Github Top Repositories
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Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
Show more📈 Analytical overview of Telegram channel Github Top Repositories
Channel Github Top Repositories (@githubre) in the English language segment is an active participant. Currently, the community unites 14 247 subscribers, ranking 13 996 in the Education category and 28 216 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 247 subscribers.
According to the latest data from 03 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 260 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.95%. Within the first 24 hours after publication, content typically collects 0.62% reactions from the total number of subscribers.
- Post reach: On average, each post receives 136 views. Within the first day, a publication typically gains 89 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as repository, fork, programming, statistic, description.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Thanks to the high frequency of updates (latest data received on 04 September, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
14 247
Subscribers
+324 hours
+1077 days
+26030 days
Posts Archive
14 247
🎯 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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English-level-up-tips is a comprehensive guide to learning English, created by byoungd. The repo offers a detailed, step-by-step approach to improving English skills, covering key areas such as understanding, vocabulary, listening, reading, speaking, and writing.
The guide also explores the role of
AI in language learning, providing practical tips on how to utilize tools like Gemini and ChatGPT to enhance the learning experience.
Whether you're a beginner or an advanced learner, this repo has something to offer. The author's passion for language learning and commitment to sharing knowledge make this guide an invaluable resource.
The repo's features
14 247
🎯 topoteretes/cognee landed on trending. Worth a proper look.
🔗 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 enables agents to recall, connect, and act with full context. Key features include vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation.
To get started, you can
install Cognee using pip, then configure the LLM API key and run the pipeline using the provided example code. The platform also includes a CLI for easy interaction and a local UI for visualization.
Technical highlights include support for multiple LLM providers, a flexible architecture, and a growing community of developers. Audience includes AI researchers, developers, and organizations looking to build more intelligent and persistent AI agents.
Cognee is ideal for building company brains, creating knowledge infrastructure, and developing persistent and learning agents. With its easy-to-use interface and extensive documentation, Cognee is perfect for anyone looking to take their AI projects to the next level.
Use Cognee to build intelligent AI agents that learn, remember, and improve over time — the future of AI depends on it.
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🧠 Channel: https://t.me/GithubRe14 247
⚡ smicallef/spiderfoot is making waves. Here's the full picture.
🔗 https://github.com/smicallef/spiderfoot
📝 SpiderFoot automates OSINT for threat intelligence and mapping your attack surface.
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SpiderFoot is an open-source intelligence (OSINT) automation tool that integrates with various data sources and uses multiple methods for data analysis. It has a web-based UI and can also be used via the command-line. Written in
Python 3, SpiderFoot has over 200 modules and features like YAML-configurable correlation engine, CSV/JSON/GEXF export, and TOR integration for dark web searching.
Key features include:
- Highly configurable
- Fully documented
- Visualisations
- API key export/import
- SQLite back-end for custom querying
You can use SpiderFoot for offensive or defensive purposes, targeting entities like IP addresses, domain names, and more. The tool can perform tasks such as host enumeration, email address extraction, and threat intelligence queries.
To get started, you can install SpiderFoot using pip and run it using python3 ./sf.py -l 127.0.0.1:5001.
SpiderFoot is perfect for security professionals, researchers, and anyone interested in OSINT. With its vast array of features and modules, SpiderFoot is a powerful tool for gathering and analyzing intelligence.
One-liner takeaway: SpiderFoot is the ultimate OSINT tool for anyone looking to automate their intelligence gathering and analysis.
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🧠 Channel: https://t.me/GithubRe14 247
💡 mikumifa/biliTickerBuy just hit the trending charts — here's why it matters.
🔗 https://github.com/mikumifa/biliTickerBuy
📝 b站会员购购票辅助工具
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biliTickerBuy is an open-source, free, and easy-to-use tool designed to assist with Bilibili membership purchases. Its primary purpose is to provide a simple and efficient way to buy Bilibili memberships.
The
main features of this tool include a user-friendly interface and streamlined purchase process. To use the tool, simply follow the installation guide and usage instructions.
From a technical standpoint, the tool is built with a focus on non-intrusive design, ensuring that it does not interfere with the target server.
This tool is ideal for individuals looking for a convenient and easy-to-use solution for Bilibili membership purchases.
In short, biliTickerBuy is a must-have tool for anyone looking to simplify their Bilibili membership buying experience: automate your purchases with ease.
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🧠 Channel: https://t.me/GithubRe14 247
🚀 Meet mukul975/Anthropic-Cybersecurity-Skills: a gem from today's GitHub trending list.
🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills
📝 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
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Anthropic Cybersecurity Skills is an open-source library of 754 production-grade cybersecurity skills, covering 26 security domains and mapped to six industry frameworks, including MITRE ATT&CK, NIST CSF, and MITRE F3. This library enables AI agents to possess the security skills of a senior analyst, guiding them through expert-level security investigations in seconds.
The library is designed to be AI-native, with YAML frontmatter for sub-second discovery and structured Markdown for step-by-step execution. It includes skills such as cloud security, threat hunting, and web application security, making it a comprehensive resource for cybersecurity professionals.
To get started, users can clone the repository or use
npx skills add mukul975/Anthropic-Cybersecurity-Skills. The library is compatible with various platforms, including Claude Code, GitHub Copilot, and OpenAI Codex CLI.
The project aims to bridge the cybersecurity workforce gap by providing AI agents with structured domain knowledge. It is an independent, community-created project, not affiliated with Anthropic PBC.
Key takeaway: Give your AI agent the security skills of a senior analyst with Anthropic Cybersecurity Skills, and revolutionize your security investigations.
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🧠 Channel: https://t.me/GithubRe
14 247
💡 DeusData/codebase-memory-mcp just hit the trending charts — here's why it matters.
🔗 https://github.com/DeusData/codebase-memory-mcp
📝 High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
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Meet codebase-memory-mcp, the fastest and most efficient code intelligence engine for AI coding agents. This powerful tool indexes an average repository in milliseconds and answers structural queries in under 1ms. It supports 158 languages and ships as a single static binary for macOS, Linux, and Windows, making it easy to download, install, and use.
The key features of codebase-memory-mcp include:
*
Extreme indexing speed: indexes the Linux kernel in 3 minutes
* Plug and play: single static binary with no dependencies or API keys required
* High-quality parsing: uses tree-sitter AST analysis and hybrid LSP semantic type resolution
* Built-in graph visualization: 3D interactive UI for exploring the knowledge graph
* 14 MCP tools: search, trace, architecture, impact analysis, and more
To get started, simply run the install.sh script (or install.ps1 on Windows) and restart your coding agent. You can also manually install the binary and configure it to work with your preferred coding agent.
With codebase-memory-mcp, you can enjoy features like semantic search, structural search, and cross-service linking, making it an essential tool for any developer. So why wait? Try codebase-memory-mcp today and experience the power of code intelligence like never before - it's like having a superpower for your code.
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🧠 Channel: https://t.me/GithubRe
14 247
🚀 Meet bytedance/deer-flow: a gem from today's GitHub trending list.
🔗 https://github.com/bytedance/deer-flow
📝 An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
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DeerFlow is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills. Its key features include skills and tools, sub-agents, sandbox and file system, context engineering, and long-term memory.
To get started with DeerFlow, you can clone the repository, run the setup wizard, and choose your LLM provider, web search, and execution preferences. The project uses
config.yaml for configuration and supports multiple deployment options, including Docker and local development.
Some technical highlights of DeerFlow include its support for various LLM providers, such as OpenAI and Claude Code, and its integration with InfoQuest, a powerful search and crawling toolset. The project also provides a range of documentation and resources, including a official website with real demos and tutorials.
DeerFlow is designed for developers and researchers who want to build and deploy complex AI systems. Its one-line agent setup feature allows you to easily integrate DeerFlow with other tools and platforms.
In summary, DeerFlow is a powerful and flexible platform for building and deploying AI systems, and its extensible skills and super agent harness make it an ideal choice for developers and researchers who want to push the boundaries of what is possible with AI.
The one-liner takeaway: DeerFlow empowers you to build and deploy AI systems that can do almost anything, with its super agent harness and extensible skills at your fingertips.
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🧠 Channel: https://t.me/GithubRe14 247
🚀 Meet koala73/worldmonitor: a gem from today's GitHub trending list.
🔗 https://github.com/koala73/worldmonitor
📝 Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
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The World Monitor is a real-time global intelligence dashboard that leverages AI to aggregate news, track geopolitics, and monitor infrastructure. Its key features include
500+ curated news feeds, a dual map engine, and a Country Instability Index (CII). To use, simply git clone the repository, npm install, and npm run dev. The project boasts a tech stack that includes Vanilla TypeScript, Vite, and Tauri 2, with a deployment process that utilizes Vercel Edge Functions and Railway relay. The audience for this project includes researchers, educators, and professionals seeking to stay informed about global events. With its permissive AGPL-3.0-only license, the World Monitor is an excellent choice for those seeking a customizable and self-hosted solution. One-liner takeaway: Stay ahead of the curve with the World Monitor, your one-stop-shop for real-time global intelligence.
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🧠 Channel: https://t.me/GithubRe
14 247
⚡ ZhuLinsen/daily_stock_analysis is making waves. Here's the full picture.
🔗 https://github.com/ZhuLinsen/daily_stock_analysis
📝 LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.
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Daily Stock Analysis is an AI-powered stock analysis system that provides users with daily reports on their selected stocks. The system uses machine learning models to analyze market data, news, and social media sentiment to generate buy, sell, or hold recommendations.
Key Features:
The system supports multiple markets, including A-shares, Hong Kong stocks, US stocks, and more. It also provides a web-based interface for users to configure their stock portfolios, view analysis reports, and receive notifications.
Usage:
Users can configure the system to analyze their selected stocks and receive daily reports via various channels, including WeChat, Telegram, and email.
Technical Highlights:
The system uses a range of AI models, including Anspire and AIHubMix, to analyze market data and generate recommendations. It also supports multiple data sources, including TickFlow and AkShare.
Audience:
The system is designed for individual investors and financial professionals who want to stay on top of market trends and make informed investment decisions.
One-liner Takeaway:
Daily Stock Analysis is a powerful tool for investors who want to leverage AI-powered insights to make informed investment decisions and stay ahead of the market.
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🧠 Channel: https://t.me/GithubRe
14 247
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14 247
🌟 penpot/penpot caught my eye on GitHub Trending today.
🔗 https://github.com/penpot/penpot
📝 Penpot: The open-source design tool for design and code collaboration
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Penpot is an open-source design platform for teams building digital products at scale. Its key strength lies in giving you full ownership of your design infrastructure, with support for
self-hosting, open standards like SVG, CSS, HTML, and JSON, and real-time collaboration.
The platform is designed for both designers and developers, with features like Design Tokens, Components, and Variants for scalable and consistent UI. The MCP server enables multi-directional workflows between design and code, and a powerful open API and plugin system makes the workspace programmable.
To get started, you can use Penpot in the browser or deploy it on your own servers. The community is active, with a contributing guide and many resources available, including documentation, tutorials, and dev diaries.
Penpot is licensed under the Mozilla Public License, and is an open-source project by Kaleidos.
Take control of your design infrastructure with Penpot - the ultimate open-source design platform for teams!
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🧠 Channel: https://t.me/GithubRe
14 247
📌 Spotted on GitHub Trending: tursodatabase/turso — let's break it down.
🔗 https://github.com/tursodatabase/turso
📝 Turso is an in-process SQL database, compatible with SQLite.
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Meet Turso Database, an in-process SQL database that's compatible with SQLite. It's designed to provide a seamless experience for users who want a lightweight, easy-to-use database solution.
The key features of Turso Database include SQLite compatibility, concurrent writes using multi-version concurrency control, and change data capture for real-time tracking of database changes. It also supports multi-language bindings for languages like Go, JavaScript, Java, .NET, Python, and Rust.
To get started with Turso Database, you can use the
tursodb command-line interface. Simply run tursodb to start the interactive shell, where you can execute SQL statements. You can also build and run the latest development version using cargo run.
Turso Database supports various programming languages, including Rust, JavaScript, Python, Go, .NET, and Java. For example, you can use the following Rust code to connect to a database:
let db = Builder::new_local("sqlite.db").build().await?;
let conn = db.connect()?;
let res = conn.query("SELECT * FROM users", ()).await?;
JavaScript code to connect to a database:
import { connect } from '@tursodatabase/database';
const db = await connect('sqlite.db');
const stmt = db.prepare('SELECT * FROM users');
const users = stmt.all();
console.log(users);
14 247
🚀 Meet chopratejas/headroom: a gem from today's GitHub trending list.
🔗 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 sent to LLMs by 60-95%. This library, proxy, and MCP solution works with various agents, including Claude, Codex, and Copilot, to compress tool outputs, logs, RAG chunks, files, and conversation history. Key features include:
-
compress(messages) function in Python or TypeScript
- headroom proxy for zero-code changes
- headroom wrap for agent compatibility
- Cross-agent memory sharing
- Reversible compression with original caching
Technical highlights include:
- ContentRouter for content type detection
- SmartCrusher, CodeCompressor, and Kompress-base for compression
- CacheAligner for prefix stabilization
Audience: Developers and users of AI agents, particularly those working with LLMs.
To get started, simply install Headroom using pip install headroom-ai[all] or npm install headroom-ai, then pick your mode and see the savings.
One-liner takeaway: Headroom reduces the tokens sent to LLMs, resulting in significant cost savings without compromising accuracy, making it a valuable tool for AI agent users.
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🧠 Channel: https://t.me/GithubRe