[hodgehog11]

AI tools seem excellent at getting through boilerplate stuff at the start of a project. But as time goes on and you have to think about what you are doing, it'll be faster to write it yourself than to convey it in natural language to an LLM. I don't see this as an issue with the tool, but just getting a better idea of what it is really good for.

[Nextgrid]

The role of a software engineer is to condense the (often unclear) requirements, business domain knowledge, existing code (if any) and their skills/experience into a representation of the solution in a very concise language: a programming language.

Having to instead express all that (including the business-related part, since the agent has no context of that) in a verbose language (English) feels counter-productive, and is counter-productive in my experience.

I've successfully one-shotted easy self-contained, throwaway tasks ("make me a program that fills Redis with random keys and values" - Claude will one-shot that) but when it comes to working with complex existing codebases I've never seen the benefits - having to explain all the context to the agent and correcting its mistakes takes longer than just doing it myself (worse, it's unpredictable - I know roughly how long something will take, but it's impossible to tell in advance whether an agent will one-shot it successfully or require longer babysitting than just doing it manually from the beginning).

[pbalau]

We are going to end up having boilerplate natural language text, that's been tested and proven to get the same output every time. Then we'll have a sort of transpiler and maybe a sub language of English, to make prompting easier. Then we will source control those prompts. What we actually do today, with extra steps.

[kerblang]

I tend to think of it as magical incantations rather than boilerplate, and the AI is Harry Potter, except in this case Harry is a pathological liar & psychopath - but a useful one...