zlacker

>>tablet+(OP)
One thing that I find truly amazing is just the simple fact that you can now be fuzzy with the input you give a computer, and get something meaningful in return. Like, as someone who grew up learning to code in the 90s it always seemed like science fiction that we'd get to a point where you could give a computer some vague human level instructions and get it more or less do what you want.

>>gdubs+Z
The other side of the coin is that if you give it a precise input, it will fuzzily interpret it as something else that is easier to solve.

>>progva+12
It will, or it might? Because if every time you use an LLM is misinterprets your input as something easier to solve, you might want to brush up on the fundamentals of the tool

(I see some people are quite upset with the idea of having to mean what you say, but that's something that serves you well when interacting with people, LLMs, and even when programming computers.)

>>Booris+F2
Might, of course. And in my experience it's what happens most times I ask a LLM to do something I can't trivially do myself.

>>progva+O3
Well everyone's experience is different, but that's been a pretty atypical failure mode in my experience.

That being said, I don't primarily lean on LLMs for things I have no clue how to do, and I don't think I'd recommend that as the primary use case either at this point. As the article points out, LLMs are pretty useful for doing tedious things you know how to do.

Add up enough "trivial" tasks and they can take up a non-trivial amount of energy. An LLM can help reduce some of the energy zapped so you can get to the harder, more important, parts of the code.

I also do my best to communicate clearly with LLMs: like I use words that mean what I intend to convey, not words that mean the opposite.