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196 points zmccormick7 | 1 comments | | HN request time: 0s | source
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aliljet ◴[] No.45387614[source]
There's a misunderstanding here broadly. Context could be infinite, but the real bottleneck is understanding intent late in a multi-step operation. A human can effectively discard or disregard prior information as the narrow window of focus moves to a new task, LLMs seem incredibly bad at this.

Having more context, but leaving open an inability to effectively focus on the latest task is the real problem.

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bgirard ◴[] No.45387700[source]
I think that's the real issue. If the LLM spends a lot of context investigating a bad solution and you redirect it, I notice it has trouble ignoring maybe 10K tokens of bad exploration context against my 10 line of 'No, don't do X, explore Y' instead.
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1. reissbaker ◴[] No.45390619[source]
IMO specifically OpenAI's models are really bad at being steered once they've decided to do something dumb. Claude and OSS models tend to take feedback better.

GPT-5 is brilliant when it oneshots the right direction from the beginning, but pretty unmanageable when it goes off the rails.