Alternatives
Products that do what Epstein Files Search does
1.4M Epstein files — instant semantic & fuzzy search
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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…
Jan 2026 · epstein.trynia.ai
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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.
Nov 2025 · searchepsteinfiles.com
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AI search through millions of declassified Epstein documents
Feb 2026 · epstein-gpt.com
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Fast and accurate Chat, Search with all Epstein DOJ Files
Mar 2026 · epsteingpt.org
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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…
Dec 2025 · github.com
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2025 · blog.wilsonl.in
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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…
Feb 2026 · jeescholar.com
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Hi HN! I'm Jonathan and I built Hacker Search (https://hackersearch.net), a semantic search engine for Hacker News. Type a keyword or a description of what you're interested in, and you'll get top links from HN surfaced to you along with brief summaries. Unlike HN's otherwise very valuable search feature, Hacker Search doesn't require you to get your keywords exactly right. That's achieved by leveraging OpenAI's latest embedding models alongside more traditional indexes extracted from the scraped and cleaned up contents of the links. I think there are many more interesting things…
2024 · hackersearch.net
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Mar 2026 · huggingface.co
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Hello HN! I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0. TL;DR The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own…
2022 · github.com
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2024 · app.airtrain.ai
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We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer. The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge…
2022 · beta.sayhello.so
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Sep 2025 · github.com
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credit to @RhysSullivan on github for creating this
Dec 2025 · epstein-files-browser.vercel.app
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2023 · github.com
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We built a search engine that shows you the most engaging stories/topics being shared across Twitter, Facebook, Linkedin, and Google+. We crawled over 15 million articles the past 3 months, retrieved the total number of Facebook likes, tweets, Google+’s etc and built a search index around it. Here's what our infrastructure looks like: Rails/Redis: We use the Sidekiq gem as a message queue. We have hundreds of workers that do the crawling, data mining, and number crunching. ElasticSearch: We built the search index using ElasticSearch, with the data imported from our Postgres…
2013 · buzzsumo.com
- 22BK
Hey HN! I got nerd-sniped by Bloom Filters this weekend, specifically for searching datasets with high "cardinality" (number of unique items). They're an _amazing_ data structure that, at a fixed size, tracks potential set membership. That means unlike normal b-tree indexes, they don't grow with the number of unique items in the dataset. This makes them great for "needle in a haystack" search (logs, document) as implementations like VictoriaMetrics and Bing's BitFunnel show. I've used them in the past, but they've never been center-stage in my projects. I wanted high cardinality keyword…
2025 · github.com
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Feb 2026 · epsteingraph.com
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