SlothTidy AI organisiert den lokalen Workspace automatisch mit Google Gemma
Das Open-Source-Projekt SlothTidy AI sortiert Downloads, Code-Snippets, Belege und Tagesaufgaben lokal auf dem Rechner. Angetrieben von Google Gemma fasst es den Arbeitstag als Overnight Digest zusammen, ohne dass Daten in die Cloud wandern.
Hacktoberfest Open-Source AI Challenge Woche 3: Slow and Steady Einreichung 🦥
Dies ist eine Einreichung für die Hacktoberfest Open-Source AI Challenge Woche 3: Slow and Steady.
Moderne Entwickler und Wissensarbeiter leiden unter digitaler Ermüdung: überfüllte Download-Ordner, unsortierte Code-Snippets, chaotische Belege und verstreute tägliche Aufgaben.
Inspiriert vom International Sloth Day und dem Challenge-Thema "Slow and Steady" habe ich SlothTidy AI gebaut, einen ambienten, local-first Background-Workspace-Organizer und täglichen Morning-Digest-Generator, der auf Open-Weight-KI-Modellen basiert.
Statt ein aufdringlicher, hyperaktiver Assistent zu sein, verkörpert SlothTidy das friedliche, bedächtige Tempo des Faultiers:
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 Demo Web UI Dashboard: Enthält ein interaktives Glassmorphic-Interface im Dark Mode zum Testen des Workspace-Aufräumens mit einem Klick, von Dry-Run-Audits und der Echtzeit-Erstellung des Morning Digest. Terminal CLI: Bietet eine übersichtliche Kommandozeilen-Oberfläche ( sloth status, sloth tidy, sloth digest, sloth serve ). Sofortige Verifikation: Ein in sich geschlossener Webserver ohne Abhängigkeiten und eine umfassende Unit-Test-Suite (100 % bestanden).
Terminal-Ausgabe $ python3 -m sloth_ai.cli tidy --folder examples/inbox --dry-run 🔍 Scanning files in: examples/inbox... [DRY RUN] Successfully processed 3 files: • meeting_agenda_q4.md --> [work_projects] Summary: Classified 'meeting_agenda_q4.md' as work projects based on local semantic markers. • invoice_october.txt --> [finance_receipts] Summary: Classified 'invoice_october.txt' as finance receipts based on local semantic markers. • db_helper.py --> [developer_code] Summary: Classified 'db_helper.py' as developer code based on local semantic markers. $ python3 -m sloth_ai.cli digest --file examples/notes/oct_25_thoughts.md ======================================================= 🌅 Digest - oct_25_thoughts.md ======================================================= Overview: Synthesized 6 log lines into 5 primary action items. All items tracked locally without external cloud telemetry. Action Items: [ ] TODO: Submit Hacktoberfest Open-Source AI Challenge Week 3 ("Slow and Steady") [ ] Review open pull requests on the repository [ ] Fix edge-case in JSON streaming response parser [ ] Prepare presentation deck for next week [ ] Walk the dog in the evening Sloth Wisdom: "Slow and steady wins the race.
Nimm einen Atemzug und lass die Maschine die Schwerstarbeit erledigen." ======================================================= Enter fullscreen mode Exit fullscreen mode Code-Repository: [ https://github.com/rajJasani/sloth-tidy-ai ] Lizenz: Permissive Open-Source-Lizenz MIT So läuft es lokal SlothTidy AI wurde mit der Python-Standardbibliothek entwickelt, sodass es ohne Installation sofort läuft und keinerlei pip-Abhängigkeiten benötigt:
# 1. Clone the repository git clone https://github.com/rajjasani/sloth-tidy-ai.git cd sloth-tidy-ai # 2. (Optional) Run with your favorite open-weight model via Ollama ollama pull gemma2:2b # 3. Check system status and local inference engine python3 -m sloth_ai.cli status # 4. Perform a dry-run tidy audit on sample files python3 -m sloth_ai.cli tidy --folder examples/inbox --dry-run # 5. Generate a morning digest python3 -m sloth_ai.cli digest --file examples/notes/oct_25_thoughts.md # 6. Launch the interactive Web Dashboard python3 -m sloth_ai.cli serve --port 8765 # Open http://localhost:8765 in your browser Enter fullscreen mode Exit fullscreen mode Wie ich es gebaut habe SlothTidy AI wurde von Grund auf mit einer sauberen, modularen Architektur entwickelt:
Automatisch übersetzt. Das Original auf Englisch ist unten aufklappbar.
Original (Englisch) anzeigen
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.
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:
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 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 $ python3 -m sloth_ai.cli tidy --folder examples/inbox --dry-run 🔍 Scanning files in: examples/inbox... [DRY RUN] Successfully processed 3 files: • meeting_agenda_q4.md --> [work_projects] Summary: Classified 'meeting_agenda_q4.md' as work projects based on local semantic markers. • invoice_october.txt --> [finance_receipts] Summary: Classified 'invoice_october.txt' as finance receipts based on local semantic markers. • db_helper.py --> [developer_code] Summary: Classified 'db_helper.py' as developer code based on local semantic markers. $ python3 -m sloth_ai.cli digest --file examples/notes/oct_25_thoughts.md ======================================================= 🌅 Digest - oct_25_thoughts.md ======================================================= Overview: Synthesized 6 log lines into 5 primary action items. All items tracked locally without external cloud telemetry. Action Items: [ ] TODO: Submit Hacktoberfest Open-Source AI Challenge Week 3 ("Slow and Steady") [ ] Review open pull requests on the repository [ ] Fix edge-case in JSON streaming response parser [ ] Prepare presentation deck for next week [ ] Walk the dog in the evening Sloth Wisdom: "Slow and steady wins the race.
Take a breath and let the machine do the heavy lifting." ======================================================= Enter fullscreen mode Exit fullscreen mode Code Repository: [ https://github.com/rajJasani/sloth-tidy-ai ] License: Permissive open-source MIT License 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:
# 1. Clone the repository git clone https://github.com/rajjasani/sloth-tidy-ai.git cd sloth-tidy-ai # 2. (Optional) Run with your favorite open-weight model via Ollama ollama pull gemma2:2b # 3. Check system status and local inference engine python3 -m sloth_ai.cli status # 4. Perform a dry-run tidy audit on sample files python3 -m sloth_ai.cli tidy --folder examples/inbox --dry-run # 5. Generate a morning digest python3 -m sloth_ai.cli digest --file examples/notes/oct_25_thoughts.md # 6. Launch the interactive Web Dashboard python3 -m sloth_ai.cli serve --port 8765 # Open http://localhost:8765 in your browser Enter fullscreen mode Exit fullscreen mode How I Built It SlothTidy AI was built from the ground up with clean modular architecture: