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Weekly round-up: Kubernetes 1.15, Istio 1.2, TIOBE Index update & more

JAXenter Editorial Team
© Shutterstock / RetroClipArt (modified)

Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, we welcomed the latest version of Kubernetes as well as Istio 1.2. Let’s have a look.

Kubernetes 1.15 arrives

The second Kubernetes release for 2019 is here! Kubernetes 1.15 has continuous improvement and extensibility as its main themes and it brings a nice list of improvements. This release includes a total of 25 enhancements including two features moving to stable and 13 in beta.

Check out all the interesting highlights here.

Istio 1.2 is out

The Istio team is back with a prompt release of Istio 1.2. The previous major release, Istio 1.1, took quite some time to go out due to some heavily manual work on testing and infrastructure. For that reason, 1.2 focuses on improving the stability of the features introduced in Istio 1.1 and the past several 1.1.x releases, and improving general product health.

Have a look at new release here.

TIOBE Index for June 2019

According to the TIOBE Index, Java is the most used programming language. It’s no surprise to see it rank so high on the index, month after month, with only a few dips and slips. Overall, Java popularity remains fairly consistent, marking it as a safe workhorse of a language for any developer. It stands right beside C in this regard.

However, will Java remain standing, unshaken in future indexes to come? Check out the latest update of the TIOBE Index here and find out more!

VS Code introduces a package installer for Java developers

News for Java developers: VS Code announced a new installer for Java. The package auto detects if you have the most recent components and installs and configures stable version of Java tools with the press of a button. Will you be giving VS Code a try or do you prefer another code editor?

Find out all the details here.

Meet PyTorch Hub

Currently, in beta phase, meet PyTorch Hub. According to its documentation, it is a “pre-trained model repository designed to facilitate research reproducibility“. This API and workflow allow for easier, more transparent reproducibility, which is an important part of machine learning and reliable data science.

Check it out here.

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