Search Results for "android runtime permissions"
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Showing 9 open source projects for "android runtime permissions"
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples.... -
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ByteHook
ByteHook is an Android PLT hook library
ByteHook is a ByteDance-hosted project whose name suggests a hooking or instrumentation library, likely used for hooking system calls or API calls for monitoring, sandboxing or instrumentation. The repository appears to aim at low-level hooking/injection capabilities, perhaps to support runtime introspection, behavioral monitoring, or hooking-based instrumentation (e.g. for security, tracing, sandboxing, or debugging). Because hooking is a common technique for intercepting library or system... -
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ChatMCP
ChatMCP is an AI chat client implementing the Model Context Protocol
ChatMCP is a cross‑platform AI chat client that implements the Model Context Protocol (MCP) to provide unified chat experiences across environments—including desktop, mobile, and web—with synchronization and protocol support tailored for MCP. You can install MCP Server from MCP Server Market, MCP Server Market is a collection of MCP Server, you can use it to chat with different data. Tested on major distributions: Ubuntu, Fedora, Arch Linux, openSUSE. Improved Experience: Latest versions... -
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td
Telegram client, in Go. (MTProto API)
Telegram MTProto API client in Go for users and bots. -
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Agent Payments Protocol (AP2)
Building a Secure and Interoperable Future for AI-Driven Payments
...In effect, AP2 aims to define a secure, interoperable protocol that allows software agents to act on behalf of users—making payments or shopping decisions autonomously—while preserving necessary security, auditability, and trust. The repository contains sample scenarios (in Python, Android, etc.) that illustrate how agents, servers, and payments flows would work under the protocol. It includes “types” definitions (the core message and object schema) and example agent implementations to demonstrate the mechanics of agent-to-agent and agent-to-server interactions. The design emphasizes flexibility: although their samples use a particular Agent Development Kit (ADK) or runtime, the protocol is intended to be independent of those choices. -
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Porcupine
On-device wake word detection powered by deep learning
...Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands. ... -
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pipeless
A computer vision framework to create and deploy apps in minutes
Pipeless is an open-source computer vision framework to create and deploy applications without the complexity of building and maintaining multimedia pipelines. It ships everything you need to create and deploy efficient computer vision applications that work in real-time in just minutes. Pipeless is inspired by modern serverless technologies. It provides the development experience of serverless frameworks applied to computer vision. You provide some functions that are executed for new... -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning models in production environments. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes obligatory when running a model. ... -
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