DigUp Overhauls Mac Search With Google’s New Model: Locate a Specific Shot in a Video Even If You Forget the Filename
47 minutes ago
Dongcha Beating AI News Flash: Finding files on Mac typically requires remembering names or keywords. If you only recall a zebra appearing in a video segment, or a podcast episode discussing sleep, traditional file search tools are of little help. Independent developer Abdur Rahim’s open-source tool DigUp solves this by enabling direct content-based file searches. Type in "zebra in video" and it will jump to the exact frame where the zebra appears; search for "segment discussing sleep" and it will pinpoint the relevant timestamp in the podcast. It can also search photos, PDFs, and even open the exact page of a specific clause in a contract. DigUp leverages Google DeepMind’s EmbeddingGemma 2 model, released on October 6. This model converts text, images, audio, and video into comparable numerical features, allowing users to find corresponding visuals or audio based on a text description. DigUp pre-indexes selected folders, and during searches, it can also precisely match details like invoice numbers and error codes. All indexing and search processes run locally on Mac, eliminating the need to upload private files to the cloud. Free and open-source, DigUp has earned over 3,000 GitHub stars in less than a week since its launch. It currently only supports Macs with Apple’s custom silicon, requiring macOS 14 or later. First-time use requires downloading an approximately 865 MB model and spending time building the index.
Physical Intelligence opens its robot model API: Picking strawberries, assembling servers, new actions can be trained in just 4 days.
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