I've been building a searchable ball-by-ball cricket archive, and one of my favorite collections to create is googlies.

This video is a compilation of some memorable IPL googlies. If you have a favorite googly that isn't included, let me know—I might add it to the next playlist.

I'm also experimenting with themed playlists like:

• Googly setups
• Doosra masterclasses
• Outswing to inswing traps
• Slow-ball deception
• Dot-ball pressure leading to wickets

Would love to hear what other bowling themes you'd watch.

https://reddit.com/link/1ttxcjk/video/r8vyv4dx5p4h1/player

A giant cosmic family tree of humanity. One person at the center, with relationship lines branching outward to parents, grandparents, cousins, distant relatives, and eventually connecting to every human on Earth. The network becomes a glowing neural-web surrounding the planet, showing humanity as one interconnected family. Futuristic holographic interface, intelligence platform visualization, millions of profile nodes, relationship graph, dark background, cinematic lighting, Palantir-inspired analytics screen, highly detailed, realistic data network, blue neon connections, global ancestry map.

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