Operations | Monitoring | ITSM | DevOps | Cloud

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. In this edition, we’ll learn about digital humans, which can be used in virtual spaces to enable operations of various industry applications.

Nobody likes using an unstable mobile app or even worse, an app that crashes on them. In fact, 9 out of 10 US and UK consumers report uninstalling a mobile application due to poor performance. Crash rates and snappy experiences matter for all applications, but especially for mobile apps. Mobile app crashes and poor performance not only cause users to abandon an app but can also trigger the app to be ranked lower in Apple App Store and Google Play Store search results.

One of the things we love about working in the cloud is the ease and scalability it brings to application development. It enables us to build out applications, APIs and any infrastructure that is needed from prototyping an idea, through to self scaling deployments. Monitoring and troubleshooting production-level serverless applications is always tricky, Especially working across a number of services and the many logs they can produce.

Since the release of Ninja Ticketing, one of our partners’ most frequent requests has been to add ticketing functionality to the Ninja mobile app. Today, we’re excited to announce that the Ninja mobile app now includes key ticketing functionality, including the ability to: This new functionality comes to Ninja on top of existing capabilities for endpoint management, including: App Store or Google Play.

Machine learning has garnered a lot of attention in the past few years. The reason behind this might be the high amount of data from applications, the ever-increasing computational power, the development of better algorithms, and a deeper understanding of data science. We have already talked about artificial intelligence (AI) in a previous blog post. In this opportunity, we will learn about machine learning, what it is and how it works with examples and ITSM applications.

Nate Lee here, and I’m one of the founders of Speedscale. The founding team’s worked at several observability and testing companies like New Relic, Observe Inc, and iTKO over the last decade. Speedscale traffic replay was borne out of a frustration from reacting to problems (even if they were minor) that could have been prevented with better testing.

When developing the new exception landing pages we recently launched (like insert exception link here), I wanted to pull some statistics from Reddit. While looking through various ways to integrate, I found an easy approach that I want to share with you in this post. You probably already know Reddit, the highly active social news aggregation and discussion forum. I've found myself using Reddit more and more over the last couple of years, with the dotnet subreddit in particular.

You’ve heard about artificial intelligence everywhere. It’s been represented and discussed in popular media like films and television, as well as in extensive and potentially fearmongering think-pieces saying that its sentience could spell the end of our times. But one thing is for certain: artificial intelligence is very real and — despite what certain sci-fi films might tell you — a really helpful tool for us humans.