HackStack

Project Info

Tune.AI

Devpost

Inspiration

As the job market becomes more competitive and fiercer than ever, everyone experiences significant hardship when trying to find a job. The situation is not getting any better soon as layoffs occur more frequently and more people begin looking for jobs. On average, a single application yields an 8.3% chance of landing a job interview, and is even more difficult to secure an offer. As such, Tune.AI aims to reduce the hassle of job search and matches you with the most suitable job!

What it does

Tune.AI allows users to automatically tailor their resumes according to each specific job posting and also provide cover letters that match with everything on the users' resumes, the company the user is applying to, and the specific job posting's requirement. Tune.AI aims to help users increase their chance of landing interviews with fewer applications and in much less time, with higher accuracy of landing a job that matches their existing skillsets.

How we built it

Our Platform is comprised of: A resume parser we buildtfrom scratch A Recommendation system to help rank current job postings according to the user's resume An algorithm to help select the most relevant experiences/skills/projects to the job posting that the user wants to apply to AIs that generate tuned resumes and tailored cover letters

Challenges we ran into

Our project consists of a lot of components and we experienced significant hardships when trying to integrate everything. Steep learning curve for different third party applications that we were trying to integrate Working together in sync and ensuring that we meet our project timeline

Accomplishments we're proud of

Being able to incorporate the latest technologies (LLM, database, etc) into our platform Creating our resume parser that successfully parsed multiple different resumes of different variations Matching and connecting people with suitable companies

What we learned

How to collaborate on different components concurrently and connecting them How to integrate third-party apps effectively to help us achieve our goals Stay awake throughout the night to get the job done

What's next

We hope to enhance the platform's functionality, user engagement, and accuracy by allowing users to interactively add, modify, and validate their skills and experiences through a more engaging UI, possibly using gamification. We also hope to implement a feedback loop where users can provide feedback on the recommendations and generated content, which can be used to fine-tune the algorithms.

Analysis

Compare with all teams

View

Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • JavaScriptIn code
  • Next.jsIn code
  • PythonIn code
  • ReactIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • FlaskClaimed
  • HTMLClaimed

7 of 9 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

143 KB

Source files

79

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

0 stars