AI is great for when you’re typing out 30 slight variations of the same thing, and you can just be like “see what I’m doing here? Do it for the other 30 variables” and it does it just fine.
It’s also great if you have a general knowledge of something but don’t know the details. Like today I needed to do some database introspection using queries in Snowflake, I knew exactly what I needed but not where the database schema is located etc., so I let GPT write the query instead.
Or some time ago I needed to get all instances of classes implementing a specific generic interface in .NET, the code eventually dabbled into the very specifics of the runtime, it would’ve taken me much longer to find out with documentation.
All in all, it’s my opinion that AI is great if two conditions are met:
you know exactly what you want to do and you can specify it to very tiny details
you have the knowledge to verify whether the result makes sense without running the code (or at least the knowledge that it can’t break your app or computer)
AI is great for when you’re typing out 30 slight variations of the same thing, and you can just be like “see what I’m doing here? Do it for the other 30 variables” and it does it just fine.
It’s also great if you have a general knowledge of something but don’t know the details. Like today I needed to do some database introspection using queries in Snowflake, I knew exactly what I needed but not where the database schema is located etc., so I let GPT write the query instead.
Or some time ago I needed to get all instances of classes implementing a specific generic interface in .NET, the code eventually dabbled into the very specifics of the runtime, it would’ve taken me much longer to find out with documentation.
All in all, it’s my opinion that AI is great if two conditions are met:
*pastes list of 100 numbers"
"Flunky, put this in a where in SQL statement. "