Вже доступно! Дослідження Telegram за 2025 — головні інсайти року 
Github Top Repositories
Відкрити в Telegram
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
Показати більше📈 Аналітичний огляд Telegram-каналу Github Top Repositories
Канал Github Top Repositories (@githubre) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 13 227 підписників, посідаючи 15 419 місце в категорії Освіта та 32 691 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 13 227 підписників.
За останніми даними від 06 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 344, а за останні 24 години на 12, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 1.18%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.79% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 156 переглядів. Протягом першої доби публікація в середньому набирає 104 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 1.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як repository, fork, programming, statistic, description.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Завдяки високій частоті оновлень (останні дані отримано 08 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
13 227
Підписники
+1224 години
+997 днів
+34430 день
Архів дописів
13 225
🎯 nicobailon/pi-subagents landed on trending. Worth a proper look.
🔗 https://github.com/nicobailon/pi-subagents
📝 Pi extension for async subagent delegation with truncation, artifacts, and session sharing
──────────────────────────────
Pi Subagents is a GitHub repository that enables Pi to delegate tasks to focused child agents, enhancing productivity and efficiency. The key features of pi-subagents include code review, scouting, implementation, parallel audits, and background jobs.
To get started, simply install
pi-subagents using pi install npm:pi-subagents. You can then use natural language to ask Pi for delegation, such as "Use reviewer to review this diff" or "Ask oracle for a second opinion on my current plan".
The repository includes various built-in agents, such as scout, researcher, planner, worker, and reviewer, each designed for specific tasks. You can also customize these agents or create new ones to suit your needs.
The extension provides a range of workflows, from simple code reviews to complex implementation plans. It also supports background runs, parallel reviewers, and saved workflows, making it a versatile tool for various use cases.
Key benefits of pi-subagents include improved code quality, increased productivity, and enhanced collaboration. With its flexible and customizable architecture, pi-subagents is an essential tool for anyone looking to streamline their workflow and improve their overall development experience.
Takeaway: With pi-subagents, you can supercharge your productivity by delegating tasks to specialized child agents, freeing you up to focus on high-level decision-making and strategy.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
📌 Spotted on GitHub Trending: anthropics/claude-code — let's break it down.
🔗 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.
──────────────────────────────
The Claude Code repository on GitHub is home to an innovative coding tool that streamlines your development workflow. This agentic tool understands your codebase and assists with routine tasks, explains complex code, and manages git workflows - all through natural language commands.
You can use Claude Code in your terminal, IDE, or even tag it on GitHub. To get started, you can install it using various methods like
curl or brew on MacOS/Linux, or irm or winget on Windows.
The repository also includes several plugins that extend the functionality of Claude Code with custom commands and agents. If you encounter any issues, you can report bugs directly within the tool using the /bug command or file a GitHub issue.
Claude Code is designed for developers who want to boost their productivity and collaborate with others. The tool is supported by a community of developers on Discord, where you can get help, share feedback, and discuss your projects.
Claude Code collects feedback and usage data, but the developers have implemented various safeguards to protect user data, including limited retention periods and restricted access to user session data.
Overall, Claude Code is a game-changer for developers - with its natural language interface and automated workflows, you can code faster and smarter.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
🌟 Crosstalk-Solutions/project-nomad caught my eye on GitHub Trending today.
🔗 https://github.com/Crosstalk-Solutions/project-nomad
📝 Project N.O.M.A.D, is a self-contained, offline survival computer packed with critical tools, knowledge, and AI to keep you informed and empowered—anytime, anywhere.
──────────────────────────────
Project N.O.M.A.D. is a self-contained, offline-first knowledge and education server that empowers users with critical tools and AI. Its key features include AI chat with a knowledge base, an information library with offline Wikipedia and medical references, an education platform with Khan Academy courses, and offline maps.
To get started, users can install Project N.O.M.A.D. on any Debian-based operating system using a
quick install script or a more customizable docker compose template. The project is designed to be hardware-agnostic, with minimum specs requiring a 2 GHz dual-core processor, 4GB RAM, and 5 GB free disk space.
From a technical standpoint, Project N.O.M.A.D. uses Docker to orchestrate its tools and resources, and includes built-in capabilities like encryption, encoding, and data analysis via CyberChef.
This project is perfect for educators, researchers, and individuals looking for a reliable, offline knowledge and education platform. With its lightweight design and customizable installation options, Project N.O.M.A.D. is an excellent choice for those who want to stay informed and empowered anywhere, anytime.
In a nutshell, Project N.O.M.A.D. is the ultimate offline knowledge companion - take the internet with you, wherever you go.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe13 225
⚡ supermemoryai/supermemory is making waves. Here's the full picture.
🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
──────────────────────────────
Supermemory is a state-of-the-art memory and context engine for AI, designed to give your AI a brain that can learn, remember, and recall information across conversations. With key features like automatic fact extraction, user profiling, hybrid search, and connectors for Google Drive, Gmail, and more, Supermemory is a powerful tool for building AI applications.
To use Supermemory, you can either use the app and browser extension for personal use or integrate the API into your AI products. The API provides a single interface for memory, RAG, user profiles, and connectors, making it easy to add memory and context to your agents and apps.
Some of the technical highlights of Supermemory include its ability to extract memories, build user profiles, and return relevant context without requiring embedding pipelines, vector DB config, or chunking strategies. It also supports framework integrations with popular AI frameworks like Vercel AI SDK, LangChain, and OpenAI Agents SDK.
Supermemory is designed for anyone building AI applications, from developers and researchers to companies and individuals looking to create more intelligent and context-aware AI systems.
In summary,
Supermemory is a game-changer for AI applications, providing a powerful and easy-to-use memory and context engine that can help you build more intelligent and human-like AI systems - give your AI a brain that never forgets.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
🔥 FareedKhan-dev/train-llm-from-scratch is trending — and it deserves your attention.
🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch
📝 A straightforward method for training your LLM, from downloading data to generating text.
──────────────────────────────
Introduction to FareedKhan-dev/train-llm-from-scratch
The FareedKhan-dev/train-llm-from-scratch GitHub repository provides a comprehensive guide to training a large language model (LLM) from scratch using PyTorch. The project is based on the paper "Attention is All You Need" and allows users to train their own billion or million parameter LLM using a single GPU.
Key Features and Usage
The repository includes the following key features:
- Implementation of a transformer model from scratch using PyTorch
- Scripts for downloading and preprocessing the dataset
- Configurable training parameters
- Support for training models with different architectures
To use the repository, simply clone it, install the required dependencies, and run the provided scripts to download and preprocess the data. You can then train the model using the
train_transformer.py script.
Technical Highlights
The project includes the following technical highlights:
- Implementation of a transformer model with attention mechanisms
- Use of PyTorch for building and training the model
- Support for configurable training parameters, such as vocabulary size and transformer configuration
Audience
The repository is targeted towards developers and researchers interested in natural language processing and large language models. It provides a comprehensive guide to training an LLM from scratch and can be used as a starting point for further research and development.
Takeaway
Train your own large language model from scratch with FareedKhan-dev/train-llm-from-scratch and unlock the power of natural language processing with a single GPU.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe13 225
📌 Spotted on GitHub Trending: revfactory/harness — let's break it down.
🔗 https://github.com/revfactory/harness
📝 A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
──────────────────────────────
Harness is a team-architecture factory for Claude Code, designed to turn domain descriptions into coordinated agent teams with specialized skills. With six pre-defined team-architecture patterns, it's perfect for complex tasks.
Key features include
agent team design, skill generation, and orchestration. Usage is straightforward: simply trigger the plugin with prompts like "Build a harness for this project" and it will generate agent definitions and skills tailored to your domain.
Technical highlights include support for Progressive Disclosure and inter-agent data passing. The plugin is suitable for a wide range of users, from developers to business strategists.
One-liner takeaway: With Harness, you can boost output quality by 60% and achieve a 100% win rate across 15 software engineering tasks!
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
🔍 Deep-diving into OpenBMB/VoxCPM — fresh off the trending list.
🔗 https://github.com/OpenBMB/VoxCPM
📝 VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning
──────────────────────────────
VoxCPM2 is a tokenizer-free Text-to-Speech system that generates continuous speech representations via an end-to-end diffusion autoregressive architecture. This
2B parameter model supports 30 languages, Voice Design, Controllable Voice Cloning, and 48kHz studio-quality audio output.
Key features include:
- 30-Language Multilingual: synthesize text in any supported language
- Voice Design: create a new voice from a natural-language description
- Controllable Cloning: clone any voice with optional style guidance
- Ultimate Cloning: reproduce every vocal nuance from a reference audio and transcript
- 48kHz High-Quality Audio: output studio-quality audio
- Context-Aware Synthesis: automatic prosody and expressiveness inference
- Real-Time Streaming: low-latency streaming with RTF as low as ~0.13
To get started, you can use the Python API or CLI Usage for tasks like text-to-speech, voice design, and controllable voice cloning. The VoxCPM class provides methods like generate for text-to-speech synthesis and generate_streaming for real-time streaming.
For production deployment, you can use Nano-vLLM-VoxCPM for high-throughput serving or vLLM-Omni for multi-tenant deployments with native VoxCPM2 support.
Takeaway: VoxCPM2 is a powerful, open-source TTS system that offers unparalleled flexibility and quality for multilingual speech synthesis, voice design, and voice cloning, making it an ideal choice for production environments.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
🔍 Deep-diving into github/docs — fresh off the trending list.
🔗 https://github.com/github/docs
📝 The open-source repo for docs.github.com
──────────────────────────────
The github/docs repository is home to GitHub's open-source documentation, where anyone can contribute. This repo is for public contributions, while GitHub employees use a separate private repository, github/docs-internal, which syncs frequently with the public one.
Key features include
content files (.md files in /content and select /data sections), while infrastructure files, workflows, and site-building code are not open for external modification. To get started, contributors can refer to resources like Finding ways to contribute to open source on GitHub and Set up Git.
The project is dual-licensed under Creative Commons Attribution 4.0 and MIT License. This repository is for developers, writers, and anyone interested in contributing to GitHub's documentation.
Contribute to github/docs and help shape the future of GitHub's documentation - every contribution counts, no matter how small.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe13 225
🚀 Meet EveryInc/compound-engineering-plugin: a gem from today's GitHub trending list.
🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
──────────────────────────────
Compound Engineering Plugin is a game-changer for developers, making each unit of engineering work easier than the last. The plugin inverts traditional development by emphasizing planning and review, with 80% of the work done before writing code. It includes a range of skills and agents to streamline the development process, from brainstorming and planning to execution and review.
Key features of the plugin include
/ce-brainstorm, /ce-plan, /ce-work, and /ce-code-review, which work together to create a seamless development cycle. The plugin also includes a /ce-compound feature, which allows developers to document their learnings and make future work easier.
The plugin is designed to be easy to use and can be installed through various platforms, including Claude Code, Codex, and GitHub Copilot. It's suitable for a wide range of developers, from individuals to large teams.
One of the most significant technical highlights of the plugin is its ability to compound knowledge, allowing developers to build on previous work and make future changes easier. The plugin also includes a range of pre-built agents that can be customized to meet the needs of individual developers or teams.
Overall, the Compound Engineering Plugin is a powerful tool that can help developers work more efficiently and effectively. With its emphasis on planning, review, and compounding knowledge, it's an essential tool for anyone looking to improve their development workflow. Streamline your development process and make each unit of work easier than the last – that's the power of Compound Engineering!
──────────────────────────────
🧠 Channel: https://t.me/GithubRe13 225
Don't miss this opportunity!
Once you register, you will receive future courses for free.
13 225
🔥 nesquena/hermes-webui is trending — and it deserves your attention.
🔗 https://github.com/nesquena/hermes-webui
📝 Hermes WebUI: The best way to use Hermes Agent from the web or from your phone!
──────────────────────────────
The Hermes Web UI is a lightweight, dark-themed web app that provides a convenient interface for interacting with the Hermes Agent, a sophisticated autonomous agent that runs on your server. This web app offers full parity with the CLI experience, allowing you to perform all actions from a terminal, but with the convenience of a web interface.
Key features of the Hermes Web UI include a three-panel layout for easy navigation, a circular context ring to show token usage at a glance, and model, profile, and workspace controls that are always visible while composing. The web app also supports light and dark modes, as well as customizable settings and password configuration.
To use the Hermes Web UI, you can run the
bootstrap.py script, which will detect the Hermes Agent and install any missing dependencies. You can also use the start.sh script to launch the web app. The web app is designed to be self-hosted and provider-agnostic, allowing you to use it with a variety of messaging platforms and AI providers.
In terms of technical highlights, the Hermes Web UI uses vanilla JavaScript and Python to provide a seamless and efficient user experience. The web app is also designed to be secure, with features such as SSH tunneling for secure access.
The Hermes Web UI is suitable for a wide range of users, from developers who want to interact with the Hermes Agent from a web interface, to non-technical users who want a simple and intuitive way to use the agent. Overall, the Hermes Web UI is a powerful and flexible tool that provides a convenient and user-friendly interface for interacting with the Hermes Agent.
The one-liner takeaway: Experience the power of the Hermes Agent from the convenience of a web interface with the Hermes Web UI.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
13 225
⚡ D4Vinci/Scrapling is making waves. Here's the full picture.
🔗 https://github.com/D4Vinci/Scrapling
📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
──────────────────────────────
Scrapling is an adaptive web scraping framework that simplifies the process of extracting data from websites. Its key features include selection methods, fetchers, and spiders that enable users to scale up to concurrent, multi-session crawls with pause/resume and automatic proxy rotation. The framework is designed to handle website changes and anti-bot systems, ensuring that your data extraction efforts are not disrupted.
To use
Scrapling, you can start by installing the library and then using its various components, such as the Fetcher and Spider classes, to fetch and parse web pages. For example:
from scrapling.fetchers import Fetcher
p = Fetcher.fetch('https://example.com', headless=True, network_idle=True)
products = p.css('.product', auto_save=True)
13 225
🌟 microsoft/markitdown caught my eye on GitHub Trending today.
🔗 https://github.com/microsoft/markitdown
📝 Python tool for converting files and office documents to Markdown.
──────────────────────────────
MarkItDown is a lightweight Python utility for converting various files to Markdown, ideal for use with Large Language Models (LLMs) and text analysis pipelines. It supports conversion from multiple file formats, including PDF, PowerPoint, Word, Excel, images, audio, HTML, and more. The goal is to preserve important document structure and content as Markdown, making it easily consumable by text analysis tools.
Key features include:
-
Support for multiple file formats
- Preserves document structure and content as Markdown
- Optional dependencies for activating various file formats
- Plugin support for adding new features, such as OCR
To use MarkItDown, you can install it via pip: pip install 'markitdown[all]'. Then, simply run the command markitdown path-to-file.pdf > document.md to convert a file.
MarkItDown also integrates with Azure Content Understanding and Azure Document Intelligence for higher-quality conversions. Additionally, it supports Large Language Models for image descriptions.
The project welcomes contributions and has adopted the Microsoft Open Source Code of Conduct.
In short, MarkItDown is a powerful tool for converting files to Markdown, and its flexibility and customization options make it an ideal choice for a variety of use cases. Convert your files to Markdown with ease using MarkItDown – your text analysis pipelines will thank you!
──────────────────────────────
🧠 Channel: https://t.me/GithubRe