Shivam Mittal

Full-stack engineer

Full-stack developer · Always exploring

Thoughtful software.
From idea to launch.

I enjoy planning, building, and shipping complete products.

When a challenge looks difficult, I break it into smaller problems, learn what’s needed, and use tools like LLMs to keep moving.

View projects Contact me
  1. Idea
  2. Design
  3. Build
  4. Ship
  • Building full-stack products with React, TypeScript, Node.js, and MongoDB.
  • 325 data-structure challenges solved to sharpen problem-solving skills.
  • Currently automating student data workflows at goSharpener.

3

Featured projects

325

DSA problems

1

Ongoing internship

Interactive walkthrough

How my work works

A quick look at the workflows behind three projects. Pick one to explore.

Built to be explored
WORKFLOW DEMO01 / 03

Incoming transactions

Groceries₹2,450
Transport₹860
Subscriptions₹499

Spending overview

Categories · History · Filters · Edit/restore

01 / 06Capture & overview

Use category tiles, browse and filter transactions, and edit or restore entries when needed.

The toolkit

Technical Skills

From real-time apps to offline experiences.
The tools I build with.

  • Languages

    The foundations

    • JavaScript
    • TypeScript
    • C++
    • Python
    • OOP

  • Web development

    From interface to API

    • React
    • Tailwind
    • Redux
    • Node.js
    • Express
    • REST
    • WebSockets
    • PWA (offline availability)

  • Data & infrastructure

    Store, cache & deploy

    • MongoDB
    • MySQL
    • Redis
    • Docker
    • Nginx

  • AI & productivity

    Work smarter

    • LLM
    • Advanced Excel

  • Developer tools

    The everyday essentials

    • Git
    • GitHub
    • Postman
    • VS Code

  • Engineering concepts

    How the pieces connect

    • DSA
    • Authentication
    • API Design
    • HTTP/TCP Workflow
    • Real-time Communication
    • Caching Strategy
    • Rate Limiting
    • Distributed Systems Basics

Learning in motion

Currently exploring AI

I'm learning how LLMs work, how AI supports a product from planning to deployment, and how model capabilities fit into real software.

Learning now
  1. 01 / 03

    Understand

    Inside the LLM

    Studying transformers, tokenization, embeddings, context, and why model responses can go wrong.

    TokensEmbeddingsTransformers
  2. 02 / 03

    Practice

    AI-native workflow

    Learning to use AI across ideation, architecture, design, code review, testing, and deployment.

    PlanDesignShip
  3. 03 / 03

    Explore

    AI in products

    Exploring LLM APIs, token usage, API protection, and adding AI capabilities to existing software.

    LLM APIsTokensAPI safety
Next on the mapRAG → Agents → Chatbot systems

Topics I plan to explore next.

Following the Namaste AI learning path

Beyond code

What keeps me curious

always curious

  • Building Products

    I enjoy crafting purposeful web apps—expense tools, collaboration hubs, or anything that removes friction for teams.

  • Backend Engineering

    APIs, databases, auth flows, and system diagrams are my happy place. I like when reliability is measurable.

  • Learning & Improving

    Reading docs, running experiments, and dissecting architecture keeps my curiosity wired-in.

  • Problem Solving

    I break ambiguous challenges into approachable sprints and validate with instrumentation.