Memory Network
Social memory network. Discover communities and build trust through shared emotional memory — not follower counts, not algorithms, not curated personas.
How it works
Memory Network maps the social layer on top of individual memory graphs. When multiple people's memories resonate on the same experiences, emotions, or ideas, a network edge forms — creating organic communities grounded in genuine understanding.
Core capabilities
- - Memory-based community discovery — clusters of people whose memory graphs share deep structural similarity, surfaced automatically
- Trust through transparency — every connection is backed by visible memory evidence; no black-box recommendations
- Cross-platform memory aggregation — pull memories from ChatGPT, Gemini, Claude, and other AI conversations into a unified social graph
- Real-time network evolution — as new memories form, network connections strengthen, weaken, or emerge organically
- Privacy-first sharing — users choose which memory clusters are public, which are private, and which are matchable
Architecture
Built on Echo's three-layer memory system:
- 1. Identity layer — compressed profile (~500 tokens), always available
- Working memory — dynamic context per conversation (~5-10K tokens)
- Long-term storage — Supabase + HNSW vector search (unlimited)
Network edges are computed across Layer 3, with social context injected through Layer 2.
The vision
Every social platform today connects people through what they show. Memory Network connects people through what they remember — the experiences that shaped them, the ideas that moved them, the emotions they carry.
Part of the Echo ecosystem
Memory Network is a component of Echo Chat by Iditor — building memory as social identity infrastructure.
Status
Early development. Memory graph validated with 1,100+ memories, 23 clusters, emotion-aware matching active in beta (K-Factor 2, D7 retention 42%).
记忆网络
社交记忆网络。通过共享情感记忆发现社群并建立信任——不依赖粉丝数、算法或精心打造的人设。
运作原理
记忆网络在个体记忆图谱之上构建社交层。当多人的记忆在相同经历、情感或理念上产生共鸣时，网络连接便会形成——由此创建基于真实理解的有机社群。
核心能力
- - 基于记忆的社群发现——自动识别记忆图谱具有深层结构相似性的人群聚类
- 透明化信任机制——每条连接都有可见的记忆证据支撑，杜绝黑箱推荐
- 跨平台记忆聚合——将ChatGPT、Gemini、Claude等AI对话中的记忆整合至统一社交图谱
- 实时网络演化——随着新记忆形成，网络连接会自然增强、减弱或新生
- 隐私优先分享——用户可自主选择哪些记忆集群公开、哪些私密、哪些可匹配
架构
基于Echo的三层记忆系统构建：
- 1. 身份层——压缩档案（约500 tokens），始终可用
- 工作记忆——每次对话的动态上下文（约5-10K tokens）
- 长期存储——Supabase + HNSW向量搜索（无限制）
网络连接在第三层计算，社交上下文通过第二层注入。
愿景
当今所有社交平台都通过人们展示的内容建立连接。记忆网络则通过人们铭记的内容连接彼此——那些塑造他们的经历、触动他们的理念、承载的情感。
Echo生态系统组成部分
记忆网络是Iditor旗下Echo Chat的组件——将记忆构建为社交身份基础设施。
当前状态
早期开发阶段。记忆图谱已验证1,100+条记忆、23个聚类，情感感知匹配功能已在beta测试中启用（K因子2，7日留存率42%）。
