4 ms·
I can kinda see it doing 90% of the code by volume, but it would be the easy 90%. And the remaining 10% will need to be done by someone who actually knows what
by jonplackett 2y ago
I can kinda see it doing 90% of the code by volume, but it would be the easy 90%. And the remaining 10% will need to be done by someone who actually knows what they’re doing.
Still amazing for time saving but so far I haven’t been able to get even a SwiftUI class written to the standard I’d use it as-is.
- aprilthird2021 2y agoI agree with you. The same way compiling code meant that 99% of the code was machine-generated, but you still wrote tons of code. Lots of code is boilerplate or large switch statements (esp in OOP), or other such things. All of which AI code completion makes a breeze. The actual hard parts of code (often the various ways of connecting two disparate systems), AI doesn't really help with unless you tell it what to do and how, so you're still the ultimate important decision-maker in this scenario and can't be "replaced"
- fsloth 2y agoAgree, excellent for commodity boilerplate where there is basically just a bunch of 1:1 mappings that need to be filled. Terrible for novelties and problems in a large solution space. The more degrees of freedom there is to fail, the worse it will work.
- wanderingstan 2y agoThere will also be Jevons Paradox happening: LLMs reduce the “cost” of writing code, so more code gets written. I already find myself building prototypes that I never would have attempted previously; it’s just too easy to have the LLM whip one up. LLMs also make it easier to do “grunt work” coding of writing validation checking and unit tests on non-critical code where previously I wouldn’t have bothered.