The missing layer in AI tooling: sharing what your…
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    The missing layer in AI tooling: sharing what your assistant already knows
    ai

    The missing layer in AI tooling: sharing what your assistant already knows

    Uri Shmueli September 22, 2026
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    It took my AI months to learn how I think, code, and ship. When a teammate joined the project, their...

    It took my AI months to learn how I think, code, and ship. When a teammate joined the project, their AI started from zero — same codebase, same conventions, none of the context.

    So I built memshare: peer-to-peer AI memory sharing, with consent on both sides.

    The problem

    Every AI tool today treats memory as a product feature locked inside one account. Claude remembers things for you. ChatGPT remembers things for you. Nobody else can get at it — not your teammate, not your other tools, not even you in a greppable format.

    That means:

    • A designer in Cursor builds up months of component conventions. A backend dev in Claude Code has none of it.
    • You switch from one AI tool to another and start over.
    • Onboarding a new teammate means weeks of their AI re-learning what yours already knows.

    What memshare does

    memshare treats AI memory as a data type — plain JSON files you own — not a feature of someone else's chat product.

    npm install -g memshare-mcp
    memshare init
    

    Connect it to your AI tool once, and capture happens in conversation:

    "we went with Postgres — the JSONB support decided it" → the AI calls memory_set, saved as private

    "what do you know about this project?" → the AI calls memory_get

    No commands to run. No copy-pasting. Your AI saves what it learns as you work.

    Sharing

    When you want to share context with a teammate:

    # See exactly what would go out
    memshare export --tags "project-x,architecture" --for alice --preview
    
    # Happy with it? Write the bundle
    memshare export --tags "project-x,architecture" --for alice --expires 30d
    # → ~/.memshare/bundles/bundle-a3f8c2d1.memshare.json
    

    Send that file however you want — Slack, email, AirDrop. On Alice's machine:

    memshare preview bundle-a3f8c2d1.memshare.json   # look, import nothing
    memshare import  bundle-a3f8c2d1.memshare.json   # choose item by item
    

    Alice picks each item individually. Everything lands private — receiving context is not consent to pass it on.

    The consent model

    This is the part I care most about. Sharing someone's AI context without their control is a terrible idea. memshare has four gates:

    1. You tag at creation. Every item is private or shareable. Private items never leave, even if their tags match an export.
    2. PII is caught automatically. Before anything leaves your machine, memshare scans for emails, phone numbers, credentials, government IDs, and more. Flagged items are held back.
    3. You see the exact bundle. --preview runs the same code path as the real export — there's no separate preview implementation that can drift.
    4. They choose too. The recipient previews every item and accepts or rejects individually. Bundles are content-hashed, so a file edited in transit is refused.

    It works across tools

    memshare uses MCP (Model Context Protocol), which means it works with any MCP client:

    // Cursor / Windsurf / GitHub Copilot — add to your MCP config
    {
      "mcpServers": {
        "memshare": {
          "command": "npx",
          "args": ["-y", "memshare-mcp", "serve"]
        }
      }
    }
    
    # Claude Code
    claude mcp add memshare --scope user -- npx -y memshare-mcp serve
    

    A designer in Cursor can hand component conventions to backend devs in Claude Code, and get the API contract back. Different people, different tools, same bundle format.

    The architecture is the point

              ~/.memshare/memories/*.json
              the actual product — plain JSON files
                ▲        ▲         ▲
                │        │         │
          MCP server    CLI    (future adapters)
                │
      Claude · Cursor · VS Code · Windsurf · any MCP client
    

    The memory store is the product. The MCP server is one adapter over it, the CLI is another. If MCP disappears tomorrow, your data is still sitting in a folder — human-readable, diffable, git-friendly.

    ~/.memshare/
    ├── config.json
    ├── memories/
    │   └── mem_<uuid>.json      # one file per memory
    └── bundles/
        └── bundle_<id>.memshare.json
    

    No database. No server. grep works. diff works. git works.

    Is it actually capturing?

    The honest risk: nothing in MCP can force a model to call a tool, so capture can quietly fail. memshare makes that visible:

    memshare stats
    
    12 memories, 5 in the last 14 days
    
      ▂▁▄█▂ ▁▃    14d ago → today
    
    - 9 captured by an assistant, 3 added by hand
    - 5 shareable, 7 private
    

    A flat line means capture isn't firing, and you know within days.

    Use as a library

    import { MemoryStore, selectForExport, planImport } from "memshare-mcp";
    
    const store = new MemoryStore();
    await store.add({
      content: "Team chose Postgres over MySQL",
      tags: ["db"]
    });
    
    const { included, blocked } = await selectForExport(store, {
      tags: ["db"]
    });
    

    Preview and the real action share one code path. selectForExport and planImport compute what would happen; the CLI renders that and then acts on it. No parallel implementation for a preview — consent based on a stale preview is not consent.

    Try it

    git clone https://github.com/kampana/memshare.git
    cd memshare && npm install && npm run build
    bash examples/try-it.sh
    

    The script builds two fake stores in a temp directory and runs the full flow — capture, PII blocking, export, per-item import — then cleans up. Nothing touches your real config.

    Or just install it:

    npm install -g memshare-mcp
    memshare init
    

    Open source, MIT licensed, no server, no signup.

    GitHub | Site

    I'd love feedback — especially on the consent model and whether the sharing flow feels right. Issues and PRs welcome.

    Tags

    aiopensourcemcpdevtools

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