I guess I'm officially listed as a "staff engineer". I have been at this for 20 years, and I work with multiple teams in pretty different areas, like the kernel, some media/audio logic, security, database stuff... I end up alternating a lot between using Rust, Java, C++, C, Python and Go.<p>Coding assistant LLMs have changed how I work in a couple of ways:<p>1) They make it a lot easier to context switch between e.g. writing kernel code one day and a Pandas notebook the next, because you're no longer handicapped by slightly forgetting the idiosyncrasies of every single language. It's like having smart code search and documentation search built into the autocomplete.<p>2) They can do simple transformations of existing code really well, like generating a match expression from an enum. They can extrapolate the rest from 2-3 examples of something repetitive, like converting from Rust types into corresponding Arrow types.<p>I don't find the other use cases the author brings up realistic. The AI is terrible at code review and I have never seen it spot a logic error I missed. Asking the AI to explain how e.g. Unity works might feel nice, but the answers are at least 40% total bullshit and I think it's easier to just read the documentation.<p>I still get a lot of use out of Copilot. The speed boost and removal of friction lets me work on more stacks and, consequently, lead a much bigger span of related projects. Instead of explaining how to do something to a junior engineer, I can often just do it myself.<p>I don't understand how fresh grads can get use out of these things, though. Tools like Copilot need a lot of hand-holding. You can get them to follow simple instructions over a moderate amount of existing code, which works most of the time, or ask them to do something you don't exactly know how to do without looking it up, and then it's a crapshoot.<p>The main reason I get a lot of mileage out of Copilot is exactly because I have been doing this job for two decades and understand what's happening. People who are starting in the industry today, IMO, should be very judicious with how they use these tools, lest they end up with only a superficial knowledge of computing. Every project is a chance to learn, and by going all trial-and-error with a chatbot you're robbing yourself of that. (Not to mention the resulting code is almost certainly half-broken.)