Vatsal.
Computer Engineering student focused on building reliable systems and intelligent software.
01. About Me
I'm a Computer Engineering student who enjoys understanding how systems work beneath the abstractions.
I like building things from scratch. not because frameworks are bad, but because reimplementing core ideas exposes trade-offs, edge cases, and constraints that are easy to miss otherwise. That approach has shaped how I learn everything from neural networks and backend services to games and embedded systems.
I'm comfortable working across the stack, but I'm especially interested in core CS concepts, system behavior, and real-world constraints—performance, timing, memory, and reliability. Projects that combine logic with physical or runtime limitations (like robotics, real-time games, or low-level AI implementations) tend to teach me the most.
I value clarity over buzzwords, and depth over breadth. My goal is to keep improving my fundamentals while building practical systems that behave predictably and are easy to reason about
Currently Focused On
- ▹Building verifiable RAG systems for industrial intelligence.
- ▹Optimizing low-level drivers for robotics hardware.
- ▹Deepening knowledge in Operating System design.
02. Core Skills
Languages
- C
- Python
- TypeScript
- C++
- SQL
Web & Backend
- Next.js
- React
- Node.js
- Express
- Tailwind
Databases
- PostgreSQL
- MongoDB
- SQLite
- MySQL
- Qdrant Vector DBs
Systems & Tools
- Linux/Unix
- Docker
- Git
- Embedded Systems
Interactive Systems
- Game Loops
- Physics Simulation
- State Machines
- Collision Detection
- Real-Time Systems
03. Selected Projects
System Context
High-level ML frameworks hide how backpropagation, gradient flow, and memory usage actually work, making it difficult to reason about performance and correctness.
Architectural Solve
Implemented a scalar-based automatic differentiation engine and built a small neural network framework on top of it, including forward/backward passes, activation functions, and optimizers.
Architecture
Gained a concrete understanding of computational graphs, gradient propagation, and why matrix operations dominate ML performance.
Observation
Most ML performance gains come from optimizing data layout and matrix operations, not from model architecture tweaks.
System Context
LLMs tend to hallucinate when answering technical or domain-specific questions without access to verified context.
Architectural Solve
Built a Retrieval-Augmented Generation pipeline using vector embeddings stored in PostgreSQL (pgvector), ensuring responses are grounded in retrieved documents.
Architecture
Improved answer reliability for technical queries by prioritizing retrieval quality over raw model capability.
Observation
Good retrieval and clean data matter more than larger models for accuracy.
System Context
I wanted to build a small arcade-style game while staying close to the code, instead of relying on a full game engine with heavy abstractions.
Architectural Solve
Built a 2D arcade game in Python using Pygame, implementing my own game loop, physics updates, collision handling, and state management.
Architecture
The project helped me understand how real-time systems behave when timing, physics, and input handling are managed manually.
Observation
Simple games are a great way to learn real-time loops, state machines, and physics without the overhead of a full engine.
04. Engineering Philosophy
Fundamentals First
Frameworks change quickly. Core concepts like algorithms, memory, and data flow do not. I focus on understanding the fundamentals so tools become interchangeable.
Abstractions Leak
Reliable systems require knowing what happens underneath the abstraction. I prefer understanding how things work internally rather than treating libraries as black boxes.
Build to Understand
Reimplementing systems from scratch exposes edge cases, trade-offs, and constraints that tutorials often hide.
"The most powerful tool in an engineer's arsenal is not a specific language or framework, but the ability to learn deeply and build with precision."