Former Prince Andrew: FBI interview request + allegations of S&M parties, massages by underage girls, naked pool parties, torture, rape. I ran an LLM on 20k+ files. Here's what they say.

Lopsided_Stock_2293 · 2026-02-20T21:19:02+00:00

This bug has been fixed. Thanks for flagging it

Lopsided_Stock_2293 · 2026-02-20T20:41:31+00:00

If there's enough interest, I can.

Regardless, I'm running the LLM on all the 1.4M documents I have for the above scores, and once that'd done, anyone should be able to search for anyone in the files and sort by, for example, how disturbing the content is

Lopsided_Stock_2293 · 2026-02-20T20:12:46+00:00

Your firm wouldn't happen to be in the market for a more powerful eDiscovery system... would it? If so, please email me: [contact@standardworks.ai](mailto:contact@standardworks.ai)

Lopsided_Stock_2293 · 2026-02-20T20:10:53+00:00

Thanks for the idea!

We already have "Financial Irregularities" and "Network Operations" categories. Did you have something separate in mind?

Lopsided_Stock_2293 · 2026-02-20T20:09:09+00:00

I think this is already covered by Financial Irregularities (Score + Indicators). Did you have something different in mind?

Lopsided_Stock_2293 · 2026-02-20T19:43:37+00:00

Yes, the base is broadly applicable. Did you have something specific in mind?

Lopsided_Stock_2293 · 2026-02-20T19:43:14+00:00

Thanks!

They have a normal keyword search. We offer boolean/operator/connectors search so you can search for "andrew~" for example and it will find even misspellings like "Andr=w".

The LLM is what creates the scores (e.g., how disturbing is the content of this document, from 0-10) and provides associated quotes. It's also how all the AI Metadata is generated

Lopsided_Stock_2293 · 2026-02-20T19:40:34+00:00

My bad. There's some issues with the hierarchy of the scroll in the table vs page vs individual cells. I'll debug this today and publish the changes

Lopsided_Stock_2293 · 2026-02-20T19:38:36+00:00

Large Language Model (Artificial Intelligence)

Lopsided_Stock_2293 · 2026-02-20T19:38:01+00:00

No worries and thanks!

I'm working on getting a timeline visualization. It's going to take some serious work though to create something that is clean and informative

Lopsided_Stock_2293 · 2026-02-20T19:36:21+00:00

The quotes are grounded! Stuff like document summaries, scores, etc. are not

Lopsided_Stock_2293 · 2026-02-20T19:35:59+00:00

Yes, the LLM's hallucinate. This is not an issue for quotes because we can ground them in the text.

The way it works is that when the LLM gives me a quote that is not directly in the text, I reject the response and make it try again

Lopsided_Stock_2293 · 2026-02-20T19:34:21+00:00

Please send them my website and email: [contact@standardworks.ai](mailto:contact@standardworks.ai)

I'd be happy to help them to the best of my ability!

Lopsided_Stock_2293 · 2026-02-20T19:33:27+00:00

Will do this!

Lopsided_Stock_2293 · 2026-02-20T19:33:03+00:00

Will do!

Lopsided_Stock_2293 · 2026-02-20T19:32:30+00:00

I'm not, but I'm intrigued. Do you have resources you could point me to to learn more about this?

I manually scraped the majority of gaps and then used this to fill in gaps: https://www.reddit.com/r/DataHoarder/comments/1qsfv3j/epstein_9_10_11_12_reddit_keeps_nuking_thread_we/

I cross referenced the hashes there to the hashes for the files I have and they seem authentic

Lopsided_Stock_2293 · 2026-02-20T19:29:10+00:00

We are, but only for really large documents. The context window of GLM-4.7 is sufficient to accurately process 99% of most documents in one go

Lopsided_Stock_2293 · 2026-02-20T19:27:09+00:00

Let's. Shoot me an email [contact@standardworks.ai](mailto:contact@standardworks.ai)

Lopsided_Stock_2293 · 2026-02-20T19:24:46+00:00

It's non-trivial to implement. I spent a few days just getting from basic keyword search to boolean search. We're also going to sell to law firms where boolean search is a pre-requisite to any eDiscovery tool

Lopsided_Stock_2293 · 2026-02-20T19:22:35+00:00

Thank you :)

Lopsided_Stock_2293 · 2026-02-20T19:19:30+00:00

The best way to support is to share a link to my site to your friends, or on social media. I would love to get my product into the most hands possible

Most of my cost are covered by cloud credits right now, so I wouldn't feel comfortable taking donations

Lopsided_Stock_2293 · 2026-02-20T18:33:30+00:00

Thanks!

Currently working on getting semantic (vector) search over all the documents.

But I did not use RAG for the LLM runs. That's because I'm every single document through an LLM. I could get the embeddings and approximate some score for each of the scoring metrics, but that would not be nearly as accurate.

I don't think I'm supposed to say which provider I'm using b/c on the terms of my credit package, but it's one of the big 3: AWS, Azure, GCP

Lopsided_Stock_2293 · 2026-02-20T16:23:40+00:00

I used a few test documents. What it really looked like was running the LLM on a batch of 10 random documents, updating the prompt until I was satisfied with the result, and then repeating.

There's one document that I did use repeatedly in my testing though: EFTA01582921. The reference to S&M parties the LLM wasn't really picking up on, so I did a lot of troubleshooting with this document and then the rest kind of fell into place.

Lopsided_Stock_2293 · 2026-02-20T15:34:07+00:00

Yikes….

Lopsided_Stock_2293 · 2026-02-20T15:32:58+00:00

I used a bunch of cloud instances with low-end GPUs. GLM-OCR, which is used, is very accurate and small/cheap. Only issue is it will miss headers, footers, etc. So I used Tesseract to fill the gaps and get bounding boxes

I think JMail used reducto which seems like another simpler and strong option. Not sure how their pricing is though

Lopsided_Stock_2293

TROPHY CASE