AI & LLM integrations
Tool use, retrieval, structured outputs, evaluation, and dependable workflows connected to your product and data.
AI ENGINEER · EDUCATOR · FOUNDER
I build AI systems that work outside the lab.
I'm Nicolai Nielsen, an AI and computer vision engineer helping companies turn ambitious ideas into production systems. I also teach 126,000+ engineers how to do the same.
126K+YouTube subscribers
400+technical videos
4.2M+channel views
10+ yrsbuilding with AI
01 / ABOUT
Engineering, explained clearly.
I work where applied research meets reliable software: computer vision, real-time inference, deep learning, and the systems that carry models into production.
More about Nicolai ↗02 / EXPERTISE
What I help teams build.
Computer vision systems
Detection, segmentation, tracking, video analytics, and edge inference built for real operating conditions.
Production AI pipelines
From model optimization and APIs to observability, deployment, and production infrastructure.
03 / EDUCATION
Practical AI education for engineers who build.
More than 400 videos break down machine learning, deep learning, computer vision, Python, C++, PyTorch, and real deployment workflows.
04 / BUILDING IN PUBLIC
Code, products, and programs.
OPEN SOURCE & PRODUCTS
View the work ↗Mask0, NNCODE, agents, and applied AI
Computer vision, LLM workflows, automation, and production examples used by engineers worldwide.
AI CAREER PROGRAM
Explore AI Career ↗Build skills, ship software, and create career leverage
Technical courses, agentic coding, career systems, freelancing, personal branding, and a private community.
05 / SELECTED COLLABORATIONS
UltralyticsNVIDIARoboflowTenyksDataStax
06 / TECHNICAL WRITING
Architecture, workflows, and lessons from real builds.
AI agents
Explore all technical articles ↗Designing browser agents that fail safely
A practical workflow for browser automation agents with scoped tools, checkpoints, evidence, and recovery paths.
Read the article ↗Computer visionHow to design a production computer vision pipeline
A practical architecture for taking computer vision from a notebook to an observable, maintainable production system.
Read the article ↗PerformanceA practical method for optimizing computer vision inference
How to find the real bottleneck across decoding, preprocessing, GPU inference, tracking, and result delivery.
Read the article ↗