This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine.
🌼 Still Here — A Private AI Memory Garden
Some people leave our lives, but the little things that remind us of them never really disappear.
A favorite song. A familiar voice. A small moment that stays with us for years.
I built Still Here, a private AI memory garden designed to help people preserve the stories, memories, and little details that make someone unforgettable.
Inspired by the Hacktoberfest Open-Source AI Challenge theme, Ghost in the Machine, this project explores how AI can help us keep memories alive without handing our most personal stories over to an online service.
Instead of building another chatbot, I wanted to create something more personal: a quiet space where memories can become letters, vignettes, and poems.
💡 What I Built
Still Here is a browser-based memory archive with AI-powered writing tools.
Users can write down a memory, optionally attach a photograph, and use an open-weight language model to transform their own details into a more expressive piece of writing.
The idea is simple: technology should help us tell our stories without taking ownership of them.
✨ Key Features
🌼 Personal Memory Archive: Write and preserve meaningful memories in one place.
📷 Photo Attachments: Add photographs to make a memory more personal.
✍️ AI-Powered Writing: Transform your notes into letters, short vignettes, or poems.
🔒 Privacy by Design: Memories and attached photos are stored in your browser's local storage rather than uploaded to a backend.
🤖 On-Device AI: Run an open-weight language model directly in the browser.
📴 Local-First Experience: Once the model assets are downloaded and cached, generation can run on your device without sending prompts to a remote inference API.
🛠️ How I Built It
I built Still Here as a lightweight web application using HTML, CSS, and JavaScript.
The application uses:
HTML for the application structure.
CSS for the user interface and visual styling.
JavaScript for application logic and interactions.
Transformers.js to run the language model in the browser.
SmolLM2-360M-Instruct as the open-weight model powering the writing experience.
Browser local storage to preserve memories and attached photographs locally.
There is no application backend or remote inference API.
The model is downloaded when the user chooses to load it. After that, text generation runs on the user's device.
👻 Why This Fits Ghost in the Machine
The theme made me think about the relationship between memory, technology, and the things we leave behind.
A machine cannot replace a person, recreate a life, or truly understand the meaning of every memory. But it can help someone find words for a moment they want to preserve.
Still Here brings together two ideas:
The ghost: The memories, stories, and small details of people who remain important to us.
The machine: An open-weight language model that helps shape those memories into written keepsakes.
The AI does not create the underlying memories. It works with the details the user provides, helping turn personal notes into something they can revisit and keep.
🔓 Why Open Innovation Matters
Memories can be deeply personal. I didn't want the core writing experience to depend on sending those memories to a third-party AI service.
Using an open-weight model makes a different approach possible.
Privacy: Personal memories and photos remain in browser storage, and prompts do not need to be sent to a remote inference API.
Control: The writing model can be inspected, replaced, or adapted rather than being permanently tied to one proprietary AI provider.
Accessibility: Once the required assets have been downloaded, users can generate writing on their own device without an inference account or per-message API fee.
Independence: The application has no backend service that must store or process a user's personal stories.
There are trade-offs. The model is deliberately small, so its writing capabilities may be more limited than those of a larger hosted model. The first model download also requires an internet connection, and a fully disconnected setup requires the assets to be available locally.
For this project, I believe that trade-off is worthwhile. When someone is preserving a personal memory, control over where that information goes matters.
🧩 Challenges and Learnings
One of the interesting parts of this project was bringing AI into the browser rather than relying on a conventional cloud-based inference API.
This approach requires thinking about model downloads, local execution, browser storage, and the balance between model size and writing quality.
It also reinforced an important lesson: AI applications do not always need a large backend or a complicated infrastructure to be useful.
Sometimes, a focused idea and a small open model can create a more personal experience.
🚀 Try It and Explore the Code
GitHub Repository: https://github.com/TarunHullur/Ghost_In_The_Machine.git
The repository includes the application code and instructions for running it locally.
To start the application, clone the repository and run:
git clone https://github.com/TarunHullur/Ghost_In_The_Machine.git
cd Ghost_In_The_Machine
python3 -m http.server 8000
Then open http://localhost:8000 in your browser.
Choose the option to load the open model. The initial download requires an internet connection.
Live Demo: https://stillhere11.netlify.app/
🌼 The Spirit of Still Here
We build technology to solve problems, automate tasks, and make life easier.
But technology can also help us preserve the things that make life meaningful.
Still Here is my exploration of that idea: a small, private place for memories, powered by open-source tools and designed around the belief that personal stories deserve care.
Because sometimes, the most meaningful thing a machine can do is help us remember.
Thanks for checking out my project!
I'd love to hear your thoughts, feedback, and ideas for making Still Here better.
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