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DeepPaperNote is an open-source tool that automatically converts papers (via title, link, or PDF) into structured Obsidian notes, bridging Zotero reference management and Obsidian knowledge base. It supports multiple AI agents (Claude Code / Codex), automatically identifies paper types, and extracts key information such as formulas, experimental data, and figures.
Introduces the 'Three-Pass Approach' proposed by Professor S. Keshav at the University of Waterloo, an efficient strategy for reading academic papers that helps researchers control the depth of reading based on their needs.
DeepPaperNote is an open-source tool that automates converting academic papers into structured Obsidian notes, handling metadata, figures, and note generation for deep reading.
Recommends using AI for reading papers and references the three-pass method from the classic 'How to Read a Paper' technique.
A methodology article on how to excel at AI research, emphasizing problem selection, literature reading, writing notes, and other skills, suitable for researchers.
User recommends a feature that helps with reading papers, finding it very useful.
Mu Shen's deep learning paper reading project on GitHub includes in-depth reading videos of major papers such as GPT-4, Llama 3.1, Sora, etc. Each video is about 1 hour, suitable for AI researchers and developers to deeply understand classic papers.