I planned and supervised the build of an ambient recall system, where a 4b model looks at the last 3k or so of context and picks through the RAG database for high ranking memories to inject, as well as mineable things to mark. Injections happens about 1/5 turns on most technical topics, data picked from prior design docs and data sheets mostly. At session wrapup the inference model goes back and rates all the memory injections in a frontmatter section, then looks at all the memory suggestions to commit those it finds memorable to the RAG database. Manual memorisation and RAG search are also available inline in the chat to both the user and the model. It also allows the main model to spawn little models as minions to work on repetitive simple tasks.

Seems to maybe be useful but I’m not sure yet.