RHEL fork @AlmaLinux intends to fill the gap left by the demise of the @IBM @RedHat @CentOS gnu/linux distro

AlmaLinux is an open-source, community-driven project that intends to fill the gap left by the demise of the CentOS stable release. AlmaLinux is a 1:1 binary compatible fork of RHEL® 8 and it is built by the creators of the established CloudLinux OS

AlmaLinux – Forever-Free Enterprise-Grade Operating System

Sponsored Post Learn from the experts: Create a successful blog with our brand new courseThe WordPress.com Blog

WordPress.com is excited to announce our newest offering: a course just for beginning bloggers where you’ll learn everything you need to know about blogging from the most trusted experts in the industry. We have helped millions of blogs get up and running, we know what works, and we want you to to know everything we know. This course provides all the fundamental skills and inspiration you need to get your blog started, an interactive community forum, and content updated annually.

goog enables more #android #gambling #beevil #dontbeevil #smn #bmn

goog enables more android gambling

goog are

expanding the number of countries where developers can publish licensed real-money gambling apps to include: Australia, Belgium, Canada, Colombia, Denmark, Finland, Germany, Japan, Mexico, New Zealand, Norway, Romania, Spain, Sweden, and the United States. (Existing permitted countries include Brazil, Ireland, France, and the United Kingdom.)

eclipse vert.x

eclipse vert.x

summary

is a tool-​kit for build­ing re­ac­tive ap­pli­ca­tions on the JVM. Re­ac­tive ap­pli­ca­tions are both scal­able as work­loads grow, and re­silient when fail­ures arise. A re­ac­tive ap­pli­ca­tion is re­spon­sive as it keeps la­tency under con­trol by mak­ing ef­fi­cient usage of sys­tem re­sources, and by pro­tect­ing it­self from er­rors.

bodywork #devops #automation #framework for #AI #ML and @kubernetes

Bodywork

overview

is a deployment automation framework for machine learning in Python. It helps you schedule batch jobs, serve models and deploy ML pipelines, in containers on Kubernetes.

It automates repetitive and time-consuming tasks that machine learning engineers think of as DevOps, freeing them to focus on what they do best – solving data problems with machine learning.