DeepSeek’s R1 had it’s time in the spotlight as a strong reasoning model that came ‘out of nowhere’. One of the highlights of the model was that it was released publicly, including both the training process and weights. However, one thing lacking from the paper was an overview of the pipeline. Unsurprisingly, there are a few steps involved to produce such great results.

Wouldn’t it be great if you could communicate directly with your dog? If you could ask him why he bit your furniture, or just understand what he’s barking about? While research has tried address this in the past, the problem is still far from solved, and potentially unsolvable. Let’s see why.

When training multilingual models a common problem is language mixing or code switching, in which models may respond in multiple languages when we would expect them to use just one. This can also happen in reasoning models, such as DeepSeek-R1. In their paper, they found that

Sometimes when reading books it can be hard to get a grasp for a problem in practice. This video popped up on IG showcasing one of the important principles of multi-agent systems.

With “agents” the hot topic of 2025, we should take a step back and ask ourselves, why do we want autonomous software? Let’s explore some reasons we’d want agents making decisions themselves.