What I Built
What if your computer logs were not just records of what happened, but clues to a haunting?
Meet GhostLog — The Haunted Digital Archive, a spooky AI-powered web application that transforms fictional computer logs into eerie ghost stories.
I wanted to build something that combined my interest in AI with the atmosphere of a haunted digital world. Instead of showing ordinary log messages, GhostLog reimagines them as mysterious events and unsettling narratives.
Users can enter fictional computer logs, choose a haunting style and story length, and generate a ghost story. They can then copy the story or click “Hear the Ghost” to listen to it through voice narration.
✨ Key Features
- AI-powered storytelling: Turn fictional computer logs into creepy narratives.
- Customizable horror: Choose the haunting style and story length.
- Voice narration: Listen to the generated story using ElevenLabs.
- Dark, spooky interface: A digital archive designed for the Ghost in the Machine theme.
- Local AI inference: Generate stories using an open-weight language model running locally through Ollama.
GhostLog explores the idea that even an ordinary digital trace can become the beginning of a mysterious story.
Demo
Demo:https://youtu.be/5c1n62dQcyU
The project currently runs locally. To try it, clone the repository, install the required dependencies, configure Ollama and the narration settings, and launch the Streamlit application.
I haven't deployed a public web demo yet, so the repository is the main project link.
Code
GitHub Repository: github.com/Ujwalashetty18/ghostlog
Tech stack: Python · Streamlit · Ollama · Open-weight LLM · ElevenLabs
The main application is implemented in app.py.
The repository contains the application code and .gitignore. Setup instructions and a dependency file can be added to make it easier for others to run the project.
How I Built It
I built GhostLog as a Python application with Streamlit for the interface.
The main technologies are:
- Python — application logic.
- Streamlit — interactive web interface.
- Ollama — local model inference.
- Llama 3.2 3B — open-weight language model for generating ghost stories.
- ElevenLabs API — voice narration for the generated stories.
- Git and GitHub — version control and source-code hosting.
The workflow is straightforward:
- The user enters fictional computer logs.
- The user selects a haunting style and story length.
- The local language model turns the input into a ghost story.
- The generated story appears in the interface.
- The user can copy the story or listen to its narration.
One of the most interesting parts was getting the narration feature working. I had to troubleshoot voice configuration and API restrictions before I could hear the generated stories aloud.
Building the project taught me that creating an AI application involves more than connecting a model to a user interface. Configuration, integration, debugging, and testing all matter.
Why Does Open Innovation Matter?
Open innovation made it possible for me to experiment with AI storytelling using a model that I could run on my own computer.
With Ollama and an open-weight model, I could generate stories locally instead of depending entirely on a hosted language-model API. This gave me more control over the development process and helped me experiment without needing a paid hosted LLM service.
It also made the project easier to explore and modify. I can experiment with prompts, change the storytelling logic, and investigate other compatible models as the project grows.
The voice narration currently uses ElevenLabs, so not every component is local. This combination let me explore how local AI generation and an external voice service can work together.
For me, open innovation means having the freedom to experiment, learn by building, and make technology my own.
The Spirit of It 👻
Imagine opening an old computer and finding a log entry that should not exist.
A timestamp in the middle of the night. A process that nobody remembers starting. A message that seems to know something it shouldn't.
What happened?
GhostLog takes that little spark of mystery and turns it into a story.
The part that made the idea exciting for me was hearing a story generated from computer logs spoken aloud. The archive no longer felt like a collection of text. It felt as though something inside the machine had finally found a voice.
That is the spirit of GhostLog: giving a voice to the mysteries hidden in our digital world.
Prize Categories
- Best Use of ElevenLabs — for integrating voice narration into the ghost-story experience.
Final Thoughts
GhostLog started as an experiment in combining local AI, storytelling, and voice narration. Along the way, I learned how to build the interface, connect the model, debug API integration, and publish my code on GitHub.
This project is a reminder that AI doesn't have to be only about productivity or solving serious problems. It can also be creative, playful, and a little spooky.
Sometimes, all it takes is a few lines in a log file to wake the ghost in the machine. 👻
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