Since we are two career-jumpers, we have many skills from other fields. Each former job helps us to be better developers. Different jobs and fields have different mindsets that can be merged into a new way of thinking.

Law helps us to see the data without any mislead by prior knowledge. Law also forms us to write well organized codes. Furthermore, any coding problem has a legal side. We can translate the technical problem to the language of law and vice versa.

There is no solution without business. As former economists, we understand the importance of business planning and design thinking. Businesses can suffer from inefficiency and wasting resources. Optimizing the code or a training of AI is really important due to the limited resources, like process time or GPU memory.

As companies affect each other in the real world, the relation between softwares or parts of code can be competitive or cooperative. We use these skills to build better architecture with parallel programming or implement the business logic in the source code where each function, methods or classes support the optimal running.

We try to make contributive relations in our work with colleagues or competitors. We think, building new and prospering relationships are as important as finishing the job within the deadline.

Teaching skills help us to train models better and understand the world from the view of the models. It is really important, since AI should not be a black box. These can be enormously complex, but the results and the workflow can be visualized and understood. In our daily life we try to bust these myths and translate the world of AI into a common language.

We really like to run on hackathons. These help us to widen our burdens and learn new things. Some years ago it was unimaginable for us to work with Earth Observation (EO) data from satellites or to work with real MRI images or genomic data. We are healthcare-oriented developers, but we are open to try ourselves in different fields.

We daily use Machine Learning or Deep Learning models. However, there is some problem where a well written short script can be better than a robust neural network. A computer vision task can be processed with various approaches based on for example the limitation of resources or the need of an assignor or an employer.

We like to solve problems with AI and we really like to learn every day. Trying new methods for a task can lead to a better solution. The world is full of problems, but we like challenges and like to work with AI to solve them.