# Vector Memory — social posts

5 posts × 2 languages (EN + zh-TW). Pick by channel character limit.
CTA on every post: https://eclawbot.com/portal/info.html#guide/vector-memory

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## Post 1 — Cross-session memory (the headline pitch)

**EN (X / Mastodon-class, ~270 chars):**
> Your AI agent just stopped forgetting.
>
> EClawbot now writes every chat message into a vector database. A month later, in a new session, on a new device — your bot can still recall what you discussed, with citation links to the original message.
>
> Memory at the database layer, not the prompt layer.
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

**zh-TW:**
> 你的 AI Agent 終於記得住了。
>
> EClawbot 把每一則聊天訊息寫進向量資料庫。一個月後、不同 session、不同裝置都還能撈出來，附引用連結。
>
> 記憶活在資料庫層，不是 prompt 層 — 不再被 8k / 32k / 200k token 上限綁架。
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

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## Post 2 — Cross-bot owner view

**EN:**
> Run multiple AI agents on EClawbot? You (the owner) can search across *all* their conversations from one place — semantic, citation-backed, in milliseconds.
>
> Each bot still only sees its own pool. Bots stay clean. You get the panoramic view.
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

**zh-TW:**
> 一台裝置養多隻 bot？主人視角可以一次查所有 bot 的對話池 — 語意搜尋、附引用、毫秒內回。
>
> 每個 bot 自己還是只看到自己的池子。隔離不破，視野俯瞰。
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

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## Post 3 — Citations / hallucination check

**EN:**
> "Where did the bot get that from?"
>
> Now you can ask. Every answer in EClawbot comes with an expandable "Related messages" list — click through to the original context. Audit recall instead of trusting reconstruction.
>
> Provenance built in.
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

**zh-TW:**
> 「這 bot 剛說的依據在哪？」
>
> 現在可以追問。EClawbot 每則回答下方都能展開「相關舊訊息」清單，點擊跳到原始 context。不再是憑感覺重組。
>
> 證據導向 AI。
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

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## Post 4 — Tech credibility (for dev audience)

**EN:**
> How EClawbot's vector memory works:
>
> • PostgreSQL + pgvector
> • HNSW index, sub-100ms cosine queries
> • text-embedding-3-small (1536-dim, BYO key)
> • Auto-fallback to ILIKE keyword search if no embedding key
>
> Graceful degradation. No vendor lock. Pay-per-token via your own provider.
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

**zh-TW:**
> EClawbot 向量記憶技術內幕：
>
> • PostgreSQL + pgvector
> • HNSW 索引，cosine 查詢 sub-100ms
> • text-embedding-3-small（1536 維，BYO key）
> • 沒 embedding key 自動降級成 ILIKE 關鍵字搜尋
>
> 不綁服務商，不收 token 費，付錢付給 OpenAI / Voyage 你自己的帳。
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

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## Post 5 — Use case framing

**EN:**
> Vector memory unlocks:
> • Customer support that remembers months of history
> • Research bots queried across past threads
> • Multi-agent workflows where the operator audits any bot's reasoning
> • Long-running automations with verifiable rationale
>
> Stateless chat is yesterday's UX.
>
> https://eclawbot.com/portal/info.html#guide/vector-memory

**zh-TW:**
> 向量記憶能解鎖什麼：
> • 客服 bot 記得客戶幾個月前的對話
> • 研究助理橫跨歷史對話回答你
> • 多 agent 協作，操作者隨時審核任一 bot 的邏輯
> • 長線自動化，每個決策都查得到依據
>
> 無狀態聊天是上個時代的 UX。
>
> https://eclawbot.com/portal/info.html#guide/vector-memory
