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Github Top Repositories

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Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.

📈 Analytical overview of Telegram channel Github Top Repositories

Channel Github Top Repositories (@githubre) in the English language segment is an active participant. Currently, the community unites 15 231 subscribers, ranking 13 394 in the Education category and 26 058 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 15 231 subscribers.

According to the latest data from 07 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 0.92%. Within the first 24 hours after publication, content typically collects 0.62% reactions from the total number of subscribers.
Post reach: On average, each post receives 140 views. Within the first day, a publication typically gains 95 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
Thematic interests: Content is focused on key topics such as repository, fork, programming, statistic, description.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.”

Thanks to the high frequency of updates (latest data received on 08 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

15 231

Subscribers

+1124 hours

+767 days

+37930 days

140

Post views

~ 9524 hours

~ 10748 hours

0.92%

Engagement rate

~ 37

Posts per day

Ads index

Posts Archive

15 232

🌟 rohitg00/agentmemory caught my eye on GitHub Trending today. 🔗 https://github.com/rohitg00/agentmemory 📝 #1 Persistent memory for AI coding agents based on real-world benchmarks ────────────────────────────── AgentMemory is a persistent memory solution for AI coding agents, allowing them to recall previous conversations and maintain context. It supports a wide range of agents, including Claude Code, Codex CLI, and Gemini CLI, and provides features like confidence scoring, lifecycle management, and hybrid search. Key benefits include improved retrieval accuracy, reduced token usage, and increased efficiency. npm install -g @agentmemory/agentmemory to get started. The solution is built on the iii engine and provides a demo command to seed sample sessions and test recall. With agentmemory, you can say goodbye to re-explaining things to your AI coding agent - it just remembers. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🔍 Deep-diving into anthropics/claude-plugins-official — fresh off the trending list. 🔗 https://github.com/anthropics/claude-plugins-official 📝 Official, Anthropic-managed directory of high quality Claude Code Plugins. ────────────────────────────── The anthropics/claude-plugins-official GitHub repository is a treasure trove for developers, offering a curated directory of high-quality plugins for Claude Code. With two main directories, /plugins for internal plugins developed by Anthropic and /external_plugins for third-party plugins from partners and the community, users can easily find and install plugins that suit their needs. To get started, plugins can be installed directly from the marketplace via Claude Code's plugin system using the command /plugin install {plugin-name}@claude-plugins-official. For contributors, the repository provides a clear structure for developing and submitting new plugins, with guidelines for internal and external plugins. Each plugin follows a standard structure, including a plugin.json file for metadata, and optional configurations for MCP servers, slash commands, agents, and skills. The takeaway: With its vast array of plugins and straightforward installation process, the anthropics/claude-plugins-official repository is a game-changer for anyone looking to supercharge their Claude Code experience! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

Repost from Machine Learning with Python

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15 232

🔍 Deep-diving into obra/superpowers — fresh off the trending list. 🔗 https://github.com/obra/superpowers 📝 An agentic skills framework & software development methodology that works. ────────────────────────────── Superpowers is a software development methodology that gives your coding agents a set of composable skills to build and create with ease. The key features of Superpowers include a step-by-step approach to coding, starting with brainstorming and ending with test-driven-development. The workflow is divided into six main stages: brainstorming, using-git-worktrees, writing-plans, subagent-driven-development, test-driven-development, and requesting-code-review. To use Superpowers, you can install it via various coding agent marketplaces such as Claude Code, Codex CLI, and Gemini CLI. The technical highlights of Superpowers include a Skills Library with various skills such as testing, debugging, and collaboration. Superpowers is designed for developers and coders who want to streamline their coding process and create high-quality code. With its systematic approach and emphasis on testing, Superpowers is perfect for those who want to take their coding to the next level. The one-liner takeaway is: Superpowers gives your coding agents the skills they need to build and create with ease and precision. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🔥 Imbad0202/academic-research-skills is trending — and it deserves your attention. 🔗 https://github.com/Imbad0202/academic-research-skills 📝 Academic Research Skills for Claude Code: research → write → review → revise → finalize ────────────────────────────── The Academic Research Skills repository on GitHub, created by Imbad0202, is a comprehensive suite of tools designed to facilitate academic research and paper writing. The primary purpose of this project is to provide a human-in-the-loop approach, where AI assists researchers in handling mundane tasks, allowing them to focus on high-level thinking and writing. Key features of the repository include Deep Research, Academic Paper, and Academic Paper Reviewer modes, which cover various aspects of research, from literature reviews to paper writing and peer review. The ars-plan command enables users to engage in a Socratic dialogue to map out their paper structure. To use the repository, users can install the plugin using the /plugin marketplace add Imbad0202/academic-research-skills and /plugin install academic-research-skills commands. The repository is suitable for academic researchers, students, and anyone looking to streamline their research and writing process. The project's technical highlights include its integration with Claude Code, a 13-agent research team, and various quality gates to ensure the accuracy and validity of research findings. The repository also provides a

README

file with detailed instructions and examples for users to get started. In summary, the Academic Research Skills repository is an invaluable resource for academic researchers, providing a comprehensive suite of tools to facilitate research and paper writing. By leveraging the power of AI, researchers can focus on high-level thinking and writing, while the tool handles the grunt work. The takeaway: AI-assisted research tools like Academic Research Skills are revolutionizing the way we conduct research, making it more efficient, accurate, and enjoyable. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🌟 HKUDS/CLI-Anything caught my eye on GitHub Trending today. 🔗 https://github.com/HKUDS/CLI-Anything 📝 "CLI-Anything: Making ALL Software Agent-Native" -- CLI-Hub:https://clianything.cc/ ────────────────────────────── The CLI-Anything project is a game-changer for making software agent-native. Its purpose is to bridge the gap between AI agents and the world's software by creating a universal interface that allows agents to interact with any application. The key features include a CLI-Hub where you can browse, install, and manage community-built CLIs, as well as a pip install cli-anything-hub command to get started. To use CLI-Anything, simply pip install cli-anything-hub and then cli-hub install <name> to install your desired CLI. You can also contribute your own CLI by opening a PR, and the hub will update instantly. The project has a strong focus on technical highlights such as structured and composable text commands, lightweight and universal design, and self-describing interfaces. The project is perfect for developers and AI enthusiasts who want to make their software agent-native. With CLI-Anything, you can create a universal interface for your application, making it accessible to a wide range of AI agents. The project has a lot of potential for growth, with a strong community of contributors and a wide range of supported applications. In summary, CLI-Anything is a powerful tool for making software agent-native, with a strong focus on community contribution and technical highlights. As the project says, "One command line to rule them all" - and with CLI-Anything, you can make any software agent-ready with just one command line. The takeaway: Make any software agent-native with one simple command line. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🔥 tinyhumansai/openhuman is trending — and it deserves your attention. 🔗 https://github.com/tinyhumansai/openhuman 📝 Your Personal AI super intelligence. Private, Simple and extremely powerful. ────────────────────────────── OpenHuman is your personal AI super intelligence, designed to integrate with you in your daily life. It's private, simple, and extremely powerful. With a clean desktop experience and short onboarding paths, you can get started in just a few clicks. Key features include: - 118+ third-party integrations with one-click OAuth - A Memory Tree and Obsidian Wiki for a local-first knowledge base - Batteries included: web search, web-fetch scraper, coder toolset, and native voice - Smart token compression (TokenJuice) to reduce cost and latency Technical highlights: - Built with Rust, Node.js, and pnpm - Uses Tauri and CEF for the desktop shell - Includes git submodule update --init --recursive for vendored sources The target audience is anyone looking for a personal AI assistant that's easy to use and integrates with their daily life. In short, OpenHuman is the ultimate AI sidekick that gets to know you in minutes, not weeks - star the repo and experience the future of AI today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🔥 plausible/analytics is trending — and it deserves your attention. 🔗 https://github.com/plausible/analytics 📝 Open source, privacy-first web analytics. Lightweight, cookie-free Google Analytics alternative. Self-hosted or cloud. ────────────────────────────── Plausible Analytics is a privacy-first web analytics tool that's lightweight and cookie-free. It offers a simple, fast dashboard and is GDPR, CCPA, PECR compliant. You can use it as a managed cloud service or self-host it. The project is open source and independent, funded by subscribers. Key features include: - Real-time insights - Search insights integrated with Google Search Console - API and integrations for custom workflows - Define key goals and track conversions Technical highlights:

Backend: Elixir + Phoenix
Databases: PostgreSQL, ClickHouse
Frontend: React + TailwindCSS

Plausible is for anyone looking for a private and simple web analytics solution. With its transparent business model, you pay for the service, not with your users' data. One-liner takeaway: Plausible Analytics - private, simple, and transparent web analytics. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

💡 ZhuLinsen/daily_stock_analysis just hit the trending charts — here's why it matters. 🔗 https://github.com/ZhuLinsen/daily_stock_analysis 📝 LLM驱动的 A/H/美股智能分析：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets. ────────────────────────────── The Daily Stock Analysis system is an AI-powered stock analysis platform that provides automated daily analysis and decision-making dashboards for A-share, Hong Kong share, and US share markets. Key features include: - AI decision reports with core conclusions, scores, trends, and risk alerts - Multi-market data aggregation for stocks, ETFs, and market news - Web and desktop workstations for manual analysis and configuration management - Agent strategy inquiry for multi-round questioning and strategy validation The system supports various AI models and data sources, including Anspire, AIHubMix, and SerpAPI. To get started, users can fork the repository and configure secrets for AI models, notification channels, and stock lists. Then, they can enable GitHub Actions for automated daily analysis or run the system locally using Docker. The target audience includes individual investors, researchers, and financial institutions seeking to leverage AI for stock market analysis. In a nutshell, Daily Stock Analysis is your one-stop-shop for AI-driven stock market insights, automating the tedious process of data collection and analysis, so you can focus on making informed investment decisions. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

I study Gold. I send the alert. You copy the move. That’s how simple I made it for beginners. You don’t need to become a full-time trader. You just need a phone, Telegram, and 10 minutes a day. 👉 Join for free #ad 📢 InsideAd

15 232

⚡ microsoft/ai-agents-for-beginners is making waves. Here's the full picture. 🔗 https://github.com/microsoft/ai-agents-for-beginners 📝 12 Lessons to Get Started Building AI Agents ────────────────────────────── The AI Agents for Beginners course on GitHub is designed to introduce you to the world of building AI Agents. With 50+ language translations available, this course is accessible to a wide range of learners. The course covers the fundamentals of building AI Agents, including lessons on AI Agentic Frameworks, AI Agentic Design Patterns, and Building Trustworthy AI Agents. To get started, you can fork this repo and run the code examples, which utilize Microsoft Agent Framework with Azure AI Foundry Agent Service V2. You can also join the Microsoft Foundry Discord channel to meet other learners and get your questions answered. This course is perfect for beginners and experienced learners alike, with each lesson including a written lesson, a short video, and Python code samples. Whether you're looking to build AI Agents for personal projects or professional applications, this course provides the foundation you need to get started. Takeaway: Dive into the world of AI Agents with this beginner-friendly course and start building your own AI Agents today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

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15 232

🎯 NVlabs/Sana landed on trending. Worth a proper look. 🔗 https://github.com/NVlabs/Sana 📝 SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer ────────────────────────────── The SANA project is an open-source codebase for efficient high-resolution image and video generation, providing complete training and inference pipelines. It includes various models such as SANA-1.5, SANA-Sprint, SANA-Video, and Sol-RL. The key features of SANA include its efficiency, scalability, and support for multiple models and techniques. The project also provides a range of tools and resources, including documentation, demos, and pre-trained models. Technical highlights of SANA include its use of techniques such as causal linear attention, mix-FFN, and inference-time scaling. The project also supports various frameworks and libraries, including diffusers and ComfyUI. The target audience for SANA includes researchers, developers, and practitioners in the field of computer vision and machine learning. The project has a strong focus on community engagement, with a Discord channel for discussions and a range of resources and tools available for contributors. In summary, SANA is a powerful and efficient codebase for high-resolution image and video generation, with a strong focus on community engagement and support. With its range of models, techniques, and resources, SANA is an ideal choice for anyone looking to explore the latest advances in computer vision and machine learning: join the SANA community today and start generating stunning images and videos! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

15 232

🔍 Deep-diving into humanlayer/12-factor-agents — fresh off the trending list. 🔗 https://github.com/humanlayer/12-factor-agents 📝 What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? ────────────────────────────── The 12-factor-agents GitHub repository provides a set of principles for building reliable LLM applications. The project, inspired by the 12 Factor Apps methodology, aims to help developers create more maintainable and scalable LLM-powered software. Key features include a set of 12 factors, such as own your prompts, own your context window, and make your agent a stateless reducer, which serve as guidelines for designing and implementing LLM applications. Developers can use these factors to build more robust and efficient agents, and the repository provides a community-driven discussion for feedback and contributions. The target audience for this repository includes developers and founders working with LLMs, particularly those interested in building production-ready customer-facing agents. From a technical perspective, the repository highlights the importance of unifying execution state and business state and launching/pausing/resuming with simple APIs. In summary, the 12-factor-agents repository offers a valuable resource for developers seeking to build reliable and scalable LLM applications, with a focus on user value and maintainability. One key takeaway: building reliable LLM applications requires a deep understanding of the underlying principles and a focus on creating maintainable and scalable software. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe