Tallinn Data Week 2026: have managers always been vibe coding?
My second year at the Tallinn Data Week Data Science Seminar. Why AI models need to be replaceable, how to work with sensitive data, and whether managers have always been vibe coding.
Notes on AI in my work and events where I go to learn. What I test, take away and put into practice.
In-house AI training: back in front of a group after the summer
On 16 October, I returned to in-house AI training after a long break. How did it go? The 22 survey responses gave it an average usefulness rating of 4.68 out of 5.
AI in energy: 96 decisions a day
Notes from the University of Tartu data science seminar: battery control, 96 daily decisions and using weather data in the electricity grid.
Alongside AI agents, responsibility needs careful thought
Seeing research and business perspectives side by side was especially interesting. Researchers highlighted the difficulties of accuracy, control and integration into workflows. Businesses showed how AI is already being used beyond experiments.
Why a confident AI answer may not be correct
Hallucinations and uncertainty are not simply random noise. They can be better understood, measured and accounted for through checks in a workflow.
AI-assisted development still needs expertise
Adopting AI is not a matter of flipping a switch. People retain responsibility, and specialist knowledge becomes more important, not less.
Measuring the benefits of AI
Useful discussion of AI rests on a method, measurement and an honest understanding of limitations, rather than slogans.
Getting started with AI needs a shared understanding
There is a substantial need for basic knowledge. People need more than a list of tools: they need a clear understanding of where to start and what to avoid.
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