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I'm in software, and I'm impressed with it. I think it's a good tool. If it goes mainstream, I can see it replacing simple queries that get SEO'd to hell with autogenerated unhelpful articles (e.g. "compare koa and express", "most commonly used Azure services"), and queries for documentation or basic initial code generation. I think it's better than Google.
To summarise the problem: for the sort of questions I ask, it's right 90% of the time. If I don't know anything about a topic, this looks incredible but is useless -- I have no way of knowing what it's got wrong (I can ask follow-ups, but it might get them wrong too). It also has a tendency to blag, saying meaningful things which are relevant but still wrong.
If I know a lot about a topic, or an adjacent topic, this is fine: I can read the paragraphs, identify the likely errors, and refresh myself. If I ask it to make a table of equivalent services in AWS and Azure, it is mostly correct -- and it makes a more helpful table than Azure's docs, as Azure's docs are alphabetical, but ChatGPT puts the most popular ones at the top.
When writing code, it doesn't necessarily do it the best way, and sometimes makes (large) errors. If you know roughly what you're doing, you can prompt it further to iterate, and it's a great assistant. If you don't know what you're doing, you're probably just going to propagate the errors.
As an example: I just asked it to compute the inode percentage in a volume. It wants to use
df -i(perfect, and 90% of the way there for a human). To compute the percentage, it observes that the calculation is "used divided by total": fair, but you could just read off the value. But when it comes to writing the script, it calculates "total divided by percentage", which is total nonsense.If you take the docs together with the code, you can figure out what to do a lot more easily than starting from scratch. But you have to take what it gives you with a pinch of salt.
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