Github Top Repositories
Photo
🌟 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
With
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
Takeaway: OpenMed brings the power of AI to healthcare, locally and securely, with just one line of code.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 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
🚀 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:
-
- 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
🔗 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
💡 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
- 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:
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
🔗 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
Github Top Repositories
Photo
🌟 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
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:
Takeaway: RuView turns ordinary WiFi into a contactless sensor, making it a revolutionary technology for spatial intelligence and sensing.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 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
Github Top Repositories
Photo
⚡ 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
* 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
🔗 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
💡 google/skills just hit the trending charts — here's why it matters.
🔗 https://github.com/google/skills
📝 Agent Skills for Google products and technologies
──────────────────────────────
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
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
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!
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/google/skills
📝 Agent Skills for Google products and technologies
──────────────────────────────
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!
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
Github Top Repositories
Photo
⚡ 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.
──────────────────────────────
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
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.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch
📝 A straightforward method for training your LLM, from downloading data to generating text.
──────────────────────────────
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.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
Github Top Repositories
Photo
📌 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.
──────────────────────────────
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
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
Key features include:
* Creating and running Linux containers as lightweight virtual machines
* Consuming and producing
* Pushing images to standard container registries
The repo is under active development, with a
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.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 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.
──────────────────────────────
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.
──────────────────────────────
🧠 Channel: https://t.me/GithubRe