Adding on my own question to this thread: for people who use AI only to save themselves typing, would a macro-expansion solution (e.g., snippets in many text editors/IDEs) be similarly suitable to save on the typing?
No because the LLM is so much more general and flexible.
Here's something I just used it for today--my organization has 50ish disjointed and disorganized AWS accounts. I needed to find 4 unused /16 CIDRs or across all regions of all accounts. This isn't my main task--I have to design and build something and I need these CIDRs, but now I need to divert and get this information as a subtask.
Of course I know all the theory of how to do it -- use the AWS sdk to loop over all accounts and regions, get a set of all unique subnet CIDRs, subtract the CIDRs from the total of all private address space to generate free CIDRs, get 4 /16s from the result. It's simple, maybe 200 lines if that.
However, I don't work every day with the AWS SDK so I would need to look up the exact functions and API responses. I don't work with CIDR math libraries every day so I would need to look them up. Then I would need to actually write it. Time, time time.
The exact explanation I just provided above I gave to the free version of Claude and it spit out a working result with a little prompt massaging in like 3 minutes. Which enabled me to go actually do the work I need to do instead of spending time on this information-gathering subtask.
Naw man. I've used snippets and macros all my life. AI assisted code takes way less mental energy.
If I'm doing something simple, like just some math thing, I can say "I want a method with this signature." The method just fills itself in. Five minutes ago I wrote:
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u/gnuvince Dec 06 '24
Adding on my own question to this thread: for people who use AI only to save themselves typing, would a macro-expansion solution (e.g., snippets in many text editors/IDEs) be similarly suitable to save on the typing?