The Last Lantern: Stories, Memories & Open AI
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
What I Built
I built The Last Lantern, an AI-powered storytelling experience that explores the creative side of artificial intelligence.
The idea was to build something different from a regular AI chatbot. I wanted to create a small space where technology could be used to tell stories, preserve meaningful memories, and explore folklore.
The application has three storytelling modes:
- Haunted Archives: Generates mysterious stories inspired by supernatural themes, forgotten places, and unexplained events.
- Memory Lantern: Turns personal memories and meaningful experiences into thoughtful written tributes.
- Forgotten Legends: Creates folklore-inspired stories based on myths, traditions, and legendary places.
The project uses a locally running open-weight AI model instead of depending on a paid cloud AI API.
Demo
Live Website:
https://devanshguptacu-create.github.io/The-Last-Lantern/
The frontend is hosted using GitHub Pages. To generate stories, users need to run Ollama and the required AI model locally on their computer.
Code
GitHub Repository:
https://github.com/devanshguptacu-create/The-Last-Lantern
The project currently uses three main files:
-
index.html– Application structure -
style.css– Visual design and responsive layout -
script.js– Application logic and local AI integration
How I Built It
I built the frontend using HTML, CSS, and JavaScript. For the AI part, I chose Ollama with the Qwen 2.5 3B open-weight model.
The application communicates with Ollama's local API. When a user selects a storytelling mode and enters a prompt, JavaScript prepares the request and sends it to the locally running model. The generated story is then displayed on the website.
I also added browser LocalStorage to save generated stories, along with options to copy and download them.
My main technology choices were:
- HTML5 and CSS3 for the interface
- JavaScript for application functionality
- Ollama for running AI locally
- Qwen 2.5 3B for story generation
- LocalStorage for saving stories in the browser
One interesting part of building this project was connecting a static frontend to a locally running AI model without creating a separate backend server.
Why Does Open Innovation Matter?
For me, open innovation means having the freedom to experiment, learn, and build without being limited to a single closed platform.
I chose Ollama and an open-weight model because I wanted to understand how local AI actually works, rather than simply connecting an application to a paid API.
This approach makes a few things possible:
- Developers can experiment with AI models locally.
- Users do not need a paid cloud API subscription for story generation.
- Developers can explore different open-weight models.
- Prompts can be processed locally instead of being sent to a third-party AI service.
Building The Last Lantern helped me understand that open AI is not only useful for research or complex technical applications. It can also be used to create simple, creative experiences.
The Spirit of It
The name The Last Lantern represents the idea that a story or memory can continue to have meaning even when time passes.
I wanted the project to feel a little mysterious while also having a meaningful side. The Haunted Archives and Forgotten Legends explore imagination, while Memory Lantern focuses on preserving personal experiences through writing.
This project is my attempt to combine storytelling and local AI into one experience.
Developer
Devansh Gupta
B.Tech CSE | AI & ML Enthusiast
GitHub: https://github.com/devanshguptacu-create
Built with curiosity, creativity, and open AI. 🕯️
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