Someone responded to a previous comment of mine [0] positing a Peter principle [1] of slopcoding — it will always be easier to tack on a new feature than to understand a whole system and clean it up. The equilibrium will remain at the point of near, but not total, codebase incomprehensibility.
replyHow is a newer AI going to "clean up" dropped databases, compromised computers or leaked personal data?
reply(None of above is theoretical)
Frankly this is what everyone is counting on whether they know it or not. The question though is not “will the models get good enough?”. The question is does the repo even contain enough accurate information content to determine what the system is even supposed to be doing.
replyAre they improving? I thought they were just getting more expensive
replyMythos apparently wrote a poem so beautiful it made Dario cry.
replydeleted
replyRoses are red
replyViolets are blue
AI is great
And so are you
Crocodile tears, just like the fake "fear" of its capabilities. Anything to raise another round of dumb oil money.
replyPeople are often skeptical when I say this, but there's simply no guarantee that it's possible in principle to clean up a bad architecture. If your system is "overfitted" to 10,000 requirements from 1,000 customers, it may be impossible to satisfy requirements 10,001 through 10,100 without starting over from scratch.
replyIt may be difficult, but impossible is such a big word to use here
replyIt's really not that big of a word. The CAP theorem shows that as few as three reasonable-sounding requirements with no obvious conflicts can be impossible to satisfy simultaneously. (User needs will start more flexible than strict mathematical requirements, of course, but once people start to build production workloads on top of your systems that flexibility is radically reduced.)
replyHow could anyone answer that with any level of certainty?
replyAi runs `rm -rf`
replyBeyond the Singularity, we reach the Nullarity.
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