1) Your original post asks a lot if not too much out the LLM, the expectation you have is too big, to the point that to get anywhere near decent results you need a super detailed prompt (if not several spec documents) and your conclusion stands true: it might be faster to just do it manually. That's the state of LLMs as of today. Your post neither hints at such detailed and laborious prompting nor seem to recognize you've asked it too much, displaying that you are not very comfortable with the limitations of the tool. You're still exploring what it can and what it can't do. But that also implies you're yet not an expert.
2) The second takeaway that you're not yet as comfortable with the tools as you think you are is clearly context management. 2/3k locs of code are way too much. It's a massive amount of output to hope for good results (this also ties with the quality of the prompt, with the guidelines and code practices provided, etc, etc).
3) Neither 1 or 2 are criticisms of your conclusions or opinions, if anything, they are confirmations of your point that LLMs are not there. But what I disagree with is the rush into concluding that AI coding provides net 0 benefits out of your experience. That I don't share. Instead of settling on what it could do (help with planning, writing a spec file, writing unit tests, providing the more boilerplate-y part of the code) and use the LLM to reduce the friction (and thus provide a net benefit), you essentially asked it to replace you and found out the obvious: that LLMs cannot take care of non-trivial business logic yet, and even when they can the results are nowhere near being satisfactory. But that doesn't mean that AI-assisted coding is useless and the net benefit is 0, or negative, it only becomes so as the expectations on the tool are too big and the amount of information provided is either too small to return consistent results or too large for the context to be an issue.
Your conclusion from that is "but they are doing it wrong", while also claiming they are saying things they didn't say (0 net benefits, useless, etc).
Do you see how that might undermine your point? That you feel they haven't take the time to understand the tools, but you didn't actually read what what wrote?
Even if you know better than themselves how musch they know, isn't the tool inadequate just yet for power use then when it is sooo easy to misuse?
Too much tweeking and adapting users to the needs of the tool (vs. the other way around) and there is little point using those (which is a bit of the sickness of modern day computing: 'with computers you can solve problems lightning fast that you wouldn't have without them')
Prior to LLMs, my impression was "high learning curve, high results" was a pretty popular sweet-spot with a large portion of the tech crowd. It seems weird how much LLMs seem to be an exception to this.
And it might be also something with GC, because I suppose the big boys are doing some GRPO over synthetically generated/altered source code (I would!) but obviously doing that in C++ is much more challenging - and I'd expect Rust to be straight impossible.
But take people like me, I am an MD who was always into computers, but that just ended up going down a separate series of life decision, and could never find the time or energy to actually learn to code. When GPT-4 arrived, I started trying out using it for a medically-related coding hobby project, which eventually escalated into an ongoing PhD. Now, the fact is that this whole thing would just never have happened without LLMs. I would have never even thought of starting such a project, and if I did I wouldn't have had the time, and would have never made any progress even if I did. Vibe coding enabled me to do something entirely outside the scope of my previous capabilities. And the reality is that if I hadn't been able to do everything myself (down to the point of installing hardware managing the servers I am using), the project as a whole just wouldn't have happened.
The code I produce isn't going into production anywhere, it is only used for my particular purposes, it is not exposed to the web in any way, and so typical LLM issues like security etc. are a non-issue. And while my understanding of what my code is actually doing is pretty rudimentary (for instance, basic syntax conventions is something I just never bothered to learn), this doesn't really matter in practice. If it works it works.
If you don't know what they are good at and how to use them of course you may end up with mixed results and yes, you may waste time.
That's a criticism I have also towards AI super enthusiasts (especially vibe coders, albeit you won't find much here), they often confuse the fact that LLMs often one shot 80% of the solutions with the idea that LLMs are 80% there, whereas the Pareto principle well applies to software development where it's the hardest 20% that's gonna prove difficult.