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Full-Stack ProductPrototype

SynergyLearn

Full-stack AI learning platform with IDE, code execution, and progress sync.

ReactFirebaseGeminiJudge0Monaco EditorFirestore
Full-Stack Product

S

SynergyLearn

Case study visual placeholder

Architecture flow

4 layers
Interface
Security & Identity
Retrieval & Data
AI Orchestration
Firebase Auth and Firestore progress sync
Monaco IDE with Judge0 sandbox execution
Gemini hints with graceful dependency fallbacks

Problem

Learning platforms often separate lessons, coding, feedback, and progress tracking into disconnected experiences.

My Role

Worked across frontend, backend integrations, auth, progress state, and AI hinting flows.

Solution

A React and Firebase platform combines auth, progress tracking, Monaco editing, Judge0 execution, and Gemini hints.

Stack

ReactFirebaseGeminiJudge0Monaco EditorFirestore

Case study

Problem

Learning platforms often separate lessons, coding, feedback, and progress tracking into disconnected experiences.

Proof signal

Judge0 sandbox · Monaco IDE · Gemini hints · Firebase progress sync

My Role

Worked across frontend, backend integrations, auth, progress state, and AI hinting flows.

Core product work

  • In-browser IDE
  • AI hints
  • Progress sync
  • Gamified learning

Solution

A React and Firebase platform combines auth, progress tracking, Monaco editing, Judge0 execution, and Gemini hints.

React frontend with Firebase Auth and Firestore progress tracking. Monaco powers the IDE, Judge0 executes code in a sandbox, and Gemini provides contextual hints with graceful fallbacks for external dependency failures.

Architecture Highlights

Firebase Auth and Firestore progress sync

Monaco IDE with Judge0 sandbox execution

Gemini hints with graceful dependency fallbacks

Challenges and Tradeoffs

  • Handling external service failures without breaking the learning session.
  • Keeping coding feedback useful without over-relying on AI hints.

Impact / Outcome

  • Integrated lessons, code execution, hints, and progress into one product flow.
  • Improved reliability through fallback handling around external AI and execution APIs.

Learnings

  • Full-stack AI products need reliable non-AI fallbacks.
  • Progress and feedback loops are central to learning-product UX.

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