alt Hacker News> It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context
It does learn in context. And lack of continuous learning is temporary, that is a quirk of the current stack, expect this to change rather quickly. Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able to grok codebases or do internal searches effectively and this will only improve.
> It doesn't have better reasoning beyond very localized decisions.
Do you have any basis for this claim? It contradicts a large amount of direct evidence and measurement and theory.
> This is just a bunch of words stringed together, isn't it?
Maybe to yourself? Chinchilla scaling laws and RL scaling laws are measured very accurately based on next token test loss (Chinchilla). This scales very predictably. It is related to downstream performance, but that relationship is noisy but clearly monotonic
> It does learn in context
It quite literally doesn't.
It also doesn't help that every new context is a new dawn with no knowledge if things past.
> Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able
A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.
> and this will only improve.
We've heard this mantra for quite some time now.
> Do you have any basis for this claim?
Oh. Just the fact that in every single coding session even on a small 20kloc codebase I need to spend time cleaning up large amounts of duplicated code, undo quite a few wrong assumptions, and correct the agent when it goes on wild tangents and goose hunts.
> Maybe to yourself? Chinchilla scaling laws a
yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.