This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
I built StationerySort, a lightweight AI-powered stationery inventory manager designed to make everyday organization easier for students.
Keeping track of pens, notebooks, sticky notes, and other supplies can become repetitive. Instead of manually updating quantities every time something is purchased or used, StationerySort lets users describe inventory changes in plain English.
For example, you can type "I bought 10 new blue pens" or "Used 2 sticky note pads." The AI generates a proposed inventory update, which the user can review and confirm before it is saved.
The application also tracks stock levels, highlights items that need restocking, and provides search and category filters.
Demo
Images are provided and git hub repo:https://github.com/Titus-Dsouza/StationerySort
Code
GitHub Repository: https://github.com/Titus-Dsouza/StationerySort
How I Built It
I built StationerySort using:
- Python and Flask for the backend.
- SQLite for storing stationery inventory.
- HTML, CSS, and vanilla JavaScript for the frontend.
- Gemma through Ollama for local, open-weight AI inference.
The AI assistant interprets natural-language inventory updates and generates structured proposals. A fallback rule-based parser allows the application to remain usable when Ollama is unavailable.
I also included a human-confirmation step so that AI-generated changes are reviewed before being applied to the database.
Why Does Open Innovation Matter?
For this project, open-weight AI is more than an optional feature. It is part of the actual workflow.
Using Gemma through Ollama gives users the flexibility to run a model locally rather than depend entirely on a proprietary cloud AI API. It also avoids per-request AI API charges when inference is performed locally and allows users to experiment with different compatible models.
Local inference helps keep inventory information on the user's own machine, while the fallback parser makes the application more resilient when the model runtime is unavailable.
For me, open innovation means being able to experiment, learn, and build useful AI applications without making a paid cloud service a mandatory dependency.
The Lazy Payoff
The idea behind StationerySort is simple: spend less time maintaining inventory and more time getting things done.
A short natural-language instruction can replace several manual steps, while automatic low-stock alerts help users identify what needs restocking.
It is a small project, but it explores a practical way to make everyday tasks a little less tedious.
Prize Categories
Open-source AI / open-weight models, as applicable to the challenge's eligibility criteria.
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