This article is automatically generated by n8n & AIGC workflow, please be careful to identify

Daily GitHub Project Recommendation: ebook2audiobook - Your Exclusive AI Narrator, One-Click Ebook to Audiobook Conversion!

Today, we’re bringing you a fascinating GitHub project – ebook2audiobook. Tired of staring at screens? Or want to “read” your favorite ebooks while commuting or exercising? This star project (currently boasting 12.8K+ stars and actively maintained) is tailor-made for you! It can effortlessly convert your ebooks (supporting over 1100 languages!) into audiobooks complete with chapters and metadata, and can even clone your voice.

Project Highlights

The core value of ebook2audiobook lies in its excellent text-to-speech capabilities and user-friendly design. From a technical perspective, it integrates various advanced text-to-speech (TTS) models, including Coqui XTTSv2, Bark, Vits, Fairseq, and more, ensuring the quality and diversity of the generated audio. The project not only supports CPU operation but also leverages GPU acceleration to provide an almost real-time conversion experience. Most impressively, its powerful voice cloning feature allows you to provide just one audio file and have the AI narrate your ebook in your unique voice, delivering a truly personalized audiobook experience.

From an application perspective, whether you are a student, a busy professional, or a user with reading difficulties, ebook2audiobook can significantly enhance your “reading” efficiency and convenience. It supports a wide range of ebook formats (such as .epub, .pdf, .mobi, etc.) and intelligently organizes the output audio by chapter, making it easy for you to resume listening anytime, anywhere. Imagine immersing yourself in a sea of knowledge with your familiar voice in any setting – this is undoubtedly a revolution in digital reading.

Technical Details and Applicable Scenarios

This project is primarily developed in Python and provides an intuitive Gradio Web interface, making it easy for non-technical users to get started. It can run locally on various operating systems (Windows, Mac, Linux) and supports containerized deployment via Docker. It even offers remote execution options through Hugging Face Spaces and Google Colab, making deployment and use exceptionally flexible. With a minimum memory requirement of 4GB, most devices can run it smoothly.

How to Get Started

Eager to try it out? The project’s GitHub repository provides detailed installation and usage guides:

  • GitHub Repository: https://github.com/DrewThomasson/ebook2audiobook You can choose to clone the repository and run it locally, or directly use its provided Docker image, Hugging Face Space, or Colab Notebook. Either way, the project strives to offer the simplest possible getting started process.

Call to Action

ebook2audiobook is an amazing tool that opens up a whole new dimension for ebook reading. We encourage everyone to explore this project firsthand and transform your ebooks into personalized audiobooks. If you are multilingual, the project team is actively seeking help to optimize the performance of different language models, and your contributions will benefit many more people! Like, share, and get involved to help make this project even better!

Daily GitHub Project Recommendation: Peeking into the Core of AI Giants - Unveiling Top AI Tool System Prompts and Models!

Today’s recommendation is absolutely eye-opening! We all know that many powerful AI tools have a set of carefully designed “secret weapons” behind them – their System Prompts and internal model structures. The treasure trove project x1xhlol/system-prompts-and-models-of-ai-tools is precisely what reveals these “secrets” to the public, showing you how AI giants work.

Project Highlights

This GitHub repository is more than just a code library; it’s a deep archive on the internal workings of AI, currently boasting over 92K stars and 25K forks, demonstrating its immense popularity.

  • Treasure Trove of Resources: It collects system prompts, internal tool configurations, and AI model details for dozens of well-known AI tools, including Augment Code, Claude Code, Devin AI, Cursor, Perplexity, Notion AI, VSCode Agent, and more. This is arguably one of the most comprehensive collections of AI internal instructions you’ll find!
  • Deep Technical Insights: The project provides over 30,000 lines of code and documentation, revealing how these AI tools are designed, how they interact with users, and their internal operational logic. For developers who want to deeply understand AI agents and LLM prompt engineering, this is an invaluable learning resource.
  • Dual Value: Technical and Application:
    • Technical Aspect: For AI researchers and Prompt Engineers, this is an excellent resource for learning and analyzing top-tier AI system prompt engineering practices. By studying these cases, you can understand how different AI models are guided in complex tasks, thereby optimizing your own AI application design.
    • Application Aspect: Developers can draw inspiration from this to design more efficient and robust system prompts for their AI applications. Even for AI security researchers and startups, the “AI Security Notice” specifically highlighted in the project’s README reminds us that understanding these internal mechanisms is crucial for discovering and hardening potential vulnerabilities in AI systems.

Applicable Scenarios

Whether you are an AI developer, a Prompt Engineer, an AI researcher, or simply curious about how AI works, this project can provide you with an unprecedented perspective. You can use it to enhance your Prompt Engineering skills, optimize the performance of your AI applications, and even provide better security protection for your own AI products.

How to Get Started

The project’s structure is very clear, with each AI tool having its own folder, making it easy for you to find the internal instructions for the tools you’re interested in.

🔗 GitHub Repository Link: https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools

Call to Action

If you are passionate about uncovering AI’s “black box” or are building your own AI applications, we highly recommend you dive deep into this project. Don’t forget to give it a Star, follow for the latest updates, and even join their Discord community to get firsthand updates and participate in discussions!

Daily GitHub Project Recommendation: sing-box - Your All-in-One Network Proxy Powerhouse!

Today, we’re focusing on a highly acclaimed project in the realm of network freedom and privacy: SagerNet/sing-box. This “universal proxy platform,” built with the Go language, has earned over 27K GitHub Stars due to its exceptional flexibility and powerful features, making it an ideal companion for exploring the network world.

Project Highlights

The core value of sing-box lies in its positioning as a “universal proxy platform.” It’s not just a simple proxy tool, but a comprehensive solution capable of meeting various complex network demands.

  • Technical Depth: sing-box is developed using the high-performance Go language, which means it excels in handling a large number of network requests, offering advantages in low latency and high concurrency. Its architectural design aims to support various proxy protocols and complex routing rules, providing users with ultimate customization capabilities.
  • Broad Applications: Whether you aim to bypass geo-restrictions, protect personal privacy, or set up a highly customized network proxy environment for your business or personal use, sing-box offers robust support. It helps users build secure, stable, and efficient network connections, truly achieving network freedom. Its active community (over 3K forks) also indicates its powerful scalability and wide range of application scenarios.

Applicable Scenarios

sing-box is suitable for all users with advanced network proxy needs, including but not limited to:

  • Users requiring a stable and reliable solution for circumventing internet censorship.
  • Users prioritizing online privacy, seeking to encrypt and hide their network footprint.
  • Network developers or administrators looking for a flexible and configurable platform to manage complex network traffic.

How to Get Started

Want to learn more or start using sing-box? It provides comprehensive official documentation to help you get started quickly:

Call to Action

sing-box is an open-source project that thrives on community support. We encourage you to visit its GitHub repository, light up that little ⭐, explore its code, and even contribute your efforts. Together, let’s build a freer and more secure network environment!