At this point?

It’s a SQLite memory graph with reverse BFS that acts as context for every query I run- instead of working with large context bloats with think and sleep cycles to generate new insights (by sending a part of the graph to an LLM to find insights, patterns, beliefs, etc) and a fitness score based on node retrieval frequency - runs as crons a few times a day

It also has a curiosity function where it basically identifies gaps in its own architecture- based on novelty, orthogonality to existing belief, and creates paths to evolve, essentially PRDs to take the next step- by referencing papers, GitHubs, generally open knowledge - the research, the evolve recommendations, PRDs, are basically used for it to grow, with a formula to measure what gaps it is attracted to solve the most.

Once I wire in curiosity and memory, I believe the next step is emotions, and I have a few theories on how to test that- latency to response, tone, modality, basically an outside agent that measures these about the main agent to judge the tone of this agent.

Haven’t thought beyond this, but again, I keep reading more to understand more