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Source channel @ttyUSB0w · Post #1663 · 5月12日

#出售#电子垃圾#zigbee 联系 Clansty ad Astra 价格 18 + 邮费 zigbee USB dongle 基于 TYZS13 EFR32MG13,支持 z2m 和 zha 自己刷机刷炸了,现在寄给闲鱼卖家刷,不想再出两份邮费了,刷完之后直接寄给你。你只要付寄给你的邮费 预览模式,不会发送到频道,请注意,您的用户名需要手动更新才能确保发送时和此处一致。

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GitHub Trends

@githubtrending · Post #15295 · 2025/11/11 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 · 2025/09/25 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