They are not. I think it is a good criticism though. Many people seem to be touting productivity that is only in the context of productivity towards more LLM inference operations and not productive in the sense of solving real world computing problems. There is a lot of material that suggests positive results are a kind of wish casting and people are not aware of the agency they are bringing to the interaction. The fact that you can hack things that do cool stuff is more of a reflection that you do those things, and that these models are not capable of it. That's why I recommend you work with others and you'll see your concepts that you feel are generalizable are not, and any learnings or insights are not like learning math or how to read, but more like learning a specific video game's rules. This is also why it is enthralling to you because you actually have only the illusion of controlling it.
Being a professional also involves reading about the technology like how there are studies saying that prompt engineering has hit a limit which is why people are talking about reasoning, agentic systems, this vendor vs that vendor vs offline, etc... Part of being a professional is also wanting to understand why and how something works and not disclosing my secrets to a third party vendor or otherwise wish casting over a black box that I cannot audit. Part of being a professional includes a history of natural language processing experience and knowing the limits of the digital representation of information, and having played with the early incarnations of OpenAI products and seeing them fail in the same way ever since, in the well documented ways that transformers are known to fail. There's also a real problem in the overloading of terms where the lay people are hearing "we have to cross our fingers and hear rumors and the magic box will make things correct for us" vs. machine learning experts saying "you have to limit the context to precisely the problem at hand and then their accuracy will improve" except... in machine learning terms the second is actually about as good as the first and they are both bad ways of working. There is very exciting ongoing research operating on these things in parallel to just absolute mysticism and here on HN that line is pretty much eroded away, and a lot of that is due to thought-terminating responses that blame the users.
Software worked quite well before the plagiarizing chat bots.
This is just a new fad like agile, where the process and tool obsessed developers blog and preach endlessly without delivering any results.
For them "it works". The rest of us can either join the latest madness or avoid places that use LLMs, which of course includes all open source projects that do actual work that is not measured in GitHub kLOCs and marketing speak.
I am inclined to agree with you, but Simonw's experiments are also illuminating.
And I have personally had good results with ChatGPT. I use it maybe 2 hours a month, on tasks that it is perfect for. The tasks would have taken 6 to 8 hours without ChatGPT. I don't find it at all useful in my normal programming activities.
The hate: "LLM's can't do anything without hand holding"
I think both of these takes a disingenuous.
> not productive in the sense of solving real world computing problems.
Solving problems is a pretty vague term.
> The fact that you can hack things that do cool stuff
Lots of times this is how problems actually get solved. I would argue most of the time this is how problems get solved. More so if your working with other peoples software because your not reinventing CUPS, Dashboards, VPN's, Marketing Tools, Mail clients, Chat Clients and so on... I would argue that LOTS of good software is propped up directly and indirectly by this sort of hacking.
Oh I mean for sure, but the point I was trying to make is that OP did those things, and I would argue the LLMs mostly just give him the permission to do it rather than actually producing something that he actually fully let the system do. I mean, he even says this too. I understand hacking is how things are done, but the thing that managers don't understand is that hacking is not their personal army, and you can't rub AI on things and make them better without losing determinism.
