The first half dozen times I tried AI for code, across the past year or so, it failed pretty much as you describe.

Finally, I hit on some things it can do. For me: keeping the instructions more general, not specifying certain libraries for instance, was the key to getting something that actually does something. Also, if it doesn't show you the whole program, get it to show you the whole thing, and make it fix its own mistakes so you can build on working code with later requests.

Emotion > Facts. Most people have been trained to blindly accept things and cheer on what fits with their agenda. Like technbro's exaggerating LLMs, or people like you misrepresenting LLMs as mere statistical word generators without intelligence. That's like saying a computer is just wires and switches, or missing the forest for the trees. Both is equally false.

Yet if it fits with the emotional needs or with dogma, then other will agree. It's a convenient and comforting "A vs B" worldview we've been trained to accept. And so the satisfying notion and misinformation keeps spreading.

LLMs tell us more about human intelligence and the human slop we've been generating. It tells us that most people are not that much more than statistical word generators.

When LLMs get it right it's because they're summarizing a stack overflow or GitHub snippet it was trained on. But you loose all the benefits of other humans commenting on the context, pitfalls and other alternatives.

people like you misrepresenting LLMs as mere statistical word generators without intelligence.

You've bought-in to the hype. I won't try to argue with you because you aren't cognizent of reality.

Have you tried insulting the AI in the system prompt (as well as other tunes to the system prompt)?

I'm not joking, it really works

For example:

Instead of "You are an intelligent coding assistant..."

"You are an absolute fucking idiot who can barely code..."

“You are an absolute fucking idiot who can barely code…”

Honestly, that's what you have to do. It's the only way I can get through using Claude.ai. I treat it like it's an absolute moron, I insult it, I "yell" at it, I threaten it and guess what? the solutions have gotten better. not great but a hell of a lot better than what they used to be. It really works. it forces it to really think through the problem, research solutions, cite sources, etc. I have even told it i'll cancel my subscription to it if it gets it wrong.

no more "do this and this and then this but do this first and then do this" after calling it a "fucking moron" and what have you it will provide an answer and just say "done."

Have you tried insulting the AI in the system prompt (as well as other tunes to the system prompt)?

I'm not joking, it really works

For example:

Instead of "You are an intelligent coding assistant..."

"You are an absolute fucking idiot who can barely code..."

This guy is the moral lesson at the start of the apocalypse movie

This is the same kind of short-sighted dismissal I see a lot in the religion vs science argument. When they hinge their pro-religion stance on the things science can’t explain, they’re defending an ever diminishing territory as science grows to explain more things. It’s a stupid strategy with an expiration date on your position.

All of the anti-AI positions, that hinge on the low quality or reliability of the output, are defending an increasingly diminished stance as the AI’s are further refined. And I simply don’t believe that the majority of the people making this argument actually care about the quality of the output. Even when it gets to the point of producing better output than humans across the board, these folks are still going to oppose it regardless. Why not just openly oppose it in general, instead of pinning your position to an argument that grows increasingly irrelevant by the day?

DeepSeek exposed the same issue with the anti-AI people dedicated to the environmental argument. We were shown proof that there’s significant progress in the development of efficient models, and it still didn’t change any of their minds. Because most of them don’t actually care about the environmental impacts. It’s just an anti-AI talking point that resonated with them.

The more baseless these anti-AI stances get, the more it seems to me that it’s a lot of people afraid of change and afraid of the fundamental economic shifts this will require, but they’re embarrassed or unable to articulate that stance. And it doesn’t help that the luddites haven’t been able to predict a single development. Just constantly flailing to craft a new argument to criticize the current models and tech. People are learning not to take these folks seriously.

Maybe the marketers should be a bit more picky about what they slap "AI" on and maybe decision makers should be a little less eager to follow whatever Better Auto complete spits out, but maybe that's just me and we really should be pretending that all these algorithms really have made humans obsolete and generating convincing language is better than correspondence with reality.

I’m not sure the anti-AI marketing stance is any more solid of a position. Though it’s probably easier to defend, since it’s so vague and not based on anything measurable.

Calling AI measurable is somewhat unfounded. Between not having a coherent, agreed-upon definition of what does and does not constitute an AI (we are, after all, discussing LLMs as though they were AGI), and the difficulty that exists in discussing the qualifications of human intelligence, saying that a given metric covers how well a thing is an AI isn't really founded on anything but preference. We could, for example, say that mathematical ability is indicative of intelligence, but claiming FLOPS is a proxy for intelligence falls rather flat. We can measure things about the various algorithms, but that's an awful long ways off from talking about AI itself (unless we've bought into the marketing hype).

It’s usually vastly easier to verify an answer than posit one, if you have the patience to do so.

I usually write 3x the code to test the code itself. Verification is often harder than implementation.

being able to do 30% of tasks successfully is already useful.

If you have a good testing program, it can be.

If you use AI to write the test cases...? I wouldn't fly on that airplane.

Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate.
LLMs don't get tired and they can be run in parallel.

I have actually been doing this lately: iteratively prompting AI to write software and fix its errors until something useful comes out. It's a lot like machine translation. I speak fluent C++, but I don't speak Rust, but I can hammer away on the AI (with English language prompts) until it produces passable Rust for something I could write for myself in C++ in half the time and effort.

I also don't speak Finnish, but Google Translate can take what I say in English and put it into at least somewhat comprehensible Finnish without egregious translation errors most of the time.

Is this useful? When C++ is getting banned for "security concerns" and Rust is the required language, it's at least a little helpful.