👉 🔗 Live Demo: https://ai-resume-analyzer-indol-one.vercel.app/
👉 💻 GitHub: https://github.com/abhi-123/ai-resume-analyzer
90% resumes get rejected before a human sees them.
So I built an AI Resume Analyzer to understand why.
This tool:
• Analyzes resumes against job descriptions
• Calculates an ATS-style score
• Highlights strengths, weaknesses, and gaps
• Suggests actionable improvements
But here’s where it got interesting…
I tested two models:
→ GPT-4o-mini (fast & cheap)
→ GPT-5 (slower & more expensive)
The difference?
GPT-4o-mini:
• Quick and decent insights
• More general suggestions
• Higher (but slightly generous) scores
GPT-5:
• Much deeper analysis
• More realistic scoring
• Extremely actionable suggestions (almost like a career coach)
Example:
Instead of saying “improve backend skills,” GPT-5 suggested:
→ Build microservices (REST/gRPC)
→ Deploy using Docker + Kubernetes
→ Add CI/CD pipelines and observability
That’s not just feedback — that’s a roadmap.
💡 Key takeaway:
Fast AI improves UX.
Smart AI improves decisions.
The real power is combining both.
So I added:
⚡ Fast Mode (instant results)
🧠 Deep Analysis Mode (advanced insights)
Would love your feedback 👇
What would you improve in this tool?
Have you ever tested different AI models like this?
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