Garly

Trust is especially important when an app can access location, motion sensors or emergency contacts.

However, transparency is not always as simple as publishing everything. Sharing source code and data flows can help independent review, while exposing detailed detection logic or security controls could also make a safety system easier to bypass.

Which information would actually help you decide whether to trust a personal safety app?

Would you look for a clear privacy model, permission explanations, selected public source code, independent security reviews, data-retention controls, or something else?

We re building Garly, a privacy-first AI safety companion, and one of our biggest challenges is avoiding false alarms.

A phone s sensors can detect movement, but real life is complicated. Running, travelling by bus, dropping a bag or making a sudden movement can look similar without enough context.

Feedback from our testers led us to develop adjustable sensitivity, Jogging Mode, Bag Mode and configurable stillness detection. We re now combining accelerometer, gyroscope, impact and movement interruption signals to make protection more context-aware.

The goal is not to replace emergency services or pretend that a phone can understand every situation. We want to create an additional safety layer that users can trust and control.