Projects

Overview: CyberScraper 2077 is a powerful web scraper built with Python, utilizing cutting-edge AI models from OpenAI, Gemini, and LocalLLM to extract data efficiently and stealthily. Key features: - AI-Powered Extraction: Intelligent parsing of web content. - Sleek Streamlit Interface: User-friendly for even non-technical users. - Multi-Format Support: Export data in JSON, CSV, HTML, SQL, or Excel. - Tor Network Support: Scrape .onion sites through Tor with automatic routing. - Stealth Mode: Parameters to avoid detection as a bot. - Ollama Support: Access to a library of open-source LLMs. - Async Operations: Fast scraping with lightning-fast performance. - Smart Parsing: Structures data as if it were extracted from a master netrunner. - Caching: Content-based and query-based caching using LRU cache and a custom dictionary. - Upload to Google Sheets: Easy upload of CSV data to Google Sheets. - Bypass Captcha: Bypasses captchas using the -captcha parameter. - Current Browser: Uses your local browser instance for bypassing most bot detections. - Navigate through the Pages (BETA): Scrape data from different pages. Tech stack: Python, Streamlit, LLMs (OpenAI, Gemini, LocalLLM), Tor, AI, Streamlit, LLMs (OpenAI, Gemini, LocalLLM), Streamlit, AI, Streamlit, LLMs (OpenAI, Gemini, LocalLLM), Streamlit, AI, Streamlit,

### Overview: This repository is a curated collection of hands-on data science projects tailored for beginners. Each project is a Jupyter notebook that you can clone and run immediately, covering a range of topics including data cleaning, exploratory data analysis, machine learning, deep learning, computer vision, and more. ### Key Features: - **Data Cleaning and Preprocessing**: Prepare real-world messy data. - **Exploratory Data Analysis**: Visualizations and statistical insights. - **Machine Learning**: Classification, regression, and anomaly detection. - **Deep Learning**: CNNs, transfer learning, and NLP models. - **Computer Vision**: Detection, recognition, and pose estimation. - **Clustering**: Group similar data points. - **Recommendation Systems**: Personalized recommendations. - **Anomaly Detection**: Identify unusual patterns. - **EDA Visualization**: Create visual representations of data. - **Miscellaneous Applied**: Apply data science to various fields. - **Computer Vision**: Use techniques for image recognition and analysis. ### Tech Stack: Jupyter Notebook ### Why It Matters: This repository is designed for anyone getting started with data science, whether you're a student, career switcher, or self-learner. Each project is a standalone Jupyter notebook that you can clone and run immediately, covering a range of topics including data cleaning, exploratory data analysis, machine learning, deep learning, computer vision, and more. The projects are categorized into different levels, making it easy to learn by following a structured learning path.

### Overview: This repository contains 67 design system skill files for AI-powered agentic tools, such as Claude Design, Google Stitch, Codex, Cursor, and others. Each skill can be easily integrated into projects with a single command, making it a valuable resource for designers and developers. ### Key Features: - **Skill Files**: 67 design system skill files for AI-powered tools. - **AI-Agent Instructions**: `SKILL.md` for instructions on how to use the AI agent. - **Human-Readable Design Intent**: `DESIGN.md` for design intent and rationale. - **Single Command Integration**: Pull any skill into your project with a single command. ### Tech Stack: - **TypeUI**: The tool used to preview and integrate the design skills. ### Why It Matters: These skills are essential for designers and developers who need to integrate AI-powered design tools into their projects quickly and efficiently. They provide a consistent and professional look across all projects, ensuring that the design is both effective and accessible. ### GitHub Stars: 3276 ### Primary Language: Unknown ### Repository: [bergside/awesome-design-skills](https://github.com/bergside/awesome-design-skills)

### Overview: MusicLM is an implementation of Google's MusicLM model, a state-of-the-art model for music generation using attention networks, built entirely in Pytorch. This project aims to replicate and contribute to the research community by training and utilizing the MuLan model. ### Key features: - **MusicLM Model**: A state-of-the-art model for music generation using attention networks. - **MuLan Model**: The core of the MusicLM model, consisting of text-conditioned AudioLM and MuLan embeddings. - **Pytorch Implementation**: All components of the MusicLM model are implemented in Pytorch, ensuring compatibility and ease of use. ### Tech stack: Pytorch ### Why it matters: This project aims to contribute to the research community by replicating and expanding upon Google's MusicLM model. By training and utilizing the MuLan model, this project will help advance the field of music generation and improve the quality of generated music. Additionally, it provides a valuable resource for developers and researchers interested in applying attention-based models to music generation tasks. ### Repository: lucidrains/musiclm-pytorch ### Primary language: Python ### GitHub stars: 3292 ### GitHub description: Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch. ### Usage: - **Training**: To train the MuLan model, you need to first train the AudioSpectrogramTransformer and TextTransformer components. These components are then combined with the MuLanEmbedQuantizer to obtain conditioning embeddings for

### Overview: TTS-WebUI is a versatile web-based interface for text-to-speech (TTS) applications, designed to simplify the process of creating and using TTS models. This project integrates multiple advanced TTS models and extensions, making it a powerful tool for developers and enthusiasts alike. ### Key Features: - **ACE-Step**: Enhances the quality of text-to-speech output. - **OmniVoice**: Provides a wide range of voices and accents. - **Kimi Audio**: Offers high-quality audio generation. - **Piper TTS**: Supports various languages and dialects. - **GPT-SoVITS**: Utilizes advanced language models for improved speech quality. - **CosyVoice**: Offers a pleasant and natural-sounding voice. - **XTTSv2**: Supports a wide variety of TTS models. - **DIA**: Enhances the quality of audio output. - **Kokoro**: Provides a unique and distinctive voice. - **OpenVoice**: Offers a range of voices and accents. - **ParlerTTS**: Supports multiple languages and dialects. - **Stable Audio**: Provides high-quality audio output. - **MMS**: Offers a wide variety of voices and accents. - **StyleTTS2**: Utilizes advanced language models for improved speech quality. - **MAGNet**: Enhances the quality of audio output. - **AudioGen**: Generates high-quality audio. - **MusicGen**: Generates high-quality music. - **Tortoise**: Offers a unique and distinctive voice. - **RVC**:

### Overview: This repository aims to provide a comprehensive study guide for beginners in machine learning. It covers essential Python programming, data analysis with Pandas and Numpy, and visualization techniques using Matplotlib, Seaborn, Bokeh, and Folium. ### Key Features: - Python Programming - Data Analysis with Pandas and Numpy - Visualization Techniques with Matplotlib, Seaborn, Bokeh, and Folium - Machine Learning Concepts and Algorithms ### Tech Stack: - Python - Jupyter Notebook ### Why it Matters: This repository is intended for helping individuals interested in machine learning study and practice. It provides a structured learning path with practical examples and resources to enhance understanding and application skills. ### Repository: teddylee777/machine-learning ### Primary Language: Jupyter Notebook ### GitHub Stars: 3286

### Overview: LIDA is a library designed for the automatic generation of visualizations and infographics using large language models. It leverages various large language models such as OpenAI, Azure OpenAI, PaLM, and Cohere, making it versatile and compatible with multiple visualization libraries like matplotlib, seaborn, and d3. The library is grammar-agnostic and can work with any programming language and visualization libraries, ensuring flexibility and ease of integration. ### Key Features: - **Data Summarization**: Generate a concise summary of a dataset. - **Goal Generation**: Create visualization goals based on a given data summary. - **Visualization Generation**: Generate visualizations from a data summary. - **Visualization Editing**: Edit existing visualizations. - **Visualization Explanation**: Explain visualizations. - **Visualization Evaluation and Repair**: Evaluate and repair visualizations. - **Visualization Recommendation**: Provide recommendations for better visualizations. - **Infographic Generation (beta)**: Generate infographics, currently in beta stage. ### Tech Stack: - **Python**: The primary programming language used. - **Jupyter Notebook**: The primary development environment for creating and testing visualizations. - **Large Language Models**: Supported providers include OpenAI, Azure OpenAI, PaLM, and Cohere. ### Why It Matters: LIDA simplifies the process of generating, editing, and improving visualizations, making it easier for developers and data scientists to create effective and engaging visual representations of their data. This tool is particularly useful in scenarios where traditional data visualization tools are not sufficient, such as when dealing with large datasets or complex data structures.

### Overview: nexu is a simple, open-source desktop client that connects your AI Agent to popular instant messaging platforms like WeChat, Feishu, Slack, and Discord. It works seamlessly with Claude Code, Codex, and any LLM, and supports BYOK, OAuth, and local-first setups. ### Key Features: - 🚀 Easy Installation: Download, double-click, and start using nexu right away. - 📱 Built-in Skills: Native OpenClaw 🦞 Skills and full Feishu Skills are included. - 📝 Local-first: All data stays on your machine, ensuring complete privacy. - 🚀 One-click Switch: Supports multiple LLMs and OAuth, making it flexible and easy to switch. - 📱 Works Out of the Box: Built-in support for WeChat, Feishu, Slack, and Discord, no additional setup required. - 🚀 Free, BYOK, and Pay via API Keys: No self-deployment required, and costs are managed through your own API keys. ### Tech Stack: TypeScript ### Why It Matters: nexu offers a simple, efficient, and secure way to connect your AI Agent to popular instant messaging platforms, ensuring your data stays on your machine and your privacy is fully protected.

Overview: Pezzo is an open-source, developer-first LLMOps platform designed to streamline prompt design, version management, instant delivery, collaboration, troubleshooting, observability, and more. Key features: - Seamless observation and monitoring of AI operations - Instantly deliver AI changes - Save up to 90% on costs and latency - Collaborate and manage prompts in one place - Streamline prompt design and version management Tech stack: TypeScript Why it matters: Pezzo simplifies the development and management of large-scale AI operations, enabling developers to quickly and efficiently deliver high-quality AI solutions with minimal overhead. Repository: pezzolabs/pezzo GitHub stars: 3279

### Overview: HelloAgents 是一个基于原生 Function Calling 构建的组件化多智能体框架，旨在为智能体开发提供可组合的工程组件。 ### Key features: - **Agent Loop**: 组件化多智能体框架，支持简单的工具调用循环。 - **工具执行**: 提供工具响应协议、上下文工程、会话持久化、子代理、Skills、流式输出与运行追踪。 - **知识检索与持久记忆**: 独立的检索、记忆和上下文组件，支持结构化用户画像和语义记忆检索。 - **MCP工具接入**: 提供MCP工具接入、RAG与GraphRAG、结构化用户画像、语义记忆检索和后台任务。 ### Tech stack: Python ### Why it matters: HelloAgents 为智能体开发提供了一套可组合的工程组件，简化了工具调用、上下文管理和知识检索，帮助开发者快速构建复杂的智能体应用。

Overview: Vortex is an advanced columnar file format and toolkit designed for high-performance data processing. It is the fastest and most extensible format for building data systems backed by object storage. Vortex provides blazing fast performance, including 100x faster random access reads, 10-20x faster scans, and 5x faster writes, with similar compression ratios. It is modeled after Apache DataFusion's extensible approach, offering zero-copy/zero-parse metadata support, and is open source, licensed under Apache-2.0. Key features: - **Blazing Fast Performance**: 100x faster random access reads, 10-20x faster scans, 5x faster writes, and similar compression ratios. - **Extensible Architecture**: Modeled after Apache DataFusion's extensible approach, with pluggable encoding system, type system, compression strategy, and layout strategy. - **Open Source, Neutral Governance**: A Linux Foundation (LF AI & Data) Project, Apache-2.0 Licensed. - **Integrations**: Arrow, DataFusion, DuckDB, Spark, Pandas, Polars, and more. Technical stack: Rust. Why it matters: Vortex is the fastest and most extensible format for building data systems backed by object storage, providing high performance, extensibility, and open-source governance, making it a valuable tool for data processing and storage. Repository: vortex-data/vortex GitHub stars: 3,270 GitHub description: An extensible, state-of-the

Overview: Shapash is a user-friendly library designed to make machine learning models comprehensible and transparent to everyone. Key features: - Generates webapps for easy understanding of model interactions. - Seamlessly combines local and global explainability. - Compatible with various models and data types. Tech stack: Python, Jupyter Notebook Why it matters: Shapash simplifies the interpretation of complex models, making them accessible to non-technical users and enhancing data science auditing.

Overview: This repository provides a comprehensive **Mind Map** and **KEYWORD** for students interested in Natural Language Processing (NLP). It covers foundational topics from basic probability and statistics to advanced models. Key features: - **Probability and Statistics** - **Machine Learning** - **Text Mining** - **Natural Language Processing** Tech stack: Mind Map and KEYWORD representation. Why it matters: This roadmap serves as a valuable resource for students looking to learn NLP, offering a structured approach to understanding the field through a visual mind map and essential keywords.

Overview: deepdoctection is a Python library for document understanding, including document layout analysis, OCR, document classification, and more. Key features: - Document layout analysis using Detectron2 and Transformers - OCR with support for Tesseract, DocTr, and AWS Textract - Document and token classification with LayoutLM, LiLT, and many Bert-style models - Fine-tuning and evaluating document, object detection, and token classification models - Native PDF text mining with pdfplumber - Language detection with xlm-roberta-base-language-detection - Deskewing and rotating images with jdeskew or Tesseract Tech stack: Python Why it matters: deepdoctection provides a comprehensive solution for document analysis and extraction tasks, making it a valuable tool for developers working with natural language processing and computer vision.

### Overview: NeuralNote is an audio plugin that enables state-of-the-art audio-to-MIDI transcription, making it a valuable tool for digital audio workstation users. ### Key features: - **Transcription Model Upgrade**: Replaces Spotify's Basic Pitch with MuScriptor, a transformer with 103M to 1.4B parameters. - **Multi-Instrument Transcription**: Transcribes full mixes, not just one instrument at a time, with notes grouped by instrument. - **Built-in Multi-Instrument Synth**: Listen to every transcribed instrument, with per-instrument gain, mute, and solo. - **Modern UI**: Updated user interface. - **Local Transcription**: Transcription runs on your machine, using the GPU or CPU, and your audio never leaves it. ### Tech stack: C++ ### Why it matters: NeuralNote offers a powerful transcription tool that enhances the capabilities of digital audio workstations, making it easier to transcribe audio into MIDI. This feature is particularly useful for musicians and composers who need precise transcriptions for their projects.

Overview: DeepSeek v4.1 flash 逆向工具（无限五代）是一个专为DeepSeek Harness桌面客户端设计的高性能逆向分析插件与可视化工作台系统。它彻底摒弃了提示词注入，实现了从脚本封装到专业级全功能Agent逆向工作站的蜕变，覆盖了二进制侦察、CFG、符号与类型、真实IR、受限仿真、原生调试、跨引擎修改计划，以及显式解密和报文解析等38项专业工具。五代还具有双模式动态降噪功能，支持高级场景按需展开为Full 38全景工具，确保了高效且安全的逆向分析体验。 Key features: - 38项专业逆向工具面 - 从脚本封装到专业级全功能Agent逆向工作站的蜕变 - 双模式动态降噪功能 - 高效且安全的逆向分析体验 Tech stack: JavaScript Why it matters: 这个工具系统彻底改变了逆向分析的方式，提供了专业级的逆向分析能力，同时保持了高效和安全的特性，使得开发者在进行逆向分析时能够更专注于核心问题，而无需担心提示词注入带来的偏见和问题。

Overview: AI-Crawler is an experimental data extraction app designed for developers and data scientists, allowing them to focus on analysis rather than building custom web scrapers. Key features: - Start a crawl from any given URL - Define data needs using a natural language prompt - Identify relevant pages based on the prompt - Use structured JSON or Markdown output formats Tech stack: Python Why it matters: The AI web crawler simplifies complex data acquisition tasks, making it easier for developers and data scientists to focus on analysis rather than building and maintaining custom web scrapers. Repository: oxylabs/ai-crawler-py GitHub stars: 3261

Overview: 一个面向长篇小说创作的AI辅助系统，通过Agent、世界观、写法引擎、RAG和整本生产工作流，帮助新手从灵感走向完整小说。 Key features: - 自动导演整理题材、人物目标和方向 - 从热门题材或拆书进入短篇生产 - 保存世界、角色和章节状态 - 保存和检查进度，支持多任务和长篇写作 - 写法引擎和反AI规则参与生成和检查 Tech stack: TypeScript Why it matters: 为第一次写小说的人提供了一条从灵感到完整小说的路径，同时为需要维护长期资料和研究AI创作流程的作者和开发者提供了更细的工具。

Overview: ResNeSt is a new ResNet variant designed specifically for improving the performance of downstream models such as Mask R-CNN, Cascade R-CNN, and DeepLabV3. It significantly boosts the accuracy of these models by leveraging split-attention mechanisms. Key features: - ResNeSt-50: 81.03% accuracy on ImageNet - ResNeSt-101: 82.83% accuracy on ImageNet - ResNeSt-200: 83.84% accuracy on ImageNet - ResNeSt-269: 84.54% accuracy on ImageNet Tech stack: Python Why it matters: ResNeSt is a significant improvement over traditional ResNet architectures, particularly in scenarios where high accuracy is crucial for applications like object detection and segmentation. By introducing split-attention mechanisms, it enhances the model's ability to handle complex tasks, leading to better performance in downstream tasks such as Mask R-CNN, Cascade R-CNN, and DeepLabV3.

Overview: FastEmbed is a lightweight, fast, and Python-based library designed for generating state-of-the-art embeddings, particularly for text data. It supports a variety of models and is optimized for performance and efficiency. Key features: - Supports popular text models - Lightweight with few external dependencies - Fast encoding using ONNX Runtime - Accurate embeddings compared to other libraries - Available in both CPU and GPU versions Tech stack: Python Why it matters: FastEmbed is a highly efficient and accurate library for generating embeddings, making it ideal for applications requiring fast and lightweight solutions, such as serverless environments or real-time processing. Repository: qdrant/fastembed Primary language: Python GitHub stars: 3249 GitHub description: Fast, Accurate, Lightweight Python library to make State of the Art Embedding