Everything vs Precision File Search

Everything is a Windows search engine for finding files and folders by filename. It indexes names rather than file contents and generally builds its database in a few seconds, then updates results in real time as file systems change. Search syntax includes Boolean operators, wildcards, file types, and location filters. The index can cover local, removable, and network locations. A content: function can search inside files, but content is not indexed, so those searches are slow. The maker says the name cache stays local and is not sent remotely; its support page describes the client as offline, without user data collection or telemetry. It reports that a fresh Windows 11 installation with about 250,000 files uses about 35 MB of RAM and less than 14 MB of disk space. An HTTP Server plug-in allows browser searches and file access, while ETP/FTP supports access from Everything or an FTP client. The HTTP server uses Basic authentication without TLS, and the maker advises against exposing it directly to the internet. NTFS indexing requires the Everything service or running as administrator. The freeware plan costs nothing; business and enterprise use requires a site license.

Precision File Search is a free desktop application for finding, classifying, and understanding local files on Windows, macOS, and Linux. It combines classic search with semantic search using a retrieval-augmented generation pipeline, and can answer natural-language questions with summarized responses. A trainable machine-learning model sorts scattered documents into categories users define. The maker says the application and AI models can operate locally, without cloud dependency, and that files, queries, and personal data are not sent to external servers. The project lists support for 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish. An internet connection is needed on first run to download multilingual AI models; later runs can be offline. PFS supports OpenAI-compatible inference URLs, including online providers and local engines such as Ollama and LM Studio. The README lists 8 GB of RAM for the core platform and a dedicated GPU, with at least 4 GB of VRAM for smaller models, for local LLM use. The project is licensed under MPL-2.0.