Job Hunter

Inspiration

We noticed that many people(us included) were facing difficulty getting jobs, particularly in engineering fields. But, we also noticed that a large portion of the people facing difficulty getting jobs were using outdated and unrefined approaches. In particular many assume a low percentage of responses and send out a large quantity of resumes in order to increase the quantity of responses from employers. This is clearly not the current meta though. The current state of the job market in engineering fields requires people to modify resumes to match particular job descriptions and optimize resumes for AI and human readers, so they don't get thrown into the godforsaken pile. We want to replace expensive courses and guides which charge hundreds to even thousands of dollars by enabling you to do what they hold your hand through by using accountability checks for resume building, interview/coding prep, and linkedin networking for referrals

What it does

Job Hunter is a simple, browser-based tool that helps users stay consistent in their job search while improving the quality of their applications. It gives users daily and/or weekly tasks for applying to jobs, solving interview problems, and networking on LinkedIn, and it keeps a record of their progress so they can visually track which days they hit their goals. It also includes a resume checker where users paste their resume text and receive feedback on missing metrics, unclear job titles, weak bullet points, and missing GitHub links. If the user enters a free Gemini API key, the app can also extract keywords from job descriptions and suggest more standard versions of confusing job titles.

How we built it

We built Job Hunter as a React application using Vite. All user data is stored in the browser’s localStorage. Most resume checks are done through JavaScript and pattern matching so users don’t need to rely on AI for basic feedback. We also added optional client side Gemini calls that run directly from the browser using the user’s own API key. Vite handles the final build.

Challenges we ran into

One of the biggest challenges was figuring out how to offer helpful feedback without turning the project into a redundant ChatGPT query wrapper. Early versions used a Node backend and server side API calls, but that added complexity and made it difficult to host.

Accomplishments that we're proud of

We are proud that Job Hunter ended up being both practical and simple. We managed to build a tool that keeps people accountable, provides genuinely useful resume insights, and helps them tailor and align their resume with a job description. We also succeeded in implementing AI powered features in a way that is free and easy. Overall, we’re proud of a final product could immediately be used for engineering job seekers, and has the groundwork for further improvements.

What we learned

This was also the first time we used AI API keys, and we were able to creatively solve real-world problems.

What's next for Job Hunter

In the future we will like to create a jobs tab, which will pull related jobs from external job-boards and present to users with the most related jobs they should apply for. We would like to help the user format intro messages for each of their LinkedIn contacts. One of the approaches we wanted to implement was to look at the overlap between the user and a potential LinkedIn contact to create a warm intro message. We also wanted to implement a feature where we could recommend people the user should connect/network with for a certain job they are planning to apply for.