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

Source: Dev.to

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 — it lets 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
• A reliable PDF generation pipeline

The 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 (illustrative)

• 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

From Prototype to Production

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.

Original 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.

Shifting Focus to Reliability

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 Challenges Encountered

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 Final System

The final system is defined by predictability rather than feature count:

• 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.”

Role of 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.

Conclusion

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.