Since the launch of GeoSpy AI on December 24th, 2023, I’ve been inundated with inquiries about the platform. These questions vary widely, from “Why did you build this?” to “Are you a Russian spy?”. Now seems like the perfect opportunity to address as many of these questions as possible.
Firstly, a heartfelt thanks to the OSINT community for their warm reception and support of GeoSpy. I am particularly grateful to the members of r/OSINT, who played a pivotal role in introducing GeoSpy to the global OSINT community. A special mention to Henk Van Ess, whose early coverage of GeoSpy significantly boosted our efforts.
Background and Origins
My name is Daniel, an AI researcher, hacker, and aspiring entrepreneur. Last year, I co-founded Graylark Technologies with my twin brothers, both computer science graduates. Our journey began at the age of 10, experimenting and tinkering with computers. Graylark, a blend of ‘Gray Hat’ (a hacker who ethically challenges cybersecurity systems) and ‘Lark’ (an adventurous, fun endeavor), started as an ambitious project to integrate AI with cybersecurity tools and methodologies.
Our goal was ambitious: to create an AI cyber assistant equipped with comprehensive knowledge from blackhat and defcon talks, published exploits, and extensive software documentation, capable of executing commands in a terminal with a hacker’s oversight.
Though primarily a research entity, we explored various AI applications in cybersecurity. Our projects ranged from a facial recognition platform linking faces to personal data like emails and music preferences, to ShellGPT, a GPT-powered terminal application capable of autonomously identifying and exploiting network vulnerabilities.
Another significant development was UnSeen, a system designed to make faces in photos unrecognizable to facial recognition search engines like PimEyes and FaceCheck.id. This project, along with others, was developed in ‘stealth mode’, funded by my day job in AI and drone technology, and admittedly, a less-than-ideal sleep schedule.
Enter GeoSpy
GeoSpy emerged from my interest in the long-standing challenge of photo geolocation. Despite being an already researched area, I believed in a more straightforward solution. Drawing inspiration from a paper on photo geolocation and using OpenAI’s research on image-text connections as a base, I started building GeoSpy.
In just two days, by combining different pieces of code from our past projects, I assembled a demo and launched it on geospy.web.app. I put a post on r/OSINT and didn’t think much about it. The response on Reddit was beyond my expectations — thousands of users from over a hundred countries had engaged with GeoSpy in just a few days.
The Future of GeoSpy
As long as there’s interest, my brothers and I are committed to continually improving GeoSpy. Our ambition is to create an AI photo geolocation platform capable of precise geolocation — within 100 feet in urban areas and 5 miles in rural settings.
We’re not just focused on offensive tools; for every such tool developed, we aim to create an equally potent defensive counterpart. The upcoming GeoSpy v0.2.0 will feature enhanced functionalities, such as targeted searches based on selected objects within a photo, and the ability to connect images of buildings or even unremarkable interiors to specific addresses.
This year, we plan to release an API and a platform for social media and end-users to protect their photos against techniques used by GeoSpy and other similar tools.
I welcome any questions or feedback. Feel free to reach out to me on Discord.
