# Aakhri Tareekh: An Offline AI That Finds Deadlines in College Notices

> Source: <https://dev.to/pratyushdev/aakhri-tareekh-an-offline-ai-that-finds-deadlines-in-college-notices-5d03>
> Published: 2026-10-05 06:02:26+00:00

*An offline-first college notice reader for the friend who keeps missing deadlines.*

*This is a submission for the [Hacktoberfest Weekend Challenge: Build for a Friend](https://dev.to/challenges/hacktoberfest-weekend-2026-10-01).*

College notices are often long, messy PDFs or scanned documents. Important dates can be buried inside several pages, and missing one deadline can mean missing a registration, payment, examination, or application.

I built **Aakhri Tareekh (आख़िरी तारीख)** for a friend who repeatedly discovers important college deadlines too late.

The app turns a college notice into a clear set of actions:

`unclear` instead of guessing`.ics` calendar events
The core idea is simple:

**Turn a messy college notice into one trustworthy next action.**

The project currently runs locally with **Ollama** and does not require a cloud AI API.

Demo flow:

**GitHub repository:** [https://github.com/pratyushmishra9920-ship-it/aakhri-tareekh](https://github.com/pratyushmishra9920-ship-it/aakhri-tareekh)

The project is built with:

The repository contains the complete application, setup instructions, requirements, and the dashboard screenshot.

The AI core uses the open-weight **Qwen3.5-4B** model locally through **Ollama**.

A selected notice page is rendered as an image and passed to the local model with a strict extraction prompt. The model returns structured JSON containing the title, deadlines, actions, audience, fees, documents, summary, and evidence.

I deliberately designed the extraction process to be conservative.

The system is instructed to:

`null` and This matters because a wrong deadline can be worse than no deadline at all.

For example, if a notice contains a date range such as `15 July 2026 to 19 July 2026`, the application does not arbitrarily choose one of those dates as the deadline. It preserves the range and marks it as unclear when a single calendar date cannot safely be determined.

Using an open-weight model locally changes what this application can do.

**Privacy:** College notices can contain academic, registration, payment, or student information. The document can remain on the user's machine.

**Offline-first:** After downloading the model, notice analysis can run locally without sending documents to a cloud AI service.

**No per-notice API bill:** Local inference means there is no cloud API request for every notice.

**Model freedom:** Because the application communicates with Ollama, the underlying model can be replaced or upgraded later.

**Custom behavior:** The extraction process can be specifically designed around college notices and a strict no-guessing policy rather than relying on a generic document summarizer.

The open model is therefore not just a cheaper replacement for a closed API. It gives me control over where the data goes and how the model is used.

I used AI coding assistance while developing the project.

I did not complete a DevRelay session that I can provide as a verified agent-session artifact for this submission, so I am not claiming a DevRelay-specific category.

This submission is for the **Overall Winner** of the Hacktoberfest Weekend Challenge: Build for a Friend.

I am not claiming a partner-specific prize category that requires technology I did not actually use in this project.

The project started from a very practical problem.

A friend kept missing important college deadlines because the information was buried inside notices that were difficult to scan quickly.

I could have built a generic AI document summarizer, but that would not solve the actual problem.

Instead, I focused the application on one specific outcome:

**What do I need to do, and when do I need to do it?**

That focus influenced the entire design — especially the evidence shown alongside each extracted deadline and the decision to say `unclear` instead of guessing.

The biggest lesson from building Aakhri Tareekh was that an AI application does not always need to be more confident.

For a deadline extractor, **knowing when not to guess is a feature.**

Working with a local open-weight vision model also made me think about the complete AI system rather than treating an API call as the whole application.

The project started as a simple idea for one friend, but it became a useful example of how local open AI can be combined with a focused workflow to solve a very specific real-world problem.

Built for the **Hacktoberfest Weekend Challenge: Build for a Friend**.