FSearch

FinderSearch is a native macOS file browser with fast fuzzy filename search, built with SwiftUI and AppKit and powered by the fsearch engine. It searches locally with fuzzy matching, relevance ranking, folder scoping, and filters such as ext:pdf and mtime:<7d, then presents results through icon, list, column, or gallery views with Quick Look previews and tabs. File operations include drag selection, copying and moving, batch renaming, ZIP tools, tags, sharing, Trash, undo, transfer progress, and conflict handling. It requires macOS 15 or later and is distributed as a DMG for Apple Silicon. Full Disk Access is needed to search protected folders; the app is currently not Developer ID-signed or notarized. FinderSearch is early software whose stated unsupported features include content search, AirDrop, Finder extensions, saved smart folders, folder merging, and persistent undo history. It is MIT-licensed, while its vendored fsearch backend is also MIT-licensed.

Search is a small, fast WebKit browser for macOS developed by Office Commun. It provides a single address-and-search field, tabs, reading mode, picture-in-picture video, bookmarks, history, downloads, built-in ad and tracker blocking, per-site clutter hiding, and password storage in the macOS keychain. It has no account, sync, cloud storage, telemetry, or analytics; browsing data remains on the Mac. The browser uses WebKit and lazily creates each tab's web view, while its ad blocker runs as a compiled WKContentRuleList before network requests are made and its hidden-element rules are injected at document start. It can run Chrome extensions through WebKit's extension engine, adding shims for Chrome APIs that WebKit lacks; extension support requires macOS 15.4 or later. Search supports macOS 14 or later, is distributed as a free download of about 3 MB or through Homebrew, and is licensed under the MIT License. The repository contains a Swift and SwiftUI/AppKit implementation with no dependencies beyond Apple's macOS frameworks.

DigUp is a free, open-source macOS menu bar app for searching files by their contents, including images, PDFs, documents, audio, video, screenshots, and optionally source code. It runs on Apple Silicon Macs with macOS 14 or later and keeps indexed data on the device. DigUp uses Google DeepMind's multimodal EmbeddingGemma 2 through llama.cpp and Metal. It converts images, document passages, video frames, and overlapping audio segments into a shared vector space, then combines semantic nearest-vector search with exact-word matching. Results can open a video or recording at the relevant moment, a PDF at the matching page, or code at the relevant line in a configured editor. The app reads only selected folders, excludes sensitive and ignored file types, and does not upload indexed files. It downloads the model once and checks for updates, with telemetry and accounts not required. DigUp can be installed from a DMG or Homebrew, includes a command-line tool, and is licensed under the MIT License.

Fscan is an open-source intranet security scanning tool developed as a Go project. It discovers hosts with ICMP probing, scans TCP ports, identifies services and web technologies, performs credential and hash-based authentication tests, detects selected vulnerabilities and unauthorized access, and provides exploitation and local information-gathering modules. Its inputs include IP addresses, CIDR ranges, domains, URLs, and batch files; results can be written as TXT, JSON, or CSV. The tool is organized around service, web, and local plugins. It supports common service fingerprinting, Xray- and Afrog-format web POCs, DNSLog-based detection, HTTP and SOCKS5 proxies, interface selection, rate and timeout controls, independent scan concurrency settings, and configurable exclusions. Its local modules include system and domain information collection, credential acquisition, persistence mechanisms, shells, security-software detection, and log-cleaning utilities. The repository also provides a Go SDK under pkg/fscan for embedding scan tasks in agents or security platforms, with pause/resume control and progress callbacks, plus an optional web management interface enabled through a build tag.

SCM is a local-first macOS application for searching photos and videos by visual content, visible text, and spoken dialogue. It indexes whole files with local vision models, segments videos into scenes so searches can jump to a timecode, runs Tesseract OCR for literal text matching, and uses Whisper transcripts for exact spoken-word searches. An optional local LLM can answer questions over extracted dialogue, OCR, and filename evidence with citations. The app imports files through manual selection, drag-and-drop, or watched folders. It content-hashes files for rename-proof deduplication, stores embeddings and media metadata locally, and can re-embed a library when the selected vision model changes. Model weights and language packs download once; subsequent inference runs offline, with no accounts, uploads, or telemetry. SCM is distributed as an Electron application for macOS, including a Homebrew cask and packaged DMG/ZIP builds.