Hey there! I'm Aaditya Jindal—a tech enthusiast who codes like there's no tomorrow and debugs like there's definitely a tomorrow (usually at 3 AM). By day, I'm a computer science student with a passion for building things that actually work. By night... well, I'm still coding, just with more coffee ☕.

I specialize in turning "wouldn't it be cool if..." into production-ready applications. Whether it's fine-tuning LLMs on my laptop, building voice assistants for government schemes, or teaching robots to navigate mazes for accessibility, I'm all about leveraging technology to solve real-world problems—preferably before the hackathon deadline hits.

When I'm not pushing commits or wrestling with merge conflicts, you'll find me contributing to open-source projects, participating in hackathons, and continuously expanding my technical toolkit. I believe in writing clean code, building inclusive solutions, and never, ever pushing to production on a Friday.

A no-code toolkit to finetune LLMs on your local GPU—just upload data, pick a task, and deploy later. Built for fast iteration at hackathons and in early-stage prototyping, with automatic hardware detection and a guided React UI.

• 💡 Key features: task presets (classification, Q&A, chat), dataset upload with schema hints, experiment tracking, and one-click export of fine‑tuned weights.
• 🔐 Privacy by design: everything runs locally; no cloud dependency required.
• ⚙️ Hardware‑aware: automatic GPU/CPU fallback, mixed precision, and LoRA adapters for low‑VRAM machines.
• 🧩 Integrations: export an inference endpoint you can drop into your app.
• 🛠️ Tech stack: React, FastAPI, Hugging Face Transformers, PEFT, bitsandbytes, Docker (optional).
• 🎯 Why it stands out: true no‑code finetuning that actually works on laptops, enabling rapid model iteration without MLOps overhead.

A drag-and-drop platform for designing deep learning architectures that generates production-ready PyTorch and TensorFlow code with automated shape inference. Secured first place in the HackASU hackathon by the Claude Builders' Club @ ASU.

• 🧩 Visual Prototyping: Build complex neural networks via an intuitive node-based drag-and-drop interface.
• 📐 Automatic Shape Inference: Eliminates "dimension mismatch" errors by dynamically calculating tensor shapes between layers.
• 🤖 AI-Powered Assistance: Intelligent suggestions to optimize architecture design and help select the best layers for specific tasks.
• 📦 One-Click Export: Instantly transform visual diagrams into clean, documented Python code for PyTorch or TensorFlow.
• 🛠️ Tech stack: TypeScript, React, Python, PyTorch, TensorFlow, Tailwind CSS.
• 🎯 Why it stands out: It bridges the gap between architectural intuition and code implementation, making deep learning accessible without getting bogged down in boilerplate syntax.

A production-hardened URL shortening service built to withstand high-concurrency loads, securing 3rd place at the inaugural MLH Production Engineering Hackathon in partnership with Meta.

• 🚀 Scalability at Scale: Designed and stress-tested to handle 500+ concurrent users using k6 and Nginx load balancing.
• 🛡️ Fault Tolerance: 91% test coverage coupled with chaos engineering failure-mode documentation and circuit breakers for Redis outages.
• 🚨 Full-Stack Observability: Integrated Prometheus, Grafana, Loki, and Jaeger to provide real-time metrics, logs, and distributed tracing.
• ⚙️ Architectural Precision: Optimized request handling via Gunicorn's pre-fork workers and implemented Redis caching to eliminate DB bottlenecks.
• 🛠️ Tech stack: Python, Flask, Redis, PostgreSQL, Nginx, Docker, Prometheus, Grafana, Jaeger, Trivy.
• 🎯 Why it stands out: A masterclass in "production over prototype"—prioritizing observability, security scans, and infrastructure resilience over just building a basic UI.

A lightweight framework for real‑time code streaming and sync across Jupyter notebooks and services—ideal for live demos, classrooms, and pair‑programming.

• ⚡ Real‑time: stream cell edits and execution events as they happen.
• 🔌 Multi‑runtime bridge: TypeScript client SDK with a Python bridge for Jupyter.
• 🧱 Modular: minimal core with small, focused components you can extend.
• 🧪 Dev‑friendly: clean architecture, typed end‑to‑end, and easy to reason about.
• 🛠️ Tech stack: TypeScript/Node.js, Python, WebSockets, modern tooling.
• 🎯 Why it stands out: keeps scope tight—synchronization done right, without the bloat of full collab suites.

Django‑based LMS for Neurologic Music Therapy Services of Arizona (NMTSA) featuring dual authentication, role‑based access, and accessible UI patterns tailored for neurodiverse learners.

• 🔐 Auth model: Auth0 for students/teachers; dedicated Django admin for administrators.
• 📚 Learning flows: courses, assignments, assessments, and submissions with progress tracking and gradebook views.
• 🧩 Roles & permissions: RBAC for students, teachers, and admins.
• 🎨 Accessibility: autism‑friendly UI with adjustable themes and font scaling.
• 🛠️ Tech stack: Django, HTML/CSS/JavaScript.
• 🎯 Why it stands out: designed with inclusive education in mind—simple, secure, and focused on essentials a small nonprofit actually needs.

A privacy‑first, voice‑forward assistant that helps citizens discover and understand Indian government schemes, with on‑device LLMs and secure identity.

• 🗣️ Multi‑modal I/O: converse in Hindi/English via text or voice.
• 🔐 Face authentication: protects access and personal context.
• 🧠 Orchestrated reasoning: LangGraph‑powered flows running local/private LLMs.
• 🖥️ Simple UI: Streamlit interface optimized for clarity and speed.
• 🛠️ Tech stack: Python, LangGraph, local LLMs, Streamlit.
• 🎯 Why it stands out: practical AI for public services—offline‑capable, private by default, and actually usable by non‑technical audiences.

• Portfolio & Projects: Devpost – Where I showcase my hackathon wins and projects
• Professional Network: LinkedIn – Let's connect!
• Tech Musings: X (Twitter) – Hot takes and tech discussions
• GitHub: RETR0-OS – You're already here!

Languages: Python, Java, C#, TypeScript/JavaScript, SQL, MATLAB, C
Frameworks & Tools: Django, Spring Boot, React, FastAPI, Node.js, LangGraph, Streamlit
AI/ML: Hugging Face Transformers, PyTorch, PEFT, LLM Fine-tuning, Computer Vision
Databases: MySQL, PostgreSQL
Other: Docker, Git, REST APIs, WebSockets, OAuth/Auth0, LEGO Mindstorms EV3

I'm always excited to collaborate on innovative projects, discuss the latest in AI/ML, or just geek out about technology. Whether you're a recruiter looking for someone who can ship production code and handle technical challenges, or a fellow developer wanting to collaborate—I'd love to connect!

Feel free to reach out via LinkedIn or check out my projects above. Let's build something amazing together! 🚀