Personally, I’ve had a pretty positive experience with the coding assistants, but I had to spend some time to develop intuition for the types of tasks they’re likely to do well. I would not say that this was trivial to do.

Like if you need to crap out a UI based on a JSON payload, make a service call, add a server endpoint, LLMs will typically do this correctly in one shot. These are common operations that are easily extrapolated from their training data. Where they tend to fail are tasks like business logic which have specific requirements that aren’t easily generalized.

I’ve also found that writing the scaffolding for the code yourself really helps focus the agent. I’ll typically add stubs for the functions I want, and create overall code structure, then have the agent fill the blanks. I’ve found this is a really effective approach for preventing the agent from going off into the weeds.

I also find that if it doesn’t get things right on the first shot, the chances are it’s not going to fix the underlying problems. It tends to just add kludges on top to address the problems you tell it about. If it didn’t get it mostly right at the start, then it’s better to just do it yourself.

All that said, I find enjoyment is an important aspect as well and shouldn’t be dismissed. If you’re less productive, but you enjoy the process more, then I see that as a net positive. If all LLMs accomplish is to make development more fun, that’s a good thing.

I also find that there's use for both terminal based tools and IDEs. The terminal REPL is great for initially sketching things out, but IDE based tooling makes it much easier to apply selective changes exactly where you want.

As a side note, got curious and asked GLM-4.5 to make a token field widget with React, and it did it in one shot.

It's also strange not to mention DeepSeek and GLM as options given that they cost orders of magnitude less per token than Claude or Gemini.