Comment by gcp123

While I think the title is misleading/clickbaity (no surprise given the buzzfeed connection), I'll say that the substance of the article might be one of the most honest take on LLMs I've seen from someone who actually works in the field. The author describes exactly how I use LLMs - strategically, for specific tasks where they add value, not as a replacement for actual thinking.

What resonated most was the distinction between knowing when to force the square peg through the round hole vs. when precision matters. I've found LLMs incredibly useful for generating regex (who hasn't?) and solving specific coding problems with unusual constraints, but nearly useless for my data visualization work.

The part about using Claude to generate simulated HN criticism of drafts is brilliant - getting perspective without the usual "this is amazing!" LLM nonsense. That's the kind of creative tool use that actually leverages what these models are good at.

I'm skeptical about the author's optimism regarding open-source models though. While Qwen3 and DeepSeek are impressive, the infrastructure costs for running these at scale remain prohibitive for most use cases. The economics still don't work.

What's refreshing is how the author avoids both the "AGI will replace us all" hysteria and the "LLMs are useless toys" dismissiveness. They're just tools - sometimes useful, sometimes not, always imperfect.