Jobologgy — AI Resume Optimizer

Upload a résumé (PDF / DOCX / image / screenshot / pasted text) + a job description. A free AI model (Google Gemini) rewrites it to be ATS-compliant and tailored to the JD, scores it before vs after, explains what was lacking, what it now has, and why keyword matching improved, and produces two PDFs — the optimized CV and a diagnostic report.

Stack: Next.js + React + TypeScript + Tailwind (frontend) · plain Node.js, native http, no framework (backend) · Supabase (Auth + Postgres + Storage) · Google Gemini (free tier).

jobologgy/
├── backend/     plain Node.js service (native http) — the language-agnostic integration surface
└── frontend/    Next.js demo app + a copy-paste integration module (JS and TS flavors)

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1. Prerequisites

• Node.js 18+ (uses global fetch; native http)
• A free Google Gemini API key — https://aistudio.google.com/apikey
• A Supabase project — https://supabase.com (grab the project URL, anon key, service-role key)

2. Supabase setup

1. Open your Supabase project → SQL Editor → paste and run backend/supabase/migrations/0001_init.sql. This creates the profiles + resume_jobs tables, RLS policies, and the uploads / generated storage buckets.
2. Under Authentication → Providers, keep Email enabled (it is by default).

3. Backend

cd backend
cp .env.example .env      # fill in the values
npm install               # note: puppeteer downloads Chromium on first install (slow)
npm run dev               # starts on http://localhost:8787

.env:

var	meaning
PORT	backend port (default 8787)
GEMINI_API_KEY	from Google AI Studio
SUPABASE_URL	your project URL
SUPABASE_SERVICE_ROLE_KEY	service-role key (server only — never expose to the browser)
FRONTEND_ORIGIN	allowed CORS origin, e.g. http://localhost:3000

4. Frontend

cd frontend
cp .env.local.example .env.local   # fill in the values
npm install
npm run dev                        # http://localhost:3000

.env.local:

var	meaning
NEXT_PUBLIC_SUPABASE_URL	your project URL
NEXT_PUBLIC_SUPABASE_ANON_KEY	anon/public key
NEXT_PUBLIC_BACKEND_URL	e.g. http://localhost:8787

Sign up, go to /app, upload a résumé + paste a JD, click Optimize.

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5. Integrating into your existing product

The backend is the real integration surface and it is language-agnostic — it speaks HTTP + JSON, so your existing frontend calls POST /api/analyze the same way whether it's JavaScript or TypeScript. You can also import services/analyzeResume.ts directly into your existing plain-Node backend (no framework assumptions).

For the UI, a self-contained, copy-paste feature module is provided in both flavors:

• TypeScript app → copy frontend/integration/ts/
• JavaScript app → copy frontend/integration/js/

Then set NEXT_PUBLIC_BACKEND_URL (and pass the user's Supabase JWT to the API client). See frontend/integration/README.md.

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Notes / limits

• Gemini free tier is rate-limited; the backend returns a friendly "try again shortly" on HTTP 429.
• Scanned/low-quality images depend on Gemini's OCR quality — pasted text is the most reliable path.
• The AI prompt forbids fabricating employers/dates, so the optimized CV stays truthful and ATS-safe.