Learning through interaction
We update through actual interaction: completion, postponement, dismissal, accepted recommendations, and other signals that help us adapt.
This keeps learning close to our product experience rather than detached from it.
Archives
Learning system
Our learning system is meant to make us better over time without turning us into an opaque behavioral profiler.
Transparency over silent inference
Our aim is not to collect invisible personal analytics. Our aim is to evolve in a way the user can understand and feel.
That is why explainable insights and check-ins matter as much as the weights themselves.
A calmer adaptive system
Our adaptation should help us recover neglected areas, preserve useful momentum, and reflect changing priorities.
It should not become a black box that makes the user feel observed rather than accompanied.