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Daily GitHub Project Recommendation: MediaCrawler - Your All-in-One Self-Media Data Collection Tool!

Are you still struggling with how to efficiently collect data from major self-media platforms like Xiaohongshu, Douyin, and Bilibili? Today, we bring you a star-level open-source project – NanmiCoder/MediaCrawler! It’s a powerful multi-platform self-media data collection tool with over 26,000 stars and 6,800 forks, an indispensable asset for data analysts, market researchers, and crawler enthusiasts.

Project Highlights

MediaCrawler’s most striking feature is its extensive support for mainstream Chinese self-media platforms, including Xiaohongshu, Douyin, Kuaishou, Bilibili, Weibo, Baidu Tieba, and Zhihu. Whether you want to analyze popular trends in Xiaohongshu notes, gain insights into hot comments on Douyin videos, or collect fan interaction data from Bilibili UP creators, it can lend you a hand.

From a technical perspective, this project is built upon the advanced Playwright browser automation framework. This means it can simulate real user behavior and successfully circumvent common complex JS reverse engineering challenges often found in traditional crawlers by cleverly utilizing “retaining login state” and “JS expressions to obtain signature parameters.” This greatly lowers the technical barrier, allowing more developers to easily get started and focus on the data itself rather than anti-crawling strategies.

From an application perspective, MediaCrawler offers a series of practical functionalities: you can search content by keywords, precisely crawl specified posts or video IDs, and even retrieve secondary comments and author homepage information. Furthermore, it supports login state caching, IP proxy pools, and can generate comment word clouds, providing visual support for your data analysis. All collected data can be flexibly stored in MySQL, CSV, or JSON files for subsequent processing.

How to Get Started

Want to take a closer look? MediaCrawler is written in Python, and it’s recommended to use uv for dependency management. Installation and running are very convenient. With just a few simple commands, you can start your data collection journey.

Click here to explore: NanmiCoder/MediaCrawler

Call to Action

Please note that the author has explicitly stated that this project is for learning and research purposes only. You must comply with relevant laws and regulations and must not use it for any illegal or commercial purposes. If you find this project helpful, why not give it a ⭐ Star, and feel free to contribute your strength to make MediaCrawler even more powerful!

Daily GitHub Project Recommendation: Ladybird - Building an Independent Web Browser from Scratch!

Today, we’re excited to bring you an ambitious and innovative GitHub project – Ladybird. It’s not just a browser; it’s a web browser engine built from scratch, aiming to achieve “true independence.” If you’re tired of the single-engine landscape of the web world or curious about browser technology, then Ladybird is definitely worth your attention!

Project Highlights

Ladybird’s most compelling feature is that it’s a “truly independent” browser. This means it doesn’t rely on existing engines like Chromium or Firefox. Instead, it builds a brand-new rendering and JavaScript engine from scratch, based on web standards. This is extremely rare in the current browser market and represents its core value.

From a technical standpoint, Ladybird adopts a modern multi-process architecture, including an independent UI process, multiple WebContent rendering processes, an image decoding process, and a request server process. This design not only enhances the browser’s stability and responsiveness but also effectively isolates malicious content, improving security. Each tab runs in an independent sandbox rendering process, providing users with a more reliable browsing experience.

Although Ladybird is currently in the “pre-Alpha” stage and primarily suitable for developers, it has already integrated several core components from the SerenityOS project, including:

  • LibWeb: Web rendering engine
  • LibJS: JavaScript engine
  • LibGfx: 2D graphics library and image decoding
  • LibHTTP: HTTP/1.1 client
  • As well as libraries for encryption, Unicode support, media playback, and other critical functionalities.

This indicates that its development team is steadily building a full-featured modern browser step by step. With over 44,000 stars and nearly 2,000 forks, Ladybird has attracted significant community attention, proving its unique appeal and immense potential.

Technical Details and Applicable Scenarios

Ladybird is written in C++ and can run on Linux, macOS, Windows (via WSL2), and various *Nix systems. For developers interested in low-level browser technology, web standard implementation, and operating system-level security isolation, Ladybird provides an excellent platform for learning and contributing. If you are a browser engine developer, web security researcher, or aspire to participate in shaping the future of the web, Ladybird’s codebase will open up a new world for you.

If you can’t wait to experience this independent browser or want to delve into its internal mechanisms, you can visit the project’s GitHub repository and follow its detailed Build Instructions .

GitHub Repository Link: https://github.com/LadybirdBrowser/ladybird

Call to Action

Ladybird’s goal is to build a complete, usable modern web browser, a grand and challenging vision. We encourage all developers passionate about cutting-edge technology to explore this project. Whether it’s contributing code, submitting bug reports, or participating in community discussions (join their Discord server ), your every involvement will be an important force in driving Ladybird forward. Let’s witness the growth of this independent browser engine together!

Daily GitHub Project Recommendation: Genesis - Shaping the Future of General-Purpose Robotics and Embodied AI!

🚀 Explorers, today we bring you a groundbreaking GitHub project – Genesis! This is a physical simulation platform designed for general-purpose robotics, embodied AI, and physical AI applications. With over 25,000 stars, this project is redefining our understanding of physical world simulation, bringing unprecedented possibilities to the fields of AI and robotics.

Project Highlights

Genesis is not just an ordinary simulator; it’s a versatile, future-ready platform:

  • New Paradigm for Universal Physics Engine: Genesis rebuilds a universal physics engine from the ground up, capable of simulating various materials and physical phenomena, achieving unprecedented realism and accuracy. Whether it’s rigid bodies, liquids, gases, deformable objects, or granular materials, they can all find their digital twin in Genesis.
  • Extreme Performance and User-Friendly: It stands out for being lightweight, ultra-fast, Pythonic, and user-friendly. On a single RTX 4090 GPU, it can simulate the Frank arm at an astounding 43 million frames per second, 430,000 times faster than real-time! This provides powerful support for large-scale, long-duration robotic training and testing.
  • Generative Data Engine: Genesis’s unique strength lies in its powerful generative data engine. It can transform natural language descriptions from user prompts into various data modalities, significantly automating the data generation process and greatly reducing the human cost required for developing and training AI models.
  • Ray Tracing and Differentiability: The built-in ray tracing rendering system provides realistic visual effects. At the same time, Genesis is designed to be fully differentiable, meaning it can seamlessly integrate with machine learning frameworks, supporting gradient-based optimization, which is crucial for training complex robotic policies.

Technical Details and Applicable Scenarios

Genesis is built on Python, compatible with Linux, macOS, and Windows, and supports multiple computing backends (CPU, Nvidia/AMD GPU, Apple Metal). Its core consists of a redesigned physics engine and an upper-level generative agent framework. This makes it suitable not only for robotics R&D and embodied AI training but also for physical AI research, automated data generation, and any scenario requiring high-fidelity physical simulation. For developers and researchers looking to lower the barrier to physical simulation, unify various physics solvers, and automate data generation, Genesis is undoubtedly an ideal choice.

How to Get Started

Want to learn more or start using this powerful platform? First, ensure PyTorch is installed, then easily install Genesis via PyPI:

pip install genesis-world

Alternatively, get the latest version:

pip install git+https://github.com/Genesis-Embodied-AI/Genesis.git

Detailed installation guides and rich documentation can be found on the project’s GitHub page.

Call to Action

Genesis is an open and collaborative project, and community contributions are highly welcome! Whether you want to submit new features, fix bugs, or provide suggestions for improvement, your participation will help Genesis continue to grow. Click the link below to explore this highly potential project and join the effort to build the future of embodied AI!

GitHub Repository Address: https://github.com/Genesis-Embodied-AI/Genesis

Don’t forget to like and share to let more people know about this game-changing AI and robotics platform!