alt Hacker NewsMy guess:
- AI slop is clearly negative. "Lacking in effort", "low quality".
- Vibecoding, I believe, tends to mean "letting the AI do it without putting much effort into it". So vibecoding produces AI slop.
- HDAE, that this article tries to define, is "a method for building complex software by combining human-defined architecture, constraints, milestones, acceptance criteria, and stopping points with an autonomous implementation, execution, diagnosis, and revision loop".
In other words, they say "it is actually possible to put a lot of effort into producing code with AI". And I personally don't disagree with that.
Now of course, it feels like marketing, a bit. "I will present you how I built this kernel as an effort to make you accept it as "not slop", by trying to convince you that I put a lot of effort into producing it".
IMO, it does not really matter. Whether code is written by a random person on the Internet, vibecoded by a random person on the Internet or HDAE-ed by a random person on the Internet, it should not be trusted unless audited. If it happens that vibecoded code is good, I don't see a reason not to use it. If HDAE-ed code is bad, it should not be used.
Replies
good balanced view. Regardless of how much effort is put into AI writing correctly, humans should review and understand.
the last part is tricky on these kinds of projects.
Just look at the bugs coming out in Linux or other kernels. Often they are very subtle errors based in very subtle misunderstandings.
This is why you'd want a lot of different people involved who in their combined knowledge have a good chance to understand enough to make such a project a success, even IF all they do is review the AI code.
IME it is much, much more difficult to review code that is the result of a LLM regurgitation than to review human-written code made by someone even a bit experienced in programming - even more so if the human in question is a domain expert.
For one, LLMs tend to waffle and produce code that works but is much more extensive and/or unintuitively organized than code that a human writes. The bigger thing however is testing. LLM code tends to test itself, whereas a domain expert human tends to have many more domain-specific edge cases in mind.
I played with the idea of starting a new Linux distro called "Slopware", which accepts basically everything from either humans or AI but requires rigorous testing and development processes, raising the quality to levels realistically unreachable by single human developers.
Not sure if that naming choice would have been a good idea though. :)