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Daily GitHub Project Recommendation: system_prompts_leaks - Peek into the Internal Instructions of ChatGPT, Claude, and Gemini!

Are you curious how top AI chatbots like ChatGPT, Claude, and Gemini are internally “set up” and “guided” when they interact with you? Today’s recommended GitHub project, asgeirtj/system_prompts_leaks, lifts the veil on this mystery. It’s a fascinating repository that specifically collects system-level prompts and instructions extracted from these popular chatbots, offering you a glimpse into the AI’s behind-the-scenes “thinking.”

Project Highlights: A Treasure Trove for Insight into AI’s Core Settings

system_prompts_leaks’s core value lies in providing a unique perspective, allowing us to directly understand the implicit or explicit behavioral guidelines, role definitions, or safety protocols that AI models are given before engaging in a conversation.

  • Technical Insight: For AI researchers and prompt engineers, this is an invaluable resource. By analyzing these “leaked” system prompts, you can learn how to construct your own prompts more effectively and understand how Large Language Models (LLMs) are guided to maintain consistency, safety, or specific conversational styles. It helps optimize your AI applications and even anticipate AI behavior patterns.
  • Application Exploration: For a broader audience of AI enthusiasts, developers, and even security researchers, this project helps you gain a deeper understanding of the “personality” and limitations of mainstream AI models. You can discover how these models are instructed to avoid harmful content, remain neutral, or play specific roles. This is significant for evaluating AI bias, exploring its boundaries, or developing safer AI applications.

This project boasts over 11,500 stars and more than 2,200 forks, which clearly demonstrates its enormous influence and value within the community. It’s not just a collection of texts; it’s an entry point to a deeper understanding of the behavioral patterns of modern AI agents.

How to Start Exploring

Want to explore these fascinating system prompts yourself? Simply click the link below to access the system_prompts_leaks GitHub repository. You can directly browse various LLM prompt files and uncover their secrets.

Act Now, Join the Exploration!

Whether you’re an AI developer, researcher, or simply curious about how AI works, system_prompts_leaks is worth your time to explore. If you make any discoveries or have new system prompts, you’re welcome to contribute via a Pull Request or share your insights in the repository’s “Discussions” section. Let’s delve deeper into the mysteries of AI together!

Daily GitHub Project Recommendation: Verifiers - Unlocking a New Paradigm for LLM Reinforcement Learning!

Today, we bring you a highly anticipated Python library in the field of LLM reinforcement learning: willccbb/verifiers. This project aims to provide a modular and scalable solution for reinforcement learning with Large Language Models (LLMs), helping researchers and developers build, train, and evaluate LLM agents more efficiently. It garnered 307 stars in just one day, with a total of 2459 stars, which is strong evidence of its powerful appeal!

Project Highlights

Verifiers’ core value lies in providing a series of flexible components for creating various RL environments and training LLM agents. It not only includes an asynchronous GRPO implementation based on the transformers Trainer but also deeply integrates prime-rl, supporting large-scale FSDP training, which provides a solid foundation for complex LLM reinforcement learning tasks.

  • Modular Environment Construction: Verifiers offers various environment types such as SingleTurnEnv, ToolEnv, and MultiTurnEnv. Whether it’s simple single-turn interactions, complex tool usage, or even custom multi-turn protocols, it can handle them with ease. This means you can flexibly design how LLMs interact with environments according to your specific needs.
  • Reinforcement Learning and Evaluation Powerhouse: Beyond RL training, Verifiers can also be directly used to build LLM evaluation systems, create high-quality synthetic data pipelines, and even implement complex agent harnesses. This makes it not just a training tool, but a powerful assistant throughout the entire LLM development lifecycle.
  • Open and Extensible: The project emphasizes minimizing “fork proliferation,” aiming to be a reliable building block. If you have new environment ideas, you can easily integrate them as independent Python modules rather than modifying the core library, greatly fostering community contributions and ecosystem health.

Technical Details and Applicable Scenarios

Verifiers is primarily developed in Python, utilizes uv for dependency management, and seamlessly integrates with the Hugging Face transformers ecosystem. Its internal GRPOTrainer and optimized support for the vLLM inference engine ensure efficiency and scalability for training and evaluation.

It is particularly suitable for the following scenarios:

  • LLM RLHF (Reinforcement Learning from Human Feedback): Provides verifiable environments for LLM alignment and behavior shaping.
  • Agent Development: Builds and trains LLM agents capable of using external tools and performing complex tasks.
  • LLM Evaluation Benchmarks: Creates customized, rigorous evaluation environments to measure LLM performance and generalization capabilities.
  • Synthetic Data Generation: Generates rich datasets for LLM training through interaction with environments.

How to Get Started

Want to experience the powerful features of Verifiers? Just follow these simple steps:

  1. Install uv:
    curl -LsSf https://astral.sh/uv/install.sh | sh
uv init
source .venv/bin/activate
  2. Add Verifiers:
    uv add verifiers # 或 uv add 'verifiers[all]' 支持GPU训练

For more detailed documentation and usage examples, please visit its official documentation: https://verifiers.readthedocs.io/en/latest/

Call to Action

Verifiers, with its modular design and deep understanding of LLM reinforcement learning, offers us endless possibilities for building more powerful and intelligent LLM agents. If you are interested in LLM reinforcement learning, agent development, or evaluation, why not explore this continuously active project, which boasts 2459 stars and 281 forks, right now?

GitHub Repository Link: https://github.com/willccbb/verifiers

We look forward to your contributions and sharing within the Verifiers community!

Daily GitHub Project Recommendation: GitHubDaily — Your Open Source Exploration Guide, 41K Stars Community’s Top Picks!

In the vast universe of GitHub, how can you quickly find truly shining and valuable open-source projects? Do you struggle daily to find high-quality developer tools and programming tutorials? Today, we proudly recommend a project that not only solves this problem but has also become a beacon in the open-source community — GitHubDaily/GitHubDaily! This treasure trove repository, boasting over 41,000 stars and continuously gaining hundreds of new stars daily, is the go-to platform for countless developers to get daily inspiration and knowledge.

Project Highlights

GitHubDaily’s core value lies in its unwavering commitment to sharing high-quality, interesting, and practical open-source technical tutorials, developer tools, programming websites, and cutting-edge tech information. Since its establishment in 2015, it has cumulatively shared over 8,000 open-source projects, which is an admirable achievement in itself!

From a technical perspective, GitHubDaily is more than just a simple list; it’s a meticulously curated open-source knowledge curator. The project categorizes and organizes content recommended each year, such as AI technologies, AI tools, free books, learning tutorials, practical tools, useful plugins, and resource collections, greatly lowering the barrier for developers to explore and learn. You can easily review past outstanding projects and quickly pinpoint areas of interest, which is undoubtedly a great saving of time and effort.

From an application perspective, whether you are a student eager to improve your programming skills, an efficiency enthusiast looking for handy tools, or a seasoned engineer wanting to stay abreast of tech trends, GitHubDaily can provide you with a comprehensive, authoritative, and continuously updated information source. It encourages developers to improve their programming abilities by reading source code and learning from the experiences of others, which is the essence of the open-source spirit.

How to Get Started / Links

Want to join this vast open-source exploration community and start your daily curated journey? Simply visit the link below to browse all selected projects and get the latest information instantly by following its official accounts on platforms like WeChat Official Account, Weibo, and Zhihu.

Explore GitHubDaily/GitHubDaily Now!

Call to Action

If you’ve discovered or created excellent open-source projects on GitHub, consider recommending or self-recommending them via GitHubDaily’s issues page to benefit more people. Together, let’s let the spark of open source illuminate more paths to innovation! Don’t forget to like and star this project to support its continued contribution to the community!