ResumeGlow - Real-Time ATS Optimization Without Generic Advice

Paste your resume, get instant feedback on which exact sentences kill ATS parsing, watch a live score climb as you edit, no buzzword spam. Targets freelancers and job seekers tired of 'add more keywords' nonsense.

𝕏 Post Reddit HN

Difficulty

beginner

What is it?

Resume screening is broken: applicant tracking systems parse resumes unpredictably, generic advice says 'use power words' without explaining why, and most tools charge $50+ just to see a score. ResumeGlow solves this by parsing your resume the same way real ATS systems do, highlighting parsing failures in real-time, and explaining exactly which phrases confuse parsers vs. which ones help. Users paste a resume, see a live ATS score (0-100), get section-by-section feedback showing parsing failures, then edit and watch the score update instantly. The core mechanic is a lightweight ATS parser trained on real job descriptions, showing users the exact delta between their resume and what hiring systems actually want. Why 100% buildable right now: ATS parsing libraries exist (pypdf, pdfplumber), Claude can classify resume sections and generate fix suggestions, and no training is required — just API calls against a static ruleset derived from common ATS failures documented on r/jobs and r/resumes.

Why now?

ATS complexity is at peak frustration (2026 resume formats are harder to parse than ever due to creative formatting), Claude vision API makes resume analysis cheap and accessible, and job market urgency creates high purchase intent for tools that help candidates compete.

▸Real-time ATS score (0-100) powered by Claude + heuristic parsing
▸Section-by-section feedback highlighting parser failures
▸Job-specific resume optimization (paste job description, get tailored feedback)
▸Parse history and trend tracking
▸Resume export as structured JSON

Target Audience

Job seekers and freelancers (US market: 15M job seekers actively applying, 40% use resume tools). Focus on junior developers and career-switchers aged 22-35.

Example Use Case

Jordan, a junior dev, pastes their resume, gets a 62/100 ATS score, sees that their skills section is formatted as a paragraph (parsing failure), rewrites it as a bulleted list, score jumps to 78/100 in real-time. They upgrade to pro, compare their resume against 3 job descriptions they're targeting, and find they're missing 'TypeScript' even though they know it — they add it, and their job-specific match score goes from 65% to 82%.

User Stories

▸As a junior developer, I want to know exactly why my resume fails ATS parsing, so that I can fix it instead of guessing.
▸As a career-switcher, I want to optimize my resume for specific job descriptions, so that I have a higher chance of getting interviews.
▸As a remote worker, I want to track how my resume score changes over time, so that I can measure the impact of my edits.

Acceptance Criteria

PDF Upload: done when user can upload a .pdf and it extracts text without errors. ATS Score: done when score appears in under 5 seconds and updates live as user edits. Feedback: done when each section shows 1-3 specific actionable suggestions. Freemium: done when free users are limited to 3 parses/month and see upgrade prompt on 4th parse. Stripe: done when checkout processes correctly and user gains immediate access to pro features.

Is it worth building?

$9/month × 200 paying users (12% freemium-to-paid conversion) = $1,800 MRR by month 2. $9/month × 800 paying users = $7,200 MRR by month 6.

Unit Economics

CAC: $5 via Reddit/Discord outreach (1 hour reaching out yields ~5 signups). LTV: $36 (4 months at $9/month, assuming 50% churn after month 2). Payback: 3-4 weeks. Gross margin: 90% (Claude API is the only variable cost).

Business Model

Freemium: 3 free parses/month, then $9/month for unlimited + ATS score history.

Monetization Path

Free tier limits parse frequency. Paid unlocks unlimited parsing, score history, export resume JSON, and job-specific optimization templates.

Revenue Timeline

First dollar: week 2 via beta upgrade. $1k MRR: month 2. $5k MRR: month 6. $10k MRR: month 11.

Estimated Monthly Cost

Claude API: $30 (assuming 1k parses/month at $0.03 each), Vercel: $20, Supabase: $25, Stripe fees: ~$20. Total: ~$95/month at launch.

Profit Potential

Full-time viable at $3k–$8k MRR.

Scalability

Medium — can expand to LinkedIn profile scanning, cover letter optimization, job matching API, and team plans for recruiting.

Success Metrics

Week 1: 500 signups. Week 2: 50 parses/day. Month 1: 60+ paid users. Retention: 65% 30-day.

Launch & Validation Plan

Survey 50 job seekers on r/jobs about their biggest resume frustration. Build landing page. Recruit 20 beta testers offering free 3-month pro access in exchange for feedback on score accuracy. Track conversion funnel.

Customer Acquisition Strategy

First customer: post free resume audit offer in r/jobs and r/resumes, DM 15 career-switcher communities on Discord. Broader: ProductHunt launch, Twitter/X thread on common ATS parsing failures, SEO targeting 'ATS resume score' + 'resume ATS checker', LinkedIn content about resume formatting.

What's the competition?

Competition Level

High

What's the roadmap?

Feature Roadmap

V1 (launch): Real-time ATS score, section feedback, freemium with 3 parses/month, Stripe payments. V2 (month 2-3): Job-specific resume matching, parse history dashboard, export resume as JSON, Google Sheets integration. V3 (month 4+): Cover letter scoring, LinkedIn profile ATS check, team accounts for career coaches, API for recruiting platforms.

Milestone Plan

Phase 1 (Week 1-2): Build landing page, set up Supabase auth, create PDF parser and ATS score calculation, implement real-time score display. Done when 3 beta testers can upload and see scores. Phase 2 (Week 3-4): Integrate Stripe, add freemium logic, build parse history, implement email onboarding. Done when payment flow works end-to-end and free users hit limit. Phase 3 (Month 2): Add job-specific matching, launch ProductHunt, optimize SEO, monitor churn and retention.

How do you build it?

Tech Stack

Next.js, Claude API, pdfplumber, Supabase, Stripe — build with Cursor for backend PDF parsing, Lovable for UI, v0 for resume preview component.

Suggested Frameworks

Time to Ship

10 days

Required Skills

PDF parsing, Claude API integration, React state management, basic job data structure knowledge.

Resources

pdfplumber docs, Claude API docs, Stripe docs, r/jobs and r/resumes for market validation.

MVP Scope

1. Landing page with resume upload form (Lovable). 2. PDF parser endpoint extracting text and sections (Cursor + pdfplumber). 3. Claude API prompt evaluating ATS compatibility and returning section-level feedback. 4. Real-time score calculation and display (v0 component). 5. Freemium check at upload endpoint. 6. Stripe checkout for upgrade. 7. Auth with Supabase. 8. Parse history in Postgres.

Core User Journey

Sign up -> upload resume -> receive ATS score in under 10 seconds -> see specific parsing feedback -> upgrade to monthly.

Architecture Pattern

User uploads PDF -> pdfplumber extracts text -> Claude API analyzes sections -> ATS score calculated -> Postgres stores parse record -> frontend displays score + feedback.

Data Model

User has many ResumeParsings. ResumeParsing has one ATSScore, many SectionFeedbacks. ATSScore belongs to one ResumeParsing.

Integration Points

Claude API for resume analysis, pdfplumber for PDF extraction, Stripe for payments, Supabase for auth and database, Vercel for hosting, Resend for onboarding email.

V1 Scope Boundaries

V1 excludes: cover letter optimization, LinkedIn scraping, team accounts, mobile app, job board integration, white-label.

Success Definition

A paying stranger discovers the product via ProductHunt or Reddit, uploads their resume, sees actionable ATS feedback, upgrades to monthly plan, and uses it to optimize 2-3 resumes.

Challenges

Convincing users that an ATS score matters without being just another vanity metric. PDF parsing is fragile across resume formats. Validating that the scoring actually correlates with real ATS acceptance rates.

Avoid These Pitfalls

Do not claim the score correlates to hiring success without evidence — it only scores parser compatibility. Do not build job matching until resume parsing is rock-solid. Do not overcomplicate the UI with too many settings — simplicity is the feature.

Security Requirements

Auth: Supabase Auth with Google OAuth. RLS: all parse_history and ats_scores tables have RLS policies limiting access to owner only. Rate limiting: 10 parses/min per IP. Input validation: file size limit 5MB, only .pdf accepted, sanitize extracted text before Claude API. GDPR: data deletion endpoint that removes all parse_history for a user, log all data access, document retention policy.

Infrastructure Plan

Hosting: Vercel (frontend + API routes). Database: Supabase (Postgres). CI/CD: GitHub Actions (test on push, auto-deploy main to Vercel). Environments: dev (local), staging (Vercel preview branch), prod (Vercel main). Monitoring: Sentry for error tracking, Vercel Analytics for page load and user journey. Estimated cost: $115/month.

Performance Targets

Expected load at launch: 200 DAU, 600 parses/day. API response target: under 2 seconds (includes Claude API latency). Page load target: under 1.5s (LCP). Caching: CDN for static assets, no heavy caching for API due to personalization.

Go-Live Checklist

☐Security audit of PDF parser (check for XXE/injection)
☐Payment flow tested end-to-end (free -> paid)
☐Error tracking (Sentry) live and alerting
☐Monitoring dashboard showing parse success rate
☐Custom domain resume-glow.com set up with SSL
☐Privacy policy (data retention, GDPR rights) published
☐Terms of service published
☐10+ beta users signed off and quoted for testimonials
☐Rollback plan documented (revert to manual ATS score if Claude API fails)
☐Launch post drafted for ProductHunt, r/jobs, r/resumes, and Twitter/X.

How to build it, step by step

1. npx create-next-app resume-glow --typescript. 2. npm install pdfplumber stripe @supabase/supabase-js. 3. Set up Supabase project and create users, parse_history, ats_scores tables. 4. Build PDF upload form in Lovable. 5. Create /api/parse-resume endpoint using pdfplumber and Claude API. 6. Calculate ATS score (heuristic: keyword match + section structure). 7. Build real-time score display with v0. 8. Integrate Stripe checkout. 9. Add Supabase Auth. 10. Deploy to Vercel and test end-to-end.

Generated

March 28, 2026

Model

claude-haiku-4-5-20251001

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