whats a github tool that can detect what email service is linked in?

Disclosure: I built it.

clstr.news dedupes same-event news coverage into clusters and links them into situations, running timelines of stories that span weeks. For any claim you see how many independent sources carry it vs. one report being recycled.

Example, the US-Iran war since April: 1,658 developments from 4,702 sources in one timeline:

No account needed to browse. Signing up allows you to set up monitors for specific situations, track new developments on semantic searches, and schedule daily/weekly briefings you get via email (there's a free forever tier).

All feedback welcome: what's missing, what's broken, what would be worth paying for. Though it might be useful for some in here!

I built a Telegram channel that automatically tracks military and government aircraft in the Mediterranean and Middle East, combines several public sources and posts regular updates.
It’s still an early prototype, so I’d really appreciate feedback from people interested in OSINT or flight tracking.

Hey everyone, i am running my free tool testing and improvement drive. Hope you liked it and have good fun to improve your website. Check out link

So I have been getting ads for this, and it seems plausible? But it seems similar to pimeyes plus fast people search crammed together according to ads. It just seems fake though.

It's all behind a paywall though.

Has anyone here tried it? And is it legit?

This is the link

https://www.sherlocksearch.com/app/

Im currently experimenting with this, it does depend on the model you are using, as im using a self hosted model via ollama but has alot of potential.

Currently im testing qwen3-abilated:4b, then i will go upto a 8b model because i think it halucinating the python tool output. If 8b works better then i will flip to qwen3.5-abilated:9b and feed screenshots in to check its accuracy.

I do also wonder what would happen if i connected this to an MCP server hmm.
https://github.com/Nagyhoho1234/QGISChat

#osint #qgis #llm #ai #geospatial

Hi,
A fake Instagram account impersonated my local university to post defamatory content about my girlfriend and several students. The account has now been deleted, but I have screenshots and the profile URL.
Is there any way to preserve or recover publicly available information from a deleted account for reporting purposes? Any advice would be appreciated.

I created a geospacial python tool, where i can enter coodinates and grab multiple days worth of filtered data such as SWIR. I then run these through yolo i can combine each day into a final image containing the filter data as a heatmap.

[code]

--- Starting Multi-Filter OSINT Analysis Pipeline ---

==========================================
Initializing analysis for Filter Strategy: Logistics_SWIR_Change
==========================================
  --> Processing Window [1/3]: baseline_early_june
-> Set as initial baseline frame for this filter.
  --> Processing Window [2/3]: monitoring_late_june
-> Generated change map with 47 anomalies: change_map_Logistics_SWIR_Change_monitoring_late_june.png
    Sending to Ollama (huihui_ai/qwen3.5-abliterated:9b) for tactical assessment...

[Ollama Logistics_SWIR_Change Assessment for monitoring_late_june]:
Based on the 'Logistics_SWIR_Change' filter and the timeline of early-to-late June, here is the tactical analysis of the Khuzestan AOI:

### 1. Evaluation of Bounding Boxes: Logistics & Infrastructure

The red bounding boxes are highlighting specific spectral anomalies where the terrain signature shifted during the heat of June. SWIR is particularly sensitive to moisture and surface roughness (like concrete vs. soil).

*   **The "Urban/Industrial" Cluster (Upper Right):**
    This quadrant shows the highest density of overlapping boxes. In SWIR, this area (likely a city center or industrial zone) appears as high-contrast, bright white patches.
    *   *Significance:* This suggests **urban activation** or **industrial maintenance**. As the soil dried out in late June, the cooler, reflective properties of concrete/asphalt became more distinct against the warming ground. This cluster likely represents the logistical hub of a major garrison (e.g., near Khorramshahr or Abadan).

*   **The "Reservoir" or "Staging" Zone (Bottom Right/Center):**
    There is a massive, sprawling bounding box cluster in the lower right that encompasses a textured, medium-brightness area.
    *   *Significance:* This looks like a **water body** or a lake bed. In early June (darker/better water coverage), the water edge would have receded by late June, exposing the bright SWIR signature of the lake bed or mudflats. This is a classic sign of a **dispersed logistical staging area** where temporary camps are set up along the water's edge.

*   **The Linear Intrusions (Top Left & Center):**
    Several narrow, rectangular boxes capture thin, bright streaks cutting through the darker background.
    *   *Significance:* These are almost certainly **unpaved roads** or **supply lines**. SWIR is excellent for seeing moisture on gravel roads. These boxes indicate active vehicle movement or maintenance on the transport grid.

### 2. Intensity & Distribution of Change

*   **Distribution:** The change is **highly dispersed**. It is not one solid block of change (which would imply a massive new construction), but rather a "sprinkled" pattern. This implies a **mobile or scattered force**—perhaps a light infantry brigade or a supply convoy spreading out.
*   **Intensity:**
    *   **High Intensity (The "Hot Spots"):** The brightest white pixels are in the upper right. These represent the most significant spectral shifts—likely **bare soil** exposed by vehicle churn or **new concrete** installations.
    *   **Medium Intensity (The "Edges"):** The boxes along the periphery of the main cluster (bottom center) catch the transition zones. These are the edges where the water or vegetation is meeting the drier ground, highlighting the **perimeter of an active base**.

**Summary:** The imagery suggests a transition from a "wet/vegetated" state to a "dry/textured" state. The red boxes effectively isolate the **man-made or altered features** against the natural background, pinpointing exactly where the military or logistical activity is most concentrated in the SWIR spectrum.
------------------------------------------------------------
  --> Processing Window [3/3]: monitoring_early_july
-> Generated change map with 51 anomalies: change_map_Logistics_SWIR_Change_monitoring_early_july.png
    Sending to Ollama (huihui_ai/qwen3.5-abliterated:9b) for tactical assessment...

[Ollama Logistics_SWIR_Change Assessment for monitoring_early_july]:
Based on the 'Logistics_SWIR_Change' filter and the transition from **Late June** (the first frame) to **Early July** (the current frame), here is the analysis of the Khuzestan AOI.

### 1. The Spectral Context: "The Heat Expands"
The timeline from late June to early July represents the peak of the regional heat. In SWIR imagery, this means the "soil" signature is drying out. As the vegetation dries or the ground bake-hardens, the "hard" surfaces (asphalt, concrete, compacted mud) become the brightest, most distinct features against the darker, organic background.

The increase from 47 to **51 anomalies** suggests that the military's footprint is **expanding** and becoming more defined, rather than just a general "blob" of activity.

### 2. Evaluation of Flagged Zones

*   **The "Reservoir" Edge (Bottom Right & Center):**
    The massive bounding box in the lower right (and extending up) is the dominant feature. In the previous frame, this was a sprawling, indistinct area.
    *   **Significance:** This is likely a **lake bed** or a **mudflat**. As the heat intensified in July, the water line receded, exposing the bright, textured SWIR signature of the exposed lake bed.
    *   **Tactical Use:** This provides a massive, dispersed staging area—perfect for light infantry or vehicle deployment where the ground is firm but not yet paved.

*   **The "Diagonal" Supply Line (Center to Bottom Left):**
    There is a long, sweeping box angling from the center towards the bottom left.
    *   **Significance:** This is a **major supply artery** (likely a gravel or paved road). The box isolates a specific texture that is brighter than its surroundings.
    *   **Tactical Use:** This indicates **heavy vehicle traffic**. The constant movement of convoys has churned up the soil, creating a rough, bright signature that stands out against the smoother, drier ground nearby.

*   **The "Urban Mosaic" (Top Right):**
    This quadrant remains the densest cluster, now appearing as a grid of overlapping boxes.
    *   **Significance:** This suggests an **industrial or urban center** (perhaps a refinery or a major garrison). The "grid" pattern implies a complex layout—barracks, warehouses, or parking lots.
    *   **Tactical Use:** This is the **Logistical Hub**. It appears to be the administrative heart, feeding vehicles out along the diagonal road seen in the center.

### 3. Assessment of Intensity & Distribution

*   **Distribution:** The change is **fragmented**. It is no longer one solid block of "stuff," but a network of connected boxes. This implies a **mobile force** that is stretching its line of communication across the drying landscape.
*   **Intensity:**
    *   **High Intensity (The Bright Spots):** The top right and the center of the "diagonal" feature show the highest intensity. These are the "hottest" points of activity where the military footprint is thickest.
    *   **Low Intensity (The Edges):** The edges of the boxes show the "fading" signature of the landscape where the military signature is beginning to blend in with the natural terrain.

### 4. Tactical Narrative
The force has shifted from a **"messy consolidation"** in late June to a **"defined logistical spread"** in early July. The heat has acted as a natural filter, peeling away the darker, vegetated soil to reveal the bright, hard signatures of infrastructure.

**The Story:** A large unit is operating along a drying lake bed (bottom right), utilizing the exposed mudflats for maneuvering. They are being fed by a long, diagonal supply road (center) that cuts through the AOI, connecting the "Mosaic" industrial hub in the top right to the front lines. The force is stretched, but the ground has become firm enough to support them.

### 5. Key Variables
*   **Slope:** **High** (The diagonal feature suggests movement across a slight incline or a road cutting through flat terrain).
*   **Heat:** **Intense** (The "baked" look of the bottom right confirms the heat has hardened the soil).
*   **Traffic:** **Active** (The diagonal box implies a line of communication is fully established).

**Summary:** The military footprint is **hugging the ground** that the heat has exposed. They aren't just occupying space; they are **following the hard surfaces** revealed by the drying landscape.

--- Starting Multi-Filter OSINT Analysis Pipeline ---

==========================================
Initializing analysis for Filter Strategy: Logistics_SWIR_Change
==========================================
  --> Processing Window [1/3]: baseline_early_june
-> Set as initial baseline frame for this filter.
  --> Processing Window [2/3]: monitoring_late_june
-> Generated change map with 47 anomalies: change_map_Logistics_SWIR_Change_monitoring_late_june.png
    Sending to Ollama (huihui_ai/qwen3.5-abliterated:9b) for tactical assessment...

[Ollama Logistics_SWIR_Change Assessment for monitoring_late_june]:
Based on the 'Logistics_SWIR_Change' filter and the timeline of early-to-late June, here is the tactical analysis of the Khuzestan AOI:

### 1. Evaluation of Bounding Boxes: Logistics & Infrastructure

The red bounding boxes are highlighting specific spectral anomalies where the terrain signature shifted during the heat of June. SWIR is particularly sensitive to moisture and surface roughness (like concrete vs. soil).

*   **The "Urban/Industrial" Cluster (Upper Right):**
    This quadrant shows the highest density of overlapping boxes. In SWIR, this area (likely a city center or industrial zone) appears as high-contrast, bright white patches.
    *   *Significance:* This suggests **urban activation** or **industrial maintenance**. As the soil dried out in late June, the cooler, reflective properties of concrete/asphalt became more distinct against the warming ground. This cluster likely represents the logistical hub of a major garrison (e.g., near Khorramshahr or Abadan).

*   **The "Reservoir" or "Staging" Zone (Bottom Right/Center):**
    There is a massive, sprawling bounding box cluster in the lower right that encompasses a textured, medium-brightness area.
    *   *Significance:* This looks like a **water body** or a lake bed. In early June (darker/better water coverage), the water edge would have receded by late June, exposing the bright SWIR signature of the lake bed or mudflats. This is a classic sign of a **dispersed logistical staging area** where temporary camps are set up along the water's edge.

*   **The Linear Intrusions (Top Left & Center):**
    Several narrow, rectangular boxes capture thin, bright streaks cutting through the darker background.
    *   *Significance:* These are almost certainly **unpaved roads** or **supply lines**. SWIR is excellent for seeing moisture on gravel roads. These boxes indicate active vehicle movement or maintenance on the transport grid.

### 2. Intensity & Distribution of Change

*   **Distribution:** The change is **highly dispersed**. It is not one solid block of change (which would imply a massive new construction), but rather a "sprinkled" pattern. This implies a **mobile or scattered force**—perhaps a light infantry brigade or a supply convoy spreading out.
*   **Intensity:**
    *   **High Intensity (The "Hot Spots"):** The brightest white pixels are in the upper right. These represent the most significant spectral shifts—likely **bare soil** exposed by vehicle churn or **new concrete** installations.
    *   **Medium Intensity (The "Edges"):** The boxes along the periphery of the main cluster (bottom center) catch the transition zones. These are the edges where the water or vegetation is meeting the drier ground, highlighting the **perimeter of an active base**.

**Summary:** The imagery suggests a transition from a "wet/vegetated" state to a "dry/textured" state. The red boxes effectively isolate the **man-made or altered features** against the natural background, pinpointing exactly where the military or logistical activity is most concentrated in the SWIR spectrum.

[/code]

Now from this you can see this isnt bad, and for a stage in a pipeline you can use this to identify POTENTIAL areas which to expand research.

Im using huihui_ai/qwen3.5-abliterated:9b and its pretty good at reading maps.

Is anyone doing something similar ?

#osint #geospatial #python #llm

i have created a tool to capture live images with location. raw image with geo tagging. no auth. from an osint pov, what all things has to be done.

https://tunc.live -> captured images on map

https://tunc.app -> android app

Hi, everyone. A ReverseLookup subscription is worth it for getting phone number information in Europe?

last night someone repeatedly called my bf using caller id suppression. after a few calls, they accidentally called from their real number.
i only have the phone number and a photo of theirs. i already tried reverse image search and searched the names i heard during the call, but found nothing.
i’m not asking anyone to identify this person, i’m only looking for legitimate osint techniques or public resources that can help me verify whether this phone number is associated with any public information or social media profiles, while respecting privacy and the law.
any advice would be appreciated.

what are some data breach websites that are free cuz i’m broke lol

Could you knows any quality alternative for breach.vip ?

Ive been looking for a good free phone number osint tool which gives you more data than just the provider and stuff like that. I found webvetted which was great and is free. I used my number and found my name. The only problem is its 3 searches per month which is bad. I could make lots of accounts but i dont want to do all that hastle using aliases and that. Does anyone know another free tool that gets you that much detail?

​

I'm building my toolkit and looking for recommendations on OSINT tools.

What are the best free/open-source tools you actually use daily for:

1. Username/Email lookup across socials

2. Domain/IP and metadata analysis

3. Image reverse search + geolocation

Tried Maltego and SpiderFoot already. What else should be in a 2026 toolkit?

Thanks!

Pls help or share, if you have one. Don't gatekeep it

Thanks!

Hello ! I was talking with a friend of mine about OSINT, since it's become his new passion over the past 3 weeks. While we were on a call, he started digging into me. After a few minutes he began bragging that he'd found stuff on me, but when I asked what exactly, he refused to tell me and was enjoying the fact that he was holding something over me.

After a lot of insisting, he sent me these two screenshots :

This whole thing made me pretty uncomfortable, and it also made me realize it's probably time I got into OSINT too. So I'd like to know: does anyone here recognize this website? I'd like to go check for myself what he was able to find on me.

Been building out the Tools section on Osintly and gaming lookups are now a full category. Three tools are live right now and all of them are free to use.

The Epic Games Player Search lets you resolve an Epic profile from a single identifier, whether that's a Display Name, Account ID, Creator Code, PSN username, Steam username, Xbox Gamertag, or Nintendo Account ID. Drop one in and you get the Epic profile plus every linked platform account in one view. The Xbox Player Search works off a Gamertag or XUID and pulls profile info, gamerscore, and public presence data. The PlayStation Network Player Search works the same way with an Online ID or account ID, pulling PSN profile, live presence, trophies, and public game history.

One username can chain you across Epic, PSN, Steam, and Xbox without ever touching a paid tool. All three also work through the API if you want to automate lookups at scale.

More gaming lookups are in the pipeline, Rockstar, FiveM, Roblox, among others. Full list always here: https://osint.ly/tools

Feedback and requests welcome, always.

Ever since I was a kid, I was fascinated by spy movies, hacker movies, and the huge intelligence maps you always saw on screen.

As I got older, I realized there are real platforms that solve similar problems, but they're generally proprietary and out of reach for most developers.

I kept wondering what an open-source version could look like.

So over the past few weeks I've been building Akashic.

Akashic is a self-hosted intelligence workspace that brings together aircraft, satellites, earthquakes, weather, public radio, infrastructure, country intelligence, public cameras, and other OSINT sources onto a single interactive map.

The goal isn't to replace every existing service.

It's to make exploration easier.

Instead of switching between multiple websites, you can combine layers, inspect entities directly on the map, and correlate information from different public sources in one workspace.

Current highlights:

• Open source (MIT)

• Self-hosted

• No mandatory API keys

• Built in public

This is still an early project, so I'd genuinely appreciate feedback on the direction, missing features, or things you'd like to see.

GitHub:

https://github.com/caviraoss/akashic

Demo:

https://www.youtube.com/watch?v=qsWHhuIwR8c

I recently learned that a lot of people reuse the same username on multiple platforms without even thinking about it.

Whether it's Instagram, Reddit, X, GitHub, gaming accounts, or niche forums, many people stick to the same alias (or a slight variation) because it's easier to remember. I tried searching my own username out of curiosity and was surprised how much of my public online footprint showed up.

While looking into this, I found AliasScan – Unmask Any Username Across 400+ Platforms (https://aliascan.com/). Instead of checking dozens of sites manually, it searches hundreds of publicly accessible platforms and shows where a username appears.

I can see it being useful if you're:

• Trying to find your own forgotten accounts.
• Checking whether a username is already taken before creating a new brand.
• Doing OSINT or security research.
• Investigating whether someone consistently reuses the same public username across different websites.

Just keep in mind that a matching username doesn't automatically mean it's the same person—it should only be treated as a starting point for further verification.

Has anyone else tried searching their own username across the web? Were you surprised by what you found?

Disclosure: I am related to OSINTPRO and I am looking for practical feedback, not trying to hide that this is my project.

The goal is a passive-only OSINT workspace: domain evidence, username checks, wallet tracing, repository audit leads and graph/report exports. The hard part is explaining risk to non-technical owners without turning findings into offensive instructions.

Current boundary:

• no exploits
• no brute force
• no credential attacks
• no invasive scanning
• no wallet movement guidance

Question for people who use OSINT tools: what would make a finding useful enough for a client report while still staying on the defensive side?

GitHub: https://github.com/haizen1312/osintpro