Approach

We do not ship a feature because it looks promising. Before adopting a learning method or a behavioural design, we check how large the effect is, whether it replicates, and where it applies. That means reading past the original paper into pre-registered studies, systematic reviews and meta-analyses, depending on what the question needs.

We adopt something because it has been shown to work, not because it is popular. The reverse holds too: if it has not been shown to work, it does not ship, however loud it is. What matters is not the name of the method but who it worked for, by how much, and under what conditions.

People do not quit because their willpower is weak. They quit because the design has friction. The number of taps before you start, the number of settings, the number of decisions you are asked to make — all of it decides whether a habit survives.

Instead of telling you to try harder, we delete steps. Fewer settings, usable at defaults, fewer decisions. The goal is a product that needs no willpower at all.

If we claim an effect, we publish it in a form you can check. This is a constraint on us, not a feature: if we cannot produce a number, we do not claim a result.

And we do not publish only the conclusion. We publish the baseline, the data and the metric as well, because a number without its conditions can only ever flatter us. A result is ready to publish when a reader could follow the same steps and see the same thing.