A new tool searches your LinkedIn connections for people who are mentioned in the Epstein files, just in case you don’t, understandably, want anything to do with them on the already deranged social network.
404 Media tested the tool, called EpsteIn—as in, a mash up of Epstein and LinkedIn—and it appears to work.
“I found myself wondering whether anyone had mapped Epstein’s network in the style of LinkedIn—how many people are 1st/2nd/3rd degree connections of Jeffrey Epstein?” Christopher Finke, the creator of the tool, told 404 Media in an email. “Smarter programmers than me have already built tools to visualize that, but I couldn’t find anything that would show the overlap between my network and his.”
- Archive: http://archive.today/AIkL2
- Github: https://github.com/cfinke/EpsteIn
Do read WHY they are mentionned, though. Most of his critics are mentionned a lot, and in that case it’s a good thing.
John Stuart is in there for entirely nonsensical reasons
Surely you meant someone like John Stuart.
Excuse me it’s spelt Jhon Stuart
Jhohn Blewahrt
We need to be careful in how we view the latest batch from the files. They contain lots of names of people who were not involved in the least. Bilbo Baggins and Punxsutawney Phil are in there. Lots of celebrities are in there simply because they’re referenced in an email, while they had no contact with Epstein knowledge of what was happening.
And if we’re too aggressive in how we react to people’s names popping up in searches, it gives cover to those who were complicit.
Slavov Zizek is named… In the How To Academy newsletter Epstein has subscribed to.
And yet I checked Slovenian media and they are writing that he “is in the files”.
What a disgrace.
Yeah it’s clearly one thing to be a public persona and someone mentions your name in an email, and another thing altogether to be an Epstein correspondent. That line is being shamelessly blurred by some media
The bash 3.1-beta1 reference manual is in the Epstein files.
https://www.justice.gov/epstein/files/DataSet 9/EFTA00315849.pdf
One of the commenters on the site did point out that it’s a defamation lawsuit waiting to happen.
Though defamation requires the claim to be both a lie, and made publicly (and have caused “legally redressable injury”, whatever that means, IANAL). The tool needs to be run locally, and specifically tells you that it’s searching by name and that others with the same name will be found in the results, and that’s why it gives the context and lists where in the files it came up.
So the tool itself most likely isn’t defamatory, but anyone that uses is better be damn sure that they have the correct person if they start publicly talking or writing about what it finds.
Not Punxsatawney Phil! Aside from his short rivalry with Murray over the starring role in Groundhog Day, he’s a good guy!
From a distance, it’s very hard to tell if it’s two consenting hobbits or if one is a child. It’s easy for them to find themselves on the list, poor Bilbo.
And how old even in a child hobbit? 60?
I believe they reach adulthood in their mid to late thirties. Merry and Pippin are technically in the equivalent of their late teens when they head off with Frodo in FotR
I don’t think I know anyone remotely rich enough for this to be a concern.
That’s the secondary use of the tool
If you’re not on the list you clearly lack connections and power and aren’t a good fit
This is the most LinkedInLunatic coded response ever
Son of a bitch is so poor he’s like 4 hops from knowing someone in the Epstein files.
We had six degrees of Kevin Bacon and the Erdos number, now we have the Epstein coefficient?
Yeah, I’m sure Mark Tramo, the UCLA neurology professor, is loaded. lol
Could be? Might be millionaire cause of royalties, speaker fees etc.
https://closertotruth.com/contributor/mark-tramo/
Mark Tramo is Director of The Institute for Music & Brain Science and Co-Director of the University of California Multi-Campus Music Research Initiative.
He is also Associate Clinical Professor of Neurology at the David Geffen School of Medicine at UCLA and Adjunct Professor in Ethnomusicology at the UCLA Herb Alpert School of Music. A 2015 recipient of the UC President’s Research Catalyst Award, Dr. Tramo has conducted original research on the neuroanatomy and neurophysiology of music perception and cognition for over 25 years.
Be aware that not everyone in the list has to be involved. Daniel Stenberg lead dev of curl is in there multiple times. I believe as part of the provided licenses.
I think, not as a business professional but as a human being, that if this is something that concerns you then you are also the kind of person that doesn’t even want to be on LinkedIn.
Right? Bill Gates is in the epstein files a lot.
Motherfuckers…
My post about this tool got removed from the community for no reason.
reason: AM: Violates Rules
Now you know. /s
For reference, everyone is 3rd degree at a minimum.
- 1st - You’re directly connected with them
- 2nd - The person is connected with a person you’re a 1st degree connection with
- 3rd - People that are not connected to any 1st or 2nd degree connections (I.e. everyone else)
Maybe I’m missing something, but 3rd degree would be the person is connected with a person you’re 2nd degree connection with, right? That’s why Six Degrees of Kevin Bacon is a thing.
You’re correct. 3rd-degree connections are friends of friends of your friends
LinkedIn only uses 3 degrees. The theory you’re talking about is that mathematically people should be connected within 6 degrees, but the number of degrees you go with is arbitrary. In LinkedIns case they use them to classify people in one of three categories as it relates to you.
the tool, called EpsteIn—as in, a mash up of Epstein and LinkedIn
A better mashup might have been “SteptIn”.
Is there a tool that crunches the entirety of the documents and sorts the individual words by frequency? For example, doing it the stupid way (semi-manually) I copied OP’s article into Word and replaced every space with a page break to turn the entire article into a one-word-per-line list, then plugged that into Excel and sorted alphabetically, then manually counted and deleted the repeats. Then sorted those to put the most frequent on top.
This reduced the 525 word article down to a list of 284 individual words. If I added another article to this list, the number of entries would only be increased by the number of words in the 2nd article that didn’t appear in the first one, so basically as more and more articles are added, the number of unique additions from each would be fewer and fewer. Do this to a thousands-of-pages of documents like the Epstein files, and you could instantly condense like dozens of pages worth of just the word “the” down to a single entry, making the entirety of the documents much easier to skim for highlights… like, if the word ‘velociraptor’ was just randomly hidden in the article, most readers would probably skim right passed it; but in the list below it would stand out like a sore thumb, prompting a targeted search in the full document for context. Especially if we could flag words as not interesting, and like click to knock “the” “of” “and” etc off the list.
…maybe a project for someone who actually knows what they’re doing… my skills hit a brick wall after things like ‘find and replace’ in Word, but you get the gist.
Word used: # found: The 37 Of 16 And 14 To 14 Epstein 11 In 11 Tool 9 A 8 I 8 Files 7 But 5 For 5 Is 5 Linkedin 5 Many 5 On 5 That 5 With 5 404 4 Also 4 An 4 Connections 4 Found 4 Media 4 Not 4 People 4 All 3 Anything 3 Are 3 As 3 Him 3 It 3 My 3 Network 3 Them 3 Were 3 Who 3 Already 2 Appears 2 Case 2 Common 2 Con 2 Def 2 Documents 2 DOJ 2 Dump 2 Each 2 Excerpts 2 Find 2 Finke 2 Founder 2 From 2 How 2 Jeffrey 2 Me 2 Mentioned 2 Moss 2 Name 2 Names 2 Obviously 2 Other 2 Overlap 2 Page 2 Positives 2 Repository 2 Said 2 Search 2 Their 2 This 2 Up 2 Vincenzo 2 Work 2 Your 2 5 1 22 1 35 1 1st 1 2nd 1 3rd 1 Acknowledges 1 Across 1 Adam 1 Added 1 After 1 Although 1 Anyone 1 Api 1 Appearance 1 Approached 1 Attended 1 Audio 1 Away 1 Badges 1 Based 1 Be 1 Because 1 Behind 1 Between 1 Brin 1 Built 1 Called 1 Can 1 Chose 1 Christopher 1 Company 1 Conference 1 Contained 1 Contains 1 Context 1 Could 1 Couldn’t 1 Court 1 Covered 1 Co-Worker 1 Creator 1 Days 1 Deep 1 Degree 1 Department 1 Deranged 1 Did 1 Didn’t 1 Do 1 Document 1 Does 1 Don’t 1 Down 1 Duggan 1 Easily 1 Elites 1 Email 1 Epstein’s 1 Far 1 First 1 Free 1 Fully 1 Ghislaine 1 Girls 1 Github 1 Gut 1 Hacker 1 Hacking 1 Had 1 Have 1 He 1 His 1 Hits 1 Images 1 Incidental 1 Included 1 Inclusion 1 Initial 1 Introduce 1 Investigations 1 Involvement 1 Iozzo 1 Jeff 1 Just 1 Justice’s 1 Keep 1 Know 1 Known 1 Larry 1 Last 1 Likely 1 Links 1 Lot 1 Made 1 Make 1 Mapped 1 Mash 1 Massive 1 Matching 1 Material 1 Maxwell 1 May 1 Mean 1 Mention 1 Mentions 1 Mentions 1 Mentions 1 Million 1 Moss’s 1 Multiple 1 Musk’s 1 Myself 1 Necessarily 1 Nefarious 1 Never 1 New 1 No 1 Nude 1 Number 1 Off 1 Offered 1 Only 1 Or 1 Original 1 Others 1 Output 1 Pages 1 Paid 1 Patrick 1 Peter 1 Photos 1 Pointed 1 Position 1 Post 1 Previous 1 Produce 1 Programmers 1 Publicly 1 Published 1 Purposefully 1 Reads 1 Realize 1 Recordings 1 Reddit 1 Related 1 Released 1 Relevance 1 Report 1 Reported 1 Result’s 1 Review 1 S 1 Saw 1 Scenes 1 Searched 1 Searches 1 Sergey 1 Show 1 Shows 1 Smarter 1 Social 1 Some 1 Stay 1 Stuff 1 Style 1 Suppose 1 Surprising 1 Taking 1 Tech 1 Tested 1 Than 1 Thankfully 1 There 1 These 1 Thiel 1 Those 1 Told 1 Tools 1 Total 1 Touch 1 Tried 1 Trusting 1 Understandably 1 Unredacted 1 Upload 1 Verify 1 Very 1 Videos 1 Visualize 1 Want 1 Warn 1 Way 1 We 1 Wealth 1 Website 1 Week 1 Well 1 Went 1 Where 1 Whether 1 Wikipedia 1 Wild 1 Wired 1 Women 1 Wondering 1 Would 1 Wrote 1 You 1 Zero 1 Seriously, if you’re motivated enough to do this, you should give programming a try. Python or Ruby or Javascript are ideal for this kind of thing, and you can solve problems like this in a few lines of code… just look up “word frequency in Python” or whatever language for examples.
If you want to see what the next level of this kind of analysis looks like, watch a few videos about how Elasticsearch works… not so much so you can USE Elasticsearch (although you can, it’s free), but just to get a sense of how they approach problems like this: Like imagine instead of just counting word occurrences, you kept track of WHERE in the text the word was. You could still count the number of occurrences, but also find surrounding text and do a bunch of other interesting things too.
I kind of dove a bit deeper into that kind of shenanigans for nursing school - copying entire chapters from my text books and using find and replace wildcards clip out all the bullshit like in-text citations.
Plug that fucker into some text-to-speech software, and my reading assignment just became a listening assignment!
I do wish I knew some actual code… I’ve tried to dive in and self-learn that stuff, but didn’t make it very far.
I should take an actual class.
There’s probably a nice shell multiline command that does what you want lol. cat + awk unique count + sort
I’m just forgetting is there’s an easy way to keep the line numbers or filename so you can easily go back to the full page reference.
I’m sure my LinkedIn contacts could search the epstein files on their own without my help
