aarsh = {
"name": "Aarsh Bhatnagar",
"role": "CS & AI Student β’ 2nd Year",
"focus": ["Agentic AI", "RAG Pipelines", "Full-Stack Web", "LLM Fine-tuning"],
"currently_building": "AI systems that are usable in real productsβnot just notebooks",
"philosophy": "data β tokenize β train β evaluate β deploy β ship",
"open_to": ["Collaborations", "Internships", "Open Source"]
}|
Telecom churn prediction with ML + Agentic AI, RAG pipeline, LangGraph workflows, and a live Streamlit dashboard. |
AI mock interview platform with voice interviews, resume analysis, real-time feedback, and Razorpay payment integration. |
|
Full-stack AI app that auto-generates structured exam notes from documents using OpenAI. |
Full-stack TypeScript app with JWT auth, class-based React, Prisma ORM, Neon PostgreSQL, deployed on Vercel. |
| Area | What I'm Learning |
|---|---|
| π€ Agentic AI & LLMs | LangGraph workflows, RAG pipelines, LLM fine-tuning, prompt engineering |
| π Full-Stack Web | End-to-end apps: clean UI β REST APIs β database β deployment |
| π ML Engineering | Loss curves, training loops, experiment tracking, model evaluation |
| π System Design | Scalable APIs, DB design, JWT auth patterns, cloud deployment |
- π€ ML / LLM projects (agentic workflows, RAG, fine-tuning)
- π Full-stack apps that integrate AI features
- π Data tools and dashboards
- π§© Tools that solve real problems and are actually useful
