I Built slothd: A Local-First, Offline-Capable AI Daemon That Patiently Organizes Your Messy Workspace

说明与用途

开发者创建了 slothd，本地 AI 守护进程，能够在不联网的情况下自动整理工作空间中的文件，使用 Gemma 3 和 TabPFN 模型，并通过 SQLite WAL 日志实现可逆操作。

问题

Users spend time manually organizing cluttered workspaces, risking privacy by uploading sensitive files to cloud AI services.

结果

In under a second, the demo fixture of 10 files was scanned, 2 clutter anomalies flagged, 1 duplicate set detected, and no cloud API calls were made, demonstrating 100% offline operation.

工作流

• Detect keyboard/mouse inactivity
• Scan target directory
• Extract 7 tabular signals
• Run Prior Labs TabPFN triage
• Flag anomalies
• Run Gemma 3 via Ollama for document understanding
• Generate organization plan
• Dry‑run preview
• Approve plan
• Move files
• Index in Tiger Data pgvector or local SQLite
• Provide semantic search
• Log actions in SQLite WAL
• Enable 1‑click rollback

证据

I built **slothd**, a local-first, offline-capable AI file organizer designed around the philosophy of **"Slow and Steady"**

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Uses native OS APIs to measure keyboard and mouse inactivity. It sleeps while you work and only activates when you step away.

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Fast metadata triage (1ms via local 7-signal engine, ~20ms via **Prior Labs TabPFN**) flags anomalies before invoking heavier models.

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Uses **Gemma 3** running locally in **Ollama** with strict Pydantic JSON schemas to read PDFs, invoices, and images.

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Every run is a dry‑run by default. Every approved move is written to an SQLite WAL log with a unique batch ID. If you dislike what it did, slothd undo restores every file to its exact original path.

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The entire core workflow—idle detection, filesystem scanning, tabular metadata triage, Gemma 3 document understanding via local Ollama, SQLite WAL manifests, and 1‑click rollback—executes **100% locally on your machine with zero internet calls.

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Anyone can clone and run the scan immediately: git clone https://github.com/JaniDhruv/slothd.git uv run slothd scan test_workspace/messy_inbox

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