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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.
Mostrar más📈 Análisis del canal de Telegram Github Top Repositories
El canal Github Top Repositories (@githubre) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 267 suscriptores, ocupando la posición 15 384 en la categoría Educación y el puesto 32 523 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 267 suscriptores.
Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 373, y en las últimas 24 horas de 13, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.17%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.73% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 155 visualizaciones. En el primer día suele acumular 97 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como repository, fork, programming, statistic, description.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 10 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
13 267
Suscriptores
+1324 horas
+737 días
+37330 días
Archivo de publicaciones
13 273
🎯 Panniantong/Agent-Reach landed on trending. Worth a proper look.
🔗 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.
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Agent Reach is an innovative solution that empowers your AI agents with internet capabilities. This project bridges the gap between AI agents and various online platforms, allowing them to search, read, and interact with web content seamlessly.
Key Features:
- Supports multiple platforms: YouTube, Twitter, Reddit, GitHub, and many more
- Enables AI agents to search, read, and interact with web content
- Provides a simple and unified interface for AI agents to access various online platforms
- Allows for customization and extension of supported platforms
agent-reach install is the command that sets everything up. The installation process is straightforward, and the project is well-documented with a comprehensive README.
Technical Highlights:
- Built using Python and various open-source libraries
- Utilizes a modular architecture, making it easy to add or remove supported platforms
- Prioritizes security, with features like local storage of credentials and a secure installation mode
The target audience for Agent Reach includes developers and users of AI agents, such as those using Claude Code, OpenClaw, or Cursor.
In summary, Agent Reach is a powerful tool that unlocks the full potential of AI agents by providing them with internet capabilities. With its simple installation process, customizable architecture, and focus on security, it's an excellent solution for anyone looking to enhance their AI agents' abilities.
The takeaway: Give your AI agent superpowers with Agent Reach!
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🧠 Channel: https://t.me/GithubRe
13 273
🔥 affaan-m/ECC is trending — and it deserves your attention.
🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC repository on GitHub is a game-changer for agentic work, offering a harness-native operator system that streamlines workflows across multiple AI agent platforms. With
182K+ stars and 28K+ forks, this project has gained significant traction.
At its core, ECC is a complete system that includes skills, instincts, memory optimization, continuous learning, security scanning, and research-first development. It supports 12+ language ecosystems, including TypeScript, Python, Go, and Java, making it a versatile tool for developers.
To get started, users can follow the Shorthand Guide to ECC or the Longform Guide to ECC for a deeper dive. The project also offers a Security Guide to help users navigate potential risks.
ECC is designed to work seamlessly with various AI agent harnesses, including Codex, Claude Code, Cursor, OpenCode, and Gemini. The project's v2.0.0-rc.1 release introduces a public Hermes operator story, adding a new layer of functionality to the existing reusable layer.
The ECC community is active, with a discussion forum for Q&A and show-and-tell. Users can also sponsor the project or subscribe to ECC Pro for additional features.
In summary, ECC is a powerful tool for agentic work that offers a unique combination of features, flexibility, and community support. With its harness-native operator system and 12+ language ecosystems, ECC is an essential resource for developers looking to streamline their workflows.
ECC simplifies agentic workflows - and that's a superpower.
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🧠 Channel: https://t.me/GithubRe
13 273
🎯 lfnovo/open-notebook landed on trending. Worth a proper look.
🔗 https://github.com/lfnovo/open-notebook
📝 An Open Source implementation of Notebook LM with more flexibility and features
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Unlock Your Research Potential with Open Notebook, a private, multi-model, and 100% local alternative to Google's Notebook LM. This open-source platform empowers you to control your data, choose from 18+ AI providers, and organize multi-modal content with ease.
Key features include professional podcast generation, intelligent search, and context-aware chat. With fine-grained context control and comprehensive REST API, you can customize and extend Open Notebook to fit your needs.
Whether you're a researcher, student, or professional, Open Notebook is the perfect tool for private and secure research. Get started in just 2 minutes with the quick start guide and discover a world of unlimited possibilities.
Take control of your research today and experience the power of Open Notebook - Your research, your way.
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🧠 Channel: https://t.me/GithubRe
13 273
🔥 CopilotKit/CopilotKit is trending — and it deserves your attention.
🔗 https://github.com/CopilotKit/CopilotKit
📝 The Frontend Stack for Agents & Generative UI. React + Angular. Makers of the AG-UI Protocol
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The CopilotKit GitHub repository is a game-changer for building agent-native applications that can run on any framework and surface. It provides a set of tools and features that enable developers to create
chat UI, generative UI, and human-in-the-loop workflows for various platforms, including React, Angular, Vue, and React Native.
The key features of CopilotKit include chat UI, backend tool rendering, generative UI, shared state, and human-in-the-loop workflows. It also supports self-learning agents that can improve over time with user feedback.
To get started with CopilotKit, you can use the npx copilotkit@latest create -f <framework> command for new projects or npx copilotkit@latest init for existing projects. The useAgent hook provides programmatic control over the agent connection, and the Generative UI pattern allows agents to dynamically render UI as part of their workflow.
CopilotKit is designed for developers who want to build agent-native applications that can run on multiple platforms. It's perfect for those who want to create chatbots, virtual assistants, or other types of conversational interfaces.
In a nutshell, CopilotKit is all about empowering developers to build cutting-edge, user-centric applications that can learn and adapt over time – and that's a total game-changer!
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🧠 Channel: https://t.me/GithubRe
13 273
💡 chopratejas/headroom just hit the trending charts — here's why it matters.
🔗 https://github.com/chopratejas/headroom
📝 Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
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Headroom is a context compression layer for AI agents that reduces the number of tokens your agent reads by 60-95%, resulting in significant savings. It offers multiple modes of operation, including a
library for inline compression, a proxy for zero-code-changes integration, and an agent wrap for one-command integration with popular agents like Claude and Codex.
The key features of Headroom include cross-agent memory, which allows shared memory across multiple agents, and reversible compression, which stores originals locally and allows the LLM to retrieve them on demand. Headroom also includes a range of algorithms for compressing different types of content, including JSON, code, and text.
Technical highlights of Headroom include its ability to integrate with a wide range of AI agents and frameworks, including Anthropic, OpenAI, and LangChain. It also includes a range of tools for evaluating and optimizing compression performance.
Audience: Headroom is designed for developers and users of AI agents who want to reduce the number of tokens their agents read and improve performance. It is particularly useful for those working with large datasets or complex AI models.
To get started with Headroom, simply install it using pip install headroom-ai or npm install headroom-ai, then use the headroom wrap or headroom proxy commands to integrate it with your AI agent or application.
In summary, Headroom is a powerful tool for compressing context in AI agents, offering significant savings and improved performance. With its range of modes, algorithms, and integrations, it's an essential tool for anyone working with AI agents. Try Headroom today and see the difference for yourself!
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🧠 Channel: https://t.me/GithubRe13 273
🔍 Deep-diving into NousResearch/hermes-agent — fresh off the trending list.
🔗 https://github.com/NousResearch/hermes-agent
📝 The agent that grows with you
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Hermes Agent is a self-improving AI agent that creates skills from experience, improves them during use, and builds a deepening model of the user across sessions. Key features include a real terminal interface, cross-platform conversation continuity, a closed learning loop with autonomous skill creation, and scheduled automations. It supports various models and can be run on a $5 VPS, a GPU cluster, or serverless infrastructure, with six terminal backends for flexibility. Users can interact with the agent via Telegram, Discord, Slack, WhatsApp, Signal, or CLI, and it's research-ready with batch trajectory generation and compression for training. To get started, users can install Hermes using a
curl command and follow the setup wizard. With its user-centric design and extensive documentation, Hermes Agent is perfect for users who want a personalized AI experience. One agent, endless possibilities.
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🧠 Channel: https://t.me/GithubRe13 273
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13 273
💡 github/copilot-sdk just hit the trending charts — here's why it matters.
🔗 https://github.com/github/copilot-sdk
📝 Multi-platform SDK for integrating GitHub Copilot Agent into apps and services
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The GitHub Copilot SDK is a powerful tool that allows developers to embed Copilot's agentic workflows into their applications. With SDKs available for Python, TypeScript, Go, .NET, Java, and Rust, you can define agent behavior and let Copilot handle the rest. The SDK exposes the same engine behind Copilot CLI, providing a production-tested agent runtime that can be invoked programmatically.
To get started, simply
install your preferred SDK and follow the getting started guide. The SDK manages the CLI process lifecycle automatically, and you can also connect to an external CLI server.
Some key features include:
* Support for BYOK (Bring Your Own Key)
* Multi-language support
* Customizable tool availability
* Support for custom agents, skills, and tools
The GitHub Copilot SDK is generally available and follows semantic versioning. If you encounter any issues or have feature requests, you can report them on the GitHub Issues page.
In summary, the GitHub Copilot SDK is a game-changer for developers - with its ease of use, flexibility, and powerful features, you can supercharge your development workflow and take your applications to the next level!
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🧠 Channel: https://t.me/GithubRe
13 273
🔍 Deep-diving into jwasham/coding-interview-university — fresh off the trending list.
🔗 https://github.com/jwasham/coding-interview-university
📝 A complete computer science study plan to become a software engineer.
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The Coding Interview University is a comprehensive study plan to help you become a software engineer at a top company like Amazon, Google, or Microsoft. The plan covers everything you need to know for a technical interview, from the basics of coding to advanced topics like data structures, algorithms, and system design.
Key features of the plan include a structured approach to learning, with a focus on practice and review. You'll start by choosing a programming language and then work your way through a series of topics, including
Big-O notation, data structures like arrays, linked lists, and trees, and algorithms like sorting and graph traversal.
To get started, you can download the plan as a zip file or fork the GitHub repo and create a new branch to track your progress. The plan is designed to be flexible, so you can work at your own pace and focus on the areas where you need the most improvement.
From a technical perspective, the plan covers a wide range of topics, including Javarecursion, dynamic programming, and design patterns. You'll also learn about testing, string searching, and networking, among other topics.
The plan is suitable for anyone who wants to become a software engineer, regardless of their background or experience level. Whether you're a complete beginner or an experienced coder looking to improve your skills, the Coding Interview University has something to offer. So why wait? Start your journey to becoming a software engineer today and remember: with dedication and hard work, you can land your dream job in no time!
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🧠 Channel: https://t.me/GithubRe13 273
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13 273
🎯 HKUDS/Vibe-Trading landed on trending. Worth a proper look.
🔗 https://github.com/HKUDS/Vibe-Trading
📝 "Vibe-Trading: Your Personal Trading Agent"
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Vibe-Trading is your personal trading agent, empowering you with comprehensive trading capabilities through one command. This project features a
connector-first trading layer with support for multiple brokers, including IBKR, Robinhood, and OKX. The FastAPI backend and React frontend provide a robust and user-friendly interface.
Key features include paper trading, read-only account access, and bounded autonomy with a user-committed mandate. The project is written in Python 3.11+ and is available on PyPI.
The target audience includes traders, investors, and developers interested in building custom trading strategies. To get started, simply run pip install vibe-trading-ai and explore the website and docs for more information.
With Vibe-Trading, automate your trading decisions and take your investments to the next level - trade smart, not hard.
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🧠 Channel: https://t.me/GithubRe
13 273
⚡ aquasecurity/trivy is making waves. Here's the full picture.
🔗 https://github.com/aquasecurity/trivy
📝 Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
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Trivy is a comprehensive security scanner that can detect various security issues in different targets, including container images, file systems, Git repositories, virtual machine images, and Kubernetes. It has multiple scanners that look for known vulnerabilities, configuration issues, sensitive information, and software licenses.
To use Trivy, you can install it using popular package managers like
brew or download the binary from the GitHub releases page. Trivy also has a GitHub Actions integration and a Kubernetes operator.
Here's a basic example of how to use Trivy:
trivy image python:3.4-alpine
