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No, this is a design decision of Cargo to default to using project-local cached artifacts rather than caching them at the user or system level. You can configure Cargo to do so if you'd like. The reason it doesn't do this by default is because Cargo gives crates great latitude to configure themselves via compile-time flags, and any difference in flags means you get a different compiled artifact anyway. On top of that, there's the question of what `cargo clean` should do when you have a global cache rather than a local one.
If such a thing came to C++, there would obviously be limitations around module boundaries, when different modules used a different Edition. But perhaps this could be a way forward that could allow both camps to have their cake and eat it too.
Imagine a world where the main difference between Python 2 and 3 was the frontend syntax parser, and each module could specifically which syntax ("Edition") it used...
C++ is a mess in that it has too much historic baggage while trying to adapt to a fiercely changing landscape. Like the article says, it has to make drastic changes to keep up, but such changes will probably kill 80% of its target audiences. I think putting C++ in maintenance mode and keep it as a "legacy" language is the way to go. It is time to either switch to Rust, or pick one of its successor languages and put effort into it.
1. Snake case to camelCase. Even without AI we can already complete these tasks easily. VSCode itself has command of "Transform to Camel Case" for selection. It is nice the AI can figure out which text to transform based on context, but not too impressive. I could select one ":, use "Select All Occurrences", press left, then ctrl+shift+left to select all the keys.
2. Generate boilerplate from documentation. Boilerplate are tedious, but not really time-consuming. How many of you spend 90% of time writing boilerplate instead of the core logic of the project? If a language/framework (Java used to be, not sure about now) requires me to spend that much time on boilerplate, that's a language to be ditched/fixed.
3. Turn problem description into a block of concurrency code. Unlike the boilerplate, these code are more complicated. If I already know the area, I don't need AI's help to begin with. If I don't know, how can I trust the generated code to be correct? It could miss a corner case that my question didn't specify, which I don't yet know existing myself. In the end, I still need to spend time learning Python concurrency, then I'll be writing the same code myself in no time.
In summary, my experience about AI is that if the question is easy (e.g. easy to find exactly same question in StackOverflow), their answer is highly accurate. But if it is a unique question, their accuracy drops quickly. But it is the latter case where we spend most of the time on.