Technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have led the way to software robots that reduce the manual, time-consuming, and repetitive actions performed on digital platforms. The concept of automating tasks on digital platforms is called robotic process automation (RPA). RPA is a software robot that interacts with computer-centric processes and aims to introduce a digital workforce that performs repetitive tasks previously completed by humans. This Refcard introduces RPA technology, how it works, key components, and how to set up your environment.

I’ve been regularly asked about comparisons between Docker Desktop and Rancher Desktop. As I have moved off of Rancher Desktop to work on other things at SUSE, I figure now is a good time to write up some of my thoughts. Note, there is an amazing team working on it now. They are incredibly talented and have made it better than I imagined.
First, I need to say what respect I have for the people who have worked on Docker Desktop. Having worked on a cross-platform container desktop app, I’ve learned about so many nuances you have to deal with. They’ve done a lot of subtle work that I’ve learned to appreciate.

If you’re developing web apps using Ruby on Rails, you probably already know that Rails is an MVC (Model-View-Controller) framework, which means that you have your Models responsible for data, Views responsible for templates, and Controllers responsible for request handling. But the bigger your app gets, the more features it has – the more business logic you will have. And here comes the question, where do you put your business logic? Obviously, it’s not viewed that should handle it. Controllers or Models? That will make them fat and unreadable pretty soon. That’s where Service Objects come to the rescue. In this article, we’ll find out what are Rails Service Objects and how you can use them to make your app cleaner and keep it maintainable.
Let’s say you have a project for handling cab trips; we’ll take a look at the particular controller action, which updates trip records. But it should not only update trips based on user input params (e.g., starting address, destination address, riders count, etc.), but it should also calculate some fields based on those params and save them to the database. So, we have a controller action like this:

Service mesh is the next best move that enterprises can take to overcome security and networking challenges obstructing Kubernetes deployment and container adoption. Check out some popular tools for deploying service mesh here in this blog!
What Is a Service Mesh?
Before we read about these tools, let’s know what Service mesh is in Kubernetes. A service mesh is a technology pattern that can be applied to microservices-based applications for managing networked communication between services. It ensures that the communication between the services within the containerized infrastructure is fast, reliable, and secure.  

Enums or enumerations are a new feature introduced in PHP 8.1 that contains a defined number of possible values you can use. When creating an app, you often come across scenarios where you have a predetermined list of options to select from, for instance:

A blog entry can be published, in the draft, or in review.
A player may be a medic, soldier, engineer, 
A ticket may be VIP, standing, or seated, 
and so on …

Those who have spent any time studying models and frameworks for things like audio classification projects, NLP, and/or computer vision, are likely wondering how to use Hugging Face for some of these models. Hugging Face is a platform that serves both as a community for those working with data models as well as a hub for data science models and information.
When using Hugging Face for NLP, audio classification, or computer vision users will need to know what Hugging Face has to offer for each project type as opposed to other options. Users will also need to have a deeper understanding of what a Hugging Face model is and how to use Hugging Face for their own data science projects.

Generated by Feedzy