⭐️ Shining ⭐️: This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness.
The resources include:
🎉Papers🎉: The latest papers about In-Context Learning, Prompt Engineering, Agent, and Foundation Models.
🎉Playground🎉: Large language models（LLMs）that enable prompt experimentation.
🎉Prompt Engineering🎉: Prompt techniques for leveraging large language models.
🎉ChatGPT Prompt🎉: Prompt examples that can be applied in our work and daily lives.
🎉LLMs Usage Guide🎉: The method for quickly getting started with large language models by using LangChain.
In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that’s a question for Musk):
- Those who enhance their abilities through the use of AIGC;
- Those whose jobs are replaced by AI automation.
💎EgoAlpha: Hello! human👤, are you ready?
Table of Contents
🔥 AI Spotlight: Trending Research Papers
[2026-11-11]
e-means: outperforming k-means (in one-tenth the epochs) （New）
Published: 2026-11-06
Swann Bessa, Pierre-Emmanuel Mazaré, Gergely Szilvasy, Hugo Touvron, David Picard, Matthijs Douze, Hervé Jégou - [arXiv]
InstantHMR : Real-Time Human Mesh Recovery on Your Phone （New）
Published: 2026-11-06
Mohamed Adjel, Kahina Chalabi, Vincent Bonnet - [arXiv]
WorldFoundry: Towards Unified Infrastructure for World Intelligence （New）
Published: 2026-11-07
Juanxi Tian, Jiadong Hou, Baiang Li, Xin Lin, Zhengbo Jiao, Weijia Zhang, Yeying Jin, Truong Nguyen, Haitian Zheng, Zhifei Zhang, Jianfei Yang, Ming-Hsuan Yang, Felix Heide, Wei Xiong, Enze Xie, Wanhu - [arXiv]
PhysioWave-T: Token-Efficient Wavelet Foundation Models for Biosignals （New）
Published: 2026-11-06
Yanlong Chen, Simone Benatti, Luca Benini, Cuntai Guan, Yawei Li - [arXiv]
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation （New）
Published: 2026-11-09
Bing Han, Yichuan Peng, Haoyu Dong, Yuanmeng Hu, Yongyi Jia, Fangjing Wang, Kecheng Zheng, Yiming Zhang, Yanhong Zeng, Xiaoqing Zhang, Chen Song, Rundong Zou, Anqi Dong, Yuan Guan, Ka Leong Cheng, Xin - [arXiv]
👉 Complete history news 👈
📜 Papers
You can directly click on the title to jump to the corresponding PDF link location
Survey
👉Complete paper list 🔗 for "Survey"👈
Prompt Engineering
Prompt Design
👉Complete paper list 🔗 for "Prompt Design"👈
Chain of Thought
👉Complete paper list 🔗 for "Chain of Thought"👈
In-context Learning
👉Complete paper list 🔗 for "In-context Learning"👈
Retrieval Augmented Generation
👉Complete paper list 🔗 for "Retrieval Augmented Generation"👈
Evaluation & Reliability
👉Complete paper list 🔗 for "Evaluation & Reliability"👈
Agent
👉Complete paper list 🔗 for "Agent"👈
Multimodal Prompt
👉Complete paper list 🔗 for "Multimodal Prompt"👈
Prompt Application
👉Complete paper list 🔗 for "Prompt Application"👈
Foundation Models
👉Complete paper list 🔗 for "Foundation Models"👈
👨💻 LLM Usage
Large language models (LLMs) are becoming a revolutionary technology that is shaping the development of our era. Developers can create applications that were previously only possible in our imaginations by building LLMs. However, using these LLMs often comes with certain technical barriers, and even at the introductory stage, people may be intimidated by cutting-edge technology: Do you have any questions like the following?
- ❓ How can LLM be built using programming?
- ❓ How can it be used and deployed in your own programs?
💡 If there was a tutorial that could be accessible to all audiences, not just computer science professionals, it would provide detailed and comprehensive guidance to quickly get started and operate in a short amount of time, ultimately achieving the goal of being able to use LLMs flexibly and creatively to build the programs they envision. And now, just for you: the most detailed and comprehensive Langchain beginner’s guide, sourced from the official langchain website but with further adjustments to the content, accompanied by the most detailed and annotated code examples, teaching code lines by line and sentence by sentence to all audiences.
Click 👉here👈 to take a quick tour of getting started with LLM.
This repo is maintained by EgoAlpha Lab. Questions and discussions are welcome via [email protected].
We are willing to engage in discussions with friends from the academic and industrial communities, and explore the latest developments in prompt engineering and in-context learning together.
🙏 Acknowledgements
Thanks to the PhD students from EgoAlpha Lab and other workers who participated in this repo. We will improve the project in the follow-up period and maintain this community well. We also would like to express our sincere gratitude to the authors of the relevant resources. Your efforts have broadened our horizons and enabled us to perceive a more wonderful world.
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