> but I also assume that there is very little training data available for Smalltalk.
Is this actually an issue? I was under the impression that the conclusion was that LLMs will invariably be terrible at writing good, working code (since their training sets are 90% garbage produced by humans), hence why agentic development now is heavy on verification tools and other software that can signal to the LLM whether the code works and has any code smells. Also why AI agents nowadays seem to write and execute scripts for a lot of tasks instead of spitting code directly. Hence, the volume of training data shouldn't be as relevant as it used to be.
Keep looking at your feet, you might find the point there someday.
The point isn’t the money. It’s the value.
Is that easier for you to understand? Perhaps I should refute my own argument since you are having trouble getting to a point in which you can agree or disagree?
The whole point of Fable was that it's Mythos but with extra guardrails, yeah. But now that Project Glasswing has been folded into the CVP, Mythos seems to have the exact same guardrails now
> Our TranslatePod Pros will automatically translate every language in real-time.
Wasn't this the utopian ideal of the Star Trek Universal Translator? Is it good when Gene Roddenbery writes it as utopian sci-fi, but bad when the AI people make it a reality?
Apparently quite a bit as long as you're not indiscriminately feeding random slop back in. An AI-generated artifact deemed "high quality" or somehow desirable by humans is a good training input depending on what you're trying to do. Humans don't train exclusively on external inputs either.
Is this actually an issue? I was under the impression that the conclusion was that LLMs will invariably be terrible at writing good, working code (since their training sets are 90% garbage produced by humans), hence why agentic development now is heavy on verification tools and other software that can signal to the LLM whether the code works and has any code smells. Also why AI agents nowadays seem to write and execute scripts for a lot of tasks instead of spitting code directly. Hence, the volume of training data shouldn't be as relevant as it used to be.
reply