Flathub has replaced its blanket ban on AI-generated applications with a disclosure requirement. Reviewers can still reject submissions, and manifests remain off-limits for AI-generated content.
Flathub has replaced its blanket ban on AI-generated applications with a disclosure requirement. Reviewers can still reject submissions, and manifests remain off-limits for AI-generated content.
I don’t have an issue with this. If it’s AI you can usually tell, if you can’t tell then it’s not slop and it’s fine to use.
i would strongly prefer a label to easily filter with as a first pass plus not all slop has an agents.md or claude.md but can still be riddled with bugs and security vulnerabilities
That goes for AI and human made software. You should only use security critical software that is trusted by the community.
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.