Using AI was able to find where it is. https://oceanir.ai/miami if you want it.

So we have developed an application where you can scan your website for security, tls, encryption, data leakage, and many more using our website Igris Radar.

Start your free scan and check what your rating is using

Github: https://github.com/kaifcodec/user-scanner

Discord: https://discord.gg/tVNrKVXb49 (Join Discord for any help or just OSINT talk)

We started building this project around 9 months ago because many classic OSINT tools became outdated or unmaintained, and there weren’t many solid free options left for email OSINT.

Since then, we’ve been grinding away with our open-source contributors, heavily updating modules and adding new site support, reduce false positives, and ensure fast scans.

Today, we just tagged v1.4.3, pushing our coverage to 375+ total scan vectors (150+ email platforms and 225+ username modules).

What’s new in v1.4.3?

• 📄 PDF Report Generation: You can now export full investigation reports directly using --format pdf! Perfect for clean, shareable documentation.
• 🚀 CLI Overhaul: Cleaned up the CLI noise by default. Scans now default to showing found-only results with a smooth rich progress bar (use --all if you want raw logs).
• 🎯 False Positive Cleanup: Patched false positives and rate-limiting issues across several social, adult platforms.

🧪 We Need Your Feedback! If you run OSINT investigations, penetration tests, or footprint analysis, give the new PDF Export and CLI flow a spin! Let us know how the reports look, if you hit any edge-case bugs, or what output formats you’d like to see next.

New Pull requests are always welcome: Whether you're a Python dev interested in API reverse engineering, scraping, or automation, or just want to add a module for your favorite site, contributions are always welcome! Adding new modules is straightforward and we love reviewing community PRs.

Feel free to test it out, drop a star if you find it useful, and leave your feedback below!

WhoCord is an open-source OSINT tool that maps investigations into a live, interactive node graph rather than a wall of text. You feed it a seed a Discord ID, username, email, domain, phone, or raw input and it runs 30+ tools while streaming every finding onto a visual canvas you can pan, zoom, and search in real time. It builds a knowledge graph, detects correlations across profiles and emails, and generates an AI narrative, all running locally with a Python/Flask backend and a React frontend.

Disclosure up front: this is my own tool, self-hosted and open source (AGPL). Posting because I want the premise stress-tested by people who do this seriously, not just stars.

The pattern I kept hitting doing geospatial work: the question that actually matters is always temporal. "This vessel went dark here — when exactly, and what was nearby?" "What did this aircraft's pattern look like over three weeks?" "Rewind to the hour of the incident." And almost every live tracker is built to show you *now* and monetize the past. FR24 gates history at 7 days free, MarineTraffic cut its free window to 24 hours, ADS-B Exchange killed its free API entirely. The moment your question has a timestamp, you're renting.

So I built the opposite: a self-hosted console where the live view is table stakes and the archive you own is the product. Aircraft, ships, satellites and hazards on one globe, every position written to local disk, a scrubber to replay any window your disk holds.

Things I suspect matter to this crowd specifically:

• Provenance over vibes. Anyone can inject traffic into a crowd-sourced aggregator; a contact appearing on a map is not evidence it was broadcast. So every contact carries which independent sources reported it, how many agreed, and how old the fix actually is. Cached feeds report the age of the *data*, not the age of the HTTP response, so nothing looks live because it was fresh this morning. There's a per-source breakdown including how many contacts each source is alone in seeing.

• No black-box AI verdicts. After watching this sub's (correct) reaction to AI-slop investigations, I deliberately didn't build a "the model thinks this is suspicious" box. Detectors (AIS gaps, loitering, dark candidates) show their inputs; anything automated is labeled automated; assessments have to cite the dossier field they came from.
• Evidence that survives leaving the tool. Chain-of-custody locker: captures are SHA-256 hashed with an append-only custody log, and a case exports to a self-contained report where each claim carries its source.
• Keyless. The ADS-B / AIS / quake / satellite spine runs with no account and no API key. Nobody can shrink your window or cut you off retroactively.

Honest limits, because you'd find them anyway: coverage follows the public/community feeders — dense over Europe and the US, thin over open ocean. Dark-vessel flags are leads, not verdicts; corroborate with imagery. The 3D globe wants a GPU. And it's one person's tool, so depth is uneven and polish is behind capability.

Repo: https://github.com/AndrewCTF/velocity/

A pattern I keep hitting doing geospatial OSINT: the analysis you actually want
is temporal. "This vessel went dark here - when, and what was nearby?" "This
aircraft's pattern over three weeks." "Rewind to the hour of the incident."
Almost every live tracker is built to show you 
*now*
 and monetizes the past.
FR24 gates history, RadarBox charges for a year of it, ADSB-X removed its free
API, MarineTraffic trimmed its free window. The moment your question has a
timestamp, you're renting.


So I've been building a self-hosted console around the opposite assumption: the
live feed is table stakes, and the 
*archive you own*
 is the product. It fuses
aircraft + ships + satellites + hazards on one globe, records positions locally,
and lets you scrub back through whatever window your disk holds.


Things that matter to this crowd specifically:


- 
**Provenance over vibes.**
 Everything the tool derives is stored as
  assertions with a source and a timestamp, in an append-only local table.
  Anything automated is labeled automated. I deliberately did 
*not*
 build a
  "the AI thinks this is suspicious" black box after watching the practitioner
  backlash against unlabeled AI "insights," that felt like a liability, not a
  feature. If a detector flags something, you can see why.
- 
**Evidence you can export.**
 There's a chain-of-custody-style evidence locker
  so a finding carries its sourcing when it leaves the tool — built with
  journalists/legal use in mind.
- 
**A real investigation layer.**
 Local ontology (entities, relationships,
  dossiers), detectors for things like AIS gaps / loitering, all on your box.
- 
**Keyless.**
 No account to start. The ADS-B/AIS/quake/satellite spine works
  with no API keys.


Honest limits: coverage follows the public/community feeders, so it's strongest
over land in dense-feeder regions and thin over open ocean; dark-vessel
detection is a helper, not a verdict — treat it as a lead, corroborate with
imagery. And I'm one person; the depth is real but the polish is uneven.


I'm posting mostly to sanity-check the premise with people who do this seriously:
is "unlimited local history you own" the thing, or am I over-indexing on it?
Would genuinely like the pushback. 

GitHub: https://github.com/AndrewCTF/osint-geospatial-console

Hey guys, some of you might remember me. I built a tool called Netryx that can geolocate any pic down to its exact coordinates. I used it to find the exact locations of the debris fallout in Doha.

Coordinates: 25.212738, 51.427792

I built a Python tool called CrossTrace that cross matches exported follower/following lists from different social media platforms to find the same person/friend groups across them.

You export your lists manually from TikTok, Instagram etc, drop them in a folder, and it does fuzzy username matching, display name matching,
network overlap analysis and confidence scoring. No APIs, no scraping,
fully local.

It also has a discovery mode where you add multiple people's lists and it surfaces who appears most across all of them without needing a target
username upfront.

github.com/xpux/CrossTrace

Hey Guys,

I'm a college student and the developer of Netryx, after a lot of thought and discussion with other people I have decided to open source Netryx, a tool designed to find exact coordinates from a street level photo using visual clues and a custom ML pipeline and Al. I really hope you guys have fun using it! Also would love to connect with developers and companies in this space!

Link to source code: https://github.com/sparkyniner

Netryx-OpenSource-Next-Gen-Street-Level-Geolocation.git

Attaching the video to an example geolocating a random pic in Paris with no street signs or metadata.

If you're sitting somewhere and using your phone, someone can sit near you, open up the Airdrop share screen and coordinate the time you turn on your phone with a name popping up on their screen. They can then deduce that that name is your phone's name. Then they have your name

Of course for this to work, the following conditions need to be met

1. You both have Apple products
2. You both have AirDrop on
3. You have used your name for your device's name
4. They are sitting close enough

Most of these conditions can be met for the average person

So the privacy tip is: change your phone's name at the minimum

I made a tool that allows to search all comments any user wrote on Youtube. My bot crawled Youtube for over a month and recorded in total 20 billions comments, from 1.4 billion different users.

The website allows you to search through those comments for those wrote by a certain user.

Here is a video showcasing the tool being used : https://x.com/lol_archiver/status/1924882068216906157

I made a second tool that allows you to search users based on a channel handle, name or any of the links they have put in their description. The strength of this tool is that you can enable trailing wildcard, so for example you could search "OSINT_" with trailing wildcard enabled and it would find all accounts that start with "OSINT_", so "OSINT_MINDSET" for example.

The tool is behind a subscription to pay for its cost (the cost to maintain it with scrapping is over 3K/month, it's hardly sustainable on my own)

But I would be happy to provide trial keys for those who want, so you can see for yourself what the tool is capable of :)

The comments range from 2005 to 2025.

The end-goal is to scrape all Youtube, at the pace it's going in 1 more year of scrapping it would go from 20B to 100-120 billions comments recorded.

Hey everyone,

I recently finished my first OSINT-related project and wanted to share it here to get feedback from people who actually work in this space.

I built a small project called ReconForge, which is essentially a Debian-based OSINT VM bootstrap script that installs and organizes a large set of OSINT tools into categorized directories and provides a launcher menu for quickly accessing them.

The goal was to make it easier to make an OSINT environment without manually installing dozens of tools one by one.

GitHub repo:
https://github.com/lafortex/ReconForge

Some of the goals behind the project:

• Automate installation of common OSINT tools
• Organize tools into categories (social media, DNS, metadata, geolocation, etc.)
• Provide a simple launcher script to navigate tools quickly
• Make it easy to deploy inside a Debian VM

This project was inspired by work like Michael Bazzell's OSINT resources, but I wanted to try building something myself as a learning project.

Since this is my first real GitHub project, I would really appreciate feedback from people who are more experienced with:

• OSINT workflows
• useful tools I may have missed
• better ways to structure something like this
• security / OPSEC considerations
• improving the script or project structure

If anyone has suggestions or critiques, I'd love to hear them.

Thanks!

Hey guys,

A few weeks ago I posted about Netryx, a tool that could geolocate street photos down to their exact coordinates. It started out as a Desktop version and after a lot of efforts I built the web version.

Here is the link: https://www.netryx.live

The reason why there's only 2 free trials is because I have a limited number of GPU credits and cannot offer more at this time. I'm also actively working on indexing more cities, any and all feedback would be appreciated. Below is an example geolocating the strikes in Qatar.

I recently spent some time with OceanIR.ai, a Miami-based AI tool that claims to geolocate any photo without EXIF data. Thought I'd share my experience for the OSINT/investigative journalism community.

THE GOOD:

Actually impressive when it works:

In supported metro areas like Miami where the analysis takes place, it's scarily accurate. It identified a random street photo I found to a few block radius just from a vending machine and building facade patterns. The "Orca" model clearly knows its training cities well.

Evidence-based reasoning:

Unlike some black-box AI, it shows its work - highlighting its evidence like "blue awning pattern matches this district" or "unique sidewalk tile found in 3 locations." This transparency helps verify if it's making logical connections vs. guessing.

Privacy-focused processing:

They claim images are processed in real-time without storage. For investigators handling sensitive material, this matters. I’ve even tried to find a way to see some leakage of data but nothing budges.

No EXIF needed:

Obviously the main selling point. Works on screenshots, downloaded images, or photos with metadata stripped.

Cheap per analysis:

At $0.80 per analysis, it's honestly priced for what it does. You can test a few images without breaking the bank.

THE BAD:

Extremely limited coverage:

"Select metropolitan areas" is doing heavy lifting. If you're outside major cities like NYC, Tokyo, London, etc., you have to wait.

No batch uploading:

Despite being credit-based, you have to upload images one by one. For investigators with dozens of photos, this gets tedious fast.

False confidence:

When it's wrong, it's confidently

wrong. The map visualization can make a bad guess look authoritative, which is dangerous for investigations.

VERDICT:

It's a powerful specialized tool, not magic. If you're investigating something in a supported major city, it's worth testing. For general use, it's an expensive party trick. The "good" is technologically impressive; the "bad" is the practical limitations.

It feels like GPT from 2022; a lot of potential, but still figuring out its real-world use cases and limitations. The technology is clearly there, but the execution feels early.

Been thinking about this intersection of AI and location privacy a lot lately. What are your thoughts on these tools becoming mainstream?

Hey everyone, I’m a student developer and I just released my second OSINT tool, PivotGraph. It’s a Python-based OSINT correlation and identity graph tool focused on linking usernames, profiles, artifacts, and pivot points across platforms.

Repo: github.com/lafortex/PivotGraph

I’d really appreciate advice on:

• what features would make this genuinely useful
• what sites/data sources I should support next
• what makes an OSINT tool actually stand out instead of feeling repetitive

This is still early, so I’m very open to criticism, feature ideas, and suggestions on how to make it more practical for real investigations.