[…] can’t LLMs solve the cognitive debt problem better then people? […]
Better, probably not. And I’ll give you one example. In a codebase there was an issue with Auth, after few runs of the LLM, the best suggestion resulted in 30~40% of change in the Auth workflow.
Took me a couple hours to figure out that the problem was a misconfiguration key (camelCase to snake_case in the vault store). Fixing this on the store (not on the code) fixed the Auth workflow.
This two hours were the cognitive debt. And keep in mind, I’m familiar with this part of the code. A Mechanical Parrot happy-trigger boyz would accept the change in the codebase as an attempt to fix it.
So, mechanical parrot can help? Sure. But better than people, probably not. At least not yet, not with the current set of tool, not with the promise of fully solving it.
[…] exact input and node activation is all you’d need for forensics, the only reason to refeed would old input plus new input mix, […]
This is badly wrong. The same node can point to multiple places depending on the K factor on this. And this isn’t even the T applied to it. So, the same node can infer multiple different others. This is the nature of the probabilistic of LLM.




So you are suggesting that we should have the metadata for every decision and direction. This is a
N^Namount of data. Keeping this data is wastefull (even more than the usage of LLM right now). Not saying that is is useless, but for sure this won’t help to mitigate the cognition since this data don’t carry meaning for us humans.