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Same here! Thats the main use I have for ChatGPT in any practical sense today - generating Bash commands. I set about giving it prompts to do things that I've had to do in the past - it was great at it.

Find all processes named '*-fpm' and kill the ones that have been active for more than 60 seconds - then schedule this as a Cron job to run every 60 seconds. It not only made me a working script rather than a single command but it explained its work. I was truly impressed.

Yes it can generate some code wireframes that may be useful in a given project or feature. But I can do that too, usually in about the time it'd take me to adequately form my request into a prompt. Life could get dangerous in a hurry if product management got salty enough in the requirements phase that the specs for a feature could just be dropped into some code assistant and generate product. I don't see that happening ever though - not even with tooling - product people just don't seem to think that way in the first place in my experience.

As developers we spend a lot of our time modifying existing product - and if the LLM knows about that product - all the better job it could do I suppose. Not saying that LLMs aren't useful now and won't become more useful in time - because they certainly will.

What I am saying is that we all like to think of producing code as some mystical gift that only we as experienced (BRILLIANT, HANDSOME AND TALENTED TOO!!) developers are capable of. The reality is that once we reach a certain level of career maturity, if we were ever any good in the first place, writing code becomes the easiest part of the job. So theres a new tool that automates the easiest part of the job? Ok - autocomplete code editors we're cool too like that. The IDE was a game changer too. Automated unit tests were once black magic too (remember when the QA department was scared of this?).

When some AI can look at a stack trace from a set of log files, being fully aware of the entire system architecture, locate the bug that compiled and passed testing all the way to production, recommend, implement, test and pre-deploy a fix while a human reviews the changes then we're truly onto something. Until then I'm not worried that it can write some really nice SQL against my schema with all kinds of crazy joins - because I can do that too - sometimes faster - sometimes not.

So far ChatGPT isn't smarter than me but it is a very dutiful intern that does excellent work if you're patient and willing to adequately describe the problem, then make a few tweaks at the end. "Tweaks" up to seeing how the AI approached it, throwing it out and doing it your own way too.




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