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Comment by bee_rider | Hacker News Reader

> Because LLMs don't understand things to begin with.

Ok, that’s fair. But I think the comment was making a distinction between the big picture and other types of “understanding.” I agree that it is incorrect to say LLMs understand anything, but I think that was just an informal turn of phrase. I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.

The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.

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> I’m saying I don’t think there’s something special about “big picture” information processing tasks, compared to in-detail information processing tasks, that makes them uniquely impossible for LLM.

LLMs can do neither reliably because to do that you need understanding which LLMs don't have. You need to learn from the codebase and the project, which LLMs can't do.

On top of that, to have the big picture LLMs have to be inside your mind. To know and correlate the various Google Docs and Figma files, the Slack discussions, the various notes scattered on your system etc.

They can't do that either because, well, they don't understand or learn (and no, clawdbot will not help you with that).

> The other objections seem mostly to be issues with current tooling, or the sort of capacity problems that the LLM developers are constantly overcoming.

These are not limitations of tooling, and no, LLM developers are not even close to overcoming, especially not "constantly". The only "overcoming" has been the gimmicky "1 million token context" which doesn't really work.

I would say that it's very germane to my original statement. Understanding is absolutely fundamental to strategy and it is pretty much why I can say LLMs can't be strategic.

To really strategize you have to have mental model of well, everything and be able to sift through that model to know what elements are critical or not. And it includes absolutely everything -- human psychology to understand how people might feel about certain features or usage models, the future outlook for what popular framework to choose and will it as viable next year as it is today. The geographic and geopolitics of which cloud provider to use. The knowledge of human sentiment around ethical or moral concerns. The financial outlook for VC funding and interest rates. The list goes on and on. The scope of what information may be relevant is unlimited in time and space. It needs creativity, imagination, intuition, inventiveness, discernment.

LLMs are fundamentally incapable of this.

Claude is perfectly capable of all of this. Give it access to meeting notes and notion/linear and it can elegantly connect the dots within the context of a given problem.

yes, its just a matter of capability, not skill

It routinely can't "connect dots" on a 10 kloc project with design notes right there in the same project.

It routinely cannot read files more than 2k lines long.

You can't even provide detailed CLAUDE.md instructions because "file is too large and will affect context".

But sure. "Just give it access to a magnitude more info and it will be able to do stuff".

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