Aginsi vs Paperlayer

Aginsi is an AI research co-pilot that turns reading material into summaries with key passages highlighted. It accepts articles, academic papers, PDFs, long threads, reports, and video transcripts; summaries keep their references and show the amount of compression and estimated reading time saved. The free Margin plan costs nothing and includes 10 monthly summaries for articles and PDFs. Scholar costs $9 a month and adds unlimited summaries, a library with tags and search, Markdown and PDF export, and a browser companion. Faculty costs $29 a month and adds team seats, shared shelves, batch and API access, and priority support. The product is available through web, extension, and API. Aginsi says it discards submitted source documents and retains only summaries users choose to save. Source text is processed transiently and not used to train models. Users can request access to, correction of, export of, or deletion of account data and saved summaries by contacting [email protected]. The company describes itself as a small studio in Helsinki.

Paperlayer is a web-based AI reading layer for research papers, pairing summaries and contextual margin notes with evidence in the original PDF. You can find public arXiv papers by title or author, enter an arXiv ID or paste an arXiv link. Summaries cover the paper’s problem, method, results and caveats, while highlights mark claims, methods, results, problems and caveats with adjustable density. Margin notes explain terms, equations and figures beside the text. Summaries and notes support Korean, Japanese, Chinese, Spanish, French and German, and J and K keys navigate through a paper. Paper chat is marked “Soon.” The public demo covers public arXiv papers only and accepts no private or unpublished uploads. Paperlayer says live processing sends paper and request content to OpenAI over encryption without intentionally including account, cookie or anonymous identifiers.