u/Old_Style_6945

I've built an OSINT tool that extracts relationship intelligence from Instagram photos.

**What it does:**

- 🔍 Downloads Instagram data (photos, followers, bios)

- 👤 Face detection & clustering (identifies same people across photos)

- 🕸️ Co-appearance network mapping (who meets with whom)

- 📊 PageRank influence scoring (finds hidden influencers)

- 📅 Temporal analysis (when people appear)

- 👥 Community detection (identifies social clusters)

link: https://github.com/rajeshmn47/valantir

**Sample OSINT output from a test account (500+ photos):**

https://github.com/rajeshmn47/valantir

I've been working on this for a few months - a face intelligence platform that extracts meaningful relationship data from Instagram photos.

**What it does:**

- 🔍 Automatically detects faces from downloaded Instagram photos

- 👥 Clusters similar faces together (handles different angles/lighting)

- 🏷️ Labels clusters using downloaded profile pictures or manual entry

- 🕸️ Builds co-appearance networks (who appears with whom)

- 📊 Calculates PageRank to find the most influential people

- 📅 Shows temporal patterns (when people appear over time)

**Tech stack:**

- Python (face_recognition, DBSCAN clustering)

- Node.js + MongoDB backend

- React frontend with Force Graph visualization

**Sample insights from a test account (500+ photos):**

- 62 face clusters reduced to 28 actual people after merging

- 3 distinct social communities detected

- Found the key influencer (not the one with most followers!)

- Mapped how different groups connect through 2 bridge people

**Use cases I'm exploring:**

- Political campaigns (who's attending rallies, who influences whom)

- Social research (community detection, influence mapping)

- Event analysis (who networks with whom)

**Challenges I've solved:**

- Handling Instagram's anti-scraping (GraphQL, rate limiting)

- Merging multiple clusters for same person (photo overlap + label matching)

- Visualizing 50+ node networks interactively

**Still working on:**

- Better cluster merging (fuzzy name matching)

- Exporting reports (PDF/CSV)

- Real-time processing

Would love feedback or ideas for other use cases!

Instead of manually going through hours of footage, every delivery becomes a tagged clip that you can instantly search and filter.

Examples:
🔍 “All Bumrah wickets”
🔍 “Kohli boundaries in death overs”
🔍 “Left-hand batters vs leg spin”
🔍 “All LBW dismissals”
🔍 “Powerplay sixes”

Every clip is linked with match context and analytics, so you can move from numbers → directly to video evidence in one click.

Useful for:
🏏 Analysts
📹 Content creators
📊 Scouts & coaches
🎯 Fantasy researchers

Currently improving:
• clip accuracy
• search/filter system
• automatic tagging
• analytics linked to video

If you work with cricket footage regularly and want to try it, comment “Demo” or DM me.

https://reddit.com/link/1t5zw1d/video/n2oz4u9n5nzg1/player

A scorecard can tell you a batsman got out 5 times to the same bowler.

But it won’t tell you:

• whether it was against short balls
• under dot‑ball pressure
• during the powerplay
• on slower pitches
• against specific bowling angles

https://reddit.com/link/1t5giux/video/cpswntb2ejzg1/player

That context usually lives inside hours of match footage – scattered, unlinked, hard to search.

So we built a small but powerful feature at Cricket Vision AI:

▶️ A play icon next to every key statistic.

Click it → a new tab opens with the actual video clips that make up that number. Filtered by series, season, format, league.

Now you can:

• Watch every dropped catch by a fielder, not just the count.
• See all 10 dismissals of a “bunny” pair with one click.
• Verify boundary‑per‑wicket stats with real footage.
• Compare left‑hand vs right‑hand performance – with video proof.

I got tired of scrubbing through 4-hour cricket matches to find specific balls.

So I built a system where you upload a full match and instantly get searchable ball-by-ball clips.

You can filter things like:

- wickets

- sixes

- players

- over phase

- dismissal type

Example:

“show every Bumrah yorker wicket in death overs”

Still rough, but this already saves a ridiculous amount of time.

I’ve been doing a lot of manual work going through large public image sets (events, protests, archives), and the biggest bottleneck was always the same:

→ scrolling through thousands of photos

→ spotting the same faces again and again

→ re-checking identities manually

So I built a small local tool to speed this up.

What it does:

extracts faces from image folders

clusters similar faces (DBSCAN)

lets you label a cluster once and reuse it

runs fully offline (no APIs, no uploads)

What I found useful:

grouping recurring faces quickly

reducing manual review time

creating candidate sets for further verification

Quick test: ~5000 images → ~15k faces → clustered in a few minutes on my machine

Important:

this is NOT perfect identification

there are false positives (similar faces, lighting, angles)

still requires manual verification

I’m not selling anything right now — just trying to see if this is useful for others doing OSINT or large dataset analysis.

If you’ve dealt with similar problems, I’d love to know:

how you currently handle image-heavy investigations

what breaks in your workflow

If anyone wants to test it on real datasets, I can share access.

I’ve been working with large sets of images (thousands at a time), and the biggest bottleneck has always been:

→ scrolling through everything manually
→ seeing the same faces repeatedly
→ re-checking similar images over and over

It gets exhausting fast.

So I experimented with a small workflow:

• extract faces from images
• group visually similar faces together
• label once and reuse

What surprised me:

• grouping similar faces cuts down manual work a lot
• you start seeing patterns much faster
• even imperfect clustering is still useful

It’s not perfect — similar-looking people get grouped sometimes, and lighting/angles can throw it off.

But compared to manual browsing, it’s a big improvement.

Curious how others handle this:

• do you manually scan everything?
• any tools/workflows that helped?

https://preview.redd.it/zd3b9kbigazg1.png?width=1408&format=png&auto=webp&s=ee232a0e7edae1f9734bcbad336d74cfed857044