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 is exactly what I am trying to solve and I have what I call smart repositories that demonstrates this at
https://github.com/gitsense/smart-ripgrep
https://github.com/gitsense/smart-codex
The issue I am finding is, getting the agent to pull what it needs, even when the data is there is still challenging since LLMs are trained on blind discovery where the pattern is:
grep -> read -> grep -> read ...
What is working for me now is thanks to Pi (pi.dev). I am working on a pi-brains extension that makes it dead simple to control the lifecycle for an agent so if I detect that it uses `rg` without `gsc rg`, I can block the agent and inject a steering message that says always search with context.
I can also see if they try to "read" without first looking at the files metadata and so forth.
I'm finalizing things right now, but I think pi with my brains extension should allow domain experts to better guide agents so they can find what they need, when they need it.
But your LLM training corpus covered that, right? /i
Challenge accepted!
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.
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.
> But an unbounded queue isn't a safety margin, it's a debt that keeps compounding [1]
before getting a headache.
I wish the whole thing was written better, because the idea of designing a codebase for humans and our limitations sounds fascinating. It's why I personally love type systems, you can keep less things in your head and let the type checker alert you of any possible errors.
[1]: https://shapeofthesystem.com/posts/2026/05/10/the-queue-that...
I would please urge you to read further into the manifesto itself but would also recommend you start at the foreword so you can understand the reason for the use of AI assistance in my writing.
The actual criticisms you have about the content however, I'd like to challenge:
The "adding up to one" is just a simplified gloss over softmax. It's very possible it reads poorly, and thats on me - not LLM gibberish.
As for the incoherence - I have to totally disagree. You have merged the 2 things the post keeps apart - capacity and attention over it. That a model can swallow a schema and write code is a competence humans share. We have been doing it for decades. Besides, the claim was never about us sharing capacity (other than stating it is always bounded) - it was about our attention failing in eerily similar ways.
So, AI slop, no. AI assisted, absolutely. It's sad that some judge the "who" more important than the "what" - especially for this kind of writing. But it's fair feedback nonetheless. I'll see what else I can do with assisting my delivery.
Maybe you just write like an LLM.
> to fit there "evidence"
> useing
> unoticed
> it's own right
I guess the connection between reading/listening, comprehension and retention ability on the one hand, and language generation ability on the other, isn't as strong as I'd been assuming till now.