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Source channel @githubtrending · Post #14986 · Jul 22

#rust#cli#command_line_interface#command_line_tool#dns#icmp#linux#macos#mtr#netbsd#network#networking#ping#ratatui#rust#rustlang#tool#traceroute#tui#tui_rs#windows Trippy is a powerful tool that combines traceroute and ping functions to help you analyze network problems easily. It works on Linux, BSD, macOS, and Windows, and you can install it through many package managers or directly with commands like `cargo install trippy`. Running a simple trace is as easy as typing `sudo trip example.com`. Trippy offers detailed network tracing with features like multipath strategies and unprivileged modes, making it flexible for different needs. Using Trippy helps you quickly find where network issues occur, saving time and improving troubleshooting efficiency. Full guides and documentation are available online to get you started smoothly. https://github.com/fujiapple852/trippy

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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