SNEWPapers offers semantic search across 6M+ AI-extracted American newspaper articles spanning 1730s-1960s, organized into 24 categories and 1,000+ sub-categories.
Key Takeaways
6M+ stories extracted from 3,000+ newspaper titles; corpus grows daily and is not indexed by Google or ChatGPT.
AI semantic search finds articles by concept and theme, not just keyword matches, with state and date filters.
“The Sleuth” is an on-platform AI research assistant that returns cited answers from the archive.
Collections feature lets users build and share curated research sets; “Today in History” surfaces date-matched primary sources daily.
Hacker News Comment Review
The main UX critique is that even experienced search-industry builders struggle to see concrete entry points; discoverability before authentication is a real friction point.
The founder is actively iterating on the auth/gating balance, considering free monthly search quotas and unauthenticated demo paths to reduce database load without hard-walling users.
Notable Comments
@benwills: “it’s difficult for me to concretely see how I would use this” – recommends a sample dataset or guided demo flow to anchor use cases.
