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

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

💡 Quick Navigation: What I Built · Live Demo · Code · Architecture & Funnel · Open Innovation · Lazy Payoff · Prize Categories (8 Tracks) · Discussion

Last week I ran du -sh ~/Downloads on my laptop and realized I was hoarding hundreds of cryptic PDFs ( INV-9041-final(1).pdf ), forgotten.exe installers, and unnamed screenshots—and I refused to upload my private bank receipts or.env configs to a cloud AI just to sort them. So for International Sloth Day, I built a daemon that waits until I walk away from my keyboard and cleans the mess locally while I sleep.

Workspaces, Desktop dumps, and project inboxes are where good intentions go to hibernate. Cryptic filenames pile up ( Screenshot 2026-10-18 at 2.34.12 PM.png, INV-9041-final(1).pdf, DockerDesktopInstaller.exe ). Receipts, strategy documents, archives, and configuration files end up in a flat, unorganized heap because standard file extensions tell you almost nothing about what a document actually contains.

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

🔒 Local-First & Air-Gapped by Default: 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. A genuinely air-gapped deployment simply leaves external integrations disabled. Cloud partner services (Tiger Data pgvector, SerpApi vendor lookup, ElevenLabs voice) are strictly opt-in extensions via explicit CLI flags ( --enrich-online ), and each is engineered with a complete offline fallback (local SQLite vector search, local heuristics, terminal audio chimes). No personal file or metadata ever leaves your device by default.

Every developer, creator, and researcher who wastes 20 minutes every week digging through unorganized files, manually dragging PDFs into dated folders, or putting off digital cleanup. You let the sloth run patiently while you make coffee, return to an inspected plan, and keep complete control.

The complete interactive loop demonstrates the full lifecycle:

Messy Workspace ➜ Fast Metadata Triage (TabPFN / Local Heuristic) ➜ Local AI Plan (Gemma 3) ➜ Dry-Run Preview ➜ Explicit Approval ➜ Semantic Search (Tiger Data) ➜ SQLite Manifest Inspection ➜ 1-Click Rollback Enter fullscreen mode Exit fullscreen mode 35-Second Walkthrough Demonstration

You don't need to risk your own files to evaluate slothd. The repository includes a deterministic test fixture in test_workspace/messy_inbox containing an invoice, duplicate invoice, Uber receipt, project notes, Python benchmark, configuration file, leftover Windows installer, stale zip archive, and unsorted screenshots.

Anyone can clone and run the scan immediately:

git clone https://github.com/JaniDhruv/slothd.git cd slothd uv run slothd scan test_workspace/messy_inbox Enter fullscreen mode Exit fullscreen mode In under a second, you will observe the verified metrics:

The project is 100% open-source under the MIT License:

👉 GitHub Repository: github.com/JaniDhruv/slothd

A file organizer that feeds every single file into a heavy vision model is noisy, slow, and expensive. Instead, slothd uses a structured two-stage funnel:

┌────────────────────────────────────────────────────────┐ │ Messy Target Directory │ └───────────────────────────┬────────────────────────────┘ │ 1. Deduplicate & Extract Features │ ▼ ┌────────────────────────────────────────────────────────┐ │ STAGE 1: Prior Labs TabPFN Metadata Triage │ │ (7 tabular signals: log size, depth, ext, time, etc.) │ └─────────────┬────────────────────────────┬─────────────┘ │ │ Leftover installers, archives, │ Needs deeper screenshots, duplicate clutter │ semantic classification ▼ ▼ Flagged for cleanup ┌────────────────────────┐ │ STAGE 2: Gemma 3 Local │ │ (Ollama Vision/Text) │ └──────────┬─────────────┘ │ ▼ ┌────────────────────────┐ │ SQLite Reversible Log │ │ + Tiger Data pgvector │ └────────────────────────┘ Enter fullscreen mode Exit fullscreen mode Stage 1: Lightweight Tabular Triage (Prior Labs TabPFN) Before running AI models, slothd extracts 7 tabular signals from the filesystem:

When TabPFN credentials or a local model checkpoint are configured, TabPFN evaluates these tabular feature distributions directly (taking ~15–25 ms). To guarantee strict zero-credential, air-gapped operation by default, slothd also bundles a calibrated deterministic heuristic engine ( TabPFN-Heuristic-Local ) that evaluates the exact same 7-signal feature vector in under 1 millisecond.

(Note on benchmark accuracy: The 1 ms timing reported in the deterministic test fixture reflects this local offline heuristic engine. When running against TabPFN's hosted model with credentials, inference takes ~20 ms across the batch. Both modes share the exact same Pydantic triage interface and 100% test pass rate).

Only files requiring semantic classification (unlabeled PDFs, scanned receipts, documents) are forwarded to local Gemma 3 via Ollama.

The organization pipeline enforces strict safety invariants:

Sentry was not added as a decorative vanity badge; it was an active part of the developer feedback loop during development.

The integration initializes Sentry with release slothd@0.1.0 and traces critical pipeline boundaries: scanner.scan_directory, ai.classify_batch, organizer.preview, organizer.execute, organizer.undo, and search.query.

[Transaction] slothd.organize_cycle (total: 1,248 ms) ├─ [Span] scanner.scan_directory.................... 12 ms [10 files discovered] ├─ [Span] ai.tabular_triage (TabPFN)................ 1 ms [2 anomalies flagged] ├─ [Span] ai.gemma_classify_batch (Ollama)..........

1,180 ms [Prompt: 412 tok, Resp: 86 tok] ├─ [Span] organizer.preview_manifest................ 3 ms [Manifest prepared] ├─ [Span] db.wal_log_commit......................... 4 ms [SQLite WAL sync] └─ [Span] search.index_pgvector (Tiger Data)........

48 ms [768-dim embeddings stored] Enter fullscreen mode Exit fullscreen mode Authentic Sentry Captured Trace & Issue Evidence Issue ID: 7805015409 Release: slothd@0.1.0 (production) Title: ConnectionError: Connection refused to 127.0.0.1:11434 Culprit: slothd.engine.classifier in classify_batch Trace ID: 71ac0b2ff1dc4c4788ff6a5b7098e354 Span ID: a1ea6839c23ccbb5 (op: ai.classify_batch, desc: " Gemma 3 batch classify (4 files)") Breadcrumb: [ warning ] slothd.engine.classifier: Ollama server is offline or unreachable. Fast-falling back to local classification. Handled: Yes (Circuit-breaker tripped gracefully without crashing the daemon) Enter fullscreen mode Exit fullscreen mode Observed Failure in Sentry Root Cause Revealed Resolution & Test ConnectionError on 127.0.0.1:11434 (Issue #7805015409) Ollama daemon was offline during CLI execution Added 200ms pre-flight probe, local heuristic fallback, and circuit-breaker regression test ( 3a327ea ).

ValidationError: confidence > 1.0 Gemma 3 occasionally returned confidence as a percentage ( 90 or 100 ) instead of float 0.0–1.0 Added automatic percentage normalization validator in Pydantic schema with bounds coverage ( a914b53 ). RuntimeError: generator didn't stop after throw() Tracing context manager was incorrectly re-entering on caught exceptions Cleaned up tracing wrapper to propagate exceptions correctly and verified with regression tests ( df19f29 ). Why Does Open Innovation Matter?

Personal files contain our most vulnerable data:

Uploading this stream of daily life to closed, centralized cloud APIs creates an unacceptable privacy tradeoff.

Open-weight AI (Gemma 3) and open-source tabular models (TabPFN) fundamentally change this dynamic. They make it possible to run state-of-the-art vision and tabular triage entirely inside the boundary of your own computer:

Privacy is not a checkbox added after the architecture is done; with open-source AI, privacy is the architecture.

The lazy payoff is not about moving files a few milliseconds faster. It is about removing the cognitive dread of manual digital housekeeping.

Instead of spending Sunday afternoon opening dozens of PDFs one by one, deciphering cryptic filenames, and deciding where each document belongs:

10 files scanned 2 clutter anomalies identified 1 duplicate set found 973 B reclaimable disk space 1 ms metadata triage (local deterministic 7-signal engine / TabPFN schema) 0 cloud API calls required 100% offline isolation Enter fullscreen mode Exit fullscreen mode Prize Categories Architectural Depth & Ecosystem Evidence While slothd operates completely offline out of the box, it was engineered with a modular architecture that cleanly interfaces with specialized ecosystem tools. Rather than a superficial checklist of sponsor imports, each partner integration represents an optional, verified subsystem that enhances capability without compromising our local-first core:

Every test runs in total offline isolation (no live external services required):

uv run pytest -v # 118 passed in 40.87s uv run ruff check. # All checks passed! Enter fullscreen mode Exit fullscreen mode 🦥 Parting Sloth Thoughts: Reclaiming Digital Calm We spend so much time micromanaging our digital clutter—manually dragging files into folders, hunting for lost receipts, or feeling guilty about a desktop covered in final_v2_really_final.pdf.

slothd was built on a very simple premise: your machine should quietly work for you when you're resting, not steal your attention when you're busy.

By embracing open-weights, deterministic rule fallbacks, and respectful idle scheduling, you don't have to surrender your personal documents to cloud silos or burn your laptop's battery just to keep a clean workspace. Take a breath, close your laptop lid, and let the sloth do the heavy lifting while you take a break.

I'd love to hear your thoughts and feedback:

If slothd brought a little bit of calm to your daily workflow, consider dropping a ⭐ on the GitHub repository, leaving a comment below, and sharing your experience! Happy Hacktoberfest! 🍂🦥

Expand Dhruv Jani Dhruv Jani Dhruv Jani Follow Upcoming Trainee Engineer @ Simform 🚀 Building, experimenting, and writing my way through tech. AI • Web • Open Source • Challenges If I find something interesting, I'll probably write about it. 😄 Email dhruvdjani29@gmail.com Location Vadodara, Gujarat, India Education B.Tech in Computer Engineering @ Dharmsinh Desai University Work Computer Engineering Student | Full-Stack Developer Joined Jan 31, 2026 • Oct 21 Dropdown menu Copy link Hide 🦥 A little more context behind slothd!

I wanted to build something that could quietly take care of digital clutter without constantly demanding my attention or sending my personal files to the cloud.

This is my Week 3 submission for the Hacktoberfest Open-Source AI Challenge, and I had a lot of fun bringing together local AI, idle-aware automation, and reversible file operations.

The 35-second demo and offline fixture are included in the article if you'd like to see it in action!

Now I'm curious: What's the messiest folder on your machine, and would you trust a local AI assistant to organize it? 👀