Time management has been engrained in our brains since we were young. Even when we were in grade school, we had specific times every day when we completed certain tasks, whether it be reading time, math time, lunchtime, or even nap time. Well now it’s a little different, but still essentially the same. Even though we may not have nap time every day (though I’m sure many people wished we did!), we still have certain tasks that need to be completed every day.

There has never been more pressure on development teams to build software faster and more efficiently. The rise in popularity of DevOps has largely been the result of its promise to speed up dev cycles, increase agility, and help teams resolve issues more quickly. And while the availability and sophistication of DevOps tools have improved greatly in the last few years, simply choosing the latest and greatest tools is no guarantee of a smooth, problem-free development lifecycle.

The principles of Agile project management took root in the software development and engineering fields. There are many different methodologies (such as Scrum or Kanban) on how to implement these processes, but agile does not have to be complicated and can be applied to any field (but maybe shouldn’t be). This article aims to take you a few steps beyond the simple principles of Agile, with actionable information, while avoiding the dogma of any particular methodology.

When it comes to switching to a new programming language, timing is of the essence in order to land qualified programmers for the project. The language must be new enough to bring technical progress, and at the same time tested and disseminated in such a way that there are enough qualified programmers who will enjoy working with it.

Artificial Intelligence refers to the simulation of human intelligence processes by machines, computer systems to be precise. These processes primarily include learning, reasoning, and self-correction. Machine learning is a term that is synonymous with AI. As the name suggests, machine learning refers to empowering machines to learn by themselves using the data provided and predict the best possible outcome of a complex problem.