TidyDesk AI: Let Open-Source AI Clean Up Your Messy Folders

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

Meet TidyDesk AI — a privacy-first desktop file organizer designed to help students, developers, freelancers, and anyone with a cluttered computer spend less time managing files.

We've all been there: hundreds of files in Downloads, confusing filenames, duplicate documents, and screenshots we promise to organize later.

TidyDesk AI aims to make that cleanup effortless.

Instead of manually opening files, renaming documents, and dragging everything into folders, users can select a directory and let AI suggest a clean organization plan.

The goal is simple: spend less time organizing files and more time doing something useful.

Try the live application: https://tidydesk-ai.onrender.com

TidyDesk AI is designed as a desktop application. The demo will show a cluttered test folder being analyzed, organized, and reviewed through a safe approval workflow.

GitHub Repository: https://github.com/Charlie-ai786/tidydesk-ai.git

The project is being developed as a standalone application, with a focus on local file processing, safe filesystem operations, and open-weight AI.

TidyDesk AI combines native desktop capabilities with local AI inference.

The model proposes changes, but it does not control filesystem operations directly. The application validates paths, checks for filename conflicts, and requires user approval before modifying files.

Open innovation makes it possible to build a file organizer that prioritizes privacy, control, and independence from paid AI APIs.

File collections can contain personal documents, financial records, academic work, and private screenshots. Local processing reduces the need to send this information to external AI services.

When a compatible open-weight model runs locally, inference does not consume hosted LLM API credits. The user still needs suitable hardware, and the application may have other costs, but its core AI workflow need not depend on a paid API subscription.

A modular architecture makes it possible to experiment with compatible models and choose one that balances quality, speed, memory usage, and licensing requirements.

After installation and model downloads, supported file-organization workflows can run without an internet connection.

Open models provide flexibility, but they can still make mistakes. That's why TidyDesk AI separates model suggestions from file operations. Users review the plan before changes are applied.

For me, open innovation is not just about making AI cheaper. It's about building useful software that users can control and trust.

Building TidyDesk AI: Privacy-First Desktop AI File Organizer (Hacktoberfest Week 3) You Build TidyDesk AI — Your Open-Source AI File Organizer for Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady. A privacy-first desktop application that reduces the effort required to clean up Downloads, Desktop, and custom folders using local open-weight LLMs via Ollama, with atomic operations and one-click undo.

Architected and implemented TidyDesk AI using Electron, React 19, TypeScript, Tailwind CSS, SQLite, and Ollama. Created full-featured modules: File Scanner, Rule-based Categorizer, AI Semantic Analyzer, Collision-Safe Executor, and Reversible SQLite History Stack.

$ npm run test Output 44 passed (44) across categorizer, analyzer, and dashboard suites You do the remenaning work

Resolved categorizer precedence logic for media files, aligned PostCSS and Tailwind to CommonJS for Electron compatibility, resolved TypeScript 6.0 moduleResolution deprecation, eliminated all lint warnings (0 errors, 0 warnings), verified full production build, designed custom brand SVG icon, and created comprehensive documentation.

$ npm run build Output Vite built in 2.11s; tsc built electron-dist/main.js cleanly You add readme file and gitignore file because i want ot push this on github

Generated root.gitignore and comprehensive README.md with Hacktoberfest badges, quickstart guide, project architecture diagram, and MIT License.

git push -u origin main: Updates were rejected because the remote contains work that you do not have locally

Diagnosed remote conflict caused by earlier submodule commit, aligned branch with remote, and force-pushed complete production code to https://github.com/Charlie-ai786/tidydesk-ai. All 42 files and 44 tests are now live on GitHub.

$ git push -u origin main --force Output branch 'main' set up to track 'origin/main'. To https://github.com/Charlie-ai786/tidydesk-ai.git forced update 8 of 8 messages I'll include the recorded development session once it has been successfully submitted through DevRelay.

The goal is to turn a tedious manual cleanup session into a quick review-and-approve workflow.

Instead of deciding where every file belongs, users can review AI-generated suggestions in batches and apply the changes they approve.

What I still want to measure: How long it takes to organize a sample folder manually compared with using TidyDesk AI.

I'll report actual timings after testing the application on a representative folder rather than inventing a time-saving figure.

If Gemma is integrated and tested in the final implementation, I'll document its role in file categorization and filename suggestions and confirm that the project satisfies the official category requirements.

I'll list only the partner categories for which the finished project is genuinely eligible.

The idea behind this project is simple: let open-source AI handle the repetitive work, keep users in control, and make organizing files something you no longer have to put off.