Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Back in the days when I was a junior dev, I used to marvel at my supervising senior dev’s ability to create code at insanely break-neck speed.
Within moments, he’d have a working piece of code with prototyped data almost ready and in a semi-working state. There were a lot of tabs pressed, his fingers never leaving the keyboard to touch the mouse for the duration of his demonstration.
Python has become a very popular programming language with over 8.2 million developers. One of the appeals is its flexibility, which includes options to use it over the cloud. It is increasingly common for Python developers to write and execute code over cloud servers. The main advantage of writing and executing Python code on the cloud, apart from having more features than a local computer, is the ability to share configurations and perform collaborative work easily.
The downside is that there are some challenges that Python developers face over the cloud. Since the Python ecosystem is evolving at a rapid pace, you may have problems using the most recent features. You may also have issues sharing your work. This is partly because the configuration of Python libraries (and versions) varies considerably among team members.
The nice folks of TechDays Finland provided me with the opportunity to speak about Microservices for building an IDE - The innards of JetBrains Rider. It was nice to be able to talk about some of the internals, architecture and design decisions that were made while building a .NET IDE - Rider.
If you’re interested in the slides, find them below. This story is also available as an article on CODE Magazine: Building a .NET IDE with JetBrains Rider.