想搞懂为什么长上下文和RAG不够用？EngramLab创始人亲自讲他们怎么用知识墨盒和连续学习让AI真正记住你，比传统路子更接近个人智能体。
EngramLab 联合创始人兼 CEO Dan Biderman 在一档播客中讨论了 AI 记忆系统的未来。他提到长上下文、RAG 和压缩技术最终都会失效，而 Engram 通过将知识压缩到“知识墨盒”和模型权重中来实现持续学习。他认为令牌效率与智能密不可分，个人模型可以像电子宠物一样持续进化。EngramLab 已获 9800 万美元融资，正在为百万级持续更新的 AI 记忆构建研究和基础设施。
In this episode, @EngramLab co-founder and CEO @dan_biderman joins @allenpark to cook Mediterranean ...
In this episode, @EngramLab co-founder and CEO @dan_biderman joins @allenpark to cook Mediterranean meatballs with yellow rice and talk about building AI that actually learns from you: why long context, RAG, and compaction eventually break down, how Engram compresses knowledge into cartridges and model weights, what continual learning could unlock for long-horizon agents, why token efficiency is inseparable from intelligence, how personal models could improve like Tamagotchis, and what it takes to build the research and infrastructure for millions of continuously updated AI memories. Timestamps: 0:00 Intro 0:26 Engram’s $98M Launch and Meatballs 1:45 From Naval Special Operations to AI Research 4:32 Israeli Military Culture and Founder Maturity 7:12 Why Engram Is Betting on Context and Continual Learning 9:14 Knowledge Cartridges, Compression, and Model Intuition 14:10 Trillion-Token Company Knowledge and Context Rot 18:05 Long-Context Limits, Compaction, and Neural Memory 22:20 Test-Time Training and “Destroying Prefill” 24:31 Harvey and Holistic Enterprise Queries Beyond RAG 27:02 Personal AI Models and Tamagotchi Weights 30:00 What Belongs in Weights vs. Text 32:25 Autonomous Memory and User-Specific Feedback Loops 34:20 Token Efficiency, Model Routing, and Harder Tasks 38:03 Engram’s Research Team and Product Culture 43:02 Hiring Researchers and Infrastructure Engineers 45:25 Doing More With Less 47:41 Where to Find Engram 48:19 Final Taste Test Your browser does not support the video tag. 🔗 View on Twitter 💬 1 🔄 4 ❤️ 14 👀 5856 📊 7 ⚡