SlothTidy AI: Ambient Local-First Workspace Organizer & Overnight Digest Engine (Powered by Google Gemma)

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

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What I Built

Modern developers and knowledge workers suffer from digital fatigue: cluttered download folders, unorganized code snippets, messy receipts, and scattered daily action items.

Inspired by International Sloth Day and the "Slow and Steady" challenge theme, I built SlothTidy AI — an ambient, local-first background workspace organizer and daily morning digest engine powered by open-weight AI models.

Rather than being an intrusive, hyperactive assistant, SlothTidy embraces the peaceful, deliberate pace of the sloth:

Ambient Workspace Sorter : Silently categorizes unorganized downloads, code files, notes, and receipts into semantic directories. 2. Overnight Morning Digest : Reads raw daily thoughts, terminal logs, and git commits to synthesize a calm executive morning brief with clear action items and inspiring "Sloth Wisdom". 3. 100% Zero-Cloud Privacy : Runs entirely on open-weight models (via Ollama or local deterministic semantic parser). Zero bytes or tokens ever leave the local machine.

flowchart TD
    subgraph Input ["📥 Raw Workspace Inputs"]
        A1["Messy Downloads & Inbox"]
        A2["Daily Notes & Markdown Logs"]
        A3["Git Commits & Standup Snippets"]
    end

    subgraph Core ["🦥 SlothTidy Core Engine"]
        B1["Local AI Engine\n(Ollama Gemma 2 / Llama 3.2)"]
        B2["Resilient Heuristic Fallback\n(Deterministic Semantic Parser)"]
        B3["SlothStorage\n(SQLite Audit & Rollback)"]
    end

    subgraph Actions ["⚡ Autonomous Tasks"]
        C1["Semantic Classifier & Folder Sorter\n(Receipts, Code, Notes, Projects)"]
        C2["Daily Executive Digest Generator\n(Action Items + Calm Briefing)"]
    end

    subgraph Interfaces ["🖥️ Presentation Layer"]
        D1["Modern Glassmorphic Web Dashboard\n(http://localhost:8765)"]
        D2["Rich Terminal CLI Tool\n(python3 -m sloth_ai)"]
    end

    Input --> Core
    B1 <--> B2
    Core --> Actions
    Actions --> B3
    Actions --> Interfaces

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Demo

Web UI Dashboard : Includes an interactive dark-mode glassmorphic interface to test one-click workspace tidying, dry-run audits, and real-time morning digest synthesis. #

Terminal CLI : Features a clean command-line interface (sloth status ,sloth tidy ,sloth digest ,sloth serve ). #

Instant Verification : Self-contained zero-dependency web server and comprehensive unit test suite (100% passing).

Terminal Execution Output

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Code

How to Run Locally

SlothTidy AI is engineered using Python's standard library so you can run it out of the box with zero required pip dependencies:

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How I Built It

SlothTidy AI was built from the ground up with clean modular architecture:

Dual-Core Local Inference Engine (sloth_ai/core/engine.py) : Seamlessly connects to localOllama instances ( gemma2:2b ,llama3.2 ,qwen2.5 ) for semantic classification and natural language synthesis. If Ollama is offline, it gracefully falls back to a deterministic semantic rule parser so the application never breaks. #

Storage & Audit Layer (sloth_ai/modules/storage.py) : Uses embedded SQLite to track every organized file with SHA256 integrity checks, supporting audit logs and rollbacks. #

Ambient Tidier (sloth_ai/modules/tidier.py) : Reads file snippets safely, determines context, and relocates them into structured categorical directories (work_projects ,finance_receipts ,developer_code ,learning_notes ,personal_archive ). #

Digest Synthesizer (sloth_ai/modules/digest.py) : Parses markdown logs, bullet points, and commit history into an actionable executive summary. #

Glassmorphic Web Interface (sloth_ai/server/) : Built with responsive vanilla JavaScript and CSS, delivering real-time status updates and audit controls without node build tools.

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Why Does Open Innovation Matter?

Absolute User Sovereignty & Privacy : File tidying and daily diary summarization inherently touch sensitive documents (tax receipts, bank statements, proprietary code, personal thoughts). Sending this data to closed cloud APIs poses major privacy and security risks. Open-source AI ensures that intelligence happens directly on device where data lives. 2. Offline Resilience & Zero Cost : True ambient background automation should not stop when Wi-Fi drops, nor should it incur recurring pay-per-token API bills. Open-weight models empower users with limitless, resilient, local compute. 3. Freedom to Customize : Open innovation allows developers and the community to inspect model prompts, modify categorization heuristics, fine-tune models on domain-specific files, and build custom workflows without corporate gatekeeping.