Malika Aubakirova

“The model is basically frozen. But the new experiences, new knowledge still persists. So how do we go about scaffolding it? Today, we augment it with agent harnesses. We use retrieval mechanisms like RAGs. We also, even, like, the example that I like to give is we have the system prompt that essentially serves as a tattoo.”

“I come back to just what Ilya talked about just recently. And what he said was basically, like, with AGI, we almost overshot the target. Humans are not AGI, but we still learn on the job. We learn from experience. And that's what makes kind of humans kind of unique.”

“What's really made it a special magical moment is this kind of like orchestration of the context... OpenClaw really utilizes your file system. It creates kind of memories, right? And it even has like a special bash access.”

“That's exactly what you think about RAG, companies like Pinecone, companies that build agent harnesses, memory scaffolding like Letta, Menzero.”

“Companies that build agent harnesses, memory scaffolding like Letta, Menzero.”

“Memory scaffolding like Letta, Menzero.”

“The example that I really like is given by Yu San, who is currently at Stanford... what Andrew Wiles did, he basically went into near isolation for seven years and had to invent new techniques to bridge basically two fields of branches of mathematics.”

“We see that with examples like Karpathy's auto-research project.”

“There is a great Stanford paper on this called Cartridges that kind of explains how you can update KB caches.”

“There are researchers from Berkeley and some of the other labs that are actually working on benchmarks that will hopefully help us define what is continual learning in a better form.”

“AI models, honestly, it kind of maps one-to-one to how AI models work today. So we have the training phase where we basically encompass all of the world knowledge... And so the question is, honestly, the model is basically frozen.”

“**Participants:** Elena Burger, Malika Aubakirova”