Automatically identifies and safely removes duplicate, similar, and low-value photos/videos across devices and cloud storage
Added Jan 7, 2026
Medium opportunity (71%)
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Users accumulate massive digital clutter—duplicate photos, memes from WhatsApp, forwarded videos, screenshots, and low-quality images—making manual cleanup overwhelming and risky. Existing tools only find exact duplicates or require manual sorting, leaving users afraid to delete important memories mixed with junk.
Uses AI/ML? to automatically categorize media into duplicates, similar images (different resolutions/edits), and junk content (memes, ads, screenshots). Provides a safe deletion workflow with smart suggestions, preview modes, and user-controlled batch operations across local devices and cloud storage. Prioritizes keeping the highest quality version and learns from user decisions.
Messaging apps like WhatsApp generate 100B+ media files daily; cloud storage costs are rising; AI image recognition is now accurate and affordable enough to distinguish personal photos from junk at scale.
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Showing 1-20 of 80 signals
Google scams us to buy space. When I do manual deleting I find many duplicates. This app, however, only found a handful....
App is bad, can't even see the duplicate photos. Super bad don't download it please it's just bad twin.
When I started building Sameish, I knew I wanted to solve a real problem: digital clutter. Everyone has it – those endless duplicate photos, blurry videos, and redundant files hogging space. The biggest hurdle for me was convincing myself that I could build something genuinely useful and private, without relying on cloud processing that users often distrust. So, I focused purely on on-device processing. Every scan, every comparison, every identification of identical, look-alike, or blurry content happens locally. No data ever leaves the user's device. It's been incredibly validating to see users adopt this, and especially to get feedback from my first paying customer about how much storage they reclaimed and how much they appreciated the privacy-first approach. It feels good to build something that truly respects user data while delivering a clear benefit.
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