👻 Memory Ghost: Bringing Forgotten Files Back to Life with Local AI | Gemma 3 + Ollama

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

What I Built

👻 Memory Ghost — Bringing Forgotten Files Back to Life with Local AI

Our computers are full of digital ghosts: old notes, assignments, research documents, and forgotten files whose contents we can barely remember.

Memory Ghost is a local-first AI assistant that helps users rediscover what's inside their files. Select a folder, choose a supported text file, and let AI generate a summary, identify topics, extract key points, and suggest a memory category.

I interpreted the Ghost in the Machine theme as the forgotten information hiding in our digital lives. Instead of letting useful knowledge disappear into cluttered folders, Memory Ghost helps bring it back to the surface.

The current version supports .txt, .md, .json, and .csv files.

Demo

🎬 Watch Memory Ghost in action: Watch the demo on YouTube

The video demonstrates the application running locally and shows the AI-powered file-analysis workflow.

Code

👩‍💻 GitHub Repository: Shikha18Shukla/Memory-Ghost

The project uses a React + TypeScript frontend and a Python + FastAPI backend, with local AI inference through Ollama.

How I Built It

The core of Memory Ghost is Gemma 3 (gemma3:4b) running locally through Ollama.

Here's how the pieces work together:

• React + TypeScript: Provides the user interface for selecting folders and exploring analysis results.
• FastAPI: Handles folder selection, file validation, reading supported files, and AI-analysis requests.
• Ollama: Runs the model locally on the user's machine.
• Gemma 3: Analyzes file content and generates summaries, topics, key points, and a suggested memory category.
• SQLite: An initial database schema has been added as the foundation for future persistent memory storage.

The workflow is simple: choose a folder, select a supported file, send its text to the local model, and review the generated results.

The current version is a working prototype. Saving and searching previously analyzed memories are planned improvements, rather than completed features.

Why Does Open Innovation Matter?

I chose an open-weight model because I wanted to explore AI beyond the usual hosted-API workflow.

Using Gemma 3 with Ollama made it possible to build an application around local inference, giving users more control over where their files are processed and allowing developers to experiment with the model on their own machines.

Open innovation makes experimentation more accessible. Developers can learn from available tools, adapt them to specific problems, and build practical applications without depending exclusively on a proprietary hosted AI service.

For Memory Ghost, this matters because the application works with personal notes and documents. Local inference can reduce the need to send their contents to an external AI provider, although local execution alone does not guarantee complete security.

This project is my exploration of how open-weight AI can make everyday information easier to rediscover.

My Agent Session

DevRelay session: Not included. This section is optional, so the submission can proceed without it.

The Spirit of It

Somewhere on our computers, there are notes we wrote months ago, ideas we thought we'd lost, and documents we know contain something important.

We don't always lose information by deleting it. Sometimes, we lose it simply by forgetting where it is.

Memory Ghost is about bringing those forgotten digital memories back to life. 👻

The goal isn't to make the AI remember everything for us. It's to make the knowledge we've already saved easier to find, understand, and use again.

Prize Categories

• Best Use of Gemma: Memory Ghost uses Gemma 3 (gemma3:4b) through Ollama as its core text-analysis model.

Memory Ghost was developed by adapting the existing LazyDesk-AI codebase. The project acknowledges this origin, and I am checking the applicable licensing and challenge eligibility requirements before claiming contest eligibility.