Github Top Repositories

🔥 Panniantong/Agent-Reach is trending — and it deserves your attention. 🔗 https://github.com/Panniantong/Agent-Reach 📝 Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. ────────────────────────────── Agent Reach 是一个能力层，让你的 AI Agent 能够访问互联网。它可以帮助你的 Agent 读取网页、搜索推特、看 YouTube 视频、刷小红书 等。只需告诉你的 Agent "帮我安装 Agent Reach"，它就会自己完成安装和配置。 主要特点： * 零配置：安装后无需任何配置，即可使用 * 支持多平台：包括网页、YouTube、Twitter、Reddit、Facebook、Instagram 等 * 安全：Cookie 和 Token 只存储在本地，且支持安全模式 * 开源：完全开源，欢迎社区贡献 使用方法： 1. 告诉你的 Agent "帮我安装 Agent Reach" 2. 等待安装和配置完成 3. 即可使用 Agent Reach 的功能 注意：需要登录的平台（如 Twitter、小红书）需要配置 Cookie 或登录态。 One-liner takeaway: Agent Reach gives your AI Agent internet superpowers with zero configuration and top-notch security. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 shiyu-coder/Kronos caught my eye on GitHub Trending today. 🔗 https://github.com/shiyu-coder/Kronos 📝 Kronos: A Foundation Model for the Language of Financial Markets ────────────────────────────── Kronos is the first open-source foundation model for financial candlesticks, trained on data from over 45 global exchanges. It's a decoder-only model, designed to handle the unique characteristics of financial data. Key features include a novel two-stage framework, with a specialized tokenizer and a large autoregressive Transformer. To get started, you can install the dependencies with pip install -r requirements.txt, then load a pre-trained model and tokenizer from the Hugging Face Hub. The KronosPredictor class simplifies forecasting, handling data preprocessing, normalization, and prediction. Technical highlights include a family of pre-trained models with varying capacities, a live demo for visualizing forecasting results, and a model zoo with readily accessible models. The model is designed for diverse quantitative tasks, such as forecasting and backtesting. The target audience includes quantitative analysts, traders, and researchers interested in applying AI to financial markets. With Kronos, you can unlock new insights and make more informed investment decisions. Kronos is a game-changer: forecast your financial future with precision and ease! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: antirez/ds4 — let's break it down. 🔗 https://github.com/antirez/ds4 📝 DeepSeek 4 Flash and PRO local inference engine for Metal, CUDA and ROCm ────────────────────────────── DwarfStar is a native inference engine optimized for DeepSeek V4 Flash and also supporting GLM 5.2 and DeepSeek V4 PRO on high-memory machines. It's designed to be self-contained and narrow in scope, not a general-purpose GGUF runner. The engine includes tools and data for GGUF, imatrix, quality, and speed. The project supports multiple backends: Metal on Macs, NVIDIA CUDA including multi-GPU systems, and ROCm on Strix Halo systems. It's capable of running on consumer hardware, like MacBooks, and can also be used to turn servers into multi-user LLM servers with good results. Some of the key features include: - Running capable models on consumer hardware - Turning old servers into multi-user LLM servers - Supporting pipeline parallelism to glue multiple systems together - Experimental DSpark speculative decoding for faster generation The project is still in the beta stage and is very fast-changing, so instabilities are possible. The code is developed with strong assistance from AI, including GPT 5.5, 5.6, and Claude Fable, and is not suitable for those who are not comfortable with AI-developed code. In summary: DwarfStar is an optimized inference engine for select models, with a focus on speed and efficiency, making it a great tool for those looking to run capable models on consumer hardware - you can now run a super-smart AI model on your MacBook! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet donnemartin/system-design-primer: a gem from today's GitHub trending list. 🔗 https://github.com/donnemartin/system-design-primer 📝 Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards. ────────────────────────────── The System Design Primer is a comprehensive GitHub repository designed to help you learn how to build systems at scale. Its primary purpose is to provide an organized collection of resources, including system design topics, interview questions, and study guides, to aid in becoming a better engineer. The repository offers key features such as Anki flashcard decks for retaining key system design concepts, interactive coding challenges, and a vast array of system design topics, including performance, scalability, latency, and availability. To use this repository, you can start by reviewing the system design topics, practicing with interview questions, and utilizing the provided resources to improve your understanding of system design. From a technical standpoint, the repository covers a wide range of topics, including load balancers, reverse proxies, databases, caching, and security. It also provides information on communication protocols, such as TCP and UDP, and design patterns, such as microservices and service discovery. The repository is suitable for a broad audience, including software engineers, system architects, and anyone interested in learning about system design. Whether you're preparing for a system design interview or simply looking to improve your skills, the System Design Primer is an invaluable resource. In summary, the System Design Primer is a treasure trove of system design knowledge, and its contributions are welcome from the open-source community. So, dive in and explore the repository to take your system design skills to the next level: designing scalable systems is not just about handling traffic, it's about creating a better user experience. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 microsoft/generative-ai-for-beginners just hit the trending charts — here's why it matters. 🔗 https://github.com/microsoft/generative-ai-for-beginners 📝 21 Lessons, Get Started Building with Generative AI ────────────────────────────── Get started with Generative AI using the microsoft/generative-ai-for-beginners GitHub repository. This comprehensive course offers 21 lessons to teach you the fundamentals of building Generative AI applications. The course covers topics such as Introduction to Generative AI and LLMs, Exploring and comparing different LLMs, Using Generative AI Responsibly, and Understanding Prompt Engineering Fundamentals. You'll learn through a combination of video introductions, written lessons, and Python and TypeScript code samples. To get started, you'll need basic knowledge of Python or TypeScript and a Github account to fork the repository. You can use either Azure OpenAI Service, Microsoft Foundry Models, OpenAI API, or Foundry Local to run the code. The course is multi-language supported with translations available in over 50 languages. Join the Microsoft Foundry Discord server to meet other learners and get support. In summary, this course is perfect for beginners and experienced developers alike, providing a comprehensive introduction to Generative AI and hands-on experience with building applications. Dive in and start building your Generative AI skills today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: microsoft/AI-For-Beginners — let's break it down. 🔗 https://github.com/microsoft/AI-For-Beginners 📝 12 Weeks, 24 Lessons, AI for All! ────────────────────────────── The AI-For-Beginners GitHub repository provides a comprehensive 12-week curriculum for learning Artificial Intelligence. This beginner-friendly course covers key topics such as symbolic AI, neural networks, and deep learning, with practical lessons, quizzes, and labs using popular frameworks like TensorFlow and PyTorch. The curriculum is translated into over 50 languages, making it accessible to a global audience. To get started, you can clone the repository locally or use the automated translations. The course is suitable for beginners, and no prior experience in AI is required. You can join the community on Discord to connect with other learners and instructors. With this curriculum, you'll gain a solid understanding of AI concepts and be able to apply them in real-world projects. Start your AI journey today and become proficient in building intelligent systems with this free and open-source resource! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: TencentCloud/TencentDB-Agent-Memory — let's break it down. 🔗 https://github.com/TencentCloud/TencentDB-Agent-Memory 📝 TencentDB Agent Memory is a team-level memory hub for AI Agents — turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks. ────────────────────────────── The TencentDB Agent Memory is a powerful tool designed to enhance the capabilities of AI agents by providing them with a robust memory system. This system allows agents to learn from workflows, retain task context, and reuse past experiences, thereby sparing humans from having to repeat themselves. The key features of this tool include symbolic short-term memory and layered long-term memory. The symbolic short-term memory offloads heavy tool logs and condenses them into compact Mermaid symbols, while the layered long-term memory distills fragmented conversations into structured personas and scenes. When integrated with OpenClaw, it can cut token usage by up to 61.38% and improve the pass rate by 51.52%. The tool is designed to work with various platforms, including OpenClaw and Hermes, and provides a zero-config setup for easy installation. The target audience for this tool includes developers and researchers working with AI agents, particularly those interested in improving the memory and learning capabilities of these agents. In summary, the TencentDB Agent Memory is a game-changer for AI agents, enabling them to remember what should be remembered, so people can focus on judgment, creation, and work that truly matters. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: esengine/DeepSeek-Reasonix — let's break it down. 🔗 https://github.com/esengine/DeepSeek-Reasonix 📝 DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability — leave it running. ────────────────────────────── Reasonix is a DeepSeek-native AI coding agent designed for terminal use, engineered around prefix-cache stability for low token costs. This legacy TypeScript line is in maintenance mode, with active development moved to the Go rewrite in the main-v2 branch. Key features include cache stability, DeepSeek API integration, and filesystem tools. To use Reasonix, simply install it globally with npm install -g reasonix and run reasonix code my-project to get started. Technical highlights include a cache-first loop and byte-stable prefix-cache mechanic. The target audience is developers and users who want a thinking partner with MCP attached but no disk access. With a strong focus on community involvement, Reasonix has a bilingual Discord for setup help, workflow showcases, feature ideas, and contributor-only PR coordination. In short, Reasonix is your AI-powered coding sidekick for the terminal, designed to keep token costs low and productivity high. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 firecrawl/pdf-inspector caught my eye on GitHub Trending today. 🔗 https://github.com/firecrawl/pdf-inspector 📝 Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions. ────────────────────────────── The pdf-inspector library is a fast and lightweight Rust-based solution for PDF classification and text extraction. Its key features include smart classification, text extraction, and Markdown conversion, all of which can be performed without the need for OCR services. This library can be used in various programming languages, including Python, Node.js, and Rust, and can also be run in browser WebAssembly environments. The pdf-inspector has been benchmarked against other popular libraries, and its results show that it outperforms them in terms of speed and accuracy. The library is designed to handle various types of PDFs, including those with mixed content, tables, and images. It also supports CID font decoding and multi-column layout detection, making it a robust solution for PDF processing needs. Overall, pdf-inspector is a powerful tool for anyone looking to work with PDFs, and its ease of use, speed, and accuracy make it an ideal choice for a wide range of applications. One-liner takeaway: pdf-inspector is the ultimate PDF processing library that saves you time and resources by skipping expensive OCR services for text-based PDFs. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 zhaoxuya520/reverse-skill just hit the trending charts — here's why it matters. 🔗 https://github.com/zhaoxuya520/reverse-skill 📝 Reverse Engineering / Authorized Penetration Testing / Security Research Skill Router Pack AI-powered routing + On-demand toolchain bootstrapping + Self-evolving knowledge base Supports Claude Code, Kiro, Cursor, Cline, and other AI coding clients 逆向/渗透/安全技能路由包 - AI 自动路由 + 按需自举工具链 + 自动进化经验库 | 支持 Claude Code / Kiro / Cursor / Cline 等代码 AI 客户端 ────────────────────────────── The reverse-skill GitHub repository is a comprehensive cybersecurity skills router designed to navigate complex security tasks with ease. Its primary purpose is to route AI agents, such as Claude Code or Codex CLI, to the right methodology and tools for a given task, ensuring a repeatable workflow. Key features of reverse-skill include: * MASTER-ROUTING.md: A PRIMARY fast ladder for routing tasks to the right skills * routing.md: A task-to-skill routing matrix for various scenarios * skills/: A directory containing various skills, including APK reverse, binary reverse, and more To get started with reverse-skill, you'll need to: * Clone the repository using git clone https://github.com/zhaoxuya520/reverse-skill.git * Refresh the tool index using the provided scripts for your platform (Windows, Linux, or macOS) The repository is built with a range of technologies, including Python, Node.js, PowerShell, and Bash, and integrates various tools like IDA Pro, radare2, and Ghidra. Audience: The reverse-skill repository is designed for cybersecurity professionals, AI agents, and anyone interested in navigating complex security tasks with ease. In summary, the reverse-skill repository is a powerful tool for cybersecurity professionals, providing a comprehensive framework for navigating complex security tasks with ease. With its PRIMARY fast ladder, task-to-skill routing matrix, and extensive skills directory, reverse-skill is an indispensable resource for anyone looking to streamline their cybersecurity workflows: Automate your cybersecurity tasks with reverse-skill and take your skills to the next level! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: lyogavin/airllm — let's break it down. 🔗 https://github.com/lyogavin/airllm 📝 AirLLM 70B inference with single 4GB GPU ────────────────────────────── AirLLM is a cutting-edge solution that enables large language models to run on limited GPU resources without sacrificing performance. Its key features include inference memory reduction, allowing 70B models to run on a single 4GB GPU card, and support for various models, such as Llama 3.1, DeepSeek-V3, and Kimi K3. To use AirLLM, simply install the package with pip install airllm and initialize the model with AutoModel.from_pretrained("model_id"). The library also supports model compression for up to 3x inference speedup and prefetching to overlap model loading and computation. AirLLM is geared towards data scientists and researchers working with large language models, providing an efficient and scalable solution for inference and deployment. With its extensive support for popular models and continuous updates, AirLLM is an essential tool for anyone looking to push the boundaries of language modeling. One-liner takeaway: AirLLM revolutionizes large language model deployment by drastically reducing memory requirements, making it possible to run massive models like Kimi K3 on a single 4GB GPU card! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe