GHOSTBYTE：以本地 AI 保存記憶並生成鬼故事

GHOSTBYTE 👻 — A Little Memory. An Endless Afterlife.

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

What I Built

GHOSTBYTE is a local-first, AI-powered memory archive that gives forgotten memories a second life.

What if the memories we save could tell us stories we never imagined?

Inspired by Halloween, Día de los Muertos, and the stories we carry about people and places from our past, I built GHOSTBYTE to preserve personal memories and transform them into something new.

GHOSTBYTE lets users:

👻 Preserve Memories: Save personal stories, important moments, people, places, and details in a persistent archive.
🔎 Ask the Archive: Ask questions about saved memories and explore fragments of the past with local AI.
🕯️ Generate Ghost Stories: Transform selected memories into eerie, melancholic, supernatural, or psychological horror fiction.
🗄️ Keep Memories Persistent: Store memories and generated stories in Microsoft SQL Server.
🔒 Keep Data Local: Run AI inference on your own computer instead of sending personal stories to a hosted AI API.

The idea behind GHOSTBYTE is simple: our memories deserve a place that doesn't depend on a third-party AI service staying online.

Some memories fade. Others become part of the machine.

Demo

Run GHOSTBYTE locally:

Install Node.js, Microsoft SQL Server, and Ollama.
Download the model using ollama pull qwen2.5:1.5b.
Configure the database connection in your .env file.
Start the application with npm start.
Open http://127.0.0.1:3000.

The application runs locally, and the AI uses your local Ollama installation.

Demo Image:

GHOSTBYTE is currently designed to run locally.

Code

GitHub repository: https://github.com/Bharatefb/Ghost-Byte-AI.git

The project uses a straightforward architecture:

public/ — HTML, CSS, and browser JavaScript.
server.js — Node.js and Express backend.
database/schema.sql — SQL Server database schema.
.env — Local configuration for the database and Ollama.
Ollama — Local inference using Qwen2.5 1.5B.

The code is designed to keep the application understandable and extensible, without requiring a hosted AI API.

How I Built It

GHOSTBYTE combines traditional web technologies with open-weight AI running locally.

Technology stack:

Frontend: HTML, CSS, and vanilla JavaScript.
Backend: Node.js and Express.
AI model: Qwen2.5 1.5B through Ollama.
Database: Microsoft SQL Server.
Inference: Local model execution through the Ollama API.

The frontend lets users create and explore memories, ask questions, and generate stories. Express handles API requests and communicates with SQL Server and Ollama.

SQL Server provides persistent storage for personal memories and generated fiction. Ollama runs Qwen2.5 1.5B locally, allowing the application to generate responses without relying on a hosted AI inference API.

I initially planned to use Qwen 3.5 2B, but I switched to Qwen2.5 1.5B to improve responsiveness on my development machine. This also made model selection an important part of the project: the model can be changed to suit the hardware and desired performance.

After the initial installation, model download, and dependency setup, the core application can work offline, provided the database, application server, and local model are running.

Why Does Open Innovation Matter?

Personal memories are among the most private things we have.

I didn't want GHOSTBYTE to require uploading family stories, personal experiences, or sensitive details to a remote AI provider just to explore them.

Using an open-weight model through Ollama makes local inference possible. Memories stay in the locally configured SQL Server database, and AI requests are sent to the local Ollama service rather than a hosted AI API.

Open innovation makes several things possible:

Privacy and control: Personal stories can remain on a computer controlled by the user.
Offline capability: Once dependencies and the model are installed, core features can work without internet access.
Model flexibility: Developers can experiment with different compatible local models and adjust prompts to change the experience.
No hosted AI request fees: Local inference avoids per-request charges from a hosted AI provider, although running the computer still consumes resources.
Long-term independence: The project does not depend on one proprietary AI API being available forever.

For a project about preserving memories, this independence matters. A private archive should remain useful even when a cloud service changes its pricing, access rules, or availability.

Open AI makes GHOSTBYTE more than a storytelling interface. It makes a locally controlled memory archive possible.

The Spirit of It

Imagine saving a memory about your grandmother's old house.

Years later, you open GHOSTBYTE and ask what you remember about that place. The archive helps you revisit the details you preserved. Then you select that memory, choose Eerie and atmospheric, and ask the machine to turn it into a ghost story.

The resulting story might describe a hallway clock stopping at midnight or footsteps echoing through an empty room.

Those supernatural details are fiction, not recovered facts. The real memory remains yours; the machine simply gives it another form.

That is the spirit of GHOSTBYTE: preserving the past while letting imagination wander through it.

A little memory. An endless afterlife. 👻

Built with Node.js, Microsoft SQL Server, Ollama, and Qwen2.5 1.5B.

Some memories fade. Others become part of the machine.