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 14 191 suscriptores, ocupando la posición 14 012 en la categoría Educación y el puesto 28 382 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 191 suscriptores.
Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 256, y en las últimas 24 horas de 0, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.05%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.69% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 149 visualizaciones. En el primer día suele acumular 98 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 30 agosto, 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.
14 191
Suscriptores
Sin datos24 horas
+257 días
+25630 días
Archivo de publicaciones
14 209
🌟 AstrBotDevs/AstrBot caught my eye on GitHub Trending today.
🔗 https://github.com/AstrBotDevs/AstrBot
📝 AI Agent Assistant & development framework that integrates lots of IM platforms, LLMs, plugins and AI feature, and can be your openclaw alternative. ✨
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AstrBot is an open-source, all-in-one chatbot platform that integrates with mainstream instant messaging apps, providing reliable and scalable conversational AI infrastructure. Its key features include AI LLM conversations, multimodal support, and plugin extensions with over 1000 plugins available.
astrbot supports multiple platforms, including QQ, WeChat, Telegram, and Slack, and offers various deployment methods, such as one-click deployment, Docker, and desktop application deployment. The platform is suitable for individuals, developers, and teams looking to build AI applications within their workflows. With its internationalization support and web chat UI, AstrBot enables users to create production-ready AI applications quickly. Whether you're building a personal AI companion or an enterprise knowledge base, AstrBot has got you covered - revolutionize your conversations with AstrBot today!
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🧠 Channel: https://t.me/GithubRe
14 209
🚀 Meet MoonshotAI/kimi-cli: a gem from today's GitHub trending list.
🔗 https://github.com/MoonshotAI/kimi-cli
📝 Kimi Code CLI is your next CLI agent.
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Kimi CLI is an AI agent that runs in your terminal, designed to assist with software development tasks and terminal operations. It can read and edit code, execute shell commands, search and fetch web pages, and even plan and adjust actions during execution.
The key features of Kimi CLI include a shell command mode that allows you to run shell commands without leaving the interface, a VS Code extension for integration with Visual Studio Code, and support for IDE integration via ACP (Agent Client Protocol) for compatibility with various editors and IDEs. Additionally, it offers Zsh integration and MCP support for Model Context Protocol tools.
To get started with Kimi CLI, you can refer to the
Getting Started guide, which provides installation and usage instructions. For development, you can clone the repository, prepare the development environment, and use various make commands to run, format, and test the code.
The target audience for Kimi CLI appears to be developers who want to leverage AI capabilities to enhance their productivity and workflow. However, note that Kimi CLI is evolving into Kimi Code CLI, and this project will be gradually wound down.
One-liner takeaway: Kimi CLI is a terminal AI agent that's evolving into Kimi Code CLI, so try the next-generation terminal AI agent for a more enhanced experience!
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🧠 Channel: https://t.me/GithubRe
14 209
🔍 Deep-diving into every-app/open-seo — fresh off the trending list.
🔗 https://github.com/every-app/open-seo
📝 Open source alternative to Semrush and Ahrefs
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OpenSEO is an open-source alternative to expensive SEO tools like Semrush and Ahrefs. It's an all-in-one SEO tool that you can control, with a modern and simple UI focused on workflows like keyword research, rank tracking, and competitor insights.
You can use
OpenSEO with any AI agent, and it even offers pre-built skills for popular agents like Claude Code, OpenClaw, or Hermes.
The tool is pay-as-you-go, so you only pay for what you use, and you can self-host it using Docker or Cloudflare.
OpenSEO is perfect for anyone looking for an affordable and customizable SEO solution.
Take control of your SEO with OpenSEO - it's the people's SEO tool.
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🧠 Channel: https://t.me/GithubRe
14 209
🌟 Robbyant/lingbot-map caught my eye on GitHub Trending today.
🔗 https://github.com/Robbyant/lingbot-map
📝 A feed-forward 3D foundation model for reconstructing scenes from streaming data
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LingBot-Map is a cutting-edge repo that introduces a geometric context transformer for streaming 3D reconstruction. This innovative approach unifies coordinate grounding, dense geometric cues, and long-range drift correction within a single streaming framework. The key features of LingBot-Map include its high-efficiency streaming inference, achieving stable performance at ~20 FPS on 518×378 resolution over long sequences exceeding 10,000 frames, and its state-of-the-art reconstruction capabilities, outperforming existing streaming and iterative optimization-based approaches.
To get started with LingBot-Map, users can follow the
installation instructions, which involve creating a conda environment, installing PyTorch, and installing the lingbot-map package. The repo also provides a quick start guide, which enables users to run their first scene with a single command. Additionally, the interactive demo allows users to visualize 3D scenes interactively, while the offline rendering pipeline enables batch rendering for long sequences.
The target audience for LingBot-Map includes researchers and developers in the field of 3D reconstruction, as well as professionals working with 3D modeling and computer vision. The repo's technical highlights, such as its feed-forward architecture and paged KV cache attention, make it an attractive solution for applications requiring efficient and accurate 3D reconstruction.
In a nutshell, LingBot-Map is a game-changer for streaming 3D reconstruction, and its efficiency, accuracy, and ease of use make it a must-try for anyone working in the field: Reconstruct your world in 3D, effortlessly!
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🧠 Channel: https://t.me/GithubRe
14 209
💡 topoteretes/cognee just hit the trending charts — here's why it matters.
🔗 https://github.com/topoteretes/cognee
📝 Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
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Cognee is an open-source AI memory platform that gives AI agents persistent long-term memory across sessions. It ingests data in any format, builds a self-hosted knowledge graph, and lets every agent recall, connect, and act with full context. Key features include vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation.
To
use Cognee, simply install it with pip, configure the LLM, and run the pipeline with four operations — remember, recall, forget, and improve. It's available as a plugin for OpenClaw, Claude Code, and has Rust and TypeScript clients.
Technical highlights include reliable and trustworthy agents, persistent and learning agents, and knowledge infrastructure. Cognee is perfect for developers and researchers who want to give their AI agents a brain that never forgets.
One-liner takeaway: Cognee helps AI agents remember everything, so they can make smarter decisions and take actions with full context.
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🧠 Channel: https://t.me/GithubRe
14 209
🔍 Deep-diving into jamiepine/voicebox — fresh off the trending list.
🔗 https://github.com/jamiepine/voicebox
📝 The open-source AI voice studio. Clone, dictate, create.
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The Voicebox GitHub repository offers an open-source AI voice studio that provides a range of features for voice cloning, speech generation, and dictation. With
7 TTS engines and support for 23 languages, users can clone voices from reference samples, generate speech, and even dictate into any text field with a global hotkey. The app also includes post-processing effects like pitch shift, reverb, and delay, as well as unlimited generation length with automatic chunking and crossfading.
Some of the key technical highlights include a REST API and a built-in MCP server for integrating voice I/O into other apps and agents. The app is built with Tauri (Rust) for native performance and runs on macOS, Windows, Linux, Docker, and other platforms.
The target audience for Voicebox appears to be developers, content creators, and anyone interested in exploring the possibilities of AI voice technology. With its complete privacy guarantee and local-first approach, Voicebox is an attractive option for those who value data security and flexibility.
In short, Voicebox is a powerful tool that puts the full voice I/O stack at your fingertips - and with its open-source approach, the possibilities for innovation and customization are endless: Take control of your voice, and let your voice be heard.
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🧠 Channel: https://t.me/GithubRe14 209
📌 Spotted on GitHub Trending: kvcache-ai/ktransformers — let's break it down.
🔗 https://github.com/kvcache-ai/ktransformers
📝 A Flexible Framework for Experiencing Heterogeneous LLM Inference/Fine-tune Optimizations
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The ktransformers project is a flexible framework for experiencing cutting-edge large language model (LLM) inference and fine-tuning optimizations. It focuses on efficient inference and fine-tuning through CPU-GPU heterogeneous computing. Key features include high-performance kernel operations, efficient mixture-of-experts (MoE) inference, quantization support, and easy integration with other frameworks.
The project has two main capabilities:
Inference and SFT (Fine-Tuning). Inference provides CPU-optimized kernel operations for heterogeneous LLM inference, while SFT offers fine-tuning with LLaMA-Factory integration for ultra-large MoE models.
The framework supports various models, including DeepSeek-R1, Qwen3-Next, and Kimi-K2, and provides tutorials and documentation for each. It also offers technical highlights such as AMX/AVX acceleration, MoE optimization, and quantization support.
KTransformers is suitable for researchers, developers, and anyone interested in efficient LLM inference and fine-tuning. The project is developed and maintained by MADSys Lab, Approaching.AI, and community contributors, and welcomes contributions and feedback.
Get started with KTransformers today and unlock the full potential of your LLMs: efficient inference and fine-tuning made easy!
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🧠 Channel: https://t.me/GithubRe
14 209
📌 Spotted on GitHub Trending: msitarzewski/agency-agents — let's break it down.
🔗 https://github.com/msitarzewski/agency-agents
📝 A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
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The Agency is a collection of AI agent personalities, each with deep expertise, unique voice, and deliverable-focused approach. The agents are specialized, personality-driven, and production-ready, making them a valuable addition to any team.
The
agency-agents repository provides various installation options, including a native desktop app for macOS, Linux, and Windows, as well as script-based installations for different tools like Claude Code, Cursor, and Gemini CLI.
To get started, you can download the app or use the command line to install the agents. For example:
./scripts/install.sh --tool claude-code14 209
🎯 rohitg00/ai-engineering-from-scratch landed on trending. Worth a proper look.
🔗 https://github.com/rohitg00/ai-engineering-from-scratch
📝 Learn it. Build it. Ship it for others.
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The AI Engineering from Scratch curriculum is a comprehensive, 20-phase, 503-lesson journey to master AI engineering. It's designed to bridge the gap between using AI tools and understanding how they work. With a focus on hands-on learning, each lesson follows a consistent structure: read the problem, derive the math, write the code, run the test, and keep the artifact.
Key features include:
- A
linear progression from math foundations to autonomous systems
- Implementation in four languages: Python, TypeScript, Rust, and Julia
- Reusable artifacts from each lesson, including prompts, skills, agents, and MCP servers
To get started, you can read any lesson on the website, clone and run the repository, or use the /find-your-level skill in a compatible agent to determine your starting point. Prerequisites are minimal: you should be able to write code in any language, and Python knowledge is helpful but not required.
Technical highlights of the curriculum include building algorithms from raw math, understanding backpropagation and attention mechanisms, and deploying autonomous agents. The audience for this curriculum includes anyone looking to deeply understand AI, from beginners to experienced engineers.
In short, AI Engineering from Scratch is a rigorous, hands-on curriculum that empowers you to build, understand, and deploy AI systems from the ground up. Don't just use AI — build it, and build it to last.
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🧠 Channel: https://t.me/GithubRe14 209
🚀 Meet diegosouzapw/OmniRoute: a gem from today's GitHub trending list.
🔗 https://github.com/diegosouzapw/OmniRoute
📝 Never stop coding. Free MIT AI gateway: one endpoint, 268+ providers (50+ free), 500+ models — Claude, GPT, Gemini, Kimi K3, GLM, DeepSeek. Works with Claude Code, Codex, Cursor, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, multimodal, Desktop/PWA. Built by 500+ contributors.
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OmniRoute is a free AI gateway that connects every AI tool to 250 providers, with 90+ free options, through one endpoint. It allows you to never stop coding and saves you money by maximizing subscriptions, tracking quota, and using every token before reset.
RTK + Caveman compression helps save 15-95% of eligible tokens per request. Key features include one endpoint for every tool, cost-optimized routing, and a 4-tier auto-fallback system. Technical highlights include circuit breakers, TLS stealth, and 21,000+ tests. Audience includes developers who want to save time and money while using AI tools. With OmniRoute, you can focus on coding without worrying about rate limits or expensive APIs. OmniRoute is production-grade, with a 0 to start approach, requiring no card or payment. In a nutshell, OmniRoute helps you code more, pay less.
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🧠 Channel: https://t.me/GithubRe
14 209
🌟 1jehuang/jcode caught my eye on GitHub Trending today.
🔗 https://github.com/1jehuang/jcode
📝 The most intelligent agent harness for code
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The jcode project is a next-generation coding agent harness designed for multi-session workflows, infinite customizability, and performance. It's built to raise the skill ceiling, and its key features include infinite customizability and high performance. To get started with
jcode, you can install it using a simple curl command or by using the provided PowerShell script for Windows.
One of the standout features of jcode is its resource efficiency, with a focus on optimizing every metric to the bone. This is particularly important for scaling multi-session workflows. The project includes detailed comparisons with other tools, showcasing its superiority in terms of RAM usage and boot-up time.
The jcode project is suitable for developers and power users looking to streamline their coding workflow. With its highly customizable and performant nature, it's an attractive option for those seeking to take their coding skills to the next level.
The project's technical highlights include a memory system that allows the agent to automatically recall relevant information, as well as explicit memory tools for active searching and storing of memories.
In a nutshell, jcode is the ultimate coding agent harness for those who want to code faster, better, and more efficiently - and it's available now!
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🧠 Channel: https://t.me/GithubRe
14 209
📌 Spotted on GitHub Trending: tirth8205/code-review-graph — let's break it down.
🔗 https://github.com/tirth8205/code-review-graph
📝 Local-first code intelligence graph for MCP and CLI. Builds a persistent map of your codebase so AI coding tools read only what matters, with benchmarked context reductions on reviews and large-repo workflows.
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Code Review Made Smart: The
code-review-graph repository revolutionizes code reviews by building a structural map of your codebase and providing your AI assistant with precise context, reducing token waste and increasing efficiency.
Key features include:
- Incremental updates in under 2 seconds
- Broad language coverage, including support for Jupyter notebooks and the ability to add custom languages
- Risk-scored PR reviews in CI through a GitHub Action
To get started, simply run pip install code-review-graph and code-review-graph install to set up everything.
The repository boasts an impressive 82x median per-question token reduction across 6 real repositories, making it a game-changer for developers.
In short, code-review-graph is a must-have for any development team looking to streamline their code review process and make the most out of their AI assistants.
The punchy one-liner takeaway: Stop burning tokens, start reviewing smarter with code-review-graph.
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🧠 Channel: https://t.me/GithubRe