You keep saying that major models have "tool calling built in". And that by giving them context about available APIs, the LLM can "use the API".
But you don't explain, in any of your comments, precisely how an LLM in practice is able to itself invoke an API function. Could you explain how?
A model is typically distributed as a set of parameters, interpreted by an inference framework (such as llama.cpp), and not as a standalone application that understands how to invoke external functions.
So I am very keen to understand how these "major models" would invoke a function in the absence of a chassis container application (like Claude Code, that tells the model, via a prompt prefix, what tokens the model should emit to trigger a function, and which on detection of those tokens invokes the function on the model's behalf - which is not at all the same thing as the model invoking the function itself).
Just a high level explanation of how you are saying it works would be most illuminating.
Tesla unilaterally decided to push out a refactored UI to my old Model S a few months ago, changing the location of numerous functions to new sub-menu paths, and rendering years of muscle memory useless.
Not only do I have to re-learn where everything is, but older memories interfere and confuse every time I try to access an infrequently used function.
This experience has been irritating at best. It has felt extremely unsafe on more than one occasion.
But you don't explain, in any of your comments, precisely how an LLM in practice is able to itself invoke an API function. Could you explain how?
A model is typically distributed as a set of parameters, interpreted by an inference framework (such as llama.cpp), and not as a standalone application that understands how to invoke external functions.
So I am very keen to understand how these "major models" would invoke a function in the absence of a chassis container application (like Claude Code, that tells the model, via a prompt prefix, what tokens the model should emit to trigger a function, and which on detection of those tokens invokes the function on the model's behalf - which is not at all the same thing as the model invoking the function itself).
Just a high level explanation of how you are saying it works would be most illuminating.