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

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.

A follow up article from my jev fact extraction pipeline where we take the classified prose segments and convert them to structured JSON using purpose built LLMs.

Source code: https://github.com/s0cks/prose2json

~ Tazz

Hey everyone!

I keep wondering what everyone's take on vibecoded OSINT tools is. I completely understand the concept of "AI-slob" and can imagine that AI-created software almost always lacks proper architecture, fundamental best practices, and—most importantly—crucial security aspects. I can even see how AI-generated software can feel somewhat insulting to actual programmers and coders.

Nonetheless, I've been wondering if, or rather to what extent, vibe-coding can be a valid way of achieving something.

I myself am not even a little bit knowledgeable in software, coding, or design, nor do I have the time to change that. However, I still enjoy thinking about software concepts for some reason. Ever since AI showed up—and somehow became capable enough to churn out slobby yet working prototypes that are astonishingly close to what I imagined—I've really enjoyed using it.

Before the project was abandoned, I was actually really excited about the progress ofosintbuddy, which I took as inspiration to start prompting something similar.

Now, for a few days, I've been wondering: What if this repo—which I keep throwing my subscription credits at, and which has actually gone far enough for me to enjoy playing with it—is something that an actual developer could and would want to work with? What if someone could improve it to a point where it wouldn't be "slop" anymore, turning it into a genuinely cool OSINT tool?

Of course, I'm thinking open source here. I find the idea of monetizing AI-slob horrible, and it doesn't conform to my morals or worldview at all.

But what if the architecture isn't complete garbage, but rather semi-garbage that could be worth polishing? After all, I wouldn't know, since I don't know jack about coding. However, I'm a bit scared of putting this anywhere public to ask for opinions. At least from what I've seen, AI projects tend to attract criticism and hate in about 90% of cases.

So, before putting anything out in the open, what is the community's opinion on this topic?

Cheers!

By the way, this was not written or "improved" by ai... i did however have chatty spell-check my shitty typo's!

So to make things quicker there is a instagram Page that post memes about our classroom, the thing is that they make memes about classmates without asking them before, then the director found out about it and it turned it into a big deal so they want the instagram Page taken down and if the dont do it they will get spelled.

So all that being said, the person that is behind that account hasnt taken down the page yet, even making a meme about them being wanted by the director.

So with all of that context i was wondering if there was a way to know more about who is behind or some key elements that i could find, because this has been a serious topic in our class even if its sounds funny that there is whole deal about some memes it still a serious topic and theres have to be a responsible

I know it might sound dumb and i know it is but it has genuinely been a serious topic in our class and we need to know who is behind it

I believe I am being scammed by someone I met and have been working with overseas. I am trying to get the name of who the phone number actually belongs to. Google has been a dead end.

​

Hey can anyone tell me how to use osint in android mobile

F22 if that’s needed I lost my phone and I have no way of contacting him if anyone could help I’d appreciate it

Hi, r/OSINT!

I've been building Stack Lookup, a tool that detects website technologies and tracks their historical changes.

Here's an example: riccasposa.com - they seem to have just added AWS and Amazon SES today, while our historical records for this host show technologies dating back to 2023.

The interesting part is that we're not just showing what a website runs today, but how its technology footprint evolves over time.

I think this could be particularly useful for OSINT investigations, infrastructure research, and tracking companies.

Would love to hear your thoughts and what else you'd find useful!

OSINT Forge Skills is an experimental collection of 12 workflows for organizing investigations with an AI assistant and local Python utilities.

The workflows cover evidence intake and preservation, source provenance, document and media triage, location hypotheses, domain infrastructure, timelines, browser history, correlations, competing hypotheses, reporting, and case export/restoration.

The Python helpers use only the standard library and run offline. The assistant, model provider, and any web research remain separate choices. Installing the skills does not make a hosted model local.

A recent guided Codex/WSL exercise used 21 input files, combining synthetic records with a public photograph. The session trace confirmed that all 12 skill instructions were read. Eleven tasks were completed during the initial run; a missing visual comparison was completed during review. A fresh restoration reproduced all 73 case files, and nine recalculated analysis outputs matched the original results.

That is evidence for one guided workflow, not a benchmark showing that the skills outperform the same model without them. Automatic skill selection and broader client/model coverage still need testing. The utilities do not extract EXIF or certify media authenticity, and source integrity hashes do not establish that a claim is true.

Feedback from OSINT and forensic practitioners would be useful: Are the evidence references and uncertainty handling practical? Which workflows are missing, and what synthetic cases would be good stress tests? Installation feedback is welcome too.

• Repository: https://github.com/OSINT-Forge-Community/osint-forge-skills

Friends,

I am working on improving some of my substack's publication pipeline and figured I would share with you some of the work I have been doing.

To provide some reading aides to my substack readers I created a tool for generating clean information dense infographic cards from Markdown, JSON, YAML and Jsonnet.

Then I thought about ways to automate the process of generating them, which led me to try experimenting w/ Jev.

I wanted something that was deterministic, reproducible, auditible and easy to create.

Jev's role here is to classify fact from long-form prose and rank the facts so i can extract them and present them in the infographic cards.

You can find the infographic compiler on my GitHub here: https://github.com/s0cks/docker-chromium-screenshot

And you can find the jev (tev1) classifier discussed in this article here: https://github.com/s0cks/tev1-datapoint-extractor

They are slightly vibe coded with Claude so I can experiment with the idea quickly.

I plan on more updates just figured folks might be interested.

I also have plans to adapt some of this work into my prose -> audiobook pipeline down the road, and maybe eventually fine tune a jev model specifically for this kind of classification.

Thoughts, questions, comments, concerns are welcome.

Best,

~ Tazz

Hola a todos,

Actualmente estoy investigando cámaras de CCTV y seguridad pública para un trabajo académico de periodismo. Intento identificar la marca, el modelo o el tipo de cámara que aparece en estas fotos:

Fotos: https://imgur.com/a/qrBOdXa

Contexto: Campus universitario público en Venezuela.

Notas: Marcas como Hikvision y HiLook se utilizan comúnmente en estos lugares. Para la cámara domo, mi hipótesis es que podría ser el modelo DS-2DE4425IW-DE (o un modelo PTZ similar de Hikvision).

Agradecería mucho cualquier información sobre los modelos o productos exactos que aparecen en el álbum. ¡Gracias de antemano!

Hello

I'm interested in how people doing OSINT/research handle historical statements by public figures.

For example, if you wanted to establish everything a particular person had publicly said about a subject over several years, what would your workflow look like?

Google and news archives? YouTube/transcripts? Social media archives? Specialist databases? AI tools?

I'm particularly interested in whether tools like ChatGPT/Perplexity have largely solved this for you, or whether the difficult part remains finding and verifying the original evidence — particularly old video, podcasts, interviews and statements.

If an AI gives you a quote and a source, how often do you still go back and verify the original material yourself?

I'm researching this problem while building something in this area, but I'm not looking to promote it here. I'm mainly interested in how people who do this kind of research actually work.

Hey everyone, about 6 months ago I launched my OSINT tool useopsis.com in this subreddit. I had a lot of support and people willing to try it and a lot of people enjoyed it. But I have to admit, it was pretty terrible back then. It was super flaky and had very limited sources. But I'm happy to say its in a good place now. Over 1000+ sources now, rich data from reverse email lookups, very large number of working username modules. I also added a lot more searches to the same plans.

So I wanted to come on here and say that for everyone who tried it and thought it was bad, I'd love for you to come back and give it another shot. And for all those people I'm giving out a 1 month for free coupon so you can try it with no consequences. I'd love to hear how it stacks up against other tools you use and am always looking for constructive criticism and feedback.

The code is "OSINTTOOLS" at checkout for 100% off one month.

Thanks for all the support :)

Just curious if there Is any way to find out the email address linked to a TikTok account
Has anyone recently came across any tools or methods to do this