LazySort AI: Drop the mess and let local Gemma sort it out

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

I built LazySort AI, a local-first file organizer for the folder everyone keeps promising to clean “later.”

Give it a cluttered folder and LazySort will:

It is deliberately not another extension-based sorter. A traditional rule can see that two files are PDFs; it cannot tell that one is an invoice and the other is a project report. LazySort uses the meaning of the document to decide where it belongs.

The people who get to be lazier are anyone with an overgrown Downloads folder: developers, students, freelancers, and people who would rather do almost anything than open and rename forty mystery documents.

Gemma suggests → LazySort validates → the user approves → LazySort moves Enter fullscreen mode Exit fullscreen mode The model never receives direct filesystem access. LazySort never deletes files, never overwrites an existing file, and records completed operations so the last organization run can be undone.

The repository includes a fictional sample folder at demo/upload-this-folder, so the workflow can be demonstrated without exposing personal documents.

The demo covers selecting a folder, checking that Gemma is ready, reviewing its explanations and confidence, editing and approving suggestions, organizing the files, confirming that zero files were deleted, and undoing the run.

LazySort is a local-first file organizer that uses Gemma 3 through Ollama to understand messy files, propose meaningful folders and names, and let you approve every change. It is a submission project for the Hacktoberfest 2026 Week 3: Slow and Steady challenge.

Instead of opening, identifying, renaming, and sorting files one by one, select a folder and review the organization plan Gemma prepares. LazySort never deletes files.

File names and document contents can be personal. When OLLAMA_BASE_URL points to localhost, inference happens on your machine: there is no mandatory proprietary AI API, no analytics, and no permanent storage of extracted content. You can also swap the configured Ollama model. If you configure a remote Ollama endpoint, extracted information is sent to that server—local inference is not an absolute privacy guarantee.