大模型将改变软件开发的模式
人工智能乘着算力发展的东风突飞猛进。从机器学习到基于神经网络的深度学习,注意力机制的引入和预训练模型的发展,到如今通用大语言模型,我感到我们离未来那么近。
人工智能乘着算力发展的东风突飞猛进。从机器学习到基于神经网络的深度学习,注意力机制的引入和预训练模型的发展,到如今通用大语言模型,我感到我们离未来那么近。
这是我当软件安全课程助教时,在堆溢出内容后布置的一道题目,二进制文件在此。题目考察的是堆溢出safe Unlinking的问题。
在写项目的单元测试时,我需要一些函数定义作为测试用例,于是告诉ChatGPT给我一个稀奇古怪的C函数定义,并获得如下定义:
这是我当软件安全课程助教时,在栈溢出内容后布置的一道题目,二进制文件在此。题目很简单,是最基础的栈溢出问题。
ChatGPT和GPT4无疑是近期最热的科技话题,我感觉到出现在科幻电影中的场景变得那么近。OpenAI为未来的机器人设计出了大脑,波士顿动力则设计出了身体。也许再过五年,一个让我们都感到震撼的、划时代的机器人就能出现在我们眼前!
LLVM infrastructure provides numerous interfaces to meet various requirements. However, lots of interfaces lack clear documents and example code. It is time-consuming for newcomers, including me, to find the ideal APIs and figure out their usage. To tackle this, I will write a series of articles that contain LLVM Interface in titles focusing on the useful APIs for program analysis. The contents of them will be short and concentrate more on concrete use cases than internal principles.
Rizin, which originates from Radare2, envolves fast with neat code style and friendly community. A big step of Rizin is the new implementation of shell compared with Radare2. Radare2 highly depends on switch statements to parse commands and conducts corresponding handlers. The situation becomes worse when it comes to the huge number of commands, which may still grow according to the various requirements of users.
在很多博客中,在和其他人的交流中,我常常听到这样描述clang和LLVM:clang是一个编译器前端,生成中间文件交给LLVM处理。依我来看,这样说是不准确的。
LLVM中提供了一组便捷好用的命令行参数处理接口,本文针对不同的场景介绍如何使用它们。
std::vector is a popular data structure for C++ programming and it’s essential to release the memory within the vector when it is no longer used to avoid the memory leaks.
最近一段时间,产生了自己装一台电脑的想法,或者用一个更精确的词语:攒机。