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Social Memory is my local-first attempt to solve a simple problem: saving useful social posts is easy, but finding them again when a project needs them is not. It collects selected posts from X and Threads into a local library, then lets a connected AI assistant retrieve source-linked evidence through a read-only MCP interface. The project is a development preview, and live account collection is not yet verified.
Why saved posts get lost
A bookmark or like records that a post mattered at some point. It does not make that post easy to find when you remember only its topic, approach, or a rough time frame. The useful material can end up scattered across saved posts, likes, and reposts, each with a different reason for keeping it.
Social Memory is built around that retrieval gap. The idea is to turn selected interactions on X and Threads into a personal evidence library that can be searched directly or queried by an assistant. The project does not claim that social platforms make this easy, nor does it establish how often people lose track of posts.
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Collect chosen signals into one library
You choose which signals to collect, including likes, bookmarks or saves, and reposts. A post encountered through more than one selected signal is stored once, with the reasons it was captured retained as metadata. That helps distinguish a post you bookmarked from one you merely liked without creating duplicate copies in the library.
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Search the archive with keywords
The README describes local storage in SQLite and keyword search using FTS5. This is text search, not embedding-based semantic search: the project materials do not describe a vector index that finds conceptually similar posts when they use different wording. Results depend on searchable text and what the collection process actually captured.
Give an assistant evidence, not authority over the archive
A read-only MCP interface allows Codex or Claude Code to retrieve evidence from the library. The assistant can then organize and synthesize what it receives, while the project supplies source-linked posts. As the project author puts it, “Social Memory supplies the evidence; the assistant does the interpretation.”
An illustrative request might be: “Find the implementation notes I bookmarked last week, group them by approach, and link every source.” That wording shows the intended interaction; it is not evidence that the project has returned those results in production.
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What the workflow requires—and what is not established
The current README describes a local demo that requires Node.js 22.16 or later, an unpacked Chrome extension, a local Native Messaging host, and MCP configuration for the assistant. Native host installation is implemented for macOS and Linux. Windows end-to-end operation is unverified.
The setup is therefore more involved than connecting a ready-made cloud service. The repository describes architecture and setup, but does not establish dependable, complete collection from live accounts. It specifically says live X and Threads account collection is not yet verified. The README also warns that browser changes, heuristic end-of-list detection, and posts that are unavailable can limit coverage. Extension collection is best effort while Chrome runs, not a guaranteed background service.
Those qualifications matter: a search tool can only retrieve evidence that made it into the archive. Treat Social Memory as a development preview for experimentation, not as a complete backup of your social history or a reliable source of every post you saved.
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Local storage does not guarantee a private AI session
“The source of truth remains local,” the author writes. That describes where the archive is stored, not necessarily where every later operation happens. When a connected cloud AI receives retrieved evidence, that content can leave your device under the assistant’s settings. The repository makes the same distinction: evidence returned to a cloud-connected assistant may leave the machine.
If that boundary matters for a post or account, check the assistant’s data and privacy settings before connecting it. Local-first storage and cloud-based interpretation are separate parts of the workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.License and project maturity
The project and repository identify the PolyForm Perimeter License 1.0.1. The repository says it permits use, modification, and distribution for permitted purposes, while prohibiting a product or service that competes with Social Memory; it is not an OSI-approved open-source license. Anyone considering reuse or redistribution should read the license itself rather than relying on this summary.
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The project is presented as a development preview. Its README is the best place to check current setup instructions and status, which can change over time. The materials do not establish a current release number or verified end-to-end live collection.
Who may find the idea useful
Social Memory is aimed at people who want to turn saved X and Threads posts into retrievable research notes and are comfortable with a local developer-oriented setup. Its useful distinction is the division of labor: a local archive retains posts and capture reasons, keyword search retrieves candidates, and an assistant can help group or explain the evidence.
It is a less suitable fit if you need guaranteed collection of every interaction, semantic search across loosely related ideas, a fully managed setup, or assurance that no content reaches a cloud service. The available project materials describe Social Memory’s design and limitations; they do not provide an independent comparison with other personal research libraries.
Quick Recap
Project links
- Social Memory on GitHub — setup instructions, current status, and license details.
- The project introduction by devjiro — the author’s explanation of the motivation and intended workflow.
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