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firefoxdyesterday at 9:29 PM2 repliesview on HN

If you are serious about sharing written ideas, I suggest you avoid using this type of prompts at all cost. I've worked with LLMs to write on my blog and they are pretty good at first glance [0]. But do it a few time and you'll notice that those tropes are the least of your problems. Not only all your articles will sound the same, but you'll see that same voice on other blogs, news articles, white paper, etc. It's as if they were all written by Mo Samuels. Readers are often here for the author's voice, not just the content of the text.

I often hear this here: "if you don't bother writing, why should I bother reading?" In fact, save us some time and just share the prompt.

[0]: https://idiallo.com/blog/why-we-hate-llm-articles


Replies

Lercyesterday at 9:44 PM

I have seen people suggest that the problem is that LLMs let you express any of your ideas, but the number of people with ideas worth expressing is limited.

In a sense I think this is accurate, but not inevitable. I think there is a lack of creative thinking, but it has come from a world that doesn't value it and suppresses difference.

There is a brilliant line in Treehouse of Horrors IV where Principle Skinner says "Now I've gotten word that a child is using his imagination, and I've come to put a stop to it." Which is just the perfect comment on the modern education system.

Models trained on the lack of diversity will push one way, but I think it will also avenues for expression that didnb't exist before. The balance will come from how we react and support what we would like to have happen

mobrienvyesterday at 10:37 PM

"Sharing the prompt" is a category error. It assumes the value of a piece is in the instructions given to the model, rather than the proprietary input or the iterative editing that follows. There is a hard line between using an LLM to generate content from a void and using it to synthesize specific ideas.

If someone asks a model to "write a post about X," they are outsourcing the thinking, which results in the homogenized voice everyone is tired of.