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.
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/
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.
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!
That is a good read, but as someone who tests in the first percentile for reading/listening, comprehension and retention, helps explain why many people who try to have discussions with me, shift there premise mid argument to fit there "evidence" and then cant remember where they started from, when reminded, and of course become agitated when challenged.
Right now I am puzzling over how to deal with a part time employee, who is addicted to this sort of disconected "style" of discourse, and am useing a disturb and observe approach,and as it seems to go unoticed, is informative in it's own right.
Author here, and yes, much the same root, gladly so. The piece is really just the on-ramp to the manifesto. If there's a step beyond the talk, it's trying to let simplicity fall out of the structure rather than rest on choosing it each time.
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.