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Source channel @githubtrending · Post #15295 · Nov 11
#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
Search: #kfunc
@aigcrubbish · Post #158 · 01/27/2026, 05:06 PM
[$] Implicit arguments for BPF kfuncs Linux 内核的 kfunc 机制允许 BPF 程序直接调用内核函数。目前内核中有超过 300 个 kfunc，功能涵盖字符串处理（如 `bpf_strnlen()`）到自定义调度器（如 `scx_bpf_kick_cpu()`）等。 有时，这些 kfunc 需要访问 BPF 程序无法直接获取的上下文信息，因此无法通过参数传递。Ihor Solodrai 提交的“隐式参数”补丁集旨在解决这个问题，它允许 kfunc 隐式地接收额外的上下文参数。 原文链接：https://lwn.net/Articles/1055559/ #Linux#内核#BPF#kfunc #AIGC Read more