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This research explores how large language models can predict and enhance human semantic memory search, showing that LLMs can track human thought trajectories better than other humans and improve human memory processing through collaborative interactions.
A tinkerer shares custom quality-of-life upgrades for local AI agents, including an MCP broker to reduce context overhead, temporal awareness, context warnings, auto-swapping models, and hybrid memory search with Postgres.