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Private Mind is a mobile AI app built around fully offline, on-device intelligence. It is designed for users who want AI conversations without sending personal data to cloud servers. The app keeps conversations and data on the device, which makes it useful for privacy-sensitive workflows, personal notes, local assistance, and offline use. It supports customizable AI behavior, including choosing supported models, uploading models, and adjusting system prompts. The project also includes built-in benchmarking so users can compare performance, speed, and memory usage on their own hardware. ...

Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using...

LLMFarm is a framework designed to simplify the deployment, management, and utilization of large language models in local or self-hosted environments, focusing on accessibility and efficient resource usage. It enables users to run LLMs on personal hardware or private infrastructure, reducing dependency on external APIs and improving data privacy. The system typically provides a user-friendly interface for loading models, configuring inference parameters, and interacting with them through chat or task-based workflows. It emphasizes modularity, allowing users to integrate different models, backends, or tools depending on their needs and hardware capabilities. ...

AppShark is an open-source static taint-analysis platform developed by ByteDance designed to scan Android application packages (APKs) for potential security or privacy vulnerabilities. It performs code analysis without executing the app — inspecting APK contents, tracking data flows (taints), and detecting risky patterns such as insecure file access, unsafe API usage, resource-leak possibilities, or misconfigurations. Users can customize scanning via rule sets (written in JSON), defining which types of vulnerabilities or compliance issues to detect — making AppShark flexible and adaptable to different security policies or threat models. ...

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