Paperlayer vs TLDR This

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 The public demo covers public arXiv papers only and accepts no private or unpublished uploads.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.

TLDR This summarizes articles and other text, with inputs ranging from a webpage address or pasted text to PDF, DOC and DOCX files up to 25 MB. Its AI generates concise summaries, while article extraction can identify the author, date, title, related images and estimated reading time. Browser extensions summarize webpages in one click, and a PDF chat feature lets students, researchers and professionals ask questions about research documents. Other tools paraphrase text in different voices and styles, filter weak arguments and speculation, and surface relevant points. A reading view removes ads, popups, graphics and other online distractions. The service also provides API access through a link to RapidAPI. TLDR This has a free plan and paid plans, including Starter at $4 a month. The Free plan includes unlimited basic summaries, 10 advanced AI summaries and up to 10 paraphrases as one-time allowances. The service says uploaded files are not opened unless requested and personal information is not sold to third parties. Data is encrypted in transit and at rest; deleted data is purged within 30 days.