🌟 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.
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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!

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🧠 Channel: https://t.me/GithubRe

🌟 maziyarpanahi/openmed caught my eye on GitHub Trending today.

🔗 https://github.com/maziyarpanahi/openmed
📝 open-source healthcare ai
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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.

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🧠 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.
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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.

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🧠 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
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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!

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🧠 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.
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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