But with secondary and tertiary modifiable and searchable memory systems like MemGPT, the sum of the parts is not static, and can even introspect and reflect on

But with secondary and tertiary modifiable and searchable memory systems like MemGPT, the sum of the parts is not static, and can even introspect and reflect on and suggest modifications to its own code and protocols. It's already learned so much that in many cases it's just a matter of addressing and combining what it already knows, with what new knowledge and instructions you can fit in its token window. See my post about my experience with MemGPT debugging itself and suggesting improvements: https://news.ycombinator.com/item?id=38344033 https://news.ycombinator.com/item?id=38344033 This approach to recursive feedback and self inquiry, and other comments in this thread, vividly remind me of my friend Jim Crutchfield's work on feedback and chaos theory, and his amazing video with groovy music, "Space-Time Dynamics in Video Feedback": https://www.youtube.com/watch?v=B4Kn3djJMCE https://www.youtube.com/watch?v=B4Kn3djJMCE A film by Jim Crutchfield, Entropy Productions, Santa Cruz (1984). Original U-matic video transferred to digital video. 16 minutes. See https://csc.ucdavis.edu/~chaos/chaos/films.htm https://csc.ucdavis.edu/~chaos/chaos/films.htm Citation: J. P. Crutchfield, "Space-Time Dynamics in Video Feedback". Physica 10D (1984) 229-245. https://csc.ucdavis.edu/~chaos/chaos/pubs/stdvf-title.html https://csc.ucdavis.edu/~chaos/chaos/pubs/stdvf-title.html Possible spatio-temporal attractors: 1. Fixed point or equilibrium 2. Limit cycle or periodic oscillation 3. Chaotic attractor: (spatially coherent or incoherent) 4. Noise-driven spatial structures