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klibertpyesterday at 7:02 PM0 repliesview on HN

I think the end goal the author describes:

> I can imagine soon-to-arrive interfaces where you just drag and drop components while narrating your desires with your voice, the models able to perform the “brainstorming,” “planning,” and “work” — operations that can take ten minutes or longer today — in mere seconds tomorrow.

Is impossible without ASI and more. This very vision failed to materialize in the past (4th-generation languages?) and still routinely fails, not only with models/agents, but also with humans and entire teams of humans on the other side. For a model to be that useful, ASI is the first enabling factor, but it also needs to develop mind-reading hardware and software and convince people that it's not breaching their privacy. I don't think this is going to happen. Not this millennium, at least.

There is a good case for prototypes, MVPs, and personal mods. I can imagine more and more users making use of the freedom to modify the open-source code. Technically, it was always possible, but for a normal user, it was not a realistic choice (learn to code (long and hard) or hire a programmer (expensive and inconvenient)). Even for me - a programmer by trade - fixing bugs in random pieces of software I might use (or not) once in a while was something I very rarely had the spare time and energy to do. The capabilities of the current crop of AI models/harnesses make this WAY easier: cheap on a subscription and requiring much less of my time. But that's for personal code modification. Pushing vibe-coded changes to anywhere outside of my machine is still something I wouldn't do, because I can see how mediocre the output code is. Unless the models can always write code as good as the top 20% of human-written code, their output will remain a liability. It's OK if I'm the only user; it's wrong to push such a problem to others. The issue here is that non-programmers cannot recognize when the code is good enough, which is why I used the word "always" - otherwise, pushing LLM code to others is a coin toss whether it's helpful or detrimental (for the project, the maintainers, and other users).