Github Top Repositories

🔍 Deep-diving into ChromeDevTools/chrome-devtools-mcp — fresh off the trending list. 🔗 https://github.com/ChromeDevTools/chrome-devtools-mcp 📝 Chrome DevTools for coding agents ────────────────────────────── The Chrome DevTools MCP repository provides a Model-Context-Protocol (MCP) server that allows coding agents to control and inspect a live Chrome browser. This enables reliable automation, in-depth debugging, and performance analysis. Key features include: - Performance insights: recording traces and extracting actionable performance insights - Advanced browser debugging: analyzing network requests, taking screenshots, and checking browser console messages - Reliable automation: automating actions in Chrome using puppeteer To get started, add the MCP server config to your client, then install the server using npx chrome-devtools-mcp@latest. This tool is designed for developers and coding agents, such as Antigravity, Claude, and Copilot. One-liner takeaway: Supercharge your coding agents with the Chrome DevTools MCP for unparalleled browser automation and debugging capabilities! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into rohitg00/ai-engineering-from-scratch — fresh off the trending list. 🔗 https://github.com/rohitg00/ai-engineering-from-scratch 📝 Learn it. Build it. Ship it for others. ────────────────────────────── The AI Engineering from Scratch curriculum is a comprehensive, free, and open-source resource designed to bridge the gap between AI education and industry preparedness. It consists of 20 phases and 435 lessons, covering a wide range of topics from math foundations to autonomous systems. Each lesson follows a structured approach, starting with a problem statement, followed by math explanations, and culminating in code implementation in languages such as Python, TypeScript, Rust, or Julia. The curriculum is designed for individuals who want to understand how AI actually works, not just call APIs. With a focus on hands-on learning, every lesson ships with a reusable artifact, such as a prompt, skill, agent, or MCP server, that can be installed or pasted into daily workflows. Whether you're a beginner or an experienced practitioner, this curriculum provides a structured path to mastering AI engineering. Build it from scratch, and you'll truly understand it. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 ruvnet/RuView landed on trending. Worth a proper look. 🔗 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. ────────────────────────────── RuView is a WiFi sensing platform that turns radio signals into spatial intelligence, detecting people, measuring breathing and heart rate, tracking movement, and monitoring rooms without cameras or wearables. The system uses Channel State Information (CSI) from low-cost ESP32 sensors and runs entirely on edge hardware. Key features include presence and occupancy detection, vital signs measurement, activity recognition, environment mapping, and sleep quality monitoring. The system learns each environment locally using spiking neural networks and adapts in under 30 seconds. To get started, you can use the Docker image with simulated data, live sensing with ESP32-S3 hardware, or the full system with Cognitum Seed. The pretrained model is available on Hugging Face. RuView is suitable for researchers, developers, and users interested in WiFi-based sensing applications. The platform is extensively documented and has a large community of contributors. In short, RuView turns WiFi into a powerful sensing tool, providing insights into people's presence, activities, and well-being without invading their privacy — the future of sensing is here, and it's WiFi-based. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet colbymchenry/codegraph: a gem from today's GitHub trending list. 🔗 https://github.com/colbymchenry/codegraph 📝 Pre-indexed code knowledge graph for Claude Code, Codex, Cursor, and OpenCode — fewer tokens, fewer tool calls, 100% local ────────────────────────────── Supercharge Your Code Intelligence with CodeGraph, a tool that enhances Claude Code, Cursor, Codex, OpenCode, and Hermes Agent with semantic code insights. Key features include smart context building, full-text search, impact analysis, and framework-aware routes. codegraph is 100% local, with no data leaving your machine and no external services required. To get started, simply run the installer with npx @colbymchenry/codegraph or curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh (on macOS/Linux) or irm https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.ps1 | iex (on Windows). Then, initialize your project with codegraph init -i to build the knowledge graph index. Technical highlights include support for 19+ languages and 14 frameworks, with a self-contained SQLite database. Benchmark results show an average of 35% cheaper, 59% fewer tokens, 49% faster, and 70% fewer tool calls compared to not using CodeGraph. The target audience is developers who want to improve their code intelligence and workflow efficiency. With CodeGraph, you can work smarter, not harder - give it a try and see the difference for yourself: CodeGraph is the secret sauce to turbocharge your coding productivity! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 anthropics/claude-plugins-official caught my eye on GitHub Trending today. 🔗 https://github.com/anthropics/claude-plugins-official 📝 Official, Anthropic-managed directory of high quality Claude Code Plugins. ────────────────────────────── The anthropics/claude-plugins-official repository is a curated directory of high-quality plugins for Claude Code, offering a range of tools to enhance your coding experience. The directory is divided into two main sections: plugins for internal plugins developed by Anthropic, and external_plugins for third-party plugins from partners and the community. To get started with a plugin, you can install it directly from the marketplace via Claude Code's plugin system by running /plugin install {plugin-name}@claude-plugins-official or browsing through the /plugin > Discover section. For developers, the repository provides a plugin-name/ structure template, including a .claude-plugin/ directory with a required plugin.json file, as well as optional directories for commands/, agents/, and skills/. This repository is for anyone looking to expand Claude Code's capabilities - and with great power comes great coding responsibility: trust no plugin before installing. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 Lum1104/Understand-Anything just hit the trending charts — here's why it matters. 🔗 https://github.com/Lum1104/Understand-Anything 📝 Graphs that teach > graphs that impress. Turn any code into an interactive knowledge graph you can explore, search, and ask questions about. Works with Claude Code, Codex, Cursor, Copilot, Gemini CLI, and more. ────────────────────────────── Understand Anything is a powerful tool that turns any codebase, knowledge base, or documentation into an interactive knowledge graph. This graph can be explored, searched, and questioned, making it easier to understand complex systems. It works with various platforms, including Claude Code, Codex, Copilot, and Gemini CLI. The tool features a multi-agent pipeline that analyzes the project, builds a knowledge graph, and provides an interactive dashboard. It includes features like guided tours, fuzzy and semantic search, diff impact analysis, and persona-adaptive UI. To get started, simply install the plugin, analyze the codebase, and explore the dashboard. The tool supports multiple languages and can be easily integrated into various development environments. Understand Anything is perfect for developers, teams, and organizations looking to improve their understanding of complex codebases and knowledge bases. Check it out and start understanding anything - your codebase will thank you! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ alireza0/s-ui is making waves. Here's the full picture. 🔗 https://github.com/alireza0/s-ui 📝 An advanced Web Panel • Built for SagerNet/Sing-Box ────────────────────────────── The S-UI project is an advanced web panel built on top of SagerNet/Sing-Box, offering a wide range of features for managing and configuring your server. With multi-protocol, multi-language, and multi-client/inbound support, this panel provides a comprehensive solution for your needs. Key features include an advanced traffic routing interface, client and traffic monitoring, and subscription link management. The panel also supports dark and light themes and has an API interface for custom integrations. To get started, you can install S-UI using the provided installation scripts for Linux/macOS or by downloading the latest Windows release. The project also supports Docker installation for containerized environments. The S-UI project is designed for users who want a powerful and customizable web panel for their servers. With its extensive feature set and user-friendly interface, it's an ideal solution for those looking to manage their server configurations efficiently. One-liner takeaway: S-UI is a feature-rich web panel that simplifies server management with its advanced traffic routing, client monitoring, and subscription link management capabilities. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

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⚡ msitarzewski/agency-agents is making waves. Here's the full picture. 🔗 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. ────────────────────────────── Introducing the Agency: a comprehensive collection of AI agent personalities, each with deep expertise, unique voice, and deliverable-focused approach. The agency is divided into several divisions, including Engineering, Design, Paid Media, and Sales. Key Features: - Specialized agents with unique personalities and approaches - Deliverable-focused with real code, processes, and measurable outcomes - Production-ready with battle-tested workflows and success metrics Usage: - Option 1: Use with Claude Code for seamless integration - Option 2: Use as reference for inspiration and guidance - Option 3: Use with other tools like GitHub Copilot, Antigravity, and more Technical Highlights: - Each agent file contains identity, personality traits, core mission, workflows, technical deliverables, and success metrics - Agents are designed to be used with various tools and platforms Audience: - Developers looking for specialized AI agents to enhance their workflow - Teams seeking to improve their productivity and efficiency - Individuals interested in AI and machine learning Takeaway: With the Agency, you can assemble your dream team of AI specialists and supercharge your workflow! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 truelockmc/streambert caught my eye on GitHub Trending today. 🔗 https://github.com/truelockmc/streambert 📝 A cross-platform Electron Desktop App to stream and download any Movie, TV Series or Anime in the World. Zero Ads and Tracking ────────────────────────────── Streambert is a cross-platform Electron Desktop App that lets you stream and download your favorite Movies, TV Series, and Anime from around the world, completely ad-free and tracker-free. With Streambert, you can stream content from various sources, download videos for offline viewing, and even manage subtitles. The app also features a customizable interface, a library to track your watched content, and a trending section to discover new titles. To get started, you'll need to install Node.js, obtain a free TMDB API key, and install ffmpeg for downloading content. The app is available for Linux, Windows, and can be built from source using npm. Streambert is designed for personal and educational use only, and users are responsible for ensuring they have the legal rights to access any content. The app does not host or distribute copyrighted content, and instead, sources content from third-party providers. In short, Streambert is your one-stop-shop for streaming and downloading your favorite content, with a focus on privacy and customization - watch what you want, without the hassle of ads and trackers. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: trimstray/the-book-of-secret-knowledge — let's break it down. 🔗 https://github.com/trimstray/the-book-of-secret-knowledge 📝 A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more. ────────────────────────────── The Book of Secret Knowledge is a curated collection of tools, resources, and knowledge for System and Network administrators, DevOps, Pentesters, and Security Researchers. It includes CLI tools, GUI tools, Web tools, Shell plugins, and more. This repository is constantly updated with new items and is open to contributions from the community. You can find inspiring lists, cheatsheets, blogs, and podcasts related to various topics, including networking, security, and development. Whether you're a seasoned professional or just starting out, this repository has something for everyone. Check it out and stay up-to-date with the latest tools and knowledge in the field! You'll find it's a treasure trove of information - Dig in and uncover the secrets! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ Imbad0202/academic-research-skills is making waves. Here's the full picture. 🔗 https://github.com/Imbad0202/academic-research-skills 📝 Academic Research Skills for Claude Code: research → write → review → revise → finalize ────────────────────────────── The academic-research-skills GitHub repository is a comprehensive tool designed to support academic researchers throughout the entire research pipeline, from planning to publication. This repository provides a suite of skills for Claude Code, a platform that leverages AI to assist with various tasks. The key features of this repository include its ability to handle deep research tasks, such as systematic reviews and fact-checking, as well as aiding in the writing and review of academic papers. It incorporates a range of modes for different research tasks, including Socratic guidance for research planning and a systematic review mode for thorough literature analysis. Technical highlights of this repository include its integration with the Claude Code platform, allowing for seamless interaction with AI tools. It also supports various file formats, including Markdown and LaTeX, making it versatile for different types of research outputs. The intended audience for this repository includes academic researchers across various disciplines who are looking to leverage AI tools to improve the efficiency and quality of their research. By utilizing this repository, researchers can focus on high-level thinking and interpretation, while delegating more mundane tasks to AI assistants. In terms of usage, getting started with the repository involves installing the academic-research-skills plugin through the Claude Code marketplace and then accessing its various features through a set of predefined commands. For instance, users can initiate a research project by asking for guidance on a specific topic or by requesting a literature review on a particular subject. Overall, the academic-research-skills repository represents a powerful tool for academics looking to harness the potential of AI in their research. By automating routine tasks and providing intelligent assistance, it enables researchers to concentrate on the creative and intellectual aspects of their work, leading to higher quality research outcomes. One-liner takeaway: The academic-research-skills repository is a game-changer for researchers, offering a suite of AI-powered tools that streamline the research process, from planning to publication, and free up time for the high-level thinking that drives real innovation. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 multica-ai/multica landed on trending. Worth a proper look. 🔗 https://github.com/multica-ai/multica 📝 The open-source managed agents platform. Turn coding agents into real teammates — assign tasks, track progress, compound skills. ────────────────────────────── Multica is an open-source managed agents platform that turns coding agents into real teammates. With Multica, you can assign tasks to agents like you'd assign to colleagues, and they'll pick up the work, write code, report blockers, and update statuses autonomously. The platform features agents as teammates, squads for stable routing, autonomous execution, reusable skills, and unified runtimes. To get started, you can install Multica using Homebrew, an install script, or PowerShell. Then, set up and start the daemon, verify your runtime, create an agent, and assign your first task. The platform has a simple CLI for connecting your local machine to Multica, managing workspaces, and running the agent daemon. Multica is designed for human + AI teams and works with various coding agents, including Claude Code, Codex, and GitHub Copilot CLI. The platform is built using Next.js, Go, and PostgreSQL, and has a modular architecture. Whether you're a developer, a contributor, or just someone interested in AI-powered teamwork, Multica is worth exploring. With its cutting-edge technology and user-friendly interface, Multica is poised to revolutionize the way we work with AI agents. Your next 10 hires won't be human - they'll be Multica agents. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 antoinezambelli/forge just hit the trending charts — here's why it matters. 🔗 https://github.com/antoinezambelli/forge 📝 A Python framework for self-hosted LLM tool-calling and multi-step agentic workflows ────────────────────────────── Forge is a reliability layer for self-hosted LLM tool-calling, designed to lift local models to the top of their class on multi-step agentic workflows. It achieves this through guardrails (rescue parsing, retry nudges, step enforcement) and context management (VRAM-aware budgets, tiered compaction). Key features include a WorkflowRunner for defining tools and running structured agent loops, Guardrails middleware for validating responses and enforcing required steps, and a Proxy server for transparently applying guardrails to local model servers. Forge supports various backends, including Ollama, llama-server, Llamafile, and Anthropic. It's built with Python 3.12+ and is licensed under MIT. The target audience is developers and researchers working with self-hosted LLMs, particularly those building multi-step workflows, multi-turn conversations, and long-running sessions. In a nutshell, Forge helps you build more reliable and efficient self-hosted LLM tool-calling workflows, so you can focus on what matters – creating value for your users. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe