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Source channel @githubtrending · Post #15282 · Nov 9

#shell#aesthetic#dark_mode#dynamic#hyde#hyprdots#light_mode#themes#unix_porn#wallpapers HyDE is a clean, modular, and visually appealing development environment designed for Hyprland on Arch Linux and some Arch-based distros. It offers easy installation via a script that auto-detects NVIDIA cards and configures necessary drivers, but it may conflict with existing desktop environments or theming. You can customize it with many official and community themes using a tool called themepatcher. HyDE keeps your configuration organized and separate from core scripts, making updates safer and simpler. It also supports running in a virtual machine for testing. Joining the HyDE Discord community helps you get support and share ideas. This setup benefits you by providing a stylish, maintainable, and customizable desktop environment with a smooth update process and community support. https://github.com/HyDE-Project/HyDE

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@githubtrending · Post #15295 · 11/11/2025, 05:00 PM

#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

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

#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