a Kubernetes-orchestrated container application, Helm Charts allows software vendors and developers to preconfigure their applications with sensible defaults. This enables users/deployers to change parameters of the application/chart using a consistent interface. Developers can incorporate production-ready packages while building applications in a Kubernetes environment thus eliminating deployment errors due to incorrect configuration file entries or mangled deployment recipes. Deploying and maintaining Kubernetes applications can be tedious and error prone. Helm Charts reduces the complexity of maintaining an App Catalog in a Kubernetes environment. Central App Catalog reduces duplication of charts (when shared within or between organizations) and spreads best practices by encoding them into Charts. To know more about Helm charts, check out the README files for the Elasticsearch and Kibana charts available on GitHub. In addition to this announcement, Elastic also announced its collaboration with Cloud Native Computing Foundation (CNCF) to promote and support open cloud native technologies and companies. This is another step towards Elastic’s mission towards building products in an open and transparent way. You can head over to Elastic’s official blog for an in-depth coverage of this news. Alternatively, check out MarketWatch for more insights on this article. Dejavu 2.0, the open source browser by ElasticSearch, now lets you build search UIs visually Elasticsearch 6.5 is here with cross-cluster replication and JDK 11 support How to perform Numeric Metric Aggregations with Elasticsearch

Yesterday, Jean-Baptiste Kempf, VideoLAN president announced dav1d 0.1.0. dav1d is an AV1 decoder from VideoLAN, the same company that offers the popular VLC Media Player. dav1d was first presented in Video Developer Days 2018. The first usable version of dav1d, dav1d 0.1.0 is dubbed as Gazelle. In this release users can use the API, ship the decoder, and expect to receive some support from the developers. New features in dav1d 0.1.0 Since the initial launch of dav1d in September 2018 there has been a lot of work done on it: All AV1 features are now supported, even the ones that are less known 8, 10, 12 bits, and all chroma sub-samplings are supported by dav1d 0.1.0 All AV1 files shared to the developers are supported Developers invested a lot of time to make dav1d 0.1.0 quick, while keeping a maintainable binary size. More assembly for desktop is added. Some assembly for ARMv8, and for older machines (SSSE3) has been merged. In single-thread, on ARMv8, dav1d is now as fast as libaom. With more threads it is even faster. Some more SSSE3 code is being merged. So, dav1d will soon be faster than other decoders, on all platforms. There is also some work being done on shaders, potentially to bring the Film Grain feature. Some benchmarks of dav1d 0.1.0 Biggest advantage dav1d has is its high scalability. The performance gets much better as the number of threads goes up. Results from a 32-core AMD Epyc processor: Source: Medium As you can see. aomdec caps out at 8 threads while dav1d keeps on scaling with higher number of threads. Performance on smartphone processors: Source: Medium On multiple cores, 1080p 30fps can be decoded by most high-end chips released in the past two years. On an Apple A12X, 1440p at 60fps and 4K at 30fps is possible! For more benchmarks and complete comparisons, visit the Medium Post. Presenting dav1d, a new lightweight AV1 decoder, by VideoLAN and FFmpeg A new Video-to-Video Synthesis model uses Artificial Intelligence to create photorealistic videos Mozilla shares how AV1, the new open source royalty-free video codec, works

Python team released Python versions 3.7.2rc1 and 3.6.9 rc1 yesterday.  Python 3.7.2rc1 is the release preview of the second maintenance release of Python 3.7. Python. 3.6.8rc1 is the release preview of the eighth and last maintenance release of Python 3.6. These latest releases include the addition of new features. Key Updates in Python 3.7.2rc1 A new C API for thread-local storage has been added in Python 3.7.2rc1. A new Thread Specific Storage (TSS) API has been added to CPython which takes over the existing TLS API within the CPython interpreter while removing the existing API. Deterministic .pyc files have been added. These .pyc files are called “hash-based”. Python still uses timestamp-based invalidation by default and does not generate hash-based .pyc files at runtime. Hash-based .pyc files can be generated with py_compile or compileall. Core support added in Python 3.7.2rc1 for typing module and generic types. Customized access to module attributes is allowed, meaning you can now define __getattr__() on modules and can call it whenever a module attribute is not found. Defining __dir__() on modules is also allowed. DeprecationWarning handling has been improved. The insertion-order preservation nature of dict objects has now become an official part of the Python language spec. Key Updates in Python 3.6.8rc1 Preserving Keyword Argument order has been added in Python 3.6.9rc1, meaning that **kwargs in a function signature is now guaranteed to be an insertion-order-preserving mapping. Python 3.6.8rc1 offers simple customization of subclass creation without using a metaclass.The new __init_subclass__ classmethod gets called on the base class when a new subclass is created. A new “secrets” module has been added to the standard library that reliably generates cryptographically strong pseudo-random values suited for managing secrets like account authentication, tokens, etc. A frame evaluation API has been added to CPython that makes frame evaluation pluggable at the C level. This allows debuggers and JITs to intercept frame evaluation before Python code execution begins. Python 3.6.8rc1 offers formatted string literals or f-strings. Formatted string literals work similarly to the format strings accepted by str.format(). They comprise replacement fields that are surrounded by curly braces. The replacement fields are expressions, that are evaluated at run time, and formatted using the format() protocol. For more information, check out the official release notes for Python 3.7.2rc1 and 3.6.8rc1. Python 3.7.1 and Python 3.6.7 released IPython 7.2.0 is out! SatPy 0.10.0, python library for manipulating meteorological remote sensing data, released

The KubeCon+CloudNativeCon happening at Seattle this week has excited developers with its plethora of new announcements and releases. This conference dedicated to Kubernetes and other cloud native technologies, brings together adopters and technologists from leading open source and cloud native communities to discuss new advancements at the cloud front. At this year’s conference, Google Cloud announced the beta availability of ‘Istio’ for its Google Kubernetes Engine. Istio was launched in the middle of 2017, as a result of a collaboration between Google, IBM and Lyft. According to Google, this open-source “service mesh” that is used to connect, manage and secure microservices on a variety of platforms- like Kubernetes- will play a vital role in helping developers make the most of their microservices. Yesterday, Google Cloud Director of Engineering Chen Goldberg and Director of Product Management Jennifer Lin said in a blog post that the availability of Istio on Google Kubernetes Engine will provide “more granular visibility, security and resilience for Kubernetes-based apps”. This service will be made available through Google’s Cloud Services Platform that bundles together all the tools and services needed by developers to get their container apps up and running on the company’s cloud or in on-premises data centres. In an interview with SiliconANGLE, Holger Mueller, principal analyst and vice president of Constellation Research Inc., compared software containers to “cars.” He says that  “Kubernetes has built the road but the cars have no idea where they are, how fast they are driving, how they interact with each other or what their final destination is. Enter Istio and enterprises get all of the above. Istio is a logical step for Google and a sign that the next level of deployments is about manageability, visibility and awareness of what enterprises are running.” Additional features of Istio Istio allows developers and operators to manage applications as services and not as lots of different infrastructure components. Istio allows users to encrypt all their network traffic, layering transparently onto any existing distributed application. Users need not embed any client libraries in their code to avail this functionality. Istio on GKE also comes with an integration into Stackdriver, Google Cloud’s monitoring and logging service. Istio securely authenticates and connects a developer’s services to one another. It transparently adds mTLS to a service communication, thus encrypting all information in transit. It provides a service identity for each service, allowing developers to create service-level policies enforced for each individual application transaction, while providing non-replayable identity protection. Istio is yet another step for GKE that will make it easier to secure and efficiently manage containerized applications. Head over to TechCrunch for more insights on this news. Google’s Cloud Robotics platform, to be launched in 2019, will combine the power of AI, robotics and the cloud What’s new in Google Cloud Functions serverless platform Introducing ‘Pivotal Function Service’ (alpha): an open, Kubernetes based, multi-cloud serverless framework for developer workloads

Yesterday, the FreeBSD release engineering team announced the availability of FreeBSD 12.0, which marks the first release of the stable/12 branch. This version is available for the amd64, i386, powerpc, powerpc64, powerpcspe, sparc64, armv6, armv7, and aarch64 architectures. FreeBSD is an open source, Unix-like operating system for x86, ARM, AArch64, RISC-V, MIPS, POWER, PowerPC, and Sun UltraSPARC computers. It is based on the 4.4BSD-Lite release from Computer Systems Research Group (CSRG) at the University of California at Berkeley. It comes with features like preemptive multitasking, memory protection, virtual memory, multi-user facilities, and SMP support. Following are some of the updates introduced in FreeBSD 12.0: The bsdinstall installer and zfsboot are updated to allow a UEFI+GELI installation option. GOST is removed, and LDNS now enables DANE-TA. sshd now comes with additional support for capsicum. Also, capsicum is enabled on armv6 and armv7 by default. The VIMAGE kernel configuration option is enabled by default. The NUMA option is enabled by default in the amd64 GENERIC and MINIMAL kernel configurations. The netdump driver is added for transmitting kernel crash dumps to a remote host after a system panic. The vt driver now comes with better performance, drawing text at rates ranging from 2- to 6-times faster. The UFS/FFS filesystem is updated to consolidate TRIM/BIO_DELETE commands, resulting in fewer read/write requests. This is enabled by default in the UFS/FFS filesystem and can be disabled by setting the vfs.ffs.dotrimcons sysctl to 0, or adding vfs.ffs.dotrimcons=0 to sysctl.conf. The pf packet filter can now be used within a jail using vnet. The bhyve utility is updated to add NVMe device emulation and it is now also able to be run within a jail. Various Lua loader improvements such as detecting a list of installed kernels to boot and support for module blacklists. Upgraded components Clang, LLVM, LLD, LLDB, compiler-rt, and libc++ is updated to 6.0.1. OpenSSL is updated to 1.1.1a (LTS). Unbound is updated to 1.8.1 OpenSSH is updated to 7.8p1. The vt(4) Terminus BSD Console font is updated to 4.46. KDE has been updated to version 5.12.5. The NFS version 4.1 server is updated to include pNFS server support. You can install FreeBSD 12.0 from a bootable ISO image or over the network. Some architectures also support installing from a USB memory stick. To read the entire list of update in FreeBSD 12.0, check out its release notes. LibrePCB 0.1.0 released with major changes in library editor and file format Systems programming with Go in UNIX and Linux AMD ROCm GPUs now support TensorFlow v1.8, a major milestone for AMD’s deep learning plans