SNEWPapers

Incredible scale!You mentioned training the model to handle degraded paper and faded ink. Google famously used recaptcha v1 for the same problem, having millions of users unknowingly label words from old NYT archives. How have you coped this issue?

Honestly, this is quite cool! Do you plan to expand the newspaper libraries to other countries?

Can someone else submit newspapers?

Hey Product Hunt! 👋I'm excited to share SNEWPapers — the world’s first AI-powered historical newspaper archive. We’ve read and organized 6 million+ stories from 250 years of American newspapers (1730s–1960s) so you can finally explore history by meaning, not just broken keywords.Maybe the biggest news since sliced bread for digital humanities, historians, researchers, genealogists?I built this after trying to research references in The Fourth Turning. Traditional archives dumped me into faded page scans with terrible search. So I created my own.The result: clean, summarized articles and nearly perfect full-text OCR extractions + The Sleuth (your personal AI research assistant), smart categorization (24 categories / 1,000+ sub-categories), Collections for sharing, and a fun Today in History daily feed.Quick start (10 minutes): → TutorialsA few things I’d love your thoughts on:Today in History — Would you actually open this daily?Search + Sleuth — How useful is semantic search and the AI assistant for your research?Collections — Would you use/share public collections?Pricing: 7-day free trial. I priced it ~50% below traditional archives because we actually deliver usable, intelligent access. Product Hunt special: Use PRODUCTHUNT20 for 20% off any plan (valid until May 8).Huge technical journey. I had to figure out how to acquire, store and process nearly a million high-resolution newspaper images, build custom multi-modal systems to detect and segment articles, massively improve OCR on centuries old ink, train models to understand newspaper layout and context, run prompt engineering at scale, balance cost vs quality with LLMs and vLLMs, build semantic and agentic search infrastructure that actually works on millions of documents, and scale a cost-effective GPU fleet.Some “AWS-ish” stats so far:115,000+ GPU GB-hours (OCR / Layouts)26,000+ Lambda GB-hours moving data around44.7 billion LLM/vLLM tokens processed7 months of 80+ hour work weeks (organic neural network compute)Would love your honest feedback and discoveries you make in the archive! 🫡 (here or hello@snewpapers.com)