Decloner vs Duplicate File Finder
Side-by-side comparison for macOS
Decloner
6.0Duplicate files finder
Duplicate File Finder
8.0Find and remove unwanted duplicate files and folders
| Metric | Decloner | Duplicate File Finder |
|---|---|---|
| Category | Utilities | Utilities |
| AI Score | 6.0 | 8.0 |
| 30-day Installs | 1 | 34 |
| 90-day Installs | 1 | 98 |
| 365-day Installs | 24 | 381 |
| Version | 1.6.3 | 9.1,974 |
| Auto-updates | No | Yes |
| Deprecated | Yes | No |
| GitHub Stars | - | 54 |
| GitHub Forks | - | 16 |
| Open Issues | - | - |
| License | MIT | MIT |
| Language | Python | Python |
| Last GitHub Commit | 11mo ago | 8mo ago |
| First Seen | Feb 1, 2021 | Aug 9, 2023 |
Reviews
Decloner
Decloner is a macOS app designed to efficiently find and remove duplicate files, helping users optimize their storage space. It is particularly useful for users with large collections of files who want to declutter their systems. The app offers a straightforward solution to identify redundant files, making it a handy tool for anyone looking to manage their digital storage effectively.
Decloner scans your system to identify and remove duplicate files.
Pros
- + Native macOS integration for seamless performance
- + Free and open-source under the MIT license
- + Helps users efficiently manage and optimize storage space
Cons
- - No auto-update feature, which may affect security and functionality
- - Lack of community support and engagement
Duplicate File Finder
Duplicate File Finder is a user-friendly tool designed to efficiently identify and remove duplicate files and folders. Its intuitive interface and robust performance make it ideal for users looking to free up storage space and organize their files effectively.
Finds and removes duplicate files and folders on your system.
Pros
- + User-friendly interface with clear visualization of duplicates
- + Efficient file scanning and comparison capabilities
- + Straightforward file removal process
Cons
- - Limited advanced filtering options compared to competitors
- - Performance may degrade on systems with extremely large datasets