Qdrant(@qdrant_engine)

"Continual learning isn't a training problem. It's a memory problem." @taranjeetio (co-founder & CE...

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"Continual learning isn't a training problem. It's a memory problem." @taranjeetio (co-founder & CE...

TL;DR · AI 摘要

持续学习的核心在于构建可扩展的记忆系统而非频繁重训练，Qdrant通过开源记忆层实现这一目标。

核心要点

  • 持续学习的关键是分离权重（稳定能力）与记忆（用户特定数据）
  • 五步记忆循环：观察→提取→检索→行动→遗忘/更新
  • Qdrant开源记忆层已用于生产环境

结构提纲

按章节快速跳转。

  1. §核心论点

    持续学习本质是记忆管理而非训练优化问题

  2. ·权重与记忆分离

    权重存储通用能力，记忆处理用户特定上下文

  3. ›五步记忆循环

    观察→提取→检索→行动→遗忘/更新构成完整记忆处理流程

  4. ·工程实践

    Qdrant开源记忆层实现生产级记忆系统

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • 持续学习架构
    • 核心区分
      • 权重（通用能力）
      • 记忆（用户数据）
    • 记忆处理流程
      • 观察
      • 提取
      • 检索
      • 行动
      • 遗忘/更新
    • 技术实现
      • Qdrant开源层
      • 生产环境应用

金句 / Highlights

值得收藏与分享的关键句。

#持续学习#记忆系统#Qdrant#AI代理#开源工具
打开原文

Qdrant on X: ""Continual learning isn't a training problem. It's a memory problem." @taranjeetio (co-founder & CEO, @mem0ai) breaks down why agents need memory before they need retraining at our Vector Space Day conducted in SF. - Weights = stable, general stuff (skills, reasoning patterns) https://t.co/O2Iv73fVPV" / X

Qdrant

@qdrant_engine

"Continual learning isn't a training problem. It's a memory problem."

@

taranjeetio

(co-founder & CEO,

mem0ai

) breaks down why agents need memory before they need retraining at our Vector Space Day conducted in SF. - Weights = stable, general stuff (skills, reasoning patterns) - Memory = everything tied to a specific user/team/org - fast, inspectable, reversible, portable His 5-step loop for agent memory: observe → extract → retrieve → act → forget/update Built on real lessons scaling

(open-source memory layer, running on

qdrant_engine

under the hood) in production. Full talk:

youtube.com/watch?v=yw-7Of…

4:00 PM · Jul 17, 2026

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