A lot of shit we are dealing with in the modern computer systems is an adversarial complexity that serves no purpose except making things harder for the benefit of the system's owners. The main problem is that lot of people believe this complexity is necessary.

You’ve mostly got it right, with some caveats. Most memory tools keep notes outside the model. RAG, vector databases and memory files store text, search it when you ask something, and paste what they find into the prompt. The model itself never changes. It rereads its notes every time, and if the search misses, it doesn’t know. Living Weights changes the model. When you tell it “I like blue”, it writes its own questions about the fact and trains a small change into its weights until it can answer them. Then it saves that change into the model’s files. It adds nothing to the prompt and looks nothing up. Restart it, and it still knows. Editing facts into weights isn’t new in research. ROME and MEMIT are the best-known papers. What Living Weights adds is doing it live on your own machine, with checks before anything is kept. Training facts into a model usually bleeds into other answers, and that’s where most of the work goes. Before it keeps a lesson, it checks that ordinary answers haven’t changed and that the fact hasn’t leaked onto similar ones. In one test it refused “my dog is called Ginger” because it had started answering “what’s my cat’s name?” with Ginger. Today it handles short personal facts, one at a time. Each one takes 2 to 4 minutes on an M3 Ultra and about 22 GB of memory while it learns. With thinking on, the model can still reason its way back to “I don’t know you”, and we’re fixing that now. A saved fact is hard to remove. It also doesn’t learn from conversations by itself yet. You tell it what to learn with the /learn command. So the coworker comparison works for things it should always know about you. Notes still win for documents and anything that changes often at the moment, but I am solving that. Jarvis on a Spark is where this is heading. A Spark can already serve a model that learned on a Mac, and I’m building learning on the Spark itself as we speak.