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c3z_ 22 minutes ago [-]
I've learned that for both humans and models: system > willpower. The key is entirely in designing the environment.
For me personally, that means setting up 'attention getters' for the important things in life - 'totems' that force a context switch. For AI agents, it means well-designed CLI tools that help the agent orient itself in a task and pull exactly the 'context-for-the-job' it needs right then.
This is exactly what makes building modern GenAI decision-support systems so difficult. It's no longer just about finding the right software abstractions. You now have to account for the unknown cognitive construct of a completely different intelligence.
james_ross 2 hours ago [-]
This rings very true to me, and it's why I've been mildly obsessed for a decade plus with how to share mental models between people, and now LLMs, of any domain, be it technical, commercial, scientific or anything else. My inspiration was a book called Learning How To Learn by Novak, which TBH is so dry I'm not sure anyone I've recommended it to has actually finished it :) So then I point them to a talk here:
https://www.infoq.com/presentations/concept-map/
and an app to help render the shared mental model in plain text accessible to the LLM while providing visual interactivity to the humans here:
https://thinkingtools.software/concepticon/
zby 2 hours ago [-]
It is interesting to compare this to LLMs - they also have the bounded context that you can see as the analogue to our working memory. It can contain enormously more bits of information than the 4 things the article says is the capacity of our working memory - but the 4 things can probably be much more complex internally - they are more like 4 pointers probably.
But at some level context engineering is very similar to what this article talks about.
misHQ 19 minutes ago [-]
Hello, author here. Lovely comment, and yes: not just similar, but exactly what the article talks about. In the middle. Where it unironically talks about being lost in the middle. And it seems to have made its own point, on even the careful reader!
ares623 2 hours ago [-]
Reminds me of Rich Hickey's "Simple Made Easy" talk
For me personally, that means setting up 'attention getters' for the important things in life - 'totems' that force a context switch. For AI agents, it means well-designed CLI tools that help the agent orient itself in a task and pull exactly the 'context-for-the-job' it needs right then.
This is exactly what makes building modern GenAI decision-support systems so difficult. It's no longer just about finding the right software abstractions. You now have to account for the unknown cognitive construct of a completely different intelligence.
But at some level context engineering is very similar to what this article talks about.