this post was submitted on 09 Oct 2026
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sure, that's true, but if we are being serious do you think software made nearly exclusively by a human (or humans) who care or software made mostly by a non-deterministic token generator working on behalf of a human who couldn't bother to write the code themself is more likely to be maintained long enough to gain community trust
Using AI isn't just a sign of laziness. I don't see an issue with using the tools available to do something in less time than usual. If a program is vibe coded then of course it's usually going to be of bad quality, but if it's a mix of human and human checked AI then it can still be a high quality project.
my concern isn't "laziness" - it's that i want software that is maintainable over something flashy that will be rotting in a year
someone who knows what they are doing using autocomplete is fine in my opinion if that is truly how it is being used. the problem is that once you accept an llm writing the rest of a line of boiler plate for you it is a short step into it writing a function body for you which i trust far less.
it is easy to just accept llm content as 'good enough' and become lulled into not thinking critically about the security and performance implications of the code it is popping out. /if/ humans who know what they are doing are /truly/ checking the output the same way they would scrutinize an intern they don't trust's code then.. sure. we should be serious though about the fact that that is neither what the ai companies are incentivizing nor what they are advertising.
all of that aside, my most pragmatic concern is that projects slapped together with meaningful llm aid usually are not - in practice - maintained for long unless they were already a well maintained project pre-llm mass adoption. checking a repo for something shiny and new and finding that claude is a contributor is, in my experienced, /very/ tightly correlated with finding that /every single/ top level file/directory was last changed as part of the same commit n number of months ago (n > 2).
part of why i pick and use the tools i use is because i have /some/ confidence that i won't have to drop them and deal with identifying (and then working through the hassle of sidegrading to) a new tool for that task every ~6 months. i would hate to see long maintained projects i use, or even cool new projects i follow, hamstring their maintainability in the medium and long term in exchange for a burst of speed in the short term.
I'm weary of the slippery slope fallacy embedded in your reply, I'm not sure if using some AI often results quality to fall out the window.
Also I don't really trust any new projects to be maintained for a long time, unless I trust the developer(s) have a clear commitment to supporting it in the long term.
this project (configarr) is one I've been using in myself-hosted setup clearly uses AI to help with development, although I'm not sure to what extent. It's been maintained consistently for 2 years.
Avoiding using brand new programs has worked quite well for me not being left with unmaintained software so far.
nope. apologies but you have stepped on a pet peeve of mine.
the slippery slope fallacy is someone saying: don't allow a~1~ because if we allow a~1~ then a~2~ will happen and we don't want a~2~ so we must prohibit a~1~.
the slippery slope fallacy is not someone saying: a naturally trends towards a^2^ and a^2^ would be a problem so we should avoid passing a*2.
for example, the slippery slope fallacy was committed when people used to say that letting consenting adults legally marry each other would inevitably lead to people legally marrying animals. it is not committed when, for example, people say that americans should at some point put a limit on how big their trucks are getting since the short term incentives all push in that direction despite the fact that more and more people keep dying. do you understand?
saying that it is easy to slip further and further into leaning on llms in the way i described is both observably true and actively what the companies selling the product (with all of their vast resources) are aiming to achieve. that is the [if the short term incentives will foreseeable move us from a to a^2^] part of this. the other part of this is that i believe that a future where projects are truly vibe coded (no human reviewing the code) is not one where that software is maintained in the long term [a^2^ isn't desirable] and it seems you agree on that part.
i am not saying that if people follow the short term incentives and move towards using llms more that next they will 'get lazy' (to use your word) with cooking too and start putting too much salt and oil in their food to try to quickly make it taste better. not a~1~ -> a~2~ but a -> a*2 -> a^2^. not only that but also i am not calling for banning generative ai at all. i am saying that because a^2^ is undesirable let's stop at a*2 (".../truly/ checking the output the same way [we] would scrutinize [code written by an intern we don't trust].") because that is the point that i believe delineates (broadly) positive vs negative outcomes in the long term.
all of that to say, even if you don't agree with me on generative ai, i hope you now know how to correctly identify that fallacy in the future.
Humans are lazy as long as AI is affordable and that is a big if right now more and more people well use it for more and more things. How many people remwbmwr phone numbers or directions anymore?
i don't disagree with you on the first half very broadly but that's a pretty deeply reductionist and apathetic way to look at it.
we can both identify what is likely given current incentives and then work to avoid those outcomes if they aren't desirable.
i don't find the lack of people remembering phone numbers or keeping maps in their cars undesirable.
i do find a swerve into long term unmaintainability for many new (and some existing) foss projects undesirable.