I've read the thread and in my mind you're missing that LLMs increase the surface area of visibility of a thing. It's a probe. It adds known unknowns to your train of thought. It doesn't need to be "creative" about it. It doesn't need to be complete or even "right". You can validate the unknown unknown since it is now known. It doesn't need to have a measured opinion (even though it acts as it does), it's really just topography expansion. We're getting in the weeds of creativity and idea synthesis, but if something is net-new to you right now in your topography map, what's so bad about attributing relative synthesis to the AI?
Honest, non-confrontational, non-passive aggressive question: Have you used any of the latest models in the last 6 months to do coding? Or frankly, in the last year?
What are you defining as free versus frontier, and for what purpose? For coding there is a big difference between Opus and GPT 5.3/4 versus Sonnet and other models such as open weight ones.
I just don't use the web much anymore because the experience has degraded so much over the past several years and it has become decreasingly useful at work as well. I do sometimes need to search for a document and find Kagi pretty good for that, but the old way of using a search engine to kind of explore and discover stuff just isn't viable anymore, unfortunately.
I administer software for a living so I read a lot of documentation of that software but it comes with the software so I don't ever really need to search for it; I also read and participate in some forums and us the relevant IRC channels.
If you're not familiar with the problem space, by definition you don't know whether or not that's the case. The problem spaces I do know well, I know the LLM isn't good at it, so why would I assume it's better at spaces I don't know?
I said familiar enough, not familiar. For example, let's say I'm building an app I know needs caching, the LLM is very good at telling me what types of caching to use, what libraries to use for each type, and so on, for which I can do more research if I really want to know specifically what the best library out of all the rest are, but oftentimes its top suggestion is, like I said, good enough for my purpose of e.g. caching.
I still don't get what you're saying. If you possess enough information to accurately judge the LLM's suggestions you possess enough information to decide on your own. There's not really a way around that.
Of course I'm deciding on my own, I'm not letting the LLM decide for me (although some people do). But the point is whatever the suggestion is is merely an implementation detail that either solves my problem or not, not sure what part of that is confusing. Replace LLM with glorified Google and maybe it's less confusing.
I've read the thread and in my mind you're missing that LLMs increase the surface area of visibility of a thing. It's a probe. It adds known unknowns to your train of thought. It doesn't need to be "creative" about it. It doesn't need to be complete or even "right". You can validate the unknown unknown since it is now known. It doesn't need to have a measured opinion (even though it acts as it does), it's really just topography expansion. We're getting in the weeds of creativity and idea synthesis, but if something is net-new to you right now in your topography map, what's so bad about attributing relative synthesis to the AI?