GROKINT – Using Grok AI for X OSINT

xAI’s Grok LLM has access to real-time X data, which makes it stand out from rest of the popular AI assistants, by providing up-to-date answers on any topic. As news break on X first, this capability can be extremely useful in the modern day information landscape. Beyond model training, however, xAI hasn’t shared details on the depth of X integration Grok has.

Through the open source intelligence, OSINT, lens, this kind of capability to automate social media account analysis is extremely interesting. So, armed with Grok 4 Expert model, I began investigating how far you can push the digital sleuthing capabilities of Grok. Turns out it can do quite a lot of digging!

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X is the Signal

X is not like other platforms. It’s not even close. Unfortunately, no other platform is surrounded by so much disinformation than X. Let me show you what’s really going on.

First of all, as a tech product, Twitter never was particularly remarkable. It could largely attribute its success to the fact that it was so early in the game. Sure, it was (and X still is) the only “direct line” to world leaders from Vatican to White House, and certain crowds like journalists and tech community to gathered there from the start. However, signal-to-noise ratio was abysmally low from the get-go, and the legacy 140-character format (imposed by SMS protocol’s limit) didn’t help.

Twitter got bloated. Innovation died faster than they recruited new people (and they did recruit a lot). It got ravaged by the parasitic ideology that swept across most of Silicon Valley in the 2010s. So called “Verification” system was based on the whims of the ideologues, where blue checkmarks were given and taken away based on reasons we can only guess, all the while the regular users got shadownbanned or worse.

At least now we know (thanks to the #TwitterFiles) the latter happened at least partly by US government pressure, and to his credit, being a cog in the censorship industrial complex wasn’t something that the founder Jack Dorsey was particularly happy with.

But the platform was still worth saving. Twitter had never made any money, and everyone knew it was a bad business deal for Musk. He himself said the primary reason for the purchase was to make sure there’s at least one bastion of free speech among the popular social media platforms. As Bret Weinstein says, zero is a special number: if even one platform (or university, newsroom, science journal…) allows truth-seekers to speak freely, the establishment can’t own the entire Overton window.

Joe Rogan said it bluntly: “Elon may have very well saved humanity in some way.”

That might sound dramatic, but I bet the impacts of the $44B deal will be studied by historians. It was a fork in the timeline, for sure.

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Social Media Countermeasures – Battling Long-Running Scams on YouTube, Facebook, Twitter and Instagram

For the past few years, I’ve been documenting, screenshotting, and sharing examples of criminal campaigns on the three big social media platforms: Facebook, YouTube and Twitter. I’m not that interested in speculating whether or not something is fake content, falsely amplified by nation-state sponsored threat actors (i.e. coordinated inauthentic behavior), but instead I’ve been focusing on two (a lot less media-sexy) themes:

low-tier criminals using these platforms to promote their services
so called “support scams” targeting mainly Facebook page owners

What is common across these two is the fact that they keep getting through social media platforms’ automatic filtering. I call this filtering – the good-willed type, not the censorship type – social media countermeasures. A term I think I picked up from Destin who runs Smarter Every Day YouTube channel, but I haven’t really seen it used. In a nutshell, social media platforms are trying to create countermeasures to prevent malicious behavior on their platform, and at the same time cyber criminals are developing counter-countermeasures to bob and weave their way around detection and filtering. Sometimes these criminals simply operate in a grey area not covered explicitly by a platform’s Terms of Service, making developing effective countermeasures even harder. Let’s take a look at few examples.

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