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.

That's funny, isn't it the same for dogs?

What I've heard is human short-term memory can hold seven things at once. Fortunately the mind is much more.

To me this is optimization problem. How can we solve a problem if we don't understand it? Understanding takes a lot of effort exactly because our minds are wandering through useless context all the time, and not to mention interruption.

I formulate this problem as:

> Optimize for understanding

I know how to approach solving this exact problem. In fact, I've been doing exactly this since March 2026. We need to figure out how to isolate problems and context around them. And so my best bet right now is using graphs. Links can be easily added or removed between two nodes. And context is simply a group of links and/or linked nodes.

Now. What exactly is "understanding"?

To me, this is process when we look at some unpredictable, chaotic system and then creating structure from it. The chaotic system is an entangled, spaghetti-like graph. The ordered one is one we [hopefully] have in our brains, which allows us to act on it. I don't want to repeat entire article I wrote about this so if you want you can find it on my recent project (it's not ready for HN prime-time yet but I'll post demo soon).

But tl;dr we have "chaotic/unknown graph" and "structured/understood graph", and the bottleneck is moving nodes and links between these two.

The faster we understand the world around us, the better we understand why problems appear, and how to fix them. And once I realized this, to me it became clear where we need to move forward: to connect everything together in a way that makes understanding quick.

And fun fact, I already did this: I connected my article to yours.

https://news.ycombinator.com/item?id=48706307

Even if it were written by hand, it’s a very poor and frankly stupid essay about an interesting topic. “The model's attention is a fixed quantity, and it has to add up to one, so the more things you make it look at, the less of that attention any single earlier thing can keep.” This is borderline gibberish and it outright rejects the interesting question about LLMs and attention, namely that they have very different capacities from us. LLMs can read an entire OpenAPI schema in seconds and immediately construct valid requests from it. The article first points this out, and then switches to arguing that LLMs have similar limits to us. It’s completely incoherent.