Inspiration

The job-search process is incredibly time-consuming in the modern day, primarily for students in computer science-related fields. That being said, when the BeaverHacks hackathon theme was announced to be "community" driven, our first-year team concluded that creating a personalized internship/job search engine for CS students would be a perfect idea. Ultimately, while serving as an experience that led to invaluable growth and the advancement of our development skills/capabilities, this project also directly tackles an ongoing struggle faced by the community in the current-day job market.

What it does

When the user first logs onto our website, they are provided with the opportunity to list basic information and skills about themselves that would allow our backend algorithm to match them to a list of categorized internships and job opportunities. From then on, the user can explore the vast list of opportunities (which can be found through their personalized "profile" page) and label them as either "saved" or "applied" based on personal decision/action.

How we built it

We initially split our four-person team into two groups -- two frontend & two backend developers. In this way, we were able to divide the workload into more manageable chunks, allowing us to support one another throughout this collaborative process. We utilized various resources such as NextJS, React, Supabase, and Tailwind in unison to develop our app on Vercel, with each subgroup having different responsibilities and components to individually construct. Regarding the search algorithm, we parsed through each website in our database and collected specific information to be compared with and applied to the user's inputted information. Combined, our website serves as a powerful tool for internship/job seekers looking for CS-related work experience and opportunities.

Challenges we ran into

Although each person in our team had their own experience with basic/intermediate web development, not many of us had gone as far as creating a project of this scale. Needless to say, we ran into a couple of issues, including but not limited to:

-initially scrapping our idea and starting over from scratch after putting in some hours of work

-specific component implementation taking far too long to implement due to slight miscommunication

-our .env files at times did not work properly, leading us to an elongated period of rigorous debugging

-A lot of time was spent learning about implementation methods instead of actually implementing them

-And much, much more

However, through hard work and resilience, we were able to overcome each obstacle that we faced, allowing us to present a complete project in the extremely short time that we had to build it.

Accomplishments that we're proud of

Given our limited collective experience with app/web development, we are extremely proud and excited to share a complete project with the rest of our community, and we hope to continue working in the future to further improve some of its components/applications.

What we learned

Throughout this experience, an [intense] amount of learning was accomplished by all members of our team. From learning basic structural design to implementing a fully integrated search algorithm using a vast database, our team couldn't be more pleased with the outcome of this experience, regardless of its on-paper placement/results.

What's next for InstApply

We initially had the idea of creating an AI-based search algorithm but did not have sufficient time to implement it both effectively and flawlessly. That being said, in the future, we hope to bring this idea to life, and given the amount of learning that this experience has brought to our developer capabilities, that future might be closer than we first anticipated.

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