what is awesome-on-device-ai-apps fr?

This repository is a collection of 36 finished mobile apps that run AI models entirely on the phone itself, instead of sending data to a server in the cloud. Every app can be cloned and run directly on a real Android or iOS device, and each one is built to be a working example a developer can copy, customize, and ship rather than just a small code snippet. The README explains why running AI fully on the device matters as a business decision, not only a technical one. Because no user data ever leaves the phone, apps built this way can avoid many of the legal and compliance concerns that come with sending personal data, such as health or financial information, to a cloud server. There is also no per use cost for running the AI model, since there is no cloud inference bill, and the apps keep working offline, without an internet connection, since everything runs locally on the phone's hardware. The collection is organized into categories covering chat, translation, vision, health, voice, and forecasting. Examples include a private note taking app that records, transcribes, and summarizes meetings, an AI powered keyboard that rewrites and translates text on the fly, an offline speech to text transcriber, a real time facial emotion recognition app, a tool that reads text from a camera view for travelers without a signal, and a tool that automatically detects and hides personal information like names and phone numbers before it is used. Each app in the catalog lists which AI model it uses and which platforms, such as Android, iOS, or Flutter, it supports. To run an app, a developer clones the repository, gets a free access key from a companion platform called Mlange so the chosen app can download its optimized model weights on first launch, and then opens the specific app folder in Android Studio, Xcode, or with the Flutter command line tool. The apps rely on a specialized runtime built to run these models efficiently on a phone's dedicated AI hardware chip, and testing them generally requires a real device rather than a simulator, since that hardware is not available in a simulated environment. The project is written primarily in Swift, though individual apps use Android and Flutter code as well, and the overall collection is released under the Apache 2.0 license. The full README is longer than what was shown.