I’ve been building overhead24 (https://overhead24.com), a free tracker focused on military satellites. No account and no ads, and your location never leaves your browser.

What I’m recording: every public orbit update for military satellites, logged as timestamped events: orbit changes, close approaches, satellites going quiet, new objects, launches and clusters over conflict zones. It’s kept indefinitely. It doesn’t predict anything.

The aim is a public record that shows when a country’s satellite activity changes from its normal pattern, which matters more as tension in space grows.

What it shows

• Every working military satellite, live. 826 of them on a 3D globe, with owner flags (GCAT’s military class). The 1,486 dead ones still in orbit are left out of this view, so what you see is what’s actually operating.
• Confirmed orbit changes. When a military satellite raises or lowers its orbit or changes its tilt, it’s listed with the size and direction. A change only counts once the next public orbit update shows it too, so one-off bad fits don’t show up as manoeuvres. On 19 Oct, for example, seven military satellites changed orbit, including COSMOS 2591–2594 and USA 485/486.
• Satellites closing on others. Military satellites in nearly the same orbit as another object and getting closer, the pattern inspector satellites show.
• Pairs flying together. Military satellites staying within 100 km of each other, and since when.
• Gone dark. Working military satellites whose public tracking suddenly stopped, or that left the public catalogue.
• Conflict overlay. Countries where UCDP recorded 25+ deaths in state-based conflict this year are outlined. Military satellites over them get a red ring, and there’s a live list of which ones are overhead right now. Click a country for its conflicts, this year’s deaths and when each conflict began.
  • I need to work more on this so we have more relevant stats etc
• Clusters over conflict zones. Times in the next 24 hours when 4 or more of one country’s working military satellites, from different launches, are over places where fighting was recorded, all at once. Click one to watch their paths meet on the globe. On the day I wrote this there were 13, all Chinese, over Pakistan, Sudan, Nigeria, Somalia and others.
• Military launches. Launches for armed forces or secret government missions are flagged, and each launch lists what it put in orbit. USSF-481, for example, shows nothing, because US military orbits aren’t published.

Data

• Space-Track.org (USSPACECOM): orbits, satellite catalogue, re-entries.
• GCAT (Jonathan McDowell): owners and military class.
• Launch Library 2: launches.
• UCDP: where the fighting was.
• Positions are calculated with SGP4 from public orbit data, and everything links to its source.

Limits, before anyone asks

• Public orbit data is accurate to a few km and gets worse as it ages. Not for targeting or collision avoidance.
• Classified US satellites aren’t in the public catalogue, so they’re missing.
• Passes are set by orbits days ahead, so a cluster on its own is usually a coincidence. A satellite that recently changed orbit is what makes one interesting, and those are flagged.
• Still very early

I’d really value feedback from people who do this work: what would make it useful in an investigation?

Hey everyone, just wanted to drop the new version of my weekend project haha. Fixed the painful setup from last time with Docker, added a local web dashboard, visual graphs/threads to track the AI pivots, and cleaner PDF reports.

https://github.com/Doble-2/osint-d2

https://osint-d2.angelcalderon.dev/getting-started/installation/

Hola! Espero estén bien y puedan ayudarme.

Hace unas 3 semanas estoy recibiendo de 2 a 3 llamadas al día de números desconocidos. Sucede literalmente a diario. Creo que quieren mi información bancaria o hackear mi celular.
Conocen de alguna herramienta de código abierto que pueda utilizar para identificar de dónde me están llamando (su ubicación) o al menos para enviarle algún script que le cause al menos un daño menor al atacante?

Aclaro que no tengo una gran cantidad (casi nada en realidad) de dinero en el banco, pero cambié de trabajo hace poco, cobro más que en mi trabajo anterior y aquí en mi país este año ha habido filtraciones de datos de muchas instituciones públicas y también muchos robos de credenciales bancarias y tarjetas de crédito. Creo que aunque sea poco pero quieren robarme cuando me depositan mis pagos. Tengo conocimientos muy básicos de ciberseguridad (Intro a la CS de Cisco). Les agradezco cualquier ayuda. Gracias!

I dont know if there is any alternative for this but for some unknown reason breach.vip is now down their telegram got purged as well I don't even get it why is the owner doing this he's not making money off the website like the previous owner use to do by letting people remove their minecraft data (skidsearch) and now nobody can search anything why doesn't he sell the project or something if anyone knows any alternative for minecraft db pls lmk if someone knows the reason for all this bs lmk as well pls

Hey, does anyone know how to figure out if two TikTok accounts belong to the same person? One of the accounts doesn’t have much info on it, but I have the phone number linked to the other one. I’m just trying to confirm if they’re the same person. Is there any way to do that using OSINT?

I know they use api but, How is it when for some platforms they have to be authenticated and they still manage to get the information without getting their account banned or suspended? Im planning on building a tool such as osint.industries and lolarchiver but this question makes me not do it.

Would appreciate any response.

Sometimes using old statistics, but adding uncertainty visualization to it. Mostly for myself, wanted to create the tools that should exist.

• E.g. Capture-Recapture calculator that supports multiple observations.
• Serial number estimator (German tank engine problem)
• Daily Rythm estimator / Posting time estimator
• Canary trap creator / Canary trap detector

Any comments or ideas for new tools are appreciated.
Yes, some of these are rough, not linking those here.

What is the best free search engine after the closure of breach.vip?

Hello interwebs-people. I present to you: yet another AI-Slob. I called it Argus because that name was only taken by 2 other OSINT tools, and it is supposed to be what OSINT-Buddy tried to become before it was abandoned (sadly).

So far, I've gotten the UI to a point that doesn't hurt my feelings anymore, and I've also managed to get some basic functionality out of the included tools.

Here's the deal: I'm not a dev. Not even remotely. I do have an interest in software, but to be brutally honest, my interest lies more in the idea behind a piece of software and not in actually coding that idea. So I guess, in simple terms, I'm lazy.

I like AI for what it does: it turns an idea I like into a usable reality, and I really enjoy that—even if, from an educated person's perspective, the product is most likely garbage.

However, I still believe that someone else might enjoy the illusion of what GPT created with my guidance as much as I do, so here is ARGUS!

PLEASE PLEASE PLEASE don't even remotely consider this for productive use! For now, this is something made only to play around, even if there is some actual function within!

I would, however, love to expose myself to the brutal and honest judgment of actual developers about the architecture, security, usability, and every other aspect of this tool that could potentially turn it into something production-worthy at some point. For the moment, the only way I can fix any reported issues is by pointing AI to them as well. But maybe it's worth a shot? Of course, I'd greatly appreciate any PRs that contain human- or AI-made fixes for any aspects of the tool!

And no: I'm not even remotely thinking of monetizing anything! That would be hideous...

So, here goes nothing :)

https://github.com/NikGro/argus-osint

Necesito ayuda, de verdad. Mi familia y yo estamos pasando por una situación bastante complicada porque sentimos que varias personas nos están acosando, y ya no sé qué hacer para que esto pare.

Lo que más me preocupa es que parece que podrían ser personas que nos conocen, porque saben cosas de nuestras vidas que no entiendo cómo podrían saber.

Sé que muchos me vais a decir que denuncie o que acuda a la Policía, y entiendo que es una opción, pero también me gustaría encontrar a alguien que pueda orientarme desde casa, que tenga conocimientos sobre estos temas y pueda ayudarme a entender qué está pasando y cómo proteger a mi familia.

No quiero meterme en problemas con nadie ni acusar a personas sin pruebas. Solo quiero encontrar una solución y que mi familia pueda vivir tranquila.

Si alguien sabe de ciberseguridad, privacidad digital o cómo actuar ante una situación así, le agradecería muchísimo que me ayudara o me aconsejara. Cualquier orientación es bienvenida. 🙏

Gracias a quienes os toméis el tiempo de leerlo.

Esta cuenta es privada. Sigue esta cuenta para ver sus videos o entra en TikTok para descubrir a creadores nuevos.
el usuario de aqui arriba es una de la personas que nos esta acosando.

Update - the girl has accepted my friend request on a throw away account. Do I message her? What do I even say because if I have overreacted then I don’t want it to be known it’s me?

I built an agent that finds fraud rings by following links in a graph

My entry for Graph Hacks, powered by FalkorDB (Track: Agents That Act on Connected Data)

Live demo: https://fraud-ring-finder.onrender.com/
Code: https://github.com/tanmay5142/fraud-ring-finder
Video: https://youtu.be/KhPOPTAxgtg

The problem

Fraud is rarely one bad account. It is usually a ring: a group of accounts that secretly share the same devices, cards and addresses.

In normal tables, these links are hard to see. An analyst has to join records by hand, one step at a time. Automated systems have two common problems. They flag innocent people, like two family members who share a laptop. And they often can’t say why they flagged something.

I wanted to build something that finds the whole ring from a single flagged transaction, tells real fraud apart from false alarms, and shows exactly how it decided.

The idea

I put the data in a graph. Accounts, devices, cards, addresses, IPs and transactions are nodes. “Used this device” or “lives at this address” are links between them.

When a transaction is flagged, an agent does this:

1. Finds every account connected to it by following links in FalkorDB.
2. Collects evidence: what the accounts share, how much money they moved compared with normal accounts, and how close together they were created.
3. Scores the case from 0 to 100 using simple rules you can read.
4. Explains the result in plain English.

The page then draws the ring, and you can click any path to see it light up.

Why a graph?

A ring is a chain. Account A and B share a device. B and C share a card. C and D share an address. A and D share nothing directly, but they are in the same ring.

In a graph database, one query follows the whole chain:

cypher

MATCH (a:Account {id: $id})-[:USED_DEVICE|OWNS_CARD|LIVES_AT*1..4]-(b:Account)
WHERE b.id <> $id
RETURN DISTINCT b.id AS id

That *1..4 means “keep following links, up to 4 steps.” That one line replaces a pile of joins. And if you remove FalkorDB from my project, there are no linked accounts, no scores and no paths, so nothing works. The graph is not decoration.

How I tested it

I didn’t have real fraud data, and I wouldn’t use private data anyway. So I wrote a script that makes synthetic data:

• 300 normal accounts
• 5 hidden fraud rings of 4 to 8 accounts
• 8 flagged transactions: 5 real fraud and 3 false alarms

I kept the answer key in a separate file that is never loaded into the graph. So I could check the agent against the truth.

I also added traps on purpose: family members who share a device, and cafe Wi-Fi shared by unrelated people. A weak system would flag all of them.

The result: the agent found all 5 rings with exactly the right accounts, and gave the 3 false alarms low scores.

What I learned

1. Not all links are equal.
At first, “shared IP” sounds like a strong clue. It isn’t. Lots of innocent people share one. I split the clues into strong links (device, card, address), which the search follows, and weak links (IP), which only count as extra evidence. [If you ran the experiment: When I tried following IPs too, unrelated accounts got glued into fake rings.]

2. Let the graph decide, and let the AI only explain.
The score comes from graph queries and rules. The AI model only gets the evidence the graph found and writes it up in plain words. So it can’t make things up about my data, and every decision can be traced. If there’s no AI key, the app uses a simple template explanation instead, and still works.

3. Small Cypher details matter.
One of my queries failed because after a RETURN, FalkorDB couldn’t see my path variable for sorting. The fix was to return the path length under its own name and sort by that. It was a small thing, but I learned how Cypher passes variables between parts of a query.

4. Deployment has its own surprises.
My first cloud connection failed because I pasted the port number into the host line of my settings file. The next failure was a hostname problem. I learned to read error messages carefully, because they usually point at the exact problem. [Add what finally fixed it.]

5. Commit early.
I realised halfway through that I hadn’t been saving my work to GitHub step by step. I fixed it, but next time I’ll commit after every working step. It’s safer, and it shows how the project was built.

Tools and honesty

I used an AI assistant (Claude) to plan the project and help write code, step by step. [Add what you did yourself: what you tested, fixed, changed, and what you can explain.] All the data is synthetic.

What’s next

• Run graph algorithms to find all rings at once, instead of one flagged transaction at a time
• Add a memory, so the agent remembers past cases and analyst decisions
• Try it on bigger, public datasets

Thanks for reading! If you try the demo, I’d love to hear what you think.

I built this for my own monitoring and it might be useful here. A pipeline reads about 100k articles a day from 40k sources in 40 languages, and groups coverage of the same event into one item. The page colors each country by how many events with coverage in the last three hours are about it (subject country, not where the publisher sits), refreshes every ten minutes, and keeps a permanent link per hour, so a screenshot's moment can be revisited: clstr.news/world

Under it, each situation has a dated timeline with the source count per step and links to every article, which is the part I actually use: "what has been reported about X, in order, and how widely".

There's a free API and an MCP server if you want to pull it into your own tooling: clstr.news/developers

Hey!

I'm well versed with this community now, and im aware this project isnt unique or doing anything new. I apologise for those of you who are sick of tools like these popping up in this subreddit.

Anyway, Ive spent the last 4 months getting iterative feedback from this community and this is where we are right now. I have a big update coming tonight which may be live by the time you are reading this. Im expecting about another 50 feeds that you can parse through.

Code repo: github.com/011-sam-110/provenance
live site: www.provenance-online.com

Best,
Sam 🙂

I've been working on a ransomware OSINT tracker that keeps track of ransomware victims and activity across different groups.

The repo contains a victim tracker and separate Markdown files for ransomware groups to track their activity and timelines. I've also included the actual chat leaks from Black Basta, The Gentlemen, and Conti, so there's more to explore than just victim lists.

I'm currently working on a scraper to automate updates to the Markdown files and keep the information as up to date as possible.

The idea is to have everything in one place instead of having to search through different sources to piece together a group's history or track its activity over time.

It's still a work in progress, and I'm continuing to improve the automation and data collection.

GitHub: https://github.com/MewtwoMalware/Ransomware-OSINT-Tracker

If anyone's interested in ransomware OSINT or threat intelligence, I'd appreciate any feedback or suggestions.

I think I have got a stalker, so I wanna get to know who they really are, are there any tools that can help revealing the true identity, like email connected to their insta account or atleast some associated accounts or even a slight hint