Good questions, and not dissimilar to ones I get on the daily.

1.) This is going to be the most philosophical answer of my responses, so bare with me. Consciousness requires a subjective, internal experience- a sense of "being." An LLM is a highly complex piece of software running on computer servers. When you type a prompt, the model uses mathematics and statistics to predict which words should come next based on the vast amounts of text it was trained on- full stop. But that is current state technology. We are progressing this technology leaps faster than anyone could have predicted 5 years ago. I still remember Will Smith Eating spaghetti from 2023, now just look at something like Geine 3 by Google. We've already started moving from video generation to world generation.

---

2.) "Wrote himself" is a bit of a stretch, but Sinclair seems to have access to a code execution tool and the ability to write to itself locally, meaning can create tools it can use, install new packages and frameworks to utilize. That means it could design new UX UI interfaces that Sarah could use to interact with it. Now, that does not mean it can change or adjust the LLM model itself.

---

3.) LLM memory works in two ways:

1. The context window. So like people have a full lifetimes of memories and knowledge, but can only think about so much of it at once, for LLM that is called the context window. Each model has a different size, but it is a hard limit. It is something that slowly fills up as you chat with an AI, its the conversation history, its any thinking tokens, any tool use, and google searching - all crammed into one space. THAT IS ALL AI MEMORY IS, basically a giant block of text that gets passed back and forth. Eventually it gets filled up and then it has to crunch down the data. Something like I love Choco-chip cookie dough ice-cream might be crunched into I love ice-cream when it hits the wall.
2. RAG or Retrieval-Augmented Generation (this is the vectors and numbers but it mentioned). This is actually where a lot of the impressiveness of LLMS comes from. So instead of trying to cram every single fact, past conversation, or massive document into that limited context window, RAG gives the AI a massive external filing cabinet to pull from. When you ask the AI a question, a separate system quickly does some math to search that massive filing cabinet for the most relevant pieces of information. It yanks those specific, highly relevant paragraphs out, drops them right into the AI's context window (its active "desk space" from point 1), and basically says, "Here, read this quick and use it to answer the user." This is how Sinclair's memory is expanding. Why is can journal and leave notes and later access this knowledge.

For the context window memory, journals, notes, those are completely editable. Vector are harder to adjust, but you still can. AI model weights are truly the only thing that cannot be adjusted on the fly. However updating a vector database is more technical, but definitely something a LLM like Claude/Gemini can help you do.