Artificial intelligence (AI) is reshaping industries, and software development is no exception. By integrating AI technologies like machine learning, generative AI, and natural language processing, development teams can optimize workflows, enhance code quality, and reduce time-to-market. In this article, we’ll examine AI in software development, including its benefits, challenges, and most recent developments. Let’s get started.

It’s time to stop proclaiming that “cloud native is the future”. Kubernetes has just celebrated its 10 year anniversary, and 76% of respondents to the latest CNCF Annual Survey reported that they have adopted cloud native technologies, like containers, for much or all of their production development and deployment. Cloud native isn’t the future – it’s here and now. Data-intensive workloads are no exception.

GenAI is everywhere, and it’s changing how we approach technology. If you’ve ever wanted to dive into the world of large language models (LLMs) but felt intimidated, there’s good news! Hugging Face recently launched a self-paced course that’s perfect for beginners and more experienced enthusiasts alike. It’s hands-on, approachable, and designed to work on standard hardware thanks to the small footprint of the models.

Government contractors are tasked with the monumental responsibility of managing IT operations that are not only vast but critical to their client’s security. From securing sensitive data to ensuring compliance in distributed environments, these challenges demand a powerful and efficient solution. As a trusted choice for endpoint management in the public sector, NinjaOne helps contractors and agencies optimize IT operations, enhance security, and scale quickly.

Any IT strategist must keep in mind the business goal, so that their technology initiatives are aimed at delivering, rather than services and infrastructure, the added value of reliability and optimal performance that makes them achieve business goals and be more competitive. Read on to understand what KPIs are and how they help us with proper business management.

If you want performance snapshots for the endpoints that access your organization’s Microsoft 365 and Teams solutions, Microsoft provides a good array of tools to do the job, including Call Quality Dashboard, Teams Admin, Teams Room Pro Dashboard and Service Health. But what happens at those endpoints is only a small part of your users’ digital experience. And managing that digital experience is an increasingly big accountability for most IT departments. So what are the gaps?

We love to talk about the value of observability in accelerating feedback loops by enabling teams to understand what changes they need to make to software. But a barrier that often holds teams back from completing the feedback loop is how long it takes to actually get feedback on code under development, or push code into production.

What do you do when an incident occurs, and you need to investigate and troubleshoot? Replay data. What about performing audit trails for compliance and reporting? Replay data. Need to do system testing and validation? Replay data. There are countless reasons to replay telemetry, but the ease of doing so largely depends on the tools and infrastructure you have in place. Manual replay is often cumbersome and time-consuming, requiring access to stored raw data in logs or files.

Imagine driving to work every day on the same route. You know how long it typically takes, where traffic tends to slow down, and which shortcuts can save you time. But one day, your commute takes twice as long, and you’re left wondering – was it an accident, construction, or just bad luck? Knowing what’s “normal” for your commute helps you immediately recognize when something’s off and figure out why. The same principle applies to your network.