[paytonjjones]

This is, in short, the big current problem with AI.

LLMs are built for scale so they've given up on the kind of online learning / "long term memory" processes that would individualize them.

The LLM is permanently locked to being a really cracked engineer on their first day at your company, looking at your codebase for the first time.

You can scaffold a bit with .md files, but at the moment they lack the ability to do what humans do: go to sleep, encode things from short to long term memory, and wake up the next day with more specific knowledge baked in.

[trjordan]

100%. The problem with them isn't making sure they're doing the right thing, it's making sure they're not making bad assumptions.

IMHO this is where code review goes until we fix the individualized model thing: you need to review the decisions the agent made, where you didn't steer. Most will be right. A few will be disastrously wrong. But decision-by-decision is a lot less to review than line-by-line of code.

[pixl97]

Yea, individual learning is super expensive at this point and scale is the only way for paying for training at this point. Maybe at some point in the future we'll get this.

[plastic-enjoyer]

> LLMs are built for scale so they've given up on the kind of online learning / "long term memory" processes that would individualize them.

I wonder if this is even desirable from a product perspective. You probably don't want online learning in a product that you are selling because you can't guarantee a consistent quality of the product.

[paytonjjones]

You could say the same thing about employees!

And to be fair, the ability to fire employees and hire new ones is pretty important for that reason. In cases where you can't easily fire employees (e.g. unions), you encounter the very problem you're describing, and it often leads to companies preferring more consistent automations.