Skip to content
View Shiv-aurora's full-sized avatar
3 followers · 1 following

Achievements

Achievement: QuickdrawAchievement: Pull SharkAchievement: YOLO

Achievements

Achievement: QuickdrawAchievement: Pull SharkAchievement: YOLO

Highlights

  • Pro

Block or report Shiv-aurora

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
Shiv-aurora/README.md

enter image description here

About Me

Computer Science & Economics @ Villanova

I’m Shivam - a builder, systems thinker, and an AI enthusiast

I write code that makes ideas real, whether that’s:

  • simulating financial markets with multi-agent AI
  • streamlining student life with full-stack tools (shoutout Budget Buddy & Dona AI)
  • or optimizing workflows at scale (hey, EY + Esri 👋)

I’m into products that are:

  1. ⚡ Fast
  2. 🎯 Useful
  3. ✨ Designed with intent and without fluff

💻 Tech Stack

Prog. Languages


JavaScript
TypeScript
Python
Java
C++
C#

Frameworks & Libraries


React
Django
REST API
Next.js
Node.js
Express
FastAPI
Tailwind
Bootstrap
PyTorch

sklearn		 NumPy

Databases


MySQL
MongoDB
PostgreSQL
Redis

Cloud & DevOps


AWS
Docker
GitHub
Nginx
GCP
Azure
Git
Vercel
Linux

Dev Tools & Design


Figma
Photoshop
Postman
VS Code

Random Dev Quote (for literally no reason)

Pinned Loading

  1. ai-research ai-research Public

    Python

  2. neuro-nudge neuro-nudge Public

    TypeScript

  3. Finetuned-CyberSec-Agent Finetuned-CyberSec-Agent Public

    Python

  4. context-compass context-compass Public

    Behavioral context engine for AI coding agents. Learns which code you actually need from git history, not just what's structurally connected. 86-92% recall at 35%-80% fewer tokens

    TypeScript 3