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Source channel @lambdaexpression · Post #206 · 4月20日

前段时间一直被MajdataPlay的外键输入问题困扰:有玩家反映majplay会无征兆地出现拖判和吃音,但是内屏一切正常 因为我是第一次接触游戏开发,IO这方面也完全没经验 一开始我和bb本怀疑是线程调度的问题,即:IO线程时间片被其他线程挤占了,导致IO线程无法及时处理HID设备回报。为了验证这个猜想,我们尝试提高了IO线程的优先级,照旧 接下来我怀疑是我那套框架有问题:majplay是根据上一帧与这一帧的按键状态判断按键是不是"click"。为此我重写了这部分的实现,改进了IO线程与主线程之间的交互,问题照旧....... 到这里我已经怀疑这不是majplay的锅:IO线程没有任何异常,IO线程与主线程的交互没有问题,Note判定逻辑也没有问题,那就是设备确实没有回报给majplay或者设备发过来的回报中按键确实没有按下,但是大佬说hdd没有这种问题.....(人已经快崩溃了,这完全看不透也摸不着,因为我用单片机模拟玩家打高速纵连是完全没有问题的,我在家里用手台测试也没有问题) 到最后,bb本灵光一闪,说有没有可能是led刷新率过高,把按键控制板干爆炸了?我们让大佬把led刷新间隔从16ms改成100ms,吃音问题瞬间没有了,无语了 。。。。。。。。。。。。。。。。。。。。 adx是一个控制板同时管理按键和led,为什么我没有遇到吃音问题呢,因为我的手台不是adx的... #dev

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@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

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@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