A new approach to artificial intelligence is emerging that aims to equip AI systems with genuine long-term memory, moving past the limitations of Retrieval-Augmented Generation (RAG) and vector databases. One developer is pioneering this field by creating a “Memory-as-a-Service” (BrainAPI) system. This innovative system leverages embeddings and graph structures to store knowledge, enabling AI agents to recall information as if it were an inherent part of their understanding. The goal is to enhance AI behavior by providing persistent context across multiple sessions. The developer is actively seeking community input on the best strategy for implementing AI memory: should AI rely on external databases, leverage internal self-adjusting weights, or embrace continual fine-tuning? To foster collaboration and discussion, the developer has shared relevant articles and established a Discord community. The original discussion can be found on Reddit: [https://old.reddit.com/r/artificial/comments/1nfu2lk/giving_llms_actual_memory_instead_of_fake_rag/]