Lynt: Turning a Hackathon Prototype into a Real AI Résumé Product (GitHub Finish-Up-A-Thon)

Awais G — Thu, 04 Jun 2026 20:12:01 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge

Loom link: https://www.loom.com/share/d4ac87a0474f4a9eb10e4ebedad5b7a7

What I Built

Lynt is an AI-powered résumé and cover-letter builder with a visual editor, live print-accurate preview, one-click PDF export, and a public shareable page.

The core idea is not just generating text with AI — but letting AI apply structured edits directly into the document while preserving layout, formatting, and history. Users can rewrite bullets, reorder sections, and tailor résumés to job descriptions with full undo support.

The goal is to make editing a résumé feel faster and more reliable than copy-pasting between ChatGPT and a document editor.

It started as a hackathon project called ResumeForge, originally just a markdown → PDF tool. Over time, it evolved into a full SaaS with authentication, cloud storage, document ingestion (PDF/DOCX/images), an AI editing system, and a reliable PDF generation pipeline.

Demo

This project is currently in private beta while final stability and polish are being completed.

Live: Private beta (not publicly available yet)
GitHub: Private repository
Screenshots: (see below)

Screenshots

AI applying edits directly to a résumé
Before vs after edit in editor
PDF export matching live preview
Upload → parsed résumé reconstruction flow
Public résumé share page

The Comeback Story

Lynt began as a hackathon prototype built around a simple idea: markdown → PDF export.

It worked, but it was not reliable enough for real-world use.

Early state

The original version had clear limitations:

AI suggestions could not be safely applied to documents
PDF output often did not match the editor preview
No production-grade authentication or storage layer
No protection against data loss or inconsistent edits

It felt like a demo that “almost worked,” but not a product you could trust.

Turning it into a product

The focus shifted from adding features to improving reliability and correctness.

Instead of free-form AI output, the system was rebuilt around:

Structured, deterministic document operations
Validation layer for every AI-generated change
Full undo/redo system using an action ledger
Data-loss prevention rules (no silent deletions or overwrites)
PDF parity between editor, export, and public view

A key shift was making the AI behave like an editor, not a generator.

Every change is:

structured
validated
reversible

Production issues uncovered

When moved into real-world conditions, several issues surfaced:

PDF rendering failures in serverless environments (DOMMatrix issues)
SSE streaming instability on edge runtimes
Public résumé pages breaking when underlying data was deleted
Latency spikes caused by over-aggressive validation logic

These were not feature bugs — they were production reliability issues.

The outcome

The final system is not defined by features, but by predictability:

AI edits no longer break layout
No silent content loss
PDF output matches the editor exactly
Full document history with undo/redo
Public pages remain stable and consistent

The biggest change was moving from “it works” to “it behaves reliably.”

My Experience with GitHub Copilot

Copilot helped mainly with accelerating repetitive development:

API routes and boilerplate
schema definitions
UI scaffolding
basic test generation

A Copilot coding agent was also used for a scoped feature (PR #21), which was reviewed and merged.

However, the core system design — especially the AI editing contract, validation system, and document safety model — required manual architecture decisions.

Final Notes

Lynt started as a hackathon experiment and evolved into a production-grade system focused on one goal: making AI-powered document editing reliable, deterministic, and safe.

The Finish-Up-A-Thon provided the push to complete the hardest part of any product — the reliability layer that turns a demo into something real.

DEV Community: Awais G