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gymbeauxyesterday at 5:41 AM1 replyview on HN

The quality of the output is so variable. It depends on the model, “effort level”, prompting, probably even the programming language/app functionality, and libraries involved. For example, I find LLMs are best at making simple web apps. These web apps, while simple, would still take a senior engineer perhaps a week or two to create, but LLMs can spit them out inside of an hour. Conversely, LLMs struggle with things like Docker or local model stuff. Parallelization of code is a mixed bag. In these areas I think it often would have been faster for me to write the thing by hand.


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user43928yesterday at 12:03 PM

What model struggles with Docker or local model deployment?

I have had good results in that area with GPT 5.5 in the past.

On the subscription plan I don't use anything but xhigh effort and Fable, 5.6 Sol, or now also Opus 5.

Is that the class of model that struggles with Docker for you?

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