Date: 19 Nov 2020

The title of this article may be a little provocative, but that’s a general idea. This time, it’s not just about sharing knowledge. We want to start a discussion about different approaches to programming. And also about how ignoring or underappreciating certain aspects may harm the final product. Let’s start with defining a microservice. It’s […]

Date: 12 Nov 2020

The relation between Scrum and Agile is similar to the one between squares and rectangles. If you use Scrum, you also use Agile. But if you use Agile, this doesn’t necessarily mean that you also use Scrum. The main difference between Scrum and Agile is that while Scrum is quite specific about how you do […]

Date: 09 Nov 2020


In today’s world data is king, and you need to have a clear view of its entirety if you want to make proper use of it. In this article, we’ll show you how to use widely available free tools to create a platform for streaming data from multiple sources to a single source – for […]

Date: 29 Oct 2020

Yes, you had a team meeting last quarter, but what about 1-on-1 meetings? We believe that every manager should find time to talk directly to their team members. Here’s why, and how, you can not only make it happen, but also make it work.

Date: 22 Oct 2020


Redux has been the go-to solution for state management since its conception in 2015. “You should use Redux” became such a universal truth, that so many started using it without really understanding why. And there are situations where Redux isn’t the best option. Some started to look for alternatives, including adopting other state management libraries or creating their own.

Date: 16 Oct 2020

In this article, I want to present intuition that stands behind bias-variance decomposition.
We can see the process of learning from different perspectives. In machine learning, in general, we can see learning as a process leading us to find the best hyperplane that allows us to explain our problem. In this heuristic definition there are two aspects that are key to understanding the process: “the best” and “explain our problem”. In any kind of learning, we have access only to some part of the information, so we can assume that the data we have can represent only some aspects of our problem. All this data always will be only a representation of some phenomenon, so we can intuitively feel that it will be somehow misled by different kinds of mistakes.