Show HN: Stateful Inference with 99% Token Savings (github.com)

A groundbreaking approach in AI/ML, named NLS (Neural Language Storage), has been unveiled that allows large language models (LLMs) to perform stateful inference with remarkable efficiency, achieving up to 99% token savings during conversations. Unlike traditional methods that require reprocessing entire conversation histories, NLS adeptly captures and stores internal states during message processing, enabling models to recall previous interactions without the need to resend extensive context. This innovation dramatically reduces both the computational costs and latency typically associated with prolonged dialogues, making long-term memory feasible without sacrificing performance. The implications of NLS for the AI community are profound. It not only challenges the prevailing notion that the transformer architecture inherently limits memory retention but also introduces a persistent memory mechanism that leverages cheap storage solutions, such as NVMe SSDs, to inject relevant historical states back into the model's attention during user interactions. The architecture includes features like real-time memory hot-swapping and multi-signal quality filtering, ensuring that the model's outputs remain coherent and relevant across sessions. This approach could redefine how AI agents manage context, leading to more effective and user-friendly applications in real-world scenarios, particularly in areas like personal assistants and coding agents.