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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 13 227 suscriptores, ocupando la posición 15 419 en la categoría Educación y el puesto 32 691 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 227 suscriptores.
Según los últimos datos del 06 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 344, y en las últimas 24 horas de 12, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.18%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.79% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 156 visualizaciones. En el primer día suele acumular 104 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 08 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.
13 227
Suscriptores
+1224 horas
+997 días
+34430 días
Archivo de publicaciones
13 226
🎯 Biohub/esm landed on trending. Worth a proper look.
🔗 https://github.com/Biohub/esm
📝 No description.
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The Biohub/esm GitHub repository presents a groundbreaking world model of protein biology, leveraging the latest advancements in Evolutionary Scale Modeling (ESM). This comprehensive system comprises three primary components:
ESMC, ESMFold2, and ESM Atlas.
ESMC is a state-of-the-art protein language model trained on billions of protein sequences, allowing it to learn and represent the rules of protein biology. ESMFold2 builds upon ESMC and is a state-of-the-art structure prediction model that can predict high-resolution, all-atom 3D protein structures directly from amino acid sequences. The ESM Atlas is a vast map of 6.8 billion proteins, organized according to the internal world model of ESMC, enabling the prediction of over one billion structures.
These tools can be utilized through the Biohub Platform or by running the models locally with Hugging Face. The repository provides extensive documentation, including tutorials and preprints, to facilitate understanding and usage. The target audience includes researchers, scientists, and developers interested in protein biology, structure prediction, and world modeling.
Here is a simple example of running ESMC locally:
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
# example GFP sequence
sequences = ["MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK"]
model = AutoModelForMaskedLM.from_pretrained(
"Biohub/ESMC-6B",
device_map="auto",
).eval()
tokenizer = AutoTokenizer.from_pretrained("Biohub/ESMC-6B")
inputs = tokenizer(sequences, return_tensors="pt", padding=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.inference_mode():
output = model(**inputs)
esm library and import the necessary modules. With Biohub/esm, unlock the power of world modeling in protein biology and discover new frontiers in protein structure prediction and design. The future of protein biology is here, and it's being modeled with unprecedented accuracy.
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🧠 Channel: https://t.me/GithubRe
13 226
🎯 byoungd/English-level-up-tips landed on trending. Worth a proper look.
🔗 https://github.com/byoungd/English-level-up-tips
📝 An advanced guide to learn English which might benefit you a lot 🎉 . 离谱的英语学习指南/英语学习教程/英语学习/学英语
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The English-level-up-tips GitHub repository is a comprehensive guide to learning English, dedicated to the author's past love, W. This project aims to provide a detailed and advanced guide to help individuals improve their English skills.
The guide covers various aspects of the English language, including understanding, vocabulary, listening, reading, speaking, and writing. It also features a chapter on AI and its application in language learning.
The guide is suitable for anyone looking to improve their English skills, from beginners to advanced learners. It's a valuable resource for those who want to learn English in a natural and enjoyable way.
Key features of the guide include its comprehensive coverage of the English language, its use of AI in language learning, and its focus on making learning English a fun and rewarding experience.
The guide is available for online reading on various platforms, including GitHub Pages, GitBook, and Zhihu.
In short, the English-level-up-tips guide is a must-have resource for anyone looking to improve their English skills, and with dedication and practice, you can master the English language and unlock a world of new opportunities.
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🧠 Channel: https://t.me/GithubRe13 226
🚀 Meet galilai-group/stable-worldmodel: a gem from today's GitHub trending list.
🔗 https://github.com/galilai-group/stable-worldmodel
📝 A platform for reproducible world model research and evaluation
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The stable-worldmodel repository provides a unified platform for world model research and evaluation. It offers a single interface for data collection, training, and evaluation with model-predictive control across various environments. The repository includes
reference implementations of common baselines and planning solvers, allowing researchers to focus on their core contributions.
The library supports multiple data formats, including lance, hdf5, folder, video, and lerobot, and provides tools for format conversion and benchmarking. It also features a large suite of environments, including those from the DeepMind Control Suite, Gymnasium, and OGBench, with factors of variation for evaluating zero-shot generalization.
To get started, users can install the library via PyPI and follow the quick start guide to collect data, train a world model, and evaluate it using model-predictive control. The repository is in active development, with APIs subject to change between minor versions.
The stable-worldmodel library is designed for researchers and developers working on world models, and its unified interface and reference implementations make it an ideal choice for those looking to advance the state-of-the-art in this field.
With stable-worldmodel, the world is your model, and the possibilities are endless.
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🧠 Channel: https://t.me/GithubRe
13 226
📌 Spotted on GitHub Trending: run-llama/liteparse — let's break it down.
🔗 https://github.com/run-llama/liteparse
📝 A fast, helpful, and open-source document parser
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LiteParse is a fast and lightweight, open-source PDF parsing tool that delivers high-quality spatial text parsing with bounding boxes. It runs locally on your machine, without relying on proprietary features or cloud dependencies.
Key features of LiteParse include fast text parsing using PDFium, a flexible OCR system with built-in Tesseract and support for HTTP servers, screenshot generation, and multiple output formats like JSON and text. It also supports multi-language use from Rust, Node.js/TypeScript, Python, or the browser (WASM) and is multi-platform, compatible with Linux, macOS, and Windows.
To
install LiteParse, you can use your preferred package manager. For example, you can install it via npm for Node.js/TypeScript, pip for Python, or cargo for Rust.
The CLI usage is straightforward. You can parse files using the lit parse command, generate screenshots with lit screenshot, and perform batch parsing with lit batch-parse.
Technical highlights include automatic conversion of various document formats to PDF before parsing, support for office documents via LibreOffice, and image formats via ImageMagick.
Audience: LiteParse is suitable for developers and users who need fast and accurate PDF parsing capabilities, especially those working with large volumes of documents or requiring precise text positioning information.
In summary, LiteParse is a powerful, user-friendly tool that provides fast and accurate PDF parsing, making it an excellent choice for anyone looking to extract valuable information from their documents - Parse your documents, unleash the power of your data.
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🧠 Channel: https://t.me/GithubRe13 226
🌟 cursor/plugins caught my eye on GitHub Trending today.
🔗 https://github.com/cursor/plugins
📝 Cursor plugin specification and official plugins
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The Cursor plugins repository offers a collection of official plugins for various developer tools, frameworks, and SaaS products. Each plugin is a standalone directory with its own manifest, making it easy to manage and maintain.
Key features include continual learning for incremental memory updates, thermos for deep security audits, and create-plugin for scaffolding new plugins.
To use these plugins, simply navigate to the desired plugin directory and follow the instructions in the
README.md file.
From a technical standpoint, the repository uses a marketplace.json file to list all available plugins, and each plugin has its own plugin.json manifest.
These plugins are perfect for developers and dev teams looking to streamline their workflow and improve productivity.
In short, the Cursor plugins repository is a game-changer for development teams - it's like having a superpower in your coding toolkit.
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🧠 Channel: https://t.me/GithubRe13 226
📌 Spotted on GitHub Trending: Leonxlnx/taste-skill — let's break it down.
🔗 https://github.com/Leonxlnx/taste-skill
📝 Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
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Taste Skill is an innovative GitHub repository designed to enhance the capabilities of AI agents in building premium frontends. The repo offers a range of portable agent skills that focus on improving layout, typography, motion, and spacing, resulting in more sophisticated and polished UIs.
Key features include adjustable dials for design variance, motion intensity, and visual density, allowing for customized outputs. The skills are framework-agnostic and compatible with major coding agents like Codex, Cursor, and Claude Code.
To
get started, simply install the desired skill using npx skills add https://github.com/Leonxlnx/taste-skill, then use the installed skill in your agent conversations. For image-generation skills, pair them with ChatGPT Images or similar generators to produce design images.
The repo is suitable for developers and designers looking to elevate their frontend builds with AI-driven design skills. With its unique approach to anti-slop design and extensive research backing, Taste Skill is a valuable tool for anyone seeking to create high-end interfaces.
In a nutshell, Taste Skill is a game-changer for AI-powered frontend development - revolutionizing the way we design and build premium UIs, one skill at a time.
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🧠 Channel: https://t.me/GithubRe
13 226
Turn Simple Tasks Into Rewards
Rozcash
Watch, play, complete tasks, and redeem rewards easily.
Ad. 18+
13 226
🎯 anthropics/claude-code landed on trending. Worth a proper look.
🔗 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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Claude Code is a terminal-based coding tool that uses natural language commands to help you code faster by executing routine tasks and handling git workflows. Key features include understanding your codebase and explaining complex code. To get started, install it via the recommended methods, then navigate to your project directory and run
claude.
The tool also includes plugins that extend its functionality with custom commands and agents.
For technical users, the code is available on GitHub, and the community can be reached on Discord for feedback and discussion.
Claude Code is suitable for developers of all levels looking to streamline their coding process.
One-liner takeaway: With Claude Code, you can claude your way to faster coding.
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🧠 Channel: https://t.me/GithubRe
13 226
⚡ twentyhq/twenty is making waves. Here's the full picture.
🔗 https://github.com/twentyhq/twenty
📝 The open alternative to Salesforce, designed for AI.
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The Twenty GitHub repository is an open-source CRM that provides building blocks for a custom customer relationship management system. Its key features include objects, views, workflows, and agents that can be extended as code. With Twenty, you can create a custom CRM that meets complex business needs and evolves quickly.
To get started, you can either sign up for a cloud workspace, build an app using the Twenty CLI, or self-host it on your own infrastructure. The
npx create-twenty-app my-app command scaffolds a new app, and you can define objects, fields, and views as code using the twenty-sdk/define module.
The technical stack includes TypeScript, Nx, NestJS, PostgreSQL, Redis, and React. The project is actively maintained, and the community is encouraged to contribute through discussions, feature requests, and code contributions.
The best way to summarize Twenty is: Build your custom CRM, ship it like code, and version it like the rest of your stack - with Twenty, the possibilities are endless!
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🧠 Channel: https://t.me/GithubRe13 226
⚡ EveryInc/compound-engineering-plugin is making waves. Here's the full picture.
🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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The Compound Engineering plugin is designed to make each unit of engineering work easier than the last. It inverts the traditional development approach by focusing on planning, review, and codifying knowledge to create a compound effect. The plugin provides a range of skills and agents to support this approach, including
/ce-brainstorm, /ce-plan, and /ce-code-review.
To get started, users can install the plugin using various methods, including Claude Code, Cursor, Codex, Copilot, Factory Droid, Qwen Code, and others. The plugin is designed to be flexible and adaptable, with a range of features and tools to support different workflows and use cases.
Key features of the plugin include:
* /ce-strategy for creating and maintaining a product's target problem, approach, persona, and key metrics
* /ce-ideate for generating and critically evaluating ideas
* /ce-brainstorm for interactive Q&A to think through a feature or problem
* /ce-plan for turning feature ideas into detailed implementation plans
The plugin also includes a range of technical highlights, including:
* Support for multiple installation methods
* A range of skills and agents to support different workflows and use cases
* A flexible and adaptable architecture to support customization and extension
The Compound Engineering plugin is designed for a range of users, including developers, engineers, and project managers. It is particularly suited to teams and organizations that want to improve their engineering workflow and create a more efficient and effective development process.
One-liner takeaway: Compound Engineering plugin helps teams create a more efficient and effective development process by focusing on planning, review, and codifying knowledge to create a compound effect.
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🧠 Channel: https://t.me/GithubRe13 226
💡 microsoft/markitdown just hit the trending charts — here's why it matters.
🔗 https://github.com/microsoft/markitdown
📝 Python tool for converting files and office documents to Markdown.
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MarkItDown is a lightweight Python utility that converts various file formats to Markdown, ideal for use with Large Language Models (LLMs) and text analysis pipelines. It supports a wide range of file formats, including PDF, PowerPoint, Word, Excel, images, audio, HTML, and more. The output is designed to be consumed by text analysis tools, preserving important document structure and content as Markdown.
Key features include:
-
Conversion of multiple file formats to Markdown
- Preservation of document structure and content
- Support for plugins and optional dependencies for added functionality
- Integration with Azure Content Understanding for higher-quality conversion and structured field extraction
- Integration with Azure Document Intelligence for cloud-based document conversion
To get started, you can install MarkItDown using pip install 'markitdown[all]' and use it from the command line or through the Python API. The library also supports Docker and has a range of optional dependencies for added functionality.
The target audience for MarkItDown includes developers, data scientists, and researchers working with text analysis tools and LLMs. It's particularly useful for those looking to convert complex documents into a format that can be easily analyzed and processed.
In terms of technical highlights, MarkItDown uses a range of libraries and services, including OpenAI for Large Language Model integration and Azure Content Understanding for cloud-based document conversion. It also supports plugins and optional dependencies, making it a highly customizable and extensible tool.
Overall, MarkItDown is a powerful and flexible tool for converting documents to Markdown, and its integration with LLMs and text analysis pipelines makes it a valuable resource for anyone working in this field.
With MarkItDown, you can unlock the full potential of your documents and take your text analysis to the next level.
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🧠 Channel: https://t.me/GithubRe13 226
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13 226
🔍 Deep-diving into harry0703/MoneyPrinterTurbo — fresh off the trending list.
🔗 https://github.com/harry0703/MoneyPrinterTurbo
📝 利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
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MoneyPrinterTurbo is a powerful tool that generates high-definition videos with AI-generated scripts, subtitles, and background music. Simply provide a topic or keyword, and the tool will create a short video.
Key Features:
- Supports MVC architecture for clear code structure and easy maintenance
- Offers AI-generated video scripts and customizable scripts
- Allows for batch video generation and adjustable video clip duration
- Supports multiple video sizes, including 9:16 and 16:9
- Includes subtitle generation with customizable font, position, color, and size
Technical Highlights:
- Built with
Python and utilizes Streamlit for the web interface
- Supports GPU acceleration for faster video processing
- Compatible with various LLM providers, including OpenAI and Azure
Audience:
- Content creators looking for an efficient way to generate high-quality videos
- Marketers seeking to automate their video content creation process
- Anyone interested in exploring the potential of AI-generated video content
Usage:
- Users can access the tool through a web interface or API
- Simply provide a topic or keyword, and the tool will generate a short video
- Customize video settings, such as script, subtitles, and background music, to suit your needs
MoneyPrinterTurbo is an innovative tool that simplifies video content creation, making it easier for everyone to produce high-quality videos. With its user-friendly interface and robust features, it's an excellent choice for anyone looking to streamline their video creation process.
Take your video content creation to the next level with MoneyPrinterTurbo - automate your video production and focus on what matters.
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🧠 Channel: https://t.me/GithubRe
13 226
🚀 Meet anthropics/skills: a gem from today's GitHub trending list.
🔗 https://github.com/anthropics/skills
📝 Public repository for Agent Skills
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The anthropics/skills GitHub repository is a collection of skills for Claude, a dynamic AI system that can load these skills to improve performance on specialized tasks. These skills, which are essentially folders of instructions, scripts, and resources, teach Claude how to complete specific tasks in a repeatable way, such as creating documents, analyzing data, or automating personal tasks.
The repository contains a wide range of skills, from creative applications like art and music to technical tasks like testing web apps and MCP server generation. Each skill is self-contained in its own folder with a
SKILL.md file containing the instructions and metadata that Claude uses.
To use these skills, you can register the repository as a Claude Code Plugin marketplace or use the Claude API. You can also create your own custom skills using a simple template provided in the repository. The template requires only two fields: name and description, and the markdown content contains the instructions, examples, and guidelines that Claude will follow.
The repository is a great resource for developers, partners, and anyone looking to improve Claude's performance on specific tasks. With its open-source and source-available skills, it provides a unique opportunity to learn from and contribute to the development of AI capabilities.
In a nutshell13 226
💡 EveryInc/compound-engineering-plugin just hit the trending charts — here's why it matters.
🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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The compound-engineering-plugin is designed to make each unit of engineering work easier than the last. It inverts traditional development by focusing 80% on planning and review, and 20% on execution. The plugin provides 37 skills and 51 agents to help with tasks such as brainstorming, planning, coding, reviewing, and compounding knowledge.
To use the plugin, start by installing it using
/plugin marketplace add EveryInc/compound-engineering-plugin and /plugin install compound-engineering for Claude Code, or equivalent commands for other platforms like Codex, Copilot, or Qwen Code. Once installed, run /ce-setup to configure the plugin.
Key features include /ce-brainstorm for interactive Q&A, /ce-plan for turning ideas into implementation plans, and /ce-compound for documenting learnings. The plugin is suitable for developers looking to streamline their workflow and improve productivity.
The /ce-strategy, /ce-ideate, /ce-brainstorm, /ce-plan, /ce-work, /ce-debug, /ce-code-review, /ce-compound