I taught machines to read newspapers, gave them 250 years of data, extracted everything (6 million+ stories so far), separated the ads from the content, and categorized it all. You can search semantically or with you own AI research assistant and get the actual articles with full text extraction, as well as build and share collections. As far as I know, this has never been done before, the data isn't on Google or in any LLM, only on SNEWPAPERS
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.
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 around
44.7 billion LLM/vLLM tokens processed
7 months of 80+ hour work weeks (organic neural network compute)
Would love your honest feedback and discoveries you make in the archive! 🫡 (here or [email protected])
About SNEWPapers on Product Hunt
“The World's First AI Newspaper Archive”
SNEWPapers launched on Product Hunt on April 27th, 2026 and earned 122 upvotes and 21 comments, placing #10 on the daily leaderboard. I taught machines to read newspapers, gave them 250 years of data, extracted everything (6 million+ stories so far), separated the ads from the content, and categorized it all. You can search semantically or with you own AI research assistant and get the actual articles with full text extraction, as well as build and share collections. As far as I know, this has never been done before, the data isn't on Google or in any LLM, only on SNEWPAPERS
On the analytics side, SNEWPapers competes within Education, Artificial Intelligence and Data & Analytics — topics that collectively have 551.2k followers on Product Hunt. The dashboard above tracks how SNEWPapers performed against the three products that launched closest to it on the same day.
Who hunted SNEWPapers?
SNEWPapers was hunted by Brett Shinnebarger. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of SNEWPapers including community comment highlights and product details, visit the product overview.
Brett Shinnebarger
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): → Tutorials
A 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 around
44.7 billion LLM/vLLM tokens processed
7 months of 80+ hour work weeks (organic neural network compute)
Would love your honest feedback and discoveries you make in the archive! 🫡 (here or [email protected])