Skip to content

RunnieResume

Project Summary

RunnieResume is an AI-powered resume tailoring product that helps users turn one baseline resume into job-specific versions with less repetitive manual work. Users can build or import a resume, save it as structured content, generate tailored versions from LinkedIn job posts, edit the result, and export it when ready.

I designed and built the entire product myself end to end, including the UX decisions, application logic, frontend implementation, AI workflows, background processing, data handling, monetization flows, billing logic, privacy handling, and the Chrome extension.

What it does

The core problem is simple: applying to many roles usually means rewriting the same resume again and again. RunnieResume reduces that friction by giving the user one workspace for creating, tailoring, editing, and exporting resume variants.

The workflow is designed to be straightforward:

  1. Create or import a baseline resume.
  2. Add a LinkedIn job posting.
  3. Generate a tailored version.
  4. Review and edit it.
  5. Export the final result.

Chrome Extension

CX is the Chrome extension layer I built for RunnieResume to bring the workflow directly into LinkedIn. Instead of forcing users to jump back and forth between a job post, a profile page, and a separate product tab, CX makes profile import and tailoring actions available closer to where the work actually starts.

That matters because it turns the product from a standalone web app into a more integrated workflow tool. It reduces context switching, shortens the path from discovery to action, and makes the overall experience feel more practical.

Why it is useful

The value of the product is speed without giving up control. Users do not need to start from scratch for every application, but they also are not forced to accept raw AI output as the final answer. The product keeps the process editable, structured, and reusable.

What makes it more than a demo

RunnieResume was built as a usable product, not just a single AI prompt flow. I built it with:

  • structured resume editing instead of static document storage
  • file import and parsing for faster onboarding
  • background processing for slower AI and parsing tasks
  • privacy-aware handling of sensitive resume data
  • account flows, usage limits, and paid credit support
  • editable outputs with export support

These details matter because they turn the product into a real workflow tool rather than a one-off experiment.

Tech stack

  • Supabase: used for authentication, database storage, and persisting user resumes, history, and job state.
  • OpenAI: used for parsing and tailoring resume content against job descriptions.
  • ScrapingBee: used to extract job information from LinkedIn postings.
  • Netlify Background Functions: used to run slower AI and parsing jobs without blocking the user experience.
  • LemonSqueezy: used for checkout, payments, and credit-based monetization flows.
  • Next.js 14: used to build the product UI, app routing, and server-side application flows in one codebase.
  • Tailwind CSS: used to build the interface quickly and keep styling consistent across the app.
  • Chrome Extension (CX): used to bring resume import and tailoring actions directly into the LinkedIn workflow through CX.