TGINSIGHT SIMILAR POSTS
Source channel @githubtrending · Post #14926 · Jul 8
#jupyter_notebook#artificial_intelligence#book#large_language_models#llm#llms#oreilly#oreilly_books You can learn how to use Large Language Models (LLMs) effectively through the book *Hands-On Large Language Models* by Jay Alammar and Maarten Grootendorst. This book uses nearly 300 custom illustrations to explain key concepts and practical tools for working with LLMs, including tokenization, transformers, prompt engineering, fine-tuning, and advanced text generation. It also provides runnable code examples in Google Colab, making it easy to practice and apply what you learn. This resource helps you understand and build your own LLM applications confidently, saving you time and effort in mastering complex AI technology. It’s highly recommended for anyone wanting hands-on experience with LLMs. https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
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