{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"How Enterprise AI Systems Simulate Memory Without Breaking the Token Budget","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/505131ed\"></iframe>","width":"100%","height":180,"duration":765,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/how-enterprise-ai-systems-simulate-memory-without-breaking-the-token-budget.\n             LLMs default to amnesia. Learn how to architect scalable stateful memory pipelines using NoSQL and intelligent token compression for multi-turn AI. \n            Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n            You can also check exclusive content about #ai-infrastructure, #software-architecture, #distributed-systems, #system-design, #dynamodb, #ai-orchestration, #llm-memory, #hackernoon-top-story,  and more.\n            \n            \n            This story was written by: @aditi-patodiya. Learn more about this writer by checking @aditi-patodiya's about page,\n            and for more stories, please visit hackernoon.com.\n            \n                \n                \n                Language models are stateless compute engines. To build fluid, multi-turn AI assistants at enterprise scale, you have to build the memory yourself. This deep-dive explores how to architect backend context propagation pipelines, avoid hot partitions, manage strict token budgets, and use event-driven summarization to keep your latency sub-50ms.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}