🌟 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.
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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.

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🧠 Channel: https://t.me/GithubRe

⚡ roboflow/supervision is making waves. Here's the full picture.

🔗 https://github.com/roboflow/supervision
📝 We write your reusable computer vision tools. 💜
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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.

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🧠 Channel: https://t.me/GithubRe

💡 google/skills just hit the trending charts — here's why it matters.

🔗 https://github.com/google/skills
📝 Agent Skills for Google products and technologies
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The google/skills GitHub repository is a collection of Agent Skills for Google products and technologies, including Google Cloud. The repository is under active development and provides a range of skills, such as Gemini API, BigQuery Basics, and Cloud Run Basics. To use these skills, you can install them using npx skills add google/skills and select the specific skills you need. The repository also includes recipes for onboarding to Google Cloud, authenticating to Google Cloud, and Google Cloud network observability.

The skills are organized into categories, including Google Cloud Well-Architected Framework, which covers security, reliability, cost optimization, operational excellence, performance optimization, and sustainability. Additional skills are available for Flutter and Dart in separate repositories.

If you encounter issues or need help, you can search for existing issues or open a new one in the GitHub Issue Tracker. Contributions are welcome, and you can help by reporting bugs or inaccuracies, or suggesting new skills to add to the repository.

The skills are licensed under the Apache 2.0 license, allowing you to copy, modify, and distribute them.
The takeaway: Level up your Google Cloud skills with this extensive repository of Agent Skills and start building like a pro today!

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🧠 Channel: https://t.me/GithubRe

⚡ FareedKhan-dev/train-llm-from-scratch is making waves. Here's the full picture.

🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch
📝 A straightforward method for training your LLM, from downloading data to generating text.
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The train-llm-from-scratch GitHub repository is a treasure trove for anyone looking to dive into the world of large language models (LLMs). At its core, this project allows you to train your own LLM from scratch using a single GPU, making it an inclusive starting point for both beginners and seasoned AI enthusiasts.

The repository is built around a transformer model implemented from scratch using PyTorch, following the principles outlined in the seminal paper "Attention is All You Need". This foundation is then expanded upon to include a comprehensive post-training suite, enabling the development of a modern aligned reasoning model through a series of sophisticated techniques such as SFT, Reward Model, PPO, DPO, and GRPO, all implemented in pure PyTorch.

To get started, you'll need a basic understanding of object-oriented programming, neural networks, and PyTorch, alongside a GPU capable of handling the demands of training such models. The project is meticulously organized, with clear documentation and a structured codebase that includes scripts for downloading datasets, preprocessing data, training the model, and generating text.

The project's code structure is well-documented, with separate directories for models, data loading, scripts, and configurations, making it easy to navigate and contribute to. The addition of a post-training suite and tools like a Streamlit control panel for training, evaluation, and interaction with the model further enhances its utility and accessibility.

This repository is perfect for researchers, students, and developers looking to explore the capabilities and potential of large language models. With its comprehensive approach, from the basics of transformer models to the advanced techniques of post-training, it serves as an invaluable resource for anyone aiming to contribute to or learn from the cutting-edge field of AI.

In short, train your own billion-parameter LLM from scratch and unlock the doors to a world of AI possibilities with this powerful and accessible GitHub repository - the future of AI is at your fingertips.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: apple/container — let's break it down.

🔗 https://github.com/apple/container
📝 A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon.
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Introducing the apple/container GitHub repo, a tool that lets you create and run Linux containers on your Mac as lightweight virtual machines. It's written in Swift and optimized for Apple silicon.

The tool consumes and produces OCI-compatible container images, allowing you to pull and run images from standard container registries. You can also push images you build to those registries and run them in other OCI-compatible applications.

To get started, you'll need a Mac with Apple silicon and macOS 26 or later. You can download the latest signed installer package from the GitHub release page and follow the installation instructions.

Key features include:

* Creating and running Linux containers as lightweight virtual machines
* Consuming and producing OCI-compatible container images
* Pushing images to standard container registries

The repo is under active development, with a contributing guide available for those who want to get involved.

One-liner takeaway: With the apple/container repo, you can run Linux containers on your Mac with ease, unlocking a world of possibilities for developers and users alike.

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🧠 Channel: https://t.me/GithubRe

💡 apple/container just hit the trending charts — here's why it matters.

🔗 https://github.com/apple/container
📝 A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon.
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Introduction to apple/container: The apple/container tool allows you to create and run Linux containers as lightweight virtual machines on your Mac. It's written in Swift and optimized for Apple silicon.

The tool supports OCI-compatible container images, enabling you to pull and run images from any standard container registry. Key features include low-level container, image, and process management using the Containerization Swift package.

To get started, you'll need a Mac with Apple silicon and macOS 26. You can download the latest signed installer package from the GitHub release page and follow the installation instructions.

Technical highlights include the use of Swift for development and the ability to push and pull images from container registries. The tool is ideal for developers and power users looking to leverage containerization on their Macs.

The project is under active development, with contributions welcome and encouraged.

One-liner takeaway: Run Linux containers on your Mac with ease using the apple/container tool.

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🧠 Channel: https://t.me/GithubRe

🎯 addyosmani/agent-skills landed on trending. Worth a proper look.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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The addyosmani/agent-skills GitHub repository provides a set of production-grade engineering skills for AI coding agents. These skills encode workflows, quality gates, and best practices that senior engineers use when building software, ensuring that AI agents follow them consistently across every development phase.

The repository includes 7 slash commands that map to the development lifecycle, allowing users to activate the right skills automatically. For example, /spec defines what to build, /plan plans how to build it, and /build builds incrementally.

To get started, users can install the skills using various methods, such as Claude Code, Cursor, or Antigravity CLI. The repository also includes a set of 24 skills that cover different aspects of software development, from defining and planning to building, verifying, and shipping.

The target audience for this repository includes software developers, engineers, and anyone interested in improving their coding skills and workflows. With addyosmani/agent-skills, developers can streamline their development process, ensure consistency, and produce high-quality code.

In short, addyosmani/agent-skills is a powerful tool for developers to improve their coding workflow - automate your coding workflow with agent skills and take your development to the next level.

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: maziyarpanahi/openmed — let's break it down.

🔗 https://github.com/maziyarpanahi/openmed
📝 open-source healthcare ai
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Introduction to OpenMed: OpenMed is a local-first healthcare AI that turns clinical text into structured insight with just one line of code. It features 1,000+ specialized medical models that run entirely on your own hardware, ensuring no cloud, no vendor lock-in, and no patient data leaving your network.

Key Features:
- Entity extraction: Identify and extract medical entities such as diseases, medications, and anatomy.
- PII de-identification: Detect and de-identify protected health information (PHI) to ensure HIPAA compliance.
- Model variety: Choose from over 1,000 curated models for various medical specialties and languages.
- Cross-platform compatibility: Run OpenMed on CPU, CUDA, Apple Silicon (MLX), and natively in iOS/macOS apps via OpenMedKit.

Technical Highlights:
- Python API for easy integration and deployment.
- REST service for scalable and secure API calls.
- Batch processing for efficient processing of large datasets.
- Apple MLX acceleration for significant performance gains on Apple Silicon devices.

Audience:
- Developers: Easily integrate OpenMed into your healthcare applications.
- Researchers: Leverage OpenMed's vast model repository for medical research and analysis.
- Healthcare organizations: Ensure HIPAA compliance and protect patient data with OpenMed's local-first approach.

Takeaway: With OpenMed, you can unlock the power of local-first healthcare AI and transform clinical text into actionable insights without compromising patient data security.

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🧠 Channel: https://t.me/GithubRe