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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchSocial Memory is my local-first attempt to make saved social posts findable when I need them: it collects selected likes, saves, or reposts from X and Threads into a searchable local library, then gives a connected AI assistant read-only access to retrieved evidence. The project is a development preview, and live account collection is not yet verified.
Why saved posts are hard to use later
Saving a post is easy; recalling the useful one weeks later is the harder part. A bookmark, like, or repost records an interaction, but it does not necessarily preserve a searchable trail that fits a question you have now. I built Social Memory around that gap: turn selected social interactions into a personal evidence library rather than expecting an assistant to remember posts it cannot access.
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The intended request might be: “Find the implementation notes I bookmarked last week, group them by approach, and link every source.” That is an example of the kind of task the system is designed to support, not a claim that it has returned those results in production.
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Social Memory is presented as a local library for posts from X and Threads. You choose which interactions count as collection signals, including likes, bookmarks or saves, and reposts. Signals can be selected independently. If the same post appears through more than one selected signal, the project stores it once and retains the reasons it was captured as metadata.
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That provenance matters: a saved item can be searched without losing whether you bookmarked it, liked it, or reposted it. The intended archive is stored in a local SQLite database, with keyword search powered by FTS5.
Keyword search, not semantic search
The README describes the search as keyword-based, not embedding-based semantic search. That distinction affects how you phrase a query: matching terms can help surface a post, but the documentation does not establish that the local index will find conceptually related posts that use different wording.
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How an AI assistant fits into the workflow
Social Memory exposes retrieved material through a read-only Model Context Protocol (MCP) interface for Codex or Claude Code. The connection is designed to let an assistant retrieve source-linked evidence from the library; the assistant then groups, summarizes, or otherwise interprets that material. As the project’s author puts it, “Social Memory supplies the evidence; the assistant does the interpretation.”
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThis division is deliberate. The archive provides the underlying posts and their source links, while the assistant helps answer a question using that evidence. It is not a claim that the assistant independently has access to your social accounts or can search the full history of a platform.
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What setup currently involves
The current README describes a local demo requiring Node.js 22.16 or later, an unpacked Chrome extension, a local Native Messaging host, and MCP configuration for an assistant. The documented native-host installation is implemented for macOS and Linux. Windows end-to-end operation is unverified.
The README also distinguishes locally tested extension and Chrome DOM collection from live social-account collection: “The extension and Chrome DOM collection are tested locally, but live X/Threads account collection is not yet verified.” It says browser changes, heuristic detection of the end of a list, and posts that are unavailable can limit coverage. Extension collection is best effort while Chrome is running, not a guaranteed background service, and complete account history should not be assumed.
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Because repository instructions can change, check the Social Memory repository for the current setup steps and status before relying on a particular platform or operating system. The reviewed README does not establish a current release number.
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Local storage does not guarantee an entirely local AI workflow
“Local-first” describes where Social Memory stores its archive; it does not mean every step stays on your device. When a connected assistant retrieves evidence, that content may leave the machine if the assistant uses a cloud service. The project’s author summarizes the storage principle as: “The source of truth remains local.” The privacy boundary still depends on the assistant and its settings, so consider what material you ask it to retrieve and where that assistant processes requests.
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License and reuse
The project identifies its license as PolyForm Perimeter License 1.0.1. The repository says it allows 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, redistribution, or commercial deployment should read the license text rather than relying on a short summary.
What the project does—and does not—establish
Social Memory describes a concrete bridge from selected X and Threads interactions to a local keyword-searchable archive and a read-only assistant interface. Its strongest stated idea is keeping source-linked evidence available for an assistant to organize, rather than treating a generated summary as the source of truth.
It remains a development preview, not a verified promise of complete or continuous collection. Its documented search is keyword-based, live account collection is unverified, and cloud-connected assistant use can move retrieved evidence off-device. Those boundaries are central to evaluating whether this approach fits your own workflow.
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