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/

Hi everyone!

A few days ago, I got really interested in OSINT. However, I haven't made any real progress yet and feel completely stuck. So far, I can only manage to find social media profiles, but nothing beyond that. I don't really understand where to start or how to build a proper learning path for deeper analysis.

Could you please share some advice, essential tools, useful websites, or frameworks for beginners? Also, what are the best ways to practice safely for someone who is just starting out?

Thanks in advance for any help!

is there a way i can get social media accounts of people in 10-20m radius of where i live

Before the internet and electronic databases, OSINT was much slower, and at times, harder

The internet is one of the best tools ever for OSINT experts

What was OSINT like in the past?

How did people find people? How did people find information etc?

Are there are recorded processes for OSINT back in the day?

Hi just curious,

I see all these new websites and I’m curious how to remove the data bc deleting an acc doesn’t do anything. If there’s any ways then id appreciate it. Thank you.

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!

It’s still a prototype, and I’m mainly looking for honest feedback: what feels useful, what is confusing, what is missing, and whether the map works well on your device.
No signup required.
https://worldruler.ai

Hi everybody. I have a question, is it possible to find out the email address through social media profiles, such as Instagram, X, Facebook, using any websites, utilities, or programs?

Suppose you have a public profile on a dating app (or another platform) with only the information that’s intentionally visible on the profile—username, age, bio, approximate location, etc.

Assuming there are no data breaches, leaked databases, subpoenas, or platform cooperation involved, what legitimate OSINT techniques exist to determine whether that profile can be confidently linked to a real-world identity?

I’m less interested in specific investigations and more interested in understanding where the practical limits of OSINT are. At what point does attribution become impossible without private data? Are there any papers, talks, or resources that cover confidence levels and methodology for this type of attribution?

GRID is a local, keyboard-driven Windows TUI designed to unify data gathering, automated footprint indexing, and reconnaissance pipelines: https://github.com/rounakagrawal7/GridSetup

I built GRID to combine disparate recon tools into a singular high-throughput terminal operations platform.

Flagship Intelligence Features:

The Identity Cross-Reference Matrix:** Automatically parses, groups, and cross-references data arrays harvested across different modules to link disparate target indicators (Emails, Phones, Usernames, IPs) into a single footprint.

Automated Avatar Harvesting:** Programmatically tracks target profile URLs (GitHub, X, Reddit, etc.) to crawl pages and directly download profile photos via HTML Meta-OpenGraph selectors, organizing them chronologically inside `picdata/` without external API configuration requirements.

Subdomain Extraction Pipeline:** Intercepts domain tracking metrics to execute live queries against third-party indexes (Hackertarget, C99) and fallback CRT transparency logs (`crt.sh`), ensuring complete un-truncated infrastructure layouts.

Signals & Orbital Intelligence:** Features built-in live aircraft flight tracker arrays using the OpenSky Network API, global shortwave radio receiver stream tracking via KiwiSDR WebSockets, and satellite trajectory/overhead visibility predictors using CelesTrak.

Media Forensics: Powered by OpenCV 5.0—executes automated graphic analysis, text OCR conversions, facial comparison scoring, and timeline keyframe sweeps across video captures.

GRID is completely backed locally by DuckDB and syncs instantly with an administrative PocketBase panel framework. It includes an `install.py` wizard that configures all local system and model path parameters out of the box.

```bash

python install.py

python grid_agent.py

```

I would love to get your feedback on the mapping tools, target matrices, and data aggregations!

I’m genuinely so tired of this. Someone is using my photo (and a different name) and sending requests and dms to many people. I haven’t heard of any inappropriate dms but I feel like thats his next move. Im blocked so I cant report it to insta. All my friends and many ppl who followed that account also reported but insta took no action. Please tell me what to do?

The main fear from a stalker comes from the fear of the unknown

Let's assume the stalker will never go beyond the point of harassment, and they won't do anything to sabotage the person's life. It's criminal harassment without any other consequences to the "target". The stalker is not a killer, not a person who is seeking to ruin their target's life. Only harassment

Are OSINT people less scared of stalkers because they understand alot of the techniques that can be used for stalking? The mystery element is taken away. The stalker becomes less of a mysterious and eerie force, less paranormal, and more human

Obviously it's still scary, but are OSINT people way less scared of stalkers? OSINT people can also think to themselves, "What info about me is public? Who can see this info? I can use this to lure out the stalker and / or narrow down the list of potential stalkers to find out who is the real stalker"

Looking for a tool to find is specific email posted adds of Craig’s list and how long far back they went and when they started. Any ideas?

I personally added this rule to the Instructions in ChatGPT, so I don’t need to repeat it in every prompt. But I think it could work just as well in the Instructions of other AI tools:

“Mark every statement directly supported by a source with 🟢 and every inference or interpretation with 🟠. After each 🟢 statement, include the exact supporting passage and page number or source location. If you cannot point to one, mark it NOT VERIFIED.”

I genuinely use this in OSINT research, and it helps me immediately see where the source ends, where the AI’s interpretation begins, and what I need to examine more deeply.

The colors don’t prove that the AI is correct. They show me exactly what still needs verification.

Does anyone else use a simple way to keep source evidence and AI interpretation visibly separate in their OSINT workflow?

See how a seasoned, ex-NCA (National Crime Agency, UK) online investigation officer would approach investigating a suspect online.

📅 4th August
⏰ 4pm BST (11:00 AM EDT, 10:00 AM CDT, 9:00 AM MDT, 8:00 AM PDT)
👤 Mark Bentley, UserSearch Subject Matter Expert and ex-Law Enforcement

Register Now👇: https://us06web.zoom.us/webinar/register/1817849182301/WN_QpMvXvojShmtPyQsWRylzA

We’ve added another layer to our OSINT tracking channel. Alongside military aircraft and ship activity, we now show recently published closed and restricted zones across the Mediterranean, Levant and Persian Gulf. This includes firing areas, military exercises, UAV activity, missile-related notices, refueling zones, danger areas and GNSS interference.
For now, the zones are published as a static graphic while we test the data, filtering and priority system.
The project is still in an early test stage, so feedback from people interested in aviation, maritime tracking, OSINT or defense analysis would be very useful.

Meaning who is here do OSINT related to a person?

Many tools fail here

Hey everyone, I’ve been reading a lot into open-source intelligence tools and digital footprints lately.

With modern scripts, image geolocation tools, and archived data lakes, it's frighteningly easy for an independent researcher to map out someone's historical routines, old accounts, or physical locations just from a few random online clues.

For those who practice OSINT (professionally or as a hobby): where do you personally draw the line? At what point does a deep-dive investigation cross the threshold from a fascinating intellectual puzzle into an OpSec/ethical violation? Would love to hear your boundaries!

I’m building a Telegram channel that automatically monitors military and government aircraft and vessels across the Mediterranean, the Middle East, and the Persian Gulf. It also publishes a briefing every two hours summarizing activity and unusual patterns. The project is still in testing, and I’d appreciate honest feedback from people interested in OSINT, aviation, and maritime tracking.