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    AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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  • 1
    DeepSeek R1

    DeepSeek R1

    Open-source, high-performance AI model with advanced reasoning

    DeepSeek-R1 is an open-source large language model developed by DeepSeek, designed to excel in complex reasoning tasks across domains such as mathematics, coding, and language. DeepSeek R1 offers unrestricted access for both commercial and academic use. The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely...
    Downloads: 83 This Week
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  • 2
    Kitten TTS

    Kitten TTS

    State-of-the-art TTS model under 25MB

    KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis.
    Downloads: 11 This Week
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  • 3
    whisper.cpp

    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. whisper.cpp supports integer quantization of the Whisper ggml models. ...
    Downloads: 446 This Week
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  • 4
    ncnn

    ncnn

    High-performance neural network inference framework for mobile

    ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs.
    Downloads: 22 This Week
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    Build Agents and Models on One Platform

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  • 5
    DeepSeek-V3

    DeepSeek-V3

    Powerful AI language model (MoE) optimized for efficiency/performance

    ...The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million.
    Downloads: 53 This Week
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  • 6
    nndeploy

    nndeploy

    An Easy-to-Use and High-Performance AI Deployment Framework

    nndeploy is an open-source framework designed to simplify the deployment of artificial intelligence models across multiple hardware platforms and devices. The framework focuses on making it easier to transform trained AI models into production-ready applications that can run efficiently on desktops, mobile devices, servers, and edge computing hardware. Developers can use visual workflows to design and configure AI processing pipelines by connecting modular nodes that represent different...
    Downloads: 0 This Week
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  • 7
    MiniCPM-o

    MiniCPM-o

    A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming

    MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available.
    Downloads: 0 This Week
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  • 8
    Fastbot-Android Open Source Handbook

    Fastbot-Android Open Source Handbook

    Testing tool for modeling GUI transitions

    Fastbot_Android (Fastbot 2.0) is a model-based automated testing tool by ByteDance designed to discover stability or usability issues in Android apps by modeling GUI transitions rather than relying purely on random interactions. It blends machine learning and reinforcement-learning approaches to build a transition graph of UI states and use that model to intelligently explore possible user interactions — aiming to replicate more human-like usage patterns and uncover hidden bugs, crashes, or...
    Downloads: 0 This Week
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  • 9
    Protenix

    Protenix

    A trainable PyTorch reproduction of AlphaFold 3

    Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. ...
    Downloads: 4 This Week
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    Build Securely on AWS with Proven Frameworks

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  • 10
    pipeless

    pipeless

    A computer vision framework to create and deploy apps in minutes

    ...You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud.
    Downloads: 0 This Week
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  • 11
    mirai

    mirai

    Efficient QQ Robot support library

    mirai is a high-efficiency robot library that runs on all platforms and provides QQ Android protocol support. mirai is completely free and open-source software for learning and entertainment use only. mirai will not impose fees or impose material conditions on users in any way. mirai is maintained by the entire open source community and is not the work of an individual.
    Downloads: 1 This Week
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  • 12
    KotlinDL

    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.
    Downloads: 0 This Week
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  • 13
    MemoryLeakDetector

    MemoryLeakDetector

    Native memory leak monitoring tool

    memory-leak-detector is a native memory-leak monitoring tool developed by ByteDance (originally for their video-app team) that helps developers detect, diagnose, and manage memory leaks in native (e.g. C/C++) code across Android applications. It offers relatively simple integration and wide monitoring coverage: by configuring the tool (or via broadcast control), developers can start leak detection for specific native libraries or entire processes. Upon activation, it tracks allocations /...
    Downloads: 0 This Week
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  • 14
    Mozilla JPEG Encoder Project

    Mozilla JPEG Encoder Project

    Improved JPEG encoder

    MozJPEG improves JPEG compression efficiency achieving higher visual quality and smaller file sizes at the same time. It is compatible with the JPEG standard, and the vast majority of the world's deployed JPEG decoders. MozJPEG is compatible with the libjpeg API and ABI. It is intended to be a drop-in replacement for libjpeg. MozJPEG is a strict superset of libjpeg-turbo's functionality. All MozJPEG's improvements can be disabled at run time, and in that case it behaves exactly like...
    Downloads: 11 This Week
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  • 15
    TNN

    TNN

    Uniform deep learning inference framework for mobile

    TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks.
    Downloads: 0 This Week
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  • 16
    SmartOpenCV

    SmartOpenCV

    OpenCV enhancement library for Android

    SmartOpenCV is an enhanced computer vision toolkit built on top of OpenCV that aims to simplify and extend common image processing and computer vision tasks through higher-level abstractions and utilities. It provides pre-built modules and optimized pipelines for tasks such as object detection, image transformation, and feature extraction, reducing the need for low-level implementation. The framework is designed to be more developer-friendly than raw OpenCV by offering cleaner APIs and...
    Downloads: 0 This Week
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  • 17
    Simd

    Simd

    High performance image processing library in C++

    The Simd Library is a free open source image processing library, designed for C and C++ programmers. It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions.
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    Downloads: 18 This Week
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