This article was originally published on aicoderscope.com
ML engineers aren't software engineers who happen to write some Python. They live in notebooks, build training loops, fight CUDA dependency hell, and write code that often exists in a Jupyter cell for six months before it becomes a real file. The AI coding tools built for web and backend devs have caught up with this workflow—some of them, at least.
Here's who does it well, who's faking it, and one mistake every AI tool makes with your PyTorch setup.
The CUDA trap — the one mistake every AI coding tool makes
Before comparing features, there's a specific failure mode you need to know about. Ask any AI coding assistant to help you set up PyTorch with GPU support and there's a good chance it gives you:
