The Problem
Deep learning moves at breakneck speed. Every day, researchers publish brilliant new architectures, but there is a persistent bottleneck: translating a dense, abstract block diagram from a research paper or a whiteboard sketch into clean, bug-free PyTorch boilerplate. It is a tedious, manual process that interrupts the flow of building. NeuroDecode was built to automate that handoff.
The Solution: An Autonomous AI Workforce
NeuroDecode is not a simple API wrapper. It is an agentic workflow. Instead of asking one massive model to do everything, the platform orchestrates a crew of specialized AI agents, each with one strict responsibility:
The Vision Specialist
Powered by Gemini 2.5 Flash's native multimodal SDK, this agent parses the uploaded diagram into a strict, literal list of layers, nodes, and mathematical flows without hallucinating unmentioned architectures.
The Systems Analyst
A senior-level deep learning agent that takes parsed visual data and maps it directly to PyTorch logic, generating raw, structural boilerplate code.
The Developer Advocate
The technical writer of the crew. It takes raw generated code and formats it into the clean, IDE-grade Markdown report shown in the interface.
The Tech Stack
Frontend
Next.js (App Router), Tailwind CSS v4, Lucide React, React Syntax Highlighter.
Backend & AI Engine
Python, FastAPI, CrewAI orchestration, Google Gemini 2.5 Flash.
About the Developer
Hi, I'm Ujjwal. I'm a developer specializing in AI and Machine Learning, with a heavy focus on Deep Learning and Computer Vision. I love building tools that solve actual developer bottlenecks. Drawing from my experience as a Design Lead for the Google Developer Group community, NeuroDecode was built with equal focus on premium UI/UX and robust backend orchestration.
This project was built as a rapid sprint to explore what happens when modern web frameworks meet autonomous AI crews.