I'm an AIML student who treats side projects like production software — deployed, documented, and actually usable. Full-stack is my foundation; AI/ML is where I specialize. Right now I'm in my 2nd year, but I build like I'm already on a team that ships.
My process is simple: take an idea from "what if we built..." to a live URL someone can click. I'm currently open to remote roles, internships, and freelance work where I can own both the product and the model behind it.
01 Scope it → Define the smallest version that's actually useful
02 Ship it → Deploy early — real URL, real users, no "coming soon"
03 Instrument it → Add the AI layer once the core product actually works
04 Iterate → Fix what breaks, cut what doesn't matter, repeat
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Frontend
Backend
AI / ML
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Database
Infra & Tooling
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♻️ context-gcDeterministic context-compaction library for AI agents. Prunes obsolete and superseded steps from execution traces via graph-based analysis — no extra LLM calls, and every pruned event stays fully recoverable via receipts.
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Personal finance assistant that auto-categorizes raw transactions using the Gemini API. Backed by an AWS database with secrets managed through encrypted environment variables.
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- Deepening Data Structures & Algorithms
- Sharpening system design fundamentals for full-stack + AI products
Remote or on-site roles, internships, and freelance work spanning full-stack development and applied AI/ML. If you need someone who can build the product and the model behind it, let's talk.
