Weekly round-up: Angular v8, new Kubernetes native Java framework, TensorFlow Privacy & more
Every Monday, we take a step back and look at all the cool stuff that went down during the previous week. Last week, we had news coming from all direction with some announcements *cough* Quarkus *cough* stealing the spotlight!
Quarkus brings Java on a subatomic level!
At the age of Kubernetes, microservices, serverless, and cloud-native application development that can deliver higher levels of productivity and efficiency, than the standard monolithic applications build on Java, those rapidly evolving trends call to rethink how Java can be best utilized to address these new deployment environments and application architectures.
In response to this call for evolution, Red Hat announces the release of Quarkus, a Kubernetes native Java framework. Find out more about this new tool here.
On the road to Angular v8
The beta season for Angular v8 began in mid-January and we’re eager to see the final result. The eighth beta is already here but even though Angular v8.0.0 is set for general availability around May, the team put up a warning that the plan is subject to change and a fixed schedule cannot be set. Check out what’s new in Angular 8.0.0-beta.7 here.
IntelliJ IDEA 2019 progress report
JetBrains team doesn’t rest! Barely a week has passed since the release of the first IDEA 2019.1 Beta and we are already at IDEA 2019.1 Beta 2! Check out our thread to find out all the updates brought by the latest release.
Uber open sources highly scalable P2P Docker registry
Besides their ridesharing app, Uber occasionally contributes to open source on GitHub. Previously open sourced projects include an orchestration engine, a robotics visualization framework, and a visual exploration data set. Uber Engineering recently open sourced their peer-to-peer Docker registry.
Kraken is used internally at Uber for managing and distributing Docker images. Now you too can get your hands (or tentacles) on it! Find out more information here.
Keep your ML data on the down low with TensorFlow Privacy
There’s a fine line between learning from and memorizing. Sometimes, our machine learning models have a problem with that. Enter TensorFlow Privacy, a new library that ensures the privacy of the initial dataset without any loss in performance thanks to differential privacy. Learn more about it here.