About

Jonatan Borkowski

Software Engineer · Poland

I’m a software engineer with over 10 years of commercial experience building systems that matter. If I had to describe my approach in a single sentence, it would be: I never stop pushing the boundaries of software engineering. I believe that solving challenging problems is one of the most fulfilling aspects of my work, so I constantly deepen my knowledge with “a child’s curiosity.”

I’m a lifelong learner who studied not only programming languages, software engineering architectures, and embedded systems but also human languages. I constantly push myself outside my comfort zone to explore the unknown and learn something new. Ultimately, I believe that being well-educated means being happy and kind, not just accumulating technical knowledge—a philosophy I try to carry into both my work and my interactions with others.

Where I am now

Right now, I’m the opposite of a Luddite when it comes to AI. But my mindset and state of the mind change rapidly through the early adaptation in my development workflow, the limitations, and the unconscious use of genAI. Even if my performance is not boosted tenfold, using and exploring still gives me a valuable lesson—knowledge. And this background is wide, starting from computer vision through audio, and finally to replacing gray-ish coders like me. But don’t be scared by this; it’s just another “shift”—be there with me.

Connecting the Dots

Steve Jobs said it best: “You can’t connect the dots looking forward; you can only connect them looking backward.”

The dots are scattered everywhere—preprints, papers, tutorials. You read them, get pulled into the dopamine rush of “state of the art,” wonder if you’re already behind. But keep reading. Eventually, something clicks and the dots connect.

Reading posts here about building voice agents, preparing evals, or managing context might spark doubts. That’s fine. Past experience will fill the gaps.

In such moments, I get brain squeeze—“oh fuck” moments. I’ll post about those to help you connect your own dots.

The dots only make sense looking backward. But they prepare you for what’s ahead.

Why “The Reasoning Trace"?

A reasoning trace is the path an AI takes to reach a conclusion—the visible chain of thought. I chose this name because:

  1. It reflects my focus on AI and LLMs.
  2. It represents transparency in thinking.
  3. It’s how I approach problems: step by step, showing my work.

When an LLM “thinks out loud,” it produces a reasoning trace. When I write here, I’m doing the same—sharing not just conclusions, but the path that led to them.

Get in Touch

I love connecting with other engineers and AI enthusiasts exploring similar territory. Feel free to reach out:

– Jonatan
When I’m not writing code or seeking boundaries of human perception, you can probably find me chasing wind in Tarifa or cruising the roads around Garda lake. Life’s too short to sit still.