I was recently reminded of why I think it’s a bad idea to teach beginners C++. It’s a bad idea because it is an objective mess–albeit a beautiful, twisted, tragic, wondrous mess. Despite the current state of the community, this post is not a polemic against modern C++. This post is partly a follow-up on Simon Brand’s article, Initialization in C++ is bonkers, and partly a message to every student who’s wanted to begin their education by gazing into the abyss.
C/C++ extension does not include a C++ compiler. So, you will need to install one or use which is already installed on your computer. Also, Make sure to add C++ compiler PATH to environment variable…
I’ve been working for almost a year implementing micro-services on C++11 running as Docker containers. Through my journey I’ve seen to emerge quite a bunch of interesting tools to work with C++ on…
Browser-based frontend to gdb (gnu debugger). Add breakpoints, view the stack, visualize data structures, and more in C, C++, Go, Rust, and Fortran. Run gdbgui from the terminal and a new tab will open in your browser.
libfive is a software library and set of tools for solid modeling, especially suited for parametric and procedural design. It is infrastructure for generative design, mass customization, and domain-specific CAD tools.
While adding compiled from source glew libraries in VS 2017, I encountered:
LNK4272 library machine type 'x64' conflicts with target machine type 'x86'
Here, Point 2. was really helpful !
Sourcetrail is a productivity tool for software developers on Windows, Mac and Linux. It uses static source code analysis to provide a visualization that lets you follow calls and other dependencies.
The main goal of include-what-you-use is to remove superfluous #includes. It does this both by figuring out what #includes are not actually needed for this file (for both .cc and .h files), and replacing #includes with forward-declares when possible.
A new system for creating code that manipulates tensors yields programs that are 100 times as efficient as those produced by existing software packages, with ramifications for big-data analysis and machine learning.
W. Lavrijsen, and A. Dutta. Proceedings of the 6th Workshop on Python for High-Performance and Scientific Computing, page 27--35. Piscataway, NJ, USA, IEEE Press, (2016)
D. Wu, L. Chen, Y. Zhou, and B. Xu. 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), page 1-10. (October 2015)