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Showing 3 open source projects for "algorithm"
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Turn traffic into pipeline and prospects into customersDocket is an AI-powered sales enablement platform designed to unify go-to-market (GTM) data through its proprietary Sales Knowledge Lake™ and activate it with intelligent AI agents. The platform helps marketing teams increase pipeline generation by 15% by engaging website visitors in human-like conversations and qualifying leads. For sales teams, Docket improves seller efficiency by 33% by providing instant product knowledge, retrieving collateral, and creating personalized documents. Built for GTM teams, Docket integrates with over 100 tools across the revenue tech stack and offers enterprise-grade security with SOC 2 Type II, GDPR, and ISO 27001 compliance. Customers report improved win rates, shorter sales cycles, and dramatically reduced response times. Docket’s scalable, accurate, and fast AI agents deliver reliable answers with confidence scores, empowering teams to close deals faster.
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dupeGuru
Find duplicate files
dupeGuru is a cross-platform GUI application written in Python (with Qt/Cocoa UI) that quickly detects duplicate files on your computer using flexible scanning modes—including filename fuzzy matching, content comparison, and specialized Music/Picture modes. On some linux systems pyrcc5 is not put on the path when installing python3-pyqt5, this will cause some issues with the resource files (and icons). These systems should have a respective pyqt5-dev-tools package, which should also be... -
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Duplicate Files Finder is a cross-platform application for finding and removing duplicate files by deleting, creating hardlinks or creating symbolic links. A special algorithm minimizes the amount of data read from disk, so the program is very fast.
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qlsdup
GUI based lightweight duplicate file finder / remover.
...This is basically a reimplementation of dupfinder as: it doesn't compile on my computer, the executable won't work either, the projects seems dead and I didn't wanna work through the original code. So Look'n'Feel should be somewhat the same, though some changes have been made. Algorithm is simple and suitable for large file sets with few differences: basic set of duplicate candidates is determined by file size, then candidates are compared byte-wise. So for files which don't start to differ by growing at the tail it reduces read operations greatly compared to hash-based comparision (i.e. test case: 40GB of files, 16GB RAM, many differences: initial compare (uncached by OS) around 300sec, subsequent compare (relevant file parts cached by OS) ca. 2sec) Note: this program is not related to lsdup
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