{"repo":"vsrupeshkumar/trendXtract","free":true,"listed":false,"github":"https://github.com/vsrupeshkumar/trendXtract","clone":"git clone https://github.com/vsrupeshkumar/trendXtract.git","description":"An intelligent research orchestration system that integrates large language models with scholarly data pipelines to enable contextual retrieval, cross-paper synthesis, and real-world knowledge extraction from scientific publications.","language":"Python","stars":18,"topics":["knowledge-graph","paper-analysis","research-automation","research-automation-tools","research-papers","semantic-search","vector-database"],"license":null,"category":"databases-storage","readme_excerpt":"trendXtract: AI Research Intelligence system Search. Synthesize. Discover. Build. The AI-native research platform for researchers, engineers, and founders who need to move faster than Google Scholar. -- Problem Researchers waste hours searching, reading, and synthesizing papers—only to find their questions unanswered and connections between papers invisible. Existing tools (Google Scholar, Semantic Scholar) are passive: they retrieve papers, but don't reason about them. Solution trendXtract is an AI Research Intelligence Engine that: 1. Searches millions of arXiv papers in milliseconds 2. Synthesizes insights across multiple papers using Claude AI 3. Extracts structured knowledge (methodology, contributions, limitations) 4. Compares papers with normalized differences 5. Generates implementation guides from papers 6. Detects research gaps to spark new ideas Result: What took 2 hours now takes 2 minutes. --- Key Features 1. Multi-Paper Intelligence 🤖 Ask a question and get answers synthesized across 1-5 papers with inline citations. Claude reasons across papers to reveal patterns humans miss. 2. Structured Paper Extraction 📋 One-click breakdown of any paper into: - Problem Statement — What's being solved? - Methodology — How does it work? - Key Contributions — What's novel? - Limitations — What's missing? - Applications — Real-world impact? 3. Paper Comparison Engine 📊 Select 2-5 papers. Get a normalized comparison table: - Methods & algorithms - Datasets & benchmarks - Perf","default_branch":null,"files":null,"tree":[],"storefront":"/r/vsrupeshkumar","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/vsrupeshkumar/trendXtract/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}