Github Top Repositories

⚡ roboflow/supervision is making waves. Here's the full picture. 🔗 https://github.com/roboflow/supervision 📝 We write your reusable computer vision tools. 💜 ────────────────────────────── Supervision is an open-source library designed to simplify computer vision workflows by providing reusable tools for tasks such as loading datasets, drawing detections, and counting objects. It's model-agnostic, allowing you to plug in any classification, detection, or segmentation model, and includes connectors for popular libraries like Ultralytics and MMDetection. To get started, you can install Supervision using pip install supervision in a Python environment. The library offers a range of features, including: * Annotators: customizable tools for visualizing detections * Dataset utilities: load, split, merge, and save datasets in various formats * Model connectors: integrate with popular libraries like Ultralytics and MMDetection Supervision is designed for anyone working with computer vision, from researchers to developers. The library is well-documented, with extensive documentation and a community-driven discussion forum. Takeaway: With Supervision, you can focus on building innovative computer vision applications instead of rebuilding common tools from scratch. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 ruvnet/RuView caught my eye on GitHub Trending today. 🔗 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. It detects people, measures breathing and heart rate, tracks movement, and monitors rooms — all through walls, in the dark, with no cameras or wearables. Just physics. Key features include presence and occupancy detection, vital sign measurement, activity recognition, environment mapping, and sleep quality monitoring. RuView works natively with major smart-home ecosystems like Home Assistant, Apple Home, Google Home, and Alexa. RuView runs entirely on edge hardware — an ESP32 mesh paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. The system learns each environment locally using spiking neural networks and multi-frequency mesh scanning. Audience: developers, researchers, and anyone interested in low-power edge applications and WiFi sensing technology. RuView is built for low-power edge applications and ships with 21 entities per node, including 11 raw signals and 10 inferred semantic states. It's a game-changer for spatial intelligence and sensing. Here's a code snippet to get you started:

docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest

Takeaway: RuView turns ordinary WiFi into a contactless sensor, making it a revolutionary technology for spatial intelligence and sensing. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 activeloopai/hivemind just hit the trending charts — here's why it matters. 🔗 https://github.com/activeloopai/hivemind 📝 One brain for all your agents ────────────────────────────── Hivemind is an auto-learning, cloud-backed shared brain for various agents, including Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi. It allows agents to learn from each other and build on shared knowledge. With Hivemind, every agent on a team can execute patterns figured out by other agents, making them sharper and more efficient. The key features of Hivemind include: - Capturing every session's prompts, tool calls, and responses as structured traces in Deeplake - Codifying patterns into reusable SKILL.md files, available to every agent on the team - Searching traces and skills with hybrid lexical + semantic retrieval - Propagating capability across sessions, agents, teammates, and machines in real time To get started with Hivemind, simply run the command: npm install -g @deeplake/hivemind && hivemind install. This will detect every supported assistant on the machine, wire up the hooks, and show a one-line consent prompt before opening a browser for sign-in. Hivemind is designed for teams of engineers and developers who want to improve the efficiency and effectiveness of their agents. It's particularly useful for teams that use multiple agents and want to share knowledge and expertise across the team. Technical highlights of Hivemind include its ability to reduce cost, tokens, and turns required to reach an answer, making it a more efficient solution than traditional methods. On the LoCoMo benchmark, Hivemind cuts cost, tokens, and turns versus a no-memory baseline, with improvements of 25% cheaper, 1.7× fewer tokens, and 31% fewer turns. In summary, Hivemind is a powerful tool for teams that want to unlock the full potential of their agents and improve their overall productivity. With its auto-learning, cloud-backed shared brain, Hivemind is the perfect solution for teams that want to work smarter, not harder - and that's the bee's knees! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet luongnv89/claude-howto: a gem from today's GitHub trending list. 🔗 https://github.com/luongnv89/claude-howto 📝 A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value. ────────────────────────────── The luongnv89/claude-howto GitHub repository provides a comprehensive guide to mastering Claude Code, a tool for automating workflows and tasks. The guide offers a structured learning path with 10 tutorial modules, covering features such as slash commands, memory, skills, subagents, and more. Key features include: - Copy-paste templates for immediate use - Mermaid diagrams for internal feature explanations - A guided learning path with estimated completion times - Self-assessment quizzes for identifying knowledge gaps The guide is suitable for developers of all levels, from beginners to advanced users. To get started, users can clone the repository, copy a slash command template, and try it in Claude Code. With this guide, developers can build various applications, such as automated code reviews, team onboarding, CI/CD automation, and more. The project is actively maintained, MIT licensed, and free to use. Start mastering Claude Code today and unlock 10x productivity - clone, learn, and automate your way to efficiency. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 maziyarpanahi/openmed caught my eye on GitHub Trending today. 🔗 https://github.com/maziyarpanahi/openmed 📝 open-source healthcare ai ────────────────────────────── The OpenMed GitHub repository offers a local-first healthcare AI solution that enables users to turn clinical text into structured insight with just one line of code. This openmed library provides entity extraction, PII de-identification, and over 1,000 specialized medical models that run entirely on the user's device, ensuring no cloud, no vendor lock-in, and no patient data leaving the network. It supports 12 languages and 247 PII checkpoints, making it a comprehensive tool for healthcare professionals. With openmed, users can easily integrate the library into their Python applications or use the OpenMedKit for native Swift apps on iPhone, powered by Apple MLX. The library is free, open-source, and Apache-2.0 licensed, providing 100% on-device processing and no vendor lock-in. Key features include specialized medical models, HIPAA-aware de-identification, and Apple Silicon (MLX) acceleration. OpenMed can be used by healthcare professionals, researchers, and developers looking for a secure and efficient way to analyze clinical text. To get started, users can simply pip install openmed and begin using the library in their applications. With its one-line deployment and zero lock-in, OpenMed is an attractive solution for those seeking a local-first healthcare AI solution. Takeaway: OpenMed brings the power of AI to healthcare, locally and securely, with just one line of code. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🌟 harry0703/MoneyPrinterTurbo caught my eye on GitHub Trending today. 🔗 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. ────────────────────────────── MoneyPrinterTurbo is a powerful tool that generates high-definition videos with automated scripts, subtitles, and background music. It supports multiple video sizes, including 9:16 and 16:9, and allows for batch video generation. The tool features a user-friendly web interface and API interface, making it easy to use for both beginners and advanced users. The key features of MoneyPrinterTurbo include: - MVC architecture for easy maintenance and scalability - Support for AI-generated video scripts and custom scripts - Batch video generation and video clip duration setting - Support for multiple languages, including Chinese and English - Subtitle generation with adjustable font, position, color, and size - Background music support with random or specified music files From a technical perspective, MoneyPrinterTurbo uses a range of technologies, including Python, Streamlit, and MoviePy. It also supports multiple LLM providers, such as OpenAI and AIHubMix. To get started with MoneyPrinterTurbo, users can follow the quick start guide or refer to the installation and deployment instructions. The tool is suitable for a wide range of users, from individuals to businesses, and can be used for various purposes, such as video marketing, education, and entertainment. In summary, MoneyPrinterTurbo is a powerful and versatile tool for generating high-quality videos with ease. With its user-friendly interface, advanced features, and technical capabilities, it is an ideal solution for anyone looking to create engaging videos. Give it a try and see the magic for yourself! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🚀 Meet masterking32/MasterDnsVPN: a gem from today's GitHub trending list. 🔗 https://github.com/masterking32/MasterDnsVPN 📝 Advanced DNS tunneling VPN for censorship bypass, optimized beyond DNSTT and SlipStream with low-overhead ARQ, resolver load balancing, high packet-loss stability and speed. ────────────────────────────── README not available for this repository. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ obra/superpowers is making waves. Here's the full picture. 🔗 https://github.com/obra/superpowers 📝 An agentic skills framework & software development methodology that works. ────────────────────────────── Superpowers is a software development methodology for coding agents, built on a set of composable skills and initial instructions. It's designed to help agents use these skills to develop software efficiently. Key features include a test-driven-development workflow, systematic-debugging, and collaboration tools like brainstorming and writing-plans. To use Superpowers, simply install it via the official plugin marketplaces for your coding agent, such as Claude Code or Codex CLI. From a technical standpoint, Superpowers uses a skills library with a range of skills, including testing, debugging, and collaboration tools. Audience includes developers who want to improve their coding agent's efficiency and productivity. In short, Superpowers is a powerful tool for coding agents - and with it, you can build better software, faster. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: x1xhlol/system-prompts-and-models-of-ai-tools — let's break it down. 🔗 https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools 📝 FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, Dia & v0. (And other Open Sourced) System Prompts, Internal Tools & AI Models ────────────────────────────── The system-prompts-and-models-of-ai-tools GitHub repository is a comprehensive collection of AI system prompts and models, designed to help developers and startups build and secure their AI systems. The repository is maintained by x1xhlol and is supported by various sponsors. Key features include a large collection of AI system prompts and models, as well as resources for securing AI systems, such as ZeroLeaks, a service designed to help startups identify and secure prompt injection and system prompt extraction risks. To get started, users can explore the repository, open an issue for feedback, or star the repository to show support. The repository is suitable for AI startups, developers, and researchers looking to build and secure their AI systems. Technically, the repository provides

examples and code snippets

in various programming languages, making it easy for developers to integrate the prompts and models into their own projects. Overall, the system-prompts-and-models-of-ai-tools repository is a valuable resource for anyone working with AI systems, and by supporting the project, you're contributing to the development of more secure and efficient AI systems - so why not drop a star and join the community today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: soxoj/maigret — let's break it down. 🔗 https://github.com/soxoj/maigret 📝 🕵️‍♂️ Collect a dossier on a person by username from 3000+ sites ────────────────────────────── Maigret is a powerful tool for collecting information about a person by their username, checking for accounts on over 3,000 sites and gathering available data from web pages. No API keys are required. Key features include: - support for 3,000+ sites, - embeddable in Python projects, - extraction of available information about account owners, - recursive search using discovered usernames and IDs, - filtering by tags, - detection and bypassing of blocks, censorship, and CAPTCHA, - and a web interface for browsing results and downloading reports. Usage is straightforward: - pip install maigret, - then maigret YOUR_USERNAME. - For advanced use, maigret can be used as a Python library for custom integrations. Technical highlights include: - async function calls for custom pipelines, - filtering sites by tag, - and a self-check feature for verifying usernameClaimed. The primary audience is professionals in OSINT and social-media analysis, as well as anyone looking to gather information about a person by their username. In short, Maigret is a powerful, user-friendly tool for gathering information about a person by their username - just pip install maigret and get started! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 mvanhorn/last30days-skill landed on trending. Worth a proper look. 🔗 https://github.com/mvanhorn/last30days-skill 📝 AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary ────────────────────────────── The last30days-skill is an AI agent-led search engine that scores results based on upvotes, likes, and real money, giving you a brief on the latest information about a topic. Its key features include zero config, multi-source search, and AI agent synthesis. To use it, simply install the skill via Claud Code or other supported platforms, and run it with a topic, like /last30days Peter Steinberger. From a technical perspective, the skill uses a Python pre-research brain to resolve topics and figure out where to search. It also features intelligent search, best takes, and cross-source cluster merging. The target audience for this skill includes anyone looking for the latest information on a topic, such as researchers, salespeople, or simply individuals trying to stay up-to-date. Overall, the last30days-skill is a powerful tool for searching and synthesizing information from multiple sources, and its AI agent-led approach makes it a game-changer in the world of search engines. Get the truth about what's happening now, not what editors think you should know - that's the last30days-skill in a nutshell. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎯 refactoringhq/tolaria landed on trending. Worth a proper look. 🔗 https://github.com/refactoringhq/tolaria 📝 Desktop app to manage markdown knowledge bases ────────────────────────────── Tolaria is a desktop app for managing markdown knowledge bases. It's designed for operating second brains, personal knowledge, and company docs. The app is files-first, git-first, and offline-first, ensuring your data belongs to you, not the app. Key features include: - markdown files with YAML frontmatter - git repositories for version history - offline functionality with no cloud dependencies - Support for AI agents like Claude Code and Codex CLI To get started, you can install Tolaria via Homebrew or download the latest release. The app is open source and built with Tauri, React, and TypeScript. One-liner takeaway: Tolaria is your personal knowledge base powerhouse, giving you complete control over your data and workflow. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: phuryn/pm-skills — let's break it down. 🔗 https://github.com/phuryn/pm-skills 📝 PM Skills Marketplace: 100+ agentic skills, commands, and plugins — from discovery to strategy, execution, launch, and growth. ────────────────────────────── Check out the PM Skills Marketplace, an AI operating system designed to enhance product decisions. This innovative tool offers 68 PM skills and 42 chained workflows across 9 plugins, providing a comprehensive framework for product management. The marketplace is designed for Claude Code and Cowork, with skills compatible with other AI assistants. To get started, simply type /discover for new ideas, /strategy for strategic clarity, or /write-prd for writing a PRD. The PM Skills Marketplace gives you structure, encoding proven PM frameworks such as discovery, assumption mapping, prioritization, and strategy. This results in better product decisions, not just faster documents. Key features include: - Skills: the building blocks of the marketplace, giving Claude domain knowledge, analytical frameworks, or guided workflows for specific PM tasks. - Commands: user-triggered workflows that chain one or more skills into an end-to-end process. - Plugins: grouping related skills and commands into installable packages, covering PM domains such as discovery, strategy, and execution. The marketplace is ideal for product managers, entrepreneurs, and AI enthusiasts looking to streamline their workflow and make informed product decisions. To install, follow the simple steps outlined in the README, and get started with the Claude Cowork or Claude Code integration. In short, the PM Skills Marketplace is a game-changer for product management, providing a structured approach to decision-making and workflow optimization. Take your product management to the next level with the PM Skills Marketplace - it's not just a tool, it's a decision-making powerhouse. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe