Manuel Sánchez Hernández
I explore the frontiers of knowledge and technology to help people and organizations.
mansanher@gmail.com
Barcelona, Spain
I am interested in how science, software, and AI can be turned into systems, teams, and products. For more than 15 years, that has been the focus of my work in machine learning and AI.
Most recently at Adevinta, I led the central machine learning organization and co-led generative AI strategy across European marketplaces. My work combined strategy, platform building, production systems, and large-scale AI adoption, improving both customer experience and internal productivity.
Earlier in my career, I built machine learning capabilities at Schibsted, developed automatic investment strategies at Morgan Stanley in London, and led product and supply initiatives in a Procter & Gamble joint venture in Barcelona.
I also teach and facilitate AI and machine learning for executive and technical audiences. Feel free to contact me if you are interested.
This site collects my writing and selected public projects on AI, LLMs, machine learning systems, software engineering productivity, and evaluation.
latest posts
| Mar 09, 2026 | A Pragmatic Evaluation of Software Engineering AI Tooling |
|---|---|
| Nov 11, 2025 | Rationalizing the AI bubble |
| Jul 01, 2025 | Beyond Tokens: The Context-Window Perspective on LLMs, Memory, and Mind |
| May 25, 2025 | Launching TheorIA: A Machine-Readable Atlas of Theoretical Physics |
| Apr 13, 2025 | Datasets for advancing Theoretical Physics and AI |
| Feb 01, 2025 | Selected ideas from NeurIPS 2024 |
| Jan 03, 2025 | Opening the LLM pipeline |
| Oct 06, 2024 | The path to AGI: quantifying bottlenecks |
| Feb 27, 2018 | Normalization in TensorFlow: speed is an issue |
| Jun 11, 2017 | Setting up your GPU TensorFlow platform |
selected projects
AI Tooling Evaluation
Evaluation of software-engineering AI tools
Kaggle Competition: Quora Insincere Questions
NLP competition under strict Kaggle kernel constraints, which finished with a Gold Medal