Jargon is an AI-managed zettelkasten that parses articles, papers, and videos into index card-sized key ideas. It summarizing sources, extracts ideas, links related concepts, and collapses duplicates. Semantic embeddings surface connections across the library.
Each source is parsed in context of existing cards, generating new insights that link back to the original material. The result is a knowledge base of interlinked ideas that can be explored directly or used as a RAG to answer questions. Questions also pull results from the web, which flow through the same extract/summarize/link pipeline before being synthesized with library content.
Core Loop
Ingest — Articles, PDFs, and YouTube videos are scraped and parsed Summarize — LLM distills each source into a concise summary Extract — Key ideas become standalone insight cards with source links Connect — Embeddings find related insights; duplicates collapse automatically Thread — Each node gets research questions that search the web for more sources
Features
PDF Full-Text Extraction
Academic papers and PDFs are automatically downloaded and converted to text using pdftotext . Jargon follows "full text" and DOI links from abstracts.
YouTube Transcripts
YouTube URLs are detected and transcripts are fetched directly from YouTube's API. Speakers are extracted from video titles when available.
Insight Extraction
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