Inspiration
As an engineering student, I am facing many challanges. However, with the developed technological perspective I have turned those challanges into an opportunity. Student need ways to improve themselves. That is why many studdents like me seek for opportunities(seminars,webinars,conferences,volunteering works etc). However it is pretty hard to track and keep eye on each of them, search if they are accurate and safe to apply. It takes a lot of time and effort to find the rightest one but wwith this system RPA and AI works together to find students all they need.
What it does
Finding hackathons, internships, and scholarships that are actually worth an EEE student's time means manually checking several sites every week and reading through postings that mostly aren't relevant. This tool automates that: it pulls new postings from a list of feeds, asks an AI model to score each one against my interests, and only surfaces the ones worth a second look — as a dated report I can skim in under a minute.
How we built it
A small command-line tool that automatically checks hackathon,seminars,webinar,remote jobs and opportunity RSS feeds, uses the Groq API (free, fast inference on open models like Llama 3.3) to judge how relevant each posting is to an Electrical/Electronics Engineering (EEE) student, and writes a clean Markdown digest of the best matches.
Challenges we ran into
The biggest challange I faced was for me to make this idea come true since my knowledge on python was limited. However, after making the main lines about the project I started to learn from my mistakes. This is why I learned a lot from this project aside technical knowledge and experince.
Accomplishments that we're proud of
I started to this project with 0 experience on python, aı and rpa. That is why ı solved my daily problem with something I never had a chance to challange and I also tested the knowledges I learned about RPA and AI after attending into a Seminar about it. Thanks to this tracker I made I found this Hackathon from it. It makes me proud to be honest.
What we learned
How to build a small pipeline app as independent, testable modules instead of one long script (fetch → evaluate → report) .Working with RSS/Atom feeds via "Feedparser" and handling malformed or unreachable feeds gracefully instead of crashing the whole run. Calling a generative AI API (Groq, OpenAI-compatible) directly over REST, including handling cases where the model doesn't return clean "JSON". Writing unit tests for logic that depends on external services, by isolating the parts that don't need the network (parsing, dedupe, formatting) and testing those directly.
What's next for Opportunity Tracker
I wanna turn this tracker into an application and also make an offical website for it. I believe with this way students can keep their applications and all in one application. They can also see application's process and also they can customize the app for themselves.
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