I think both your arguments are true. It all depends on the velocity of the capability growth and the fact that opportunity cost is expensive.
Once we get out of this hypergriwth phase the very same AI companies that now are giving you llms will provide a service that employed a rich mixture of optimized models that will reduce the operational costs to achieve the required results
Perhaps we should have README.md and README.ai the latter containing a bunch of stuff that humans don't want to read but agents can use to answer questions humans ask
I wonder if we're doing virtual filesystems wrong.
There is a good reason why traditionally filesystem access was mediated by the OS layer, but there are many use cases where you just want to give processes a different view of what they already can access and it could be done as a library in the same userspace process.
However, for that to work across all the processes in a session we'd need a standard way to install such a hook in all peocesses and that's achievable to some extent using LD preload but falls apart quite rapidly with statically built binaries or different libcs
Sending -asScheme to a reference is just a shorthand that actually constructs a composition[2] of a "path relative" store with the underlying store of the original reference. So the following two are identical:
This composition mechanism can be carried further with post-processing, so for example an img-scheme can be constructed by composing an image-decoder store with the previous store
Latin didn't have two letters to distinguish V and U. You can still find inscriptions with VVA (modern spelling UVA)
Later that sound morphed to a voiced labiodental fricative which is what we think of V now
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