alt Hacker NewsAnyone who thinks AI is good with writing code that is hard to write for the operator, not due to lack of basic software engineering know how but complexity of the domain, either has access to models beyond what is available to the public or is completely lost.
I believe this because every time I use AI for domains that I consider myself above competent, if it is anything beyond UI components or a simple CRUD endpoints, I cringe at the quality of what it generates.
This has made me to be extremely cautious of starting working in a new domain with AI if I want anything beyond throw away quick hacks or junk, shy of quick bug fixes perhaps.
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> I cringe at the quality of what it generates.
Besides all the other mentioned points, I think a good remaining less-discussed point is that quality in software has always been in the eye of the beholder. You may very well see the AI output as low quality, I may not, and its not necessarily clear who is right or wrong, because there was always little precedent in objectively evaluating code quality.
This is a long standing issue, and was never resolved before AI happened, and the coming of AI has not really changed things, except that AI is under a magnifying glass obviously. How do we objectively measure the quality of code? There's some general consensus on things, but surprisingly little is true professional agreed upon consensus.
If I can make up a figure, I would guess 95% of software engineering quality rhetoric, and craftmanship advice, is just strongly held opinions.
This is not something I can prove, but if I look at the (still ongoing.....) debates on very basic ideas like clean code, and the reactions from also-great programmers like Carmack, Blow & Muratori, it is clear to me that there is little consensus on even the fundamentals of software design.
If all these people can produce excellent working software while disagreeing on these fundamentals (of quality), it means we do not yet understand what the fundamentals are.
Fable (and even Opus if kept tightly under reigns) does generate high-quality code even for highly complex tasks.
It generally performs better if the tasks are broken done into small manageable pieces, and the person is actually reviewing and calling out problems, which usually requires the person to be a competent engineer in the problem domain to begin with.
But yes, I have personally used it to build what the OP calls a JIT. I would usually write that by hand and it would take me one week. The AI does it in an hour.
Three quarters of my CS class at university could barely code and/or understand code.
That’s not a joke. A lot went on to be programmers professionally. And judging by the quality of closed & open source code I witness daily those figures from university accurately depict people’s capabilities.
Now that said, if you can’t really code then using AI will be a godsend to said individuals.
When did you last try? They've improved a lot.
Also often the difficulty with writing code is simply knowing where to start - getting past the blank page. AI can help a lot with that. Often there's a task where I've got kind of writers block, but you can ask AI to do it and suddenly it's like "ah yeah, sort of but actually that's not quite right we should do it this way".
> I believe this because every time I use AI for domains that I consider myself above competent, if it is anything beyond UI components or a simple CRUD endpoints, I cringe at the quality of what it generates.
In a long run session Fable 5 generated a Disney principled (physically based) shading/lighting engine from scratch, both with a CPU (SIMD accelerated) backend _and_ a full GPU Vulkan backend. Exceptional performance too; the CPU backend runs almost realtime and literally looks better than some AAA games outright. Took it about ~8 hours wall time total time to achieve this.