Hi — I'm Dev Jindal (DevJin27).
I'm passionate about building intelligent, reliable systems and learning continuously — from algorithms and systems to ML and full‑stack apps.

• 💻 Focus: Back-end & full-stack development, APIs, systems design
• 🤖 Interests: AI, fine-tuning models, applied ML, developer tooling
• 🌱 Always learning: system design, React/Next.js, model fine-tuning
• 📫 Reach me: [email protected] | https://github.com/DevJin27

I work across the full stack and with ML tooling:

• Languages: Python, JavaScript (ES6+), SQL
• Back-end: FastAPI, Flask, REST, WebSocket, Docker
• Front-end: React, Next.js, HTML/CSS
• ML & AI: Model fine-tuning, prompt engineering, evaluation pipelines
• Datastores: MySQL, PostgreSQL, Redis
• Dev & Deploy: Git, GitHub, Docker, CI/CD

• Deepening knowledge of model fine-tuning and evaluation pipelines.
• Building RISE — a DSA Mentor AI app (see project details below).
• Improving React/Next.js front-end patterns and full-stack integration.

• Tech: Python, MySQL, HTML/CSS, JS
• Status: Completed — production-ready MVP
• Overview: Shelf Stack is an inventory and retail management system tailored for small bookstores and shops. It combines staff management, role-based access control, inventory tracking, and sales analytics into one lightweight system.
• Key features:
  • User authentication and role-based access (Admin, Manager, Cashier)
  • Inventory management: add/edit books, categories, stock counts
  • Sales & POS: create sales, discounts, returns, and generate receipts
  • Sales tracking & reports: daily/monthly sales summaries, bestsellers, low-stock alerts
  • Reporting: exportable CSV reports, simple dashboard metrics
• Architecture:
  • Flask (or FastAPI) backend with REST APIs
  • MySQL for persistent storage
  • Modular service structure for inventory, sales, and users
• Deployment & run notes:
  • Designed to run in a container or small VM; easy to deploy with Docker
  • Can integrate barcode scanners and receipt printers for POS use

• Tech: FastAPI, React (WebSockets), map visualization libraries, PostgreSQL/MySQL
• Status: Completed — realtime telemetry tracking system
• Overview: Project_map captures, processes, and visualizes real‑time telemetry from drones (or similar devices). It’s built to ingest frequent telemetry, provide live location & status updates on a map, and store historical telemetry for replay and analytics.
• Key features:
  • Real-time ingestion via REST + WebSocket streaming for low-latency updates
  • Interactive frontend map showing live locations, flight paths, telemetry overlays
  • Telemetry pipeline with storage for historical playback and diagnostics
  • Alerts for geofence breaches, low battery, or anomalous telemetry
  • Authentication for device operators and admin dashboards
• Architecture:
  • FastAPI handles telemetry ingestion, WebSocket streams, and REST endpoints
  • Front-end (React) subscribes to telemetry channels for live map updates
  • Optional message broker (Redis/Redis Pub/Sub or lightweight queue) for scaling real-time events
• How to run (example)

  # backend
  uvicorn app.main:app --reload
  
  # frontend
  cd web && npm install && npm run dev

• Notes: Built with extensibility in mind — can add ML-based anomaly detection, geospatial indexing, or integrate with cloud streaming services.

• Tech stack: FastAPI, model fine-tuning (transformer-based), React, Next.js, Python
• Status: Active development (I'm currently working on this)
• Vision: RISE is a DSA-focused AI mentor that helps learners prepare for algorithmic interviews and improves problem-solving skills using personalized coaching and automated feedback.
• Core capabilities:
  • Personalized learning paths and problem recommendations based on skill gaps
  • Problem explanation generation and step-by-step hints
  • Automated code evaluation and constructive feedback (style, complexity, correctness)
  • Adaptive difficulty and progress tracking
  • Conversational mentor UI powered by a fine-tuned model (for DSA pedagogy and scaffolding)
• Technical details:
  • FastAPI provides the API backend, evaluation sandbox, and model serving endpoints
  • Model fine-tuning pipeline for specialized DSA tutoring responses and scoring
  • React + Next.js for an interactive UI (SSR for landing and SEO; CSR for the mentor/chat experience)
  • Secure code execution sandbox for running user solutions (isolated, resource-limited)
  • Analytics and telemetry to track learning progress and model performance
• Current focus:
  • Building a robust evaluation and sandboxing system for running submissions safely
  • Fine-tuning model(s) with curated DSA dialogues and high-quality feedback examples
  • Implementing the mentor chat experience and personalized plan generator
• Example dev steps

  # backend dev
  uvicorn rise_api.main:app --reload
  
  # frontend dev
  cd client && npm install && npm run dev

• Goal: early private alpha → public beta with instructor/dev tools and integrations (GitHub, LeetCode-style problem import, etc.)

• Want to try RISE or contribute to Project_map/Shelf Stack? Reach out at [email protected] with a short note about how you'd like to help.
• If you find issues or want features, open an issue or PR in the repo (linked project repos will have contributing guidelines).

• GitHub: https://github.com/DevJin27
• Email: [email protected]
• LinkedIn: https://www.linkedin.com/in/dev-jindal-/

Thank you for stopping by — I enjoy working on systems that combine solid engineering with intelligent behavior. If you'd like to see code, demos, or discuss collaboration, drop me a message!