Epstein's emails reconstructed in a message-style UI (OCR and LLMs)
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This project reconstructs the Epstein email records from the recent U.S. House Oversight Committee releases using only public-domain documents (23,124 image files + 2,800 OCR text files).Most email pages contain only one real message, buried under layers of repeated headers/footers. I wanted to rebuild the conversations without all the surrounding noise.I used an OCR + vision-LLM pipeline to extract individual messages from the email screenshots, normalize senders/recipients, rebuild timestamps, detect duplicates, and map threads. The output is a structured SQLite database that runs client-side via SQL.js (WebAssembly).The repository includes the full extraction pipeline, data cleaning scripts, schema, limitations, and implementation notes. The interface is a lightweight PWA that displays the reconstructed messages in a phone-style UI, with links back to every original source image for verification.Live demo: https://epsteinsphone.orgAll source data is from the official public releases; no leaks or private material.Happy to answer questions about the pipeline, LLM extraction, threading logic, or the PWA implementation.
HN · launched Dec 12, 2025 · 46 upvotes (top 19%) · 0 views
Niche: digital history and reference archives · 150 launches, 6 in the last 7 days ▼ -57% · Explore this niche →
Category and tags
- Media & entertainment
- Search & retrieval
- B2C
- AI feature
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 150 launches in digital history and reference archives →
Could you build this?
Yes This is a document parsing pipeline feeding OCR results into LLMs for extraction and presenting them in a chat-style frontend UI.
Discussion
8 comments analyzed.
Competitors mentioned: jmail.world
Concerns raised: UI not working on Android/Firefox, Auto-hyphenation distorts original text formatting
Feature requests: Inline annotations identifying people mentioned, Better cross-platform testing
Competitors
Other products that read as similar to this one : 101 launches clear the similarity bar, closest 8 shown.
Attention rank: #24 of 102 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 42 days after the earliest competitor.
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