- cross-posted to:
- linux@programming.dev
- cross-posted to:
- linux@programming.dev
cross-posted from: https://piefed.social/c/linux@programming.dev/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
cross-posted from: https://piefed.social/c/linux@programming.dev/p/2509642/linus-torvalds-posts-his-first-ai-assisted-patch-to-the-linux-kernel-synthetic-test-clo
No? Are you not aware of the concept of “memories”? AI do indeed take feedback into account, if they’re designed to.
AI does not have memories.
It has a comparatively small context window that makes things worse the more it fills up.
Eh I really think it does. You don’t news to load everything into context. You need to load where everything is located into context.
That isn’t equivalent to memories.
You are mistaken. Some AI models do indeed have memories.
They save some strings in a document to later read and “remember”. Calling them memories is a reach.
My point stands that they can learn from feedback. Your memories are only information in a virtual document, too. Just as susceptible to corruption, or misinterpretation.
There is no concept of a filesystem in our brains. Weird hills, and such and so forth
Only because you don’t think of it as one. It’s just a different medium for the storage of information.
The auto-generated memories I’ve seen it create are pretty inane: “always name variables container_ when in MySpecificClass”. Not really learning engineering principles.
Similar, I’ve found AGENT.md files might nudge it in the right direction, but as always it’s a fuzzy matcher, not a learning engineer.
It’s best to generate memories manually, but I’ve had good fortune with auto-generated so far. At least using Co-pilot.
I have found it best to have another AI generate the agent.md - having a layer of interpretation can really clarify the information imparted.