This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
As a teacher, I know how easily important instructions get buried in long educational notices.
A single circular may contain an examination deadline, a fee requirement, a list of documents, and instructions for several different groups of people. Finding the relevant action often means reading the entire notice, searching for dates, and manually deciding who needs to do what.
I built Notice2Next to make that work easier.
Notice2Next transforms educational notices into actionable checklists. It helps teachers and students identify:
- What action needs to be completed.
- Who is responsible.
- Which deadline is stated.
- What fees or documents are mentioned.
- Which details are missing or need human review.
The goal is not to replace the person responsible for understanding a notice. It is to reduce the repetitive work of locating the relevant instructions and organizing them into next actions.
Demo
Watch the product video:
Notice2Next — From Dense Notices to Clear Next Actions
Live application: https://notice2next.onrender.com/
The video introduces the problem, the Notice2Next workflow, and the idea behind evidence-backed task extraction.
Code
GitHub repository: https://github.com/diusazzad/notice2next
The project is built around an open-weight language model and a separate evidence-validation layer.
How I Built It
1. Multiple ways to provide a notice
Notice2Next supports pasted text, notice photographs processed with in-browser OCR, and public notice webpages or PDF links.
This matters because people do not always receive notices as conveniently formatted digital text. A teacher may have a printed circular, while a student may find an examination notice on a university website.
2. Gemma 2 through Backboard
The extraction workflow uses Google's open-weight Gemma 2 model, served through Backboard.
The model identifies candidate tasks, responsibilities, dates, fees, and document requirements. Its output is then passed through a Python evidence layer.
3. Evidence instead of blind trust
A generated checklist should not be a black box.
The Python validation layer checks extracted details against the supplied notice text and provides supporting quotations where available. This allows users to inspect the source behind an extracted task rather than accepting an AI-generated answer without evidence.
When a deadline is missing, Notice2Next can mark it NOT STATED. Unclear or conflicting instructions should be reviewed by a person rather than silently treated as certain.
This validation layer reduces some classes of extraction errors, but it does not guarantee that every model interpretation is correct. Source quotations and the original notice still need to be reviewed when the consequences matter.
4. Useful outputs
After reviewing the checklist, users can listen to a spoken briefing and export the results to CSV or Markdown.
The application is deployed on Render, and GitHub Actions runs automated checks on pushes and pull requests.
The test suite includes six synthetic edge-case scenarios covering important failure modes. These are regression tests, not a comprehensive evaluation of real-world notice accuracy.
Why Does Open Innovation Matter?
Notice2Next uses Gemma 2 because open-weight models create options that go beyond simply calling a closed, proprietary AI endpoint.
An open-weight model can provide a basis for inspecting, adapting, evaluating, and potentially changing the model used in a workflow. That flexibility matters for tools intended to become part of everyday educational work.
There is an important distinction between the model and the deployment, however. The current Notice2Next prototype uses hosted inference through Backboard; it does not run entirely offline. Its model-backed workflow requires internet access, and notice text submitted for extraction may be processed by the configured backend and inference provider.
For that reason, users should not submit sensitive student or staff information to this prototype.
The open-weight approach gives the project flexibility, while the evidence layer adds a separate engineering safeguard around the model's output. The two pieces solve different problems: the model extracts candidate actions, and deterministic checks help users inspect the result.
The Lazy Payoff
The intended payoff is straightforward: instead of repeatedly scanning a long notice to locate every date, fee, responsibility, and document requirement, a user receives a structured starting point for review.
I have not conducted a formal timed study to quantify the minutes saved, so I will not claim a specific productivity improvement. The current project focuses on reducing repetitive reading and organizing work while keeping the person responsible for the final decision.
Prize Categories
I am entering the partner categories that align with the project's actual implementation:
- Best Use of Gemma ($200): Gemma 2 is the open-weight model used for notice extraction.
- Best Use of Render ($200): The deployed application and its user-facing AI workflow are hosted on Render.
- Best Use of ElevenLabs ($100): ElevenLabs was used to generate the narration for the product demo.
- Best Use of GitHub Copilot ($100): The project uses GitHub Actions to automate testing and repository checks, a use case listed in the challenge's category description.
The project also uses Backboard to serve Gemma 2. I have not listed Backboard as a separate prize category because I do not want to imply that the project implements its more specific qualifying use cases—such as assistant memory/RAG, model comparison, or R-CLI—without evidence that it does.
Notice2Next is a work in progress. Its purpose is to make dense notices easier to act on, while keeping extracted tasks inspectable and uncertainty visible.
From dense circulars to clear next actions.
Top comments (0)