This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine
AFTERLIGHT — A Garden of Memories ✳
What I Built
What if the memories we are afraid of losing could find a home in technology?
Introducing AFTERLIGHT, a local-first AI-powered memory garden designed to preserve the little things that make life meaningful.
Inspired by the idea of Halloween and Día de los Muertos, I wanted to explore a different side of the Ghost in the Machine theme.
Instead of building something that creates fear, I built something that keeps memories alive.
With AFTERLIGHT, users can:
- 🌼 Plant a Memory: Preserve personal stories, family traditions, recipes, and meaningful moments.
- 🕯️ Memory Garden: Organize, search, and revisit saved memories.
- ✨ The Lantern Room: Transform memories into remembrance notes, gentle ghost stories, and family-history entries.
- 🔒 Privacy First: Keep memories stored locally in the browser.
The idea is simple: technology should not replace memories. It should help us preserve them.
Demo
Live Demo: file:///C:/Users/lenovo/Desktop/AFTERLIGHT/index.html
Code
GitHub Repository: https://github.com/anushkabajpaigarg10-debug/AFTERLIGHT.git
The project is built using:
-
index.html— Application structure -
style.css— User interface and styling -
app.js— Application logic and AI integration
How I Built It
I wanted to keep the project beginner-friendly and build it using HTML, CSS, and JavaScript rather than relying on a complicated backend.
The main technologies are:
- HTML5
- CSS3
- Vanilla JavaScript
- WebLLM
- Qwen2.5 0.5B Instruct
- Browser LocalStorage
The AI Behind AFTERLIGHT
I integrated Qwen2.5 0.5B Instruct through WebLLM.
WebLLM enables language models to run directly inside the browser using WebGPU.
The AI powers the Lantern Room, where users can select a saved memory and generate different forms of writing.
The application does not require a hosted AI API or an application backend.
The first model download requires internet access. Once cached, local inference can work offline, subject to browser and device support.
Why Does Open Innovation Matter?
This is probably the most important part of AFTERLIGHT.
Memories can be deeply personal. Family stories, personal experiences, and traditions deserve privacy.
Using an open-weight model and browser-based inference makes it possible to build an AI experience without sending those memories to a hosted AI provider.
Open innovation gives this project:
1. Privacy and Control
The memories are stored in the user's browser. AI inference happens locally rather than through a hosted AI API.
2. Independence
The project does not depend on a paid AI API subscription or a proprietary hosted model.
3. Flexibility
Open-weight models make it possible to experiment with different models and adapt the AI experience.
4. Accessibility
There is no application backend or hosted AI API charge required to run the prototype.
For AFTERLIGHT, open-source AI is not just an additional feature. It is what makes the privacy-first approach possible.
My Agent Session
[Add your DevRelay agent session link if available.]
The Spirit of It
I kept coming back to one thought while building this:
Some people leave, some moments pass, and some stories slowly fade. But what if we could preserve the smallest pieces of them?
A recipe that has been passed down through generations.
A familiar phrase someone always used.
A story that everyone in the family knows but nobody has written down.
AFTERLIGHT is my small attempt to give those memories a place to stay.
It is not about bringing the past back.
It is about making sure we don't lose the parts of it that matter.
Keep the stories. Keep the memories. Keep the light.
Prize Categories
[Add the applicable partner prize categories from the challenge page.]
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