Applying to multiple roles with different requirements? Not enough space on your resume for all your content? Tailor.cv uses your LinkedIn profile to generate resumes tailored to specific job descriptions.

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Inspiration

Previously, we've all built our share of buzzword soup hackathon ideas without really determining whether there's a market for our product. This time, we decided to solve a problem for the customers we know best, us.

Once you have more than a few job experiences, you need to start choosing which ones to put on your resume. This is a hassle especially for hackathon-goers who are often jack-of-all-trades or students who haven't found their specialization yet, because we apply to all kinds of roles with different requirements. If I'm applying for a frontend role, I want to include my frontend experiences; if I'm applying for an ML role, I want to include those skills.

Previously, we've had to maintain multiple versions of our resume or customize our resume to each (of the hundreds) of jobs and internships we apply for.

What it does

In Tailor.cv, simply sign in with LinkedIn, input a job posting, and watch it generate a 1-page version of your resume optimized for the specific posting. It only includes the most relevant experiences, skills, and projects based on a classical NLP comparison of your profile and the posting.

How we built it

Frontend:

  • React
  • LaTeX2PDF

Backend:

  • Node.js
  • Express
  • Cheerio
  • ScrapedIn
  • ejs

NLP:

  • Topic Modeling
  • Keyword Analysis
  • Cosine Distance

Challenges we ran into

We pivoted multiple times early in this hackathon so we only finalized this idea by Saturday afternoon. We ended up in a time crunch but still completed all the features we set out to develop.

Accomplishments that we're proud of

Building an MVP for a product with real market potential in under 24 hours.

What we learned

LaTeX is really powerful and even classical NLP algorithms can be pretty great at their job.

What's next for Tailor.cv

After cleaning up the code for production, we aim to do some customer testing and deploy by the end of February.

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