TGTGInsighttelegram intelligenceLIVE / telegram public index
← GitHub Trends

TGINSIGHT SIMILAR POSTS

Find similar content

Source channel @githubtrending · Post #15077 · Aug 20

#c_lang#infiniband#iwarp#kernel_rdma_drivers#linux_kernel#rdma#roce#userspace_libraries You can use RDMA Core, a set of Linux userspace libraries and daemons, to work with RDMA devices for high-speed network communication. It supports many kernel drivers and provides tools and libraries like libibverbs and librdmacm to manage RDMA devices and connections. You can build it easily with cmake and install required packages depending on your Linux distribution. Using RDMA Core lets you set up software RDMA interfaces and verify them with commands like `ibv_devices` or `rdma link`. This helps you achieve faster, low-latency data transfer, which is useful for high-performance computing and networking tasks. https://github.com/linux-rdma/rdma-core

Results

2 similar posts found

Search: #faiss

当前筛选 #faiss清除筛选
GitHub Trends

@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

GitHub Trends

@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