My very first step was a mentorship from an American company. At that moment, I was finishing my studies, and I wanted to get practical experience in IT and see things in practice.

They gave me a choice: I could either start with something simple to make my first money with easy work, or I could start with something completely unknown and learn.

That kind of challenge really suits my personality, so I chose it. I started with a really simple project, and when I saw the first results, it immediately felt right. As I said at the time, “When I saw our first results, even though the project was simple, I said, ‘Okay, this is for me. I feel really good doing this, so this is for me.”

There were a lot of challenges because everything was new. It was both the beginning of my career and the beginning of AI becoming widely discussed.

At the same time, there was a lot of media coverage around AI. Every day you hear people talking about AI and making money with it.

But the way it is presented is often misleading. Everyone is talking about AI and there are a lot of videos about how to become a millionaire in 10 minutes — and that is definitely not how it works.

What beginners really need is direction and advice about how to start and what path to follow.

When I started, it was very hard to find other people who were interested in working with AI.

Even though people talked about AI a lot online, it was difficult to find people who were actually building things and learning together.

For me personally, that was one of the biggest challenges. I had no one to talk to about AI.

That’s why I believe a good community could really help people who want to learn and work in this field.

In my close environment people are supportive, even if many of them still don’t fully understand what I do.

Some people see AI professionals as people who watched a few videos about ChatGPT and decided to make money with it.

But that’s not the reality. Learning AI requires time and dedication. First, people who are starting with this need to invest in knowledge.

From the beginning, I started working with n8n, and I still use it today.

I mainly use it for API integrations and automation architecture. For discussions, planning, and advice, I often use ChatGPT.

As I often say, I started with n8n from the beginning, and I’ve never really felt like I wanted to change.

Starting from my own experience, I began with prompt building and prompt engineering.

It’s important to learn how to write prompts, verify outputs, and perform multiple checks.

Learning about APIs and integrations is also very important because many systems rely on connecting tools together. But overall, problem-solving is the key skill.

When I talk to beginners, I often tell them: start by learning about prompts and take your time to learn how to build them. And you need to know when to stop just learning and start combining work with learning.

I already automate repetitive tasks whenever possible.

But you cannot fully delegate everything to AI right away. You first need to train the system and build a knowledge base. We can’t let AI replace our judgment. We need to use AI well.

You also need to verify outputs and perform multiple checks before trusting the automation. You cannot delegate 100% without first training the AI and building a solid knowledge base.

This is a very common feeling. Because AI evolves so quickly, people often feel like they are falling behind if they take even a short break.

But the reality is different. The expansion of AI has been so fast that everyone is still learning.

As I like to remind people, no one knows everything about AI.