zlacker

>>moored+(OP)
This stuff smells like maybe the bitter lesson isn't fully appreciated.

You might as well just write instructions in English in any old format, as long as it's comprehensible. Exactly as you'd do for human readers! Nothing has really changed about what constitutes good documentation. (Edit to add: my parochialism is showing there, it doesn't have to be English)

Is any of this standardization really needed? Who does it benefit, except the people who enjoy writing specs and establishing standards like this? If it really is a productivity win, it ought to be possible to run a comparison study and prove it. Even then, it might not be worthwhile in the longer run.

>>iainme+Qb
It's all about managing context. The bitter lesson applies over the long haul - and yes, over the long haul, as context windows get larger or go away entirely with different architectures, this sort of thing won't be needed. But we've defined enough skills in the last month or two that if we were to put them all in CLAUDE.md, we wouldn't have any context left for coding. I can only imagine that this will be a temporary standard, but given the current state of the art, it's a helpful one.

>>smithk+id
Why not replace the context tokens on the GPU during inference when they become no longer relevant? i.e. some tool reads a 50k token document, LLM processes it, so then just flush those document tokens out of active context, rebuild QKV caches and store just some log entry in the context as "I already did this ... with this result"?

>>storus+Ci
Anthropic added features like this into 4.5 release:

https://claude.com/blog/context-management

> Context editing automatically clears stale tool calls and results from within the context window when approaching token limits.

> The memory tool enables Claude to store and consult information outside the context window through a file-based system.

But it looks like nobody has it as a part of an inference loop yet: I guess it's hard to train (i.e. you need a training set which is a good match for what people use context in practice) and make inference more complicated. I guess more high-level context management is just easier to implement - and it's one of things which "GPT wrapper" companies can do, so why bother?