I place a lot of importance in my own projects that every agent session captures non-obvious learnings as rules and that skills learn from their executions. Interesting approach to bring this fully local! A bit unfortunate that the RAM requirements will lock some Macs out, but I guess that's not easily changeable right now.
So How do you prevent the continuously fine-tunes itself from drifting away from its original capabilities? The idea of training LoRA adapters from user corrections is interesting, and how do you balance personalization vs. catastrophic forgetting?
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