I indexed the academic papers buried in the DOJ Epstein Files
Details
- External ID
- 47083160
- Source
- HN
- Company
- —
- Product
- I indexed the academic papers buried in the DOJ Epstein Files
- Website domain
- jeescholar.com
- Launched
- Feb. 20, 2026
- Cohort
- —
- Upvotes
- 7
- Upvotes percentile
- 0.38207547169811323
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
The DOJ released ~3.5M pages of Epstein documents across 12 datasets. Buried in them are 207 academic papers and 14 books that nobody was really talking about. From what I understand these papers aren't usually freely accesible, but since they are public documents, now they are.I don't know, thought it was interesting to see what this dude was reading. You can check it out at jeescholar.com Pipeline: 1. Downloaded all 12 DOJ datasets + House Oversight Committee release 2. Heuristic pre-filter (abstract detection, DOI regex, citation block patterns, affiliation strings) to cut noise 3. LLM classifier to confirm and extract metadata 4. CrossRef and Semantic Scholar APIs for DOI matching, citation counts, abstracts 5. 87 of 207 papers got DOI matches; the rest are identified but not in major indexes Stack: FastAPI + SQLite (FTS5 for full-text search) + Cloudflare R2 for PDFs + nginx/Docker on Hetzner. The fields represented are genuinely iteresting: there's a cluster of child abuse/grooming research, but also quantum gravity, AGI safety, econophysics, and regenerative medicine. Each paper links back to its original government PDF and Bates number. For sure not an exhaustive list. Would be happy to add more if anyone finds them.
Enrichment
- Theme
- searchable public records and archives
- Vertical
- Legal
- Function
- Search & retrieval
- Audience
- B2B
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- academic paper indexing from legal documents
- Manually corrected
- False
Could you build this?
Yes The project is a standard pipeline: extracting text/PDFs, running heuristic/LLM filters to classify papers, querying public scholarly APIs (Crossref/Semantic Scholar), and serving them in a full-text search web UI.
Discussion
No comments on this launch.
Competitors
Other products that read as similar to this one — 94 launches clear the similarity bar, closest 8 shown.
Attention rank: #64 of 95 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 101 days after the earliest competitor.
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