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Chaîne source @OnePlusGuide · Post #2729 · 17 août

🔻OXYGENOS 11: ALWAYS ON DISPLAY!🔻 #ONEPLUS#OXYGENOS11#ALWAYSONDISPLAY#AOD OnePlus si è creata una fama internazionale per la sua particolare attenzione alla Community e alle sue richieste. Beh, anche questa volta non si smentisce. Dopo mesi e mesi di attesa, finalmente OnePlus ha confermato che implementerà nella nuova OxygenOS 11 una delle funzionalità più richieste dai suoi utenti: l'Always On Display! Per chi non conoscesse questa funzionalità, rende alcuni elementi come Orologio e icone di notifiche sempre visibili sullo schermo quando si blocca il dispositivo, in maniera tale da non dover accendere il telefono per vedere l'orario o le notifiche, a discapito di un po' di batteria. E voi che ne pensate? Userete questa funzionalità? Fatecelo sapere nei nostri gruppi! Pit — Il nostro canale 👉🏻@oneplusguide I nostri gruppi 👉🏻@oneplusitcommunity

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GitHub Trends

@githubtrending · Post #15295 · 11/11/2025 17:00

#python#ai#faiss#gpt_oss#langchain#llama_index#llm#localstorage#offline_first#ollama#privacy#python#rag#retrieval_augmented_generation#vector_database#vector_search#vectors LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop. https://github.com/yichuan-w/LEANN

GitHub Trends

@githubtrending · Post #15168 · 25/09/2025 12:30

#python#ai#context#embedded#faiss#knowledge_base#knowledge_graph#llm#machine_learning#memory#nlp#offline_first#opencv#python#rag#retrieval_augmented_generation#semantic_search#vector_database#video_processing Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily. https://github.com/Olow304/memvid