Operations | Monitoring | ITSM | DevOps | Cloud

Startups often face challenges in balancing rapid development with robust infrastructure. DevOps can be the solution, but selecting the right tools can be overwhelming. Achieving a good balance of cost, scaling, security, efficiency improvement, learning curve, rapid product releases, etc. is not easy. But worry not, we will solve this problem for you today. I've compiled some of the best DevOps tools that will make the DevOps journey of a startup easier.

The software development industry reacted with excitement to the news about the partnership between JFrog and GitHub and its potential impact on software development operations as covered in our recent webinar.

In today’s digital age, providing users with quick and easy access to information is crucial. An in-app knowledge base can significantly enhance user experience by offering instant support and reducing the need for external help. It is for this reason that this knowledge base has been integrated within Turbo360 to enhance the user experience. Let’s dive into what an in-app knowledge base is, its benefits, and its impact on adding it to Turbo360.

Ensuring that an application renders properly in any browser size is important for user experience but tricky to get right. With users accessing your application on devices that have a wide range of screen sizes and aspect ratios, it’s difficult to guarantee that your application will look and function as intended for every user.

Does your organization struggle with slow software releases, frequent bugs, and siloed teams? If so, you're not alone. These challenges are common in software development processes across industries. DevOps workflows offer a solution to these problems, supporting faster delivery of high-quality apps through automation, collaboration, and communication. In this article, we’ll provide an overview of DevOps workflows, explain how to implement them, and offer best practices to ensure success.

The artificial intelligence (AI) revolution is here. If you’ve been following the meteoric rise of companies like NVIDIA, you understand that the AI revolution isn’t just a passing hype cycle. To train complex models like Large Language Models (LLMs), organizations are turning back the dial to private data centers and high-performance computing (HPC) infrastructure — which requires thousands of servers all working in harmony.