Digital Archaeologist: Rediscovering Forgotten Files with Open AI
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady . What I Built Digital Archaeologist is a project designed to help people rediscover forgotten information hidden in their digital files. Over time, we collect PDFs, notes, research documents, code snippets,

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 馃Ε
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
Digital Archaeologist is a project designed to help people rediscover forgotten information hidden in their digital files.
Over time, we collect PDFs, notes, research documents, code snippets, and project ideas. Finding one specific detail months later can mean searching through countless folders and opening files one by one.
Digital Archaeologist aims to simplify this process. The goal is to let users search their documents, discover related information, and ask questions about their digital archive.
Think of it as an archaeologist for your digital life: instead of digging through the ground to uncover the past, it searches through forgotten files to uncover useful ideas.
I want Digital Archaeologist to be lightweight, accessible, and affordable to run, even for developers without powerful computers or a budget for paid AI services.
The basic search functionality is designed to work without AI. The AI layer can then add document summaries and more intelligent answers.
I will document the final implementation and any limitations of the free-tier services used.
Digital archives can contain personal notes, unfinished projects, research, and sensitive information. Developers should have control over how this information is processed.
Open-weight models such as Gemma give developers more flexibility to experiment with AI, adapt their applications, and potentially run models on infrastructure they control.
For Digital Archaeologist, open AI makes it possible to explore document understanding without making a paid, closed AI API the only option.
Combining open-weight AI with browser-based storage can also help keep documents on the user's device, provided they are not sent to an external inference service.
Open models do not automatically guarantee privacy, so the application should clearly explain when information leaves the user's device.
The idea is simple: spend less time searching and more time using the information you already have.
Instead of opening dozens of documents to find an old project idea, a user should be able to search their archive and jump directly to the relevant source.
I plan to demonstrate this with a small collection of sample documents and measure how easily the application can retrieve a specific piece of forgotten information.
Thanks for checking out Digital Archaeologist!