University and learning in public
I started publishing Python and computer vision videos while studying. Teaching each topic forced me to understand it clearly and created a habit I still follow today.
01 / ABOUT NICOLAI
Engineer by training. Educator by instinct.
I build practical AI systems, explain how they work, and help engineers turn technical skills into products, careers, and businesses.
THE SHORT VERSION
From university projects to production AI.
My journey started at university, where I created a YouTube channel to learn in public and teach the computer vision ideas I was exploring. The process was simple: build something, understand it, explain it, and repeat.
Today I design machine learning, LLM, and computer vision applications, advise companies on production AI, create technical education for a global audience, and build tools that help engineers move faster.
I care most about what happens after a model works in a notebook. The real work includes reliable software, measurable performance, useful interfaces, deployment, and clear communication.
126K+YouTube subscribers
400+technical videos
4.2M+channel views
10+ yrsbuilding with AI
THE JOURNEY
Build, teach, and create leverage.
Production AI engineering
I moved from isolated model experiments into complete software systems with data pipelines, APIs, interfaces, deployment, monitoring, and real operating constraints.
A global engineering audience
More than 400 technical videos now help 126,000+ engineers learn computer vision, machine learning, deep learning, and applied AI.
Products, programs, and companies
I build open source systems, advise companies, lead technical education, and run the AI Career Program for engineers who want a practical path into the field.
HOW I WORK
Principles behind the systems and the teaching.
Useful beats impressive
The system should improve a real decision, workflow, or product after the demo is over.
Software surrounds the model
Data, APIs, deployment, observability, interfaces, and failure handling turn model output into a dependable product.
Clear teaching creates better engineers
If an architecture cannot be explained clearly, it is usually not understood well enough yet.
Proof creates opportunity
Working projects, measured results, open source code, and thoughtful explanations are stronger than a list of tools.