Comment by eloisius
I agree. AI is a wonderful tool for making fuzzy queries on vast amounts of information. More and more I'm finding that Kagi's Assistant is my first stop before an actual search. It may help inform me about vocabulary I'm lacking which I can then go successfully comb more pages with until I find what I need.
But I have not yet been able to consistently get value out of vibe coding. It's great for one-off tasks. I use it to create matplotlib charts just by telling it what I want and showing it the schema of the data I have. It nails that about 90% of the time. I have it spit out close-ended shell scripts, like recently I had it write me a small CLI tool to organize my Raw photos into a directory structure I want by reading the EXIF data and sorting the images accordingly. It's great for this stuff.
But anything bigger it seems to do useless crap. Creates data models that already exist in the project. Makes unrelated changes. Hallucinates API functions that don't exist. It's just not worth it to me to have to check its work. By the time I've done that, I could have written it myself, and writing the code is usually the most pleasurable part of the job to me.
I think the way I'm finding LLMs to be useful is that they are a brilliant interface to query with, but I have not yet seen any use cases I like where the output is saved, directly incorporated into work, or presented to another human that did not do the prompting.
Have you tried Opus? It's what got me past using LLMs only marginally. Standard disclaimers apply in that you need to know what it's good for and guide it well, but there's no doubt at this point it's a huge productivity boost, even if you have high standards - you just have to tell it what those standards are sometimes.