AMD has posted patches for AOD, a WMI driver for Ryzen CPUs. Posted by AMD engineer Shyam Sundar S K, it exposes CPU tunables via sysfs, including Package Power Tracking (PPT), Thermal Design Current (TDC), and Electrical Design Current (EDC) limits, plus their supported ranges. It also supports SMT toggling at the platform level and reading current memory clocks. Power tunable changes apply only after a reboot, not in real time. No GUI app has been patched yet, and GUIs are left to independent developers and desktop environments, similar to AMD's Radeon driver approach. The patches are posted on the platform-driver-x86 mailing list for review.
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Software was slowing down my Pi.
malloc has a very nice and simple interface: you ask for a certain amount of memory, and you’re given a void pointer to your block of contiguous bytes that’s been magicked into existence. Once you’re done with it, you call free with the same pointer and the memory disappears into the ether.
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A Fluke Model 407 high-voltage power supply gets rescued from the scrap heap and diagnosed on video. Mr Carlson powers it up safely using an isolated transformer and a current-limited variac, avoiding the risk of full power on old components. The vacuum-tube-and-circuit-board unit shows no visible damage but its output misbehaves. Using the original schematics, he traces the fault to a single faulty capacitor in the feedback loop that keeps output voltage constant.
RAG (Retrieval-Augmented Generation) và Agentic RAG là hai phương pháp xử lý thông tin khác nhau trong lĩnh vực AI. RAG hoạt động bằng cách truy vấn vector database để tìm thông tin liên quan trước khi tạo câu trả lời, ví dụ như với vector database có thể chứa hàng triệu document embeddings. Agentic RAG thêm vào một agent có khả năng tự suy luận, cho phép hệ thống tự động xác định cần tìm thêm thông tin hay không, như trường hợp của OpenAI's GPT-4 với function calling. Sự khác biệt chính là Agentic RAG có khả năng tự chủ trong việc quyết định cần làm gì tiếp theo thay vì chỉ phụ thuộc vào kết quả truy vấn ban đầu. Đây là kiến thức quan trọng để hiểu sự tiến hóa từ RAG cơ bản đến các hệ thống thông minh hơn có khả năng tự điều chỉnh.
Bài viết này giúp bạn hiểu rõ sự khác biệt cơ bản giữa RAG và Agentic RAG để áp dụng hiệu quả vào các dự án AI.
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