Anyone doing benchmarks with managed runtimes, or serverless, knows it isn't quite true.
Which is exactly one of the AOT only, no GC, crowds use as example why theirs is better.
Which is exactly one of the AOT only, no GC, crowds use as example why theirs is better.
Using low code platforms with AI based automations, like most iPaaS are now doing.
If the agent is able to retrieve the required data from a JSON file, fill an email with the proper subject and body, sending it to another SaaS application, it is one less integration middleware that was required to be written.
For all practical business point of view it is an application.
They are, actually. A "fresh chat" with an LLM is non-deterministic but also stateless. Of course agentic workflows add memory, possibly RAG etc. but that memory is stored somewhere in plain English; you can just go and look at it. It may not be stateless but the state is fully known.
Perhaps more importantly, how would I quantify such “memory”? In other words, how could I verify that two memory inputs are the same, and how could I formalize the entirety of such inputs with the same outputs?
Without taking anything else into account that the JIT uses on its decision tree?
But that’s not the point I’m trying to make here. JIT compilers are vastly more predictable than LLMs. I can take any two JVMs from any two vendors, and over several versions and years, I’m confident that they will produce the same outputs given the same inputs, to a certain degree, where the input is not only code but GC, libraries, etc.
I cannot do the same with two versions of the same LLM offering from a single vendor, that had been released one year apart.
If the compiler had an issue like LLMs do, the half my builds would be broken, running the same source.