A new open-source project is betting that the fastest way to find a memory is to describe it — and that the search should never leave your laptop. SCM, short for Screen Memories, is a free macOS application that applies local AI models to every photo and every frame of video in a folder, letting users search their libraries in plain language. The project appeared on GitHub this week and quickly climbed to the front page of Hacker News, where it drew more than 85 points within hours.
The pitch is simple on the surface: point SCM at any folder, and it builds a searchable index of what is actually in your media — not just filenames, but the visual content of each image and each scene inside each video. What sets it apart in a crowded field of AI photo tools is that all of it runs on the Mac itself. There are no accounts, no cloud uploads and no telemetry. Once the models download their weights on first run, the app works offline. 이 이야기에 대한 자세한 내용은 인공지능 뉴스를 참조하세요.
Five Ways to Search a Lifetime of Media
SCM organizes search into five distinct modes, each powered by a different model running locally.
The Files mode performs whole-file semantic search across photos and videos, ranking results by meaning with boosts from filenames and phrases. Scenes goes deeper: the app segments videos into individual scenes and embeds each one separately, so a query lands you on the exact shot, with a jump straight to its timecode rather than a timestamp buried in a filename.
Two text-focused modes handle the words inside media. The OCR mode, built on the Tesseract engine, reads text that is visibly present in images and video frames and matches it literally, with English plus 35 additional language packs that can be toggled on. The Dialogue mode runs Whisper speech recognition over videos and indexes the exact spoken words, with tiered levels of match exactness for hunting a specific line.
Finally, an opt-in LLMs mode turns the extracted material — dialogue transcripts, OCR text and filenames — into a local chat context. Users can ask questions about their own library, and the app returns answers with citations pointing back to the source files, all generated by a model running on the same machine.
How It Works Under the Hood
Technically, SCM is an Electron application written in JavaScript, using the Bun toolkit as its package manager and runtime. The heavy lifting is done by transformer models executed directly in the app — the default vision model is a CLIP variant whose weights, roughly 435 megabytes, are downloaded once on first use.
The index maintains itself. Watched folders auto-import new media as it arrives, content hashing deduplicates files that have merely been renamed or moved, and switching to a different model re-embeds the library in the background without blocking searches. The developer notes that menu-bar and tray features are specific to macOS, where the app is packaged for distribution.
The privacy model is the selling point. The project's documentation is explicit: media never leaves the machine, inference runs locally through transformer runtimes, and after the initial weight download everything works offline. For users with sensitive archives — medical imagery, legal footage, personal video — that architectural choice is the feature.
Built for Personal Archives
The interface reflects the same philosophy of customization. Any query can be saved as a reusable tab, so a search that took a moment to phrase becomes a permanent view over the library. The app also ships with toggleable Screenshots and Email tabs, acknowledging that a modern Mac accumulates screenshots and attachments at a pace that quickly outgrows the operating system's own search.
The underlying technique is the now-standard machinery of semantic search: a vision model converts each image and video scene into a numerical embedding, and queries are embedded the same way so that matching happens by meaning rather than keyword overlap. What is notable here is not the technique but the packaging — the same pipeline that cloud photo services run on server farms is compressed into an Electron app that a non-developer can install and run unattended over folders of their choosing.
Part of a Larger Shift Toward Local AI
SCM은 소비자 하드웨어에서 유능한 AI 모델을 실행하는 것이 참신함에서 기대로 바뀌면서 등장했습니다. Apple은 의미론적 검색을 자체 포토 앱에 꾸준히 적용해 왔으며, 오픈 소스 음성, 비전 및 텍스트 모델은 노트북에서 편안하게 실행되는 크기로 축소되었습니다. 부족한 것은 기본 모델이 아니라 통합 계층, 즉 엔지니어가 아닌 사람이 실제로 자신의 파일에서 사용할 수 있는 무언가로 연결하는 소프트웨어입니다.
이것이 바로 SCM이 차지하고 있는 틈새시장이며, SCM의 반응은 가려움증이 실제로 존재함을 시사합니다. 저장소는 대략 하루 만에 160개의 별과 8개의 포크를 수집했으며 Hacker News 댓글 작성자는 모델 크기, 인덱싱 속도 및 Windows 가용성에 대한 질문에 초점을 맞췄습니다. 현재 프로젝트는 macOS 전용이므로 마지막 질문에 대한 대답은 '아니오'입니다.
이 프로젝트는 상업적 사용과 수정을 허용하는 MIT 라이선스에 따라 출시됩니다. 크고 체계적이지 않은 사진 및 화면 녹화 아카이브를 갖고 있고 어디에나 업로드하는 것을 원칙적으로 싫어하는 사람에게는 완전한 로컬 미디어 검색 엔진이 더 이상 연구 데모가 아니라 오후 설치라는 점을 보여주는 가장 설득력 있는 데모 중 하나입니다.
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