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Local-first desktop app for ML-powered visual file search.
Enables natural language search to find files based on visual and semantic content without needing filenames. Allows visual similarity searching where users can click an image to find conceptually or visually related files. Provides a node-based graph visualization to view archives, auto-detecting clusters and similarity patterns between files. Supports file organization through 'Cubbies,' allowing users to create collections, annotate files, and build mood boards.
Local-first architecture ensures all data remains on the user's machine, providing superior privacy over cloud-based alternatives. Visual search capability removes the burden of manual file naming, tagging, or complex folder maintenance. Rust-based backend threads ensure high performance and responsiveness even when indexing large file collections. It adapts to how users think about their files rather than forcing rigid organizational hierarchies.
Category: Utilities & System Tools
Team Size: 1
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Dotient is a privacy-focused, local-first desktop application that allows users to organize and search personal files using ML-powered visual and semantic search. It eliminates the need for manual tagging or folder management by letting users describe files in natural language or find similar visual content. All processing, indexing, and embeddings occur entirely on-device, ensuring complete data privacy and offline functionality.
Dotient was developed by Declan to solve the persistent frustration of lost files in disorganized personal archives. Frustrated by cloud services that harvest user data and traditional search tools that rely solely on filenames, the developer built a local, privacy-centric solution. The project focuses on utilizing on-device machine learning to make personal file discovery intuitive, visual, and secure.
