For category #2 it’s very useful as well and ties in with the theme of the article in that it reduces the activation energy required to almost zero. I’ve been using AI relentlessly to pursue all kinds of ideas that I would otherwise simply write down and file for later. When they involve some technology or theory I know little about I can get to a working demo in less than an hour and once I have that in hand I begin exploring the concepts I’m unfamiliar with by simply asking about them: what is this part of the code doing? Why is this needed? What other options are there? What are some existing projects that do something similar? What is the theory behind this? And then also making modifications or asking for changes or features. It allows for much wider and faster exploration and let’s you build things from scratch instead of reaching for another library so you end up learning how things work at a lower level. The code does get messy but AI is also a great tool for refactoring and debugging, you just have to adjust to the faster pace of development and remember to take more frequent pauses to clean up or rebuild from a better starting point and understanding of the problem.