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

Lynt, an AI-powered résumé and cover-letter builder, evolved from a simple hackathon prototype into a production-grade SaaS platform focused on reliability and deterministic document editing. The system, originally a markdown-to-PDF tool called ResumeForge, was rebuilt to let AI apply structured edits directly into documents while preserving layout, formatting, and full undo history. The project is currently in private beta, with development centered on moving from a "demo that almost worked" to a predictable, production-ready system.

This is a submission for the GitHub Finish-Up-A-Thon Challenge 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. This project is currently in private beta while final stability and polish are being completed. 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. The original version had clear limitations: It felt like a demo that “almost worked,” but not a product you could trust. The focus shifted from adding features to improving reliability and correctness . Instead of free-form AI output, the system was rebuilt around: A key shift was making the AI behave like an editor , not a generator. Every change is: When moved into real-world conditions, several issues surfaced: DOMMatrix issues These were not feature bugs — they were production reliability issues. The final system is not defined by features, but by predictability : The biggest change was moving from “it works” to “it behaves reliably.” Copilot helped mainly with accelerating repetitive development: 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. 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.