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 14 237 suscriptores, ocupando la posición 14 012 en la categoría Educación y el puesto 28 298 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 237 suscriptores.
Según los últimos datos del 01 septiembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 294, y en las últimas 24 horas de 28, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.65% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 136 visualizaciones. En el primer día suele acumular 93 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 02 septiembre, 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.
14 237
Suscriptores
+2824 horas
+1137 días
+29430 días
Archivo de publicaciones
14 237
🎯 TencentCloud/CubeSandbox landed on trending. Worth a proper look.
🔗 https://github.com/TencentCloud/CubeSandbox
📝 Instant, Concurrent, Secure & Lightweight Sandbox for AI Agents.
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CubeSandbox is an instant, concurrent, secure, and lightweight sandbox service for AI agents. It's built on RustVMM and KVM, offering hardware-level isolation and millisecond-level startup. Key features include
sub-60ms boot, high density, and auto pause/resume for cost optimization. The service is also E2B-compatible, allowing for seamless migration.
The technical highlights of CubeSandbox include its ability to create a hardware-isolated, fully serviceable sandbox in under 60ms with less than 5MB of memory overhead. It supports both single-node deployment and easy scaling to multi-node clusters.
This project is ideal for developers and DevOps teams looking for a secure and efficient way to run AI agents. To get started, simply follow the four-step quick start guide, which includes provisioning a server, installing Cube Sandbox, creating a sandbox template, and running your first sandbox.
One-liner takeaway: With CubeSandbox, you can run thousands of secure, isolated AI agents on a single node in milliseconds.
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🧠 Channel: https://t.me/GithubRe14 237
🎯 asgeirtj/system_prompts_leaks landed on trending. Worth a proper look.
🔗 https://github.com/asgeirtj/system_prompts_leaks
📝 Extracted system prompts from Anthropic - Claude Fable 5, Opus 4.8, Claude Code, Claude Design. OpenAI - ChatGPT 5.5 Thinking, GPT 5.5 Instant, Codex. Google - Gemini 3.5 Flash, 3.1 Pro, Antigravity. xAI - Grok, Cursor, Copilot, VS Code, Perplexity, and more. Updated regularly.
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The system_prompts_leaks GitHub repository is a fascinating collection of system prompts for various AI chatbots, including Claude, ChatGPT, and Gemini. The purpose of this repository is to document and share the system prompt instructions for these AI models, allowing users to understand the underlying rules and guidelines that govern their behavior.
The repository contains a wide range of key features, including system prompts for different models, such as Claude Opus 4.8, ChatGPT GPT-5.5, and Gemini 3.5 Flash. It also includes usage examples, such as how to use the system prompts to rewrite articles or generate code.
From a technical standpoint, the repository is well-organized, with clear and concise documentation for each system prompt. The repository is also regularly updated, with new system prompts and models being added all the time.
The repository is likely to be of interest to developers and researchers working with AI chatbots, as well as anyone looking to gain a deeper understanding of how these models work.
In short, the system_prompts_leaks repository is a treasure trove of information for anyone interested in AI chatbots, and is sure to be a valuable resource for years to come:
System prompts are the secret sauce behind AI chatbots.
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🧠 Channel: https://t.me/GithubRe
14 237
🔍 Deep-diving into ruvnet/RuView — fresh off the trending list.
🔗 https://github.com/ruvnet/RuView
📝 π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.
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RuView is a WiFi sensing platform that leverages the disturbances in WiFi signals caused by human presence to detect and track people, breathing rates, and heart rates. This technology works through walls, in the dark, and without cameras or wearables, providing a unique solution for spatial intelligence and sensing.
Key features include presence detection, vital sign measurement, activity recognition, environment mapping, and sleep quality monitoring. The system integrates with major smart-home ecosystems like
Home Assistant, Apple Home, Google Home, and Alexa, allowing for seamless voice control and automation.
Technical highlights of RuView include its ability to run on low-cost ESP32 hardware, leveraging Channel State Information (CSI) to capture disturbances in WiFi signals. The platform utilizes spiking neural networks for real-time learning and adaptation, with a pre-trained model available on Hugging Face.
Audience for RuView includes developers, researchers, and smart home enthusiasts interested in exploring the potential of WiFi sensing technology for various applications, from healthcare and security to smart buildings and home automation.
To get started with RuView, users can choose from multiple options, including a Docker setup for simulated data, live sensing with ESP32-S3 hardware, or a full system with Cognitum Seed for persistent storage and advanced features.
In summary, RuView is a groundbreaking WiFi sensing platform that turns ordinary WiFi into a spatial intelligence system, providing a robust and versatile solution for various applications: RuView sees through walls, and so can you.
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🧠 Channel: https://t.me/GithubRe
14 237
🚀 Meet addyosmani/agent-skills: a gem from today's GitHub trending list.
🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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Agent Skills is a collection of production-grade engineering skills for AI coding agents, designed to streamline software development workflows. The repository provides 24 skills that cover the entire development lifecycle, from defining what to build to shipping it to production.
Key features include
8 slash commands that map to different stages of development, such as /spec, /plan, /build, and /ship. These commands activate the right skills automatically, ensuring consistency and efficiency in the development process.
To get started, users can install the skills using the skills CLI or integrate them with various agents like Claude Code, Cursor, or Codex. The skills are designed to be flexible and can be used with any agent that accepts system prompts or instruction files.
The repository also includes technical highlights such as automated testing, code review, and performance optimization. The skills are structured as workflows with steps, verification gates, and anti-rationalization tables, making it easier for developers to follow best practices and ensure high-quality code.
Agent Skills is suitable for a wide range of users, from individual developers to large teams, and can be used with various programming languages and frameworks.
In summary, Agent Skills helps developers build better software, faster.
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🧠 Channel: https://t.me/GithubRe14 237
⚡ Zackriya-Solutions/meetily is making waves. Here's the full picture.
🔗 https://github.com/Zackriya-Solutions/meetily
📝 Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper live transcription, speaker diarization, and Ollama summarization built on Rust. 100% local processing. no cloud required. Meetily (Meetly Ai -https://meetily.ai) is the #1 Self-hosted, Open-source Ai meeting note taker for macOS & Windows.
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The Zackriya-Solutions/meetily GitHub repository presents a privacy-first AI meeting assistant designed to capture, transcribe, and summarize meetings entirely on your local infrastructure. This solution is perfect for enterprises that require advanced meeting intelligence without compromising on privacy, compliance, or control.
Key features include
real-time transcription, AI-powered summaries, and multi-platform support for macOS, Windows, and Linux. The application is open source and free to use, with flexible AI provider support and custom OpenAI endpoint configuration.
The installation process is straightforward, with options for Windows, macOS, and Linux. For developers, the repository provides detailed build instructions and a contributing guide.
A Meetily PRO upgrade is available for users who need enhanced accuracy and advanced features, including custom summary templates, advanced exports, and self-hosted deployment options.
Meetily is ideal for professionals, teams, and organizations that require a privacy-first meeting assistant with enterprise-ready capabilities.
One-liner takeaway: Meetily empowers you to have total control over your meeting data with its privacy-first AI meeting assistant.
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🧠 Channel: https://t.me/GithubRe14 237
🎯 MadsLorentzen/ai-job-search landed on trending. Worth a proper look.
🔗 https://github.com/MadsLorentzen/ai-job-search
📝 AI-powered job application framework built on Claude Code. Fork it, fill in your profile, and let Claude evaluate jobs, tailor CVs, write cover letters, and prepare you for interviews.
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AI Job Search is an innovative framework that utilizes Claude Code to streamline your job application process. This powerful tool allows you to create a personalized profile, search for job openings, and even draft tailored CVs and cover letters. With its
drafter-reviewer workflow, you can ensure that your applications are both relevant and effective.
To get started, simply fork and clone the repository, then follow the quick start guide to set up your profile and install the necessary job search tools. The framework includes features like expand to enrich your profile, upskill to analyze skill gaps, and add-template to register custom LaTeX templates.
The technical highlights of this framework include its use of LaTeX for CV and cover letter compilation, as well as its ATS verification process to ensure that your applications are optimized for applicant tracking systems. The framework also includes a salary_lookup.py tool for benchmarking salaries.
Whether you're a recent graduate or an experienced professional, AI Job Search is an invaluable resource for anyone looking to take their job search to the next level. So why wait? Clone the repository and start streamlining your job search today - and remember, with AI Job Search, you can apply smarter, not harder.
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🧠 Channel: https://t.me/GithubRe
14 237
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14 237
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14 237
🌟 anthropics/claude-code caught my eye on GitHub Trending today.
🔗 https://github.com/anthropics/claude-code
📝 Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.
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Meet Claude Code, an AI-powered coding assistant that streamlines your workflow. This terminal-based tool understands your codebase and executes tasks like routine coding, code explanations, and Git workflows using natural language commands.
Key features include agentic coding and custom plugins to extend functionality. To get started, simply install Claude Code using one of the recommended methods, such as
curl -fsSL https://claude.ai/install.sh | bash for MacOS/Linux, and run claude in your project directory.
Technical highlights include support for various installation methods, a plugins directory for custom commands, and a /bug command for reporting issues.
The target audience is developers looking to boost productivity and simplify coding tasks.
In a nutshell, Claude Code is your new coding sidekick - simplifying development, one command at a time.
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🧠 Channel: https://t.me/GithubRe
14 237
⚡ steipete/CodexBar is making waves. Here's the full picture.
🔗 https://github.com/steipete/CodexBar
📝 Show usage stats for OpenAI Codex and Claude Code, without having to login.
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README not available for this repository.
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🧠 Channel: https://t.me/GithubRe
14 237
🎯 OthmanAdi/planning-with-files landed on trending. Worth a proper look.
🔗 https://github.com/OthmanAdi/planning-with-files
📝 Persistent file-based planning for AI coding agents and long-running agentic tasks. Crash-proof markdown plans that survive context loss and /clear, plus a deterministic completion gate and multi-agent shared state on disk. Manus-style. Works with Claude Code, Codex CLI, Cursor, Kiro, OpenCode and 60+ agents via the SKILL.md standard.
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Planning with Files is a persistent file-based planning skill designed for AI coding agents. It enables agents to survive context loss, crashes, and the `/clear` command by keeping `task_plan.md`, `findings.md`, and `progress.md` files on disk. The skill installs across 60+ agents via the SKILL.md standard.
Key features include opt-in autonomous and gated modes for long-running agent runs, a completion gate that holds the agent until the plan is done, and a structured run ledger. The skill has a high
96.7% pass rate and has been validated through A/B testing and security audits.
To use the skill, simply install it and start planning with your AI coding agent. The skill supports various modes, including autonomous and gated modes, and provides a range of commands for managing plans and sessions.
From a technical standpoint, the skill uses a combination of Markdown files and JSONL logs to store plan data and session history. It also includes a range of shell scripts and Python tools for managing plans and sessions.
The skill is designed for AI developers and researchers who want to improve the productivity and efficiency of their AI coding agents. It's also useful for teams working on large-scale AI projects that require careful planning and coordination.
In short, Planning with Files is a powerful tool for AI coding agents that need to survive context loss and crashes - give it a try and start planning with files today!
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🧠 Channel: https://t.me/GithubRe14 237
🚀 Meet dotnet/skills: a gem from today's GitHub trending list.
🔗 https://github.com/dotnet/skills
📝 Repository for skills to assist AI coding agents with .NET and C#
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The dotnet/skills GitHub repository is a treasure trove of curated core skills and custom agents for coding agents, focusing on .NET development. This repository includes a range of plugins such as
dotnet, dotnet-advanced, and dotnet-data, each with its own set of skills for handling specific .NET tasks.
To get started, users can install plugins using the Copilot CLI or Claude Code by adding the marketplace and installing the desired plugin. Alternatively, users can configure VS Code or Cursor to browse and install plugins from the marketplace.
From a technical standpoint, the repository follows the agentskills.io open standard, making it compatible with OpenAI Codex. The Codex CLI also supports a plugin marketplace, allowing users to register and install plugins directly.
The repository is perfect for .NET developers looking to enhance their coding skills and stay up-to-date with the latest .NET technologies. With its open-source nature, the repository encourages contributions from the community, making it a valuable resource for anyone involved in .NET development.
In a nutshell, dotnet/skills is a powerful tool for .NET developers, and its plugins can be used to supercharge coding skills - unlock your full potential with dotnet/skills!
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🧠 Channel: https://t.me/GithubRe14 237
⚡ gastownhall/gastown is making waves. Here's the full picture.
🔗 https://github.com/gastownhall/gastown
📝 Gas Town - multi-agent workspace manager
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Introduction to Gas Town: Gas Town is a multi-agent orchestration system that enables you to coordinate multiple AI coding agents, such as Claude Code and GitHub Copilot, on different tasks. The system persists work state in git-backed hooks, ensuring reliable multi-agent workflows.
Main Features:
- Multi-agent orchestration
- Persistent work state in git-backed hooks
- Support for various AI coding runtimes, including Claude Code and GitHub Copilot
- Customizable workflows using formulas (TOML-defined workflows)
- Real-time monitoring and tracking of agent progress
Example Usage:
```bash
gt install ~/gt --git
cd ~/gt
gt rig add myproject https://github.com/you/repo.git
gt crew add yourname --rig myproject
cd myproject/crew/yourname
gt mayor attach
```
The Gas Town system is designed for developers and teams who work with multiple AI coding agents and need a reliable way to manage their workflows. With its customizable formulas, real-time monitoring, and support for various AI coding runtimes, Gas Town is an ideal solution for complex software development projects.
The takeaway: Gas Town empowers developers to orchestrate AI coding agents with ease, streamlining their workflow and boosting productivity.
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🧠 Channel: https://t.me/GithubRe14 237
💡 ruvnet/RuView just hit the trending charts — here's why it matters.
🔗 https://github.com/ruvnet/RuView
📝 π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.
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RuView is a revolutionary WiFi sensing platform that transforms ordinary WiFi into a spatial intelligence system. It detects people, measures breathing and heart rate, tracks movement, and monitors rooms — all through walls, in the dark, with no cameras or wearables. Using Channel State Information (CSI) from low-cost ESP32 sensors, RuView captures disturbances in WiFi signals and turns them into actionable data.
Key features include
presence and occupancy detection, vital signs measurement, activity recognition, and environment mapping. RuView works natively with major smart-home ecosystems like Home Assistant, Apple Home, Google Home, and Alexa. It's built on RuVector and Cognitum Seed, running entirely on edge hardware with no cloud or internet required.
Technical highlights include a pretrained model published on Hugging Face, which fits in 8 KB and runs in microseconds on a Raspberry Pi. RuView also features edge intelligence with a catalog of 105 modules, multi-frequency mesh scanning, and 3D point cloud fusion.
RuView is perfect for researchers, developers, and smart home enthusiasts looking for a low-cost, camera-free, and contactless sensing solution. With its wide range of applications, RuView is set to revolutionize the way we interact with our environment. Transform your space with RuView — the future of spatial intelligence is here!
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🧠 Channel: https://t.me/GithubRe