Almost three years ago I did a little experiment: What is the effect of AI on Journalism? How easy is it, too create vaguely accurate information that looks professional, yet is totally trembling at the slightest touch - or even is plain wrong. 

I revisited this experiment now, moving beyond the experiment on the edge of acceptability and well beyond trustworthiness: moving it towards something really useful: a self-run News Website, that get's updated fully autonomously, uses a variety of sources and does proper research on every article it writes. And here it is: MockMirror, v2.0

Prediction for 2026: For AI Supported Coding, the "Loop Velocity" will become a key differentiator. With closed-loop agents like Claude Code or Antigravity, the AI doesn't just write code; it executes, identifies errors, and self-corrects.

Generative AI is probably one of the biggest innovations in the current decade. It has enormous potential to change our private and professional lifes.

Most AI applications today are focused on solving a very concrete task, automating business processes with stunning precision and efficiency. But recently another kind of AI has received a high level of attention, as it is suited to fulfill a variety of tasks and support in new domains – Generative AI. Let’s take a look at some of the most prominent examples!

In contrast to 2015, when the Data Science hype was rolling across Germany, we now do have more and more universities offering specializations or whole degrees in Data Science and tightly related disciplines. Yet, still many aspiring Data Scientists have completed different studies, e.g., computer science, physics, mathematics or economics. To be honest - I highly appreciate the diverse backgrounds of Data Scientists I have been collaborating with.

Coming from a non-DS field, you'll need to do some additional homework in order to keep up with ML natives. But in the age of MOOCs (massive open online courses), there are enough offerings. I'll try to provide an overview and give some guidance. 

Data Scientist are pack animals - they need a team to develop and achieve their maximum productivity. Leaving them as individual fighters will turn them inefficient, stuck too often, lost in complicated projects - and most likely lead to churn. But how do you build a Data Science team? How do you achieve a good skill mix? And how do you create a culture of creativity, open-mindedness and productivity? There is not the "one size fits all" answer, but at least there are some clear guidelines.