Save papers from arXiv, links from Twitter, notes from seminars, PDFs from your downloads folder. Use the browser extension, drag-and-drop, or just paste a URL into the chat. Synap ingests it all and creates the right entity type automatically — article, bookmark, note, or file.

Drop in a paper and AI extracts the title, authors, abstract, publication date, and key topics. It creates author entities, links to existing topics in your graph, and tags methodologies. No manual entry. No BibTeX wrangling. The AI proposes these changes and you approve with one click.

Over time, your Synap pod becomes a rich graph of papers, authors, topics, methodologies, and your own notes. Explore it visually with the graph view, or switch to table view to filter by topic, date range, or citation count. See which authors appear most in your reading. Find the paper that connects two fields you study.

Ask your AI agent: "What papers in my library discuss attention mechanisms in the context of genomics?" or "Find connections between my notes on transformer architectures and protein folding." Because your data is structured — not just text — the AI can give precise, relationship-aware answers instead of fuzzy keyword matches.