Epstein Files Search
1.4M Epstein files — instant semantic & fuzzy search
What it does
Search through 1.4 million declassified Epstein documents in real time, as you type. Powered by LaSearch — a full-text engine with semantic understanding and fuzzy matching. Finds what you mean, not just exact keywords. No AI, no vectors, no embeddings. Also a live demo of the LaSearch desktop app: the same instant, private search for your own files and emails, running entirely on your device.
Does the same job
all alternatives →- OAOSS AI agent that indexes and searches the Epstein filesJan 2026 · epstein.trynia.ai · ▲211
Hi HN, I built an open-source AI agent that has already indexed and can search the entire Epstein files, roughly 100M words of publicly released documents. The goal was simple: make a large, messy corpus of PDFs and text files immediately searchable in a precise way, without relying on keyword search or bloated prompts. What it does: - The full dataset is already indexed - You can ask natural language questions - Answers are grounded and include direct references to source documents - Supports both exact text lookup and semantic search Discussion around these files is often fragmented. This…
- EFEpstein Files Organized and SearchableNov 2025 · searchepsteinfiles.com · ▲329
Hey all, Throwaway in case this is assumed to be politcally motivated. I spent some time organizing the Eptstein files to make transparency a little clearer. I need to tighten the data for organizations and people a bit more, but hopeful this is helpful in research in the interim.
EpsteinGPT - ChatGPT for Epstein FilesMar 2026Fast and accurate Chat, Search with all Epstein DOJ Files
- ESEpstein's emails reconstructed in a message-style UI (OCR and LLMs)Dec 2025 · github.com · ▲46
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…
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