Marco Nieri
Marco Nieri reports on applied AI across science and industry for Brixnex, covering how machine learning is being deployed in healthcare, climate science, robotics, and fundamental research. He specialises in connecting cutting-edge academic research to real-world applications and practical implications.
Marco studied Biomedical Engineering and subsequently worked as a research associate at a university hospital, where he contributed to clinical AI validation studies. He later joined a medical-device startup working on AI-assisted diagnostics before moving into science and technology journalism.
His reporting on AI in healthcare, climate modelling, and scientific discovery is grounded in practical research experience. He is known for scrutinising AI claims carefully — distinguishing genuine breakthroughs from overstated results with the eye of someone who has worked in research environments himself.
Articles by Marco
AI in Healthcare 2026: FDA-Approved Tools and Clinical Reality 📅 March 26, 2026 AI and Climate Change: Machine Learning Fighting the Climate Crisis 📅 March 9, 2026Marco Nieri is Brixnex's Tutorials Lead, responsible for producing the step-by-step technical guides and hands-on coding content that helps Brixnex readers move from reading about AI to actually building with it. A software engineer by training with a BSc in Computer Engineering from Politecnico di Milano, Marco has spent seven years building AI-powered applications across fintech, healthtech, and media sectors.
Marco's tutorials are notable for their completeness and pragmatism: every piece of code he publishes is tested in a real environment, every library version is specified, and potential failure modes are documented alongside the happy path. His approach reflects years of professional experience where code that works in a tutorial but breaks in production is worse than no tutorial at all.
His most-read pieces include a comprehensive LoRA fine-tuning guide covering everything from dataset preparation to quantised inference, a practical introduction to neural architecture search, and a deep-dive into building production-grade RAG pipelines with proper evaluation frameworks.
Areas of Expertise
- LoRA, QLoRA, and PEFT fine-tuning techniques
- Building and optimising RAG pipelines
- Vector database integration (Pinecone, Weaviate, Chroma)
- MLOps: model serving, monitoring, and CI/CD for ML
- Python ecosystem: PyTorch, Hugging Face Transformers, LangChain
Coding Standards
Marco enforces a strict reproducibility standard for all Brixnex code content: every notebook and script must run end-to-end from a clean environment, dependencies are pinned, and tutorials are updated when major library versions introduce breaking changes. Readers can trust that Brixnex code tutorials work—not just at time of writing, but on an ongoing basis.