ArxivPaperAI is a cutting-edge tool designed to facilitate the exploration and analysis of scientific papers from the arXiv repository. Empowered by ChatGPT, it allows users to summarize complex academic documents in seconds, offering deep insights and enhancing focus. This makes it easier for researchers, students, and academics to stay updated with the latest advancements in their fields, saving valuable time and effort.

Scholarcy Browser Extension is designed for researchers, students, and academics who need a quick way to summarize lengthy research articles. By simply installing the extension, users can easily extract key points, create interactive flashcards, and link to open-access versions of citations, streamlining their research process without leaving the webpage they are on.

Virtual Scientists V2 serves as a modular AI agent framework tailored for scientific research. It defines multiple virtual scientists—Chemist, Physicist, Biologist, and Data Scientist—each equipped with domain-specific knowledge and tool integrations. These agents utilize LangChain to orchestrate API calls to sources like Semantic Scholar, ArXiv, and web search, enabling automated literature retrieval, contextual analysis, and data extraction. Users script tasks by specifying research objectives; agents autonomously gather papers, summarize methodologies and results, propose experimental protocols, generate hypotheses, and produce structured reports. The framework supports plugins for custom tools and workflows, promoting extensibility. By automating repetitive research tasks, Virtual Scientists V2 accelerates insight generation and reduces manual effort across multidisciplinary projects.