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The Last Lantern – An Offline-First AI Storytelling Experience

Devansh Gupta built The Last Lantern, an offline-first AI storytelling web app that runs entirely on a locally hosted open-weight model. The static HTML/CSS/JavaScript frontend calls Ollama's local API with the Qwen 2.5 3B model to generate stories across three modes, and uses browser LocalStorage to save, copy and download results, avoiding any paid cloud AI API or separate backend server.

by read2 min views1 publishedOct 30, 2026

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

The project uses a locally running open-weight AI model instead of depending on a paid cloud AI API.

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.

GitHub Repository:

https://github.com/devanshguptacu-create/The-Last-Lantern The project currently uses three main files:

index.html – Application structurestyle.css – Visual design and responsive layoutscript.js – Application logic and local AI integration 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:

One interesting part of building this project was connecting a static frontend to a locally running AI model without creating a separate backend server.

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:

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 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.

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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