Algorithmic Thinking Trainer & Adaptive Learning Platform
A comprehensive system profiling cognitive patterns to identify biases and provide personalized coaching.
NxtDevs is an advanced algorithmic training platform designed to go beyond simple syntax verification. It utilizes a multi-dimensional profiling engine to track user cognition across 20+ "Thinking Axes," identifying specific cognitive pitfalls such as "Greedy Bias" or "Premature Optimization."
Unlike standard coding platforms, NxtDevs integrates real-time competitive duels, generative AI coaching, and deep analytics to foster genuine problem-solving growth.
"We don't just check if your code works; we analyze how you think."
- Multi-Dimensional Tracking: Profiles users on axes like Constraint Sensitivity, Edge Case Paranoia, and Asymptotic Intuition.
- Bias Detection: Heuristic evaluation of submission history to detect patterns like over-reliance on Brute Force or premature optimization.
- Adaptive Remediation: Automatically assigns problems that target your specific cognitive weak points.
- GraphQL Data Sync: Direct integration with LeetCode's GraphQL API (
backend/services/leetcode_service.py) to fetch submission history, calendars, and problem tags. - Pattern Analysis: Heuristics engine that scans your LeetCode history to identify strengths (e.g., "Strong Graph Intuition") vs weaknesses (e.g., "Greedy Bias Detected").
- Smart Recommendations: Correlates your LeetCode difficulty distribution with internal metrics to recommend optimal training sets.
- WebSocket Orchestration: Custom-built
backend/engine/orchestrator.pyhandles state synchronization between players with sub-50ms latency. - ELO Matchmaking: Proprietary matchmaking queue (
backend/services/matchmaking.py) that pairs users based on skill bands (Δ < 300 ELO). - Live State Sync: Bidirectional communication ensuring fairness and instant feedback during 1v1 battles.
- Multi-Provider Fallback: A robust engineering layer (
backend/services/ai_service.py) that ensures 100% uptime by cascading requests: Gemini 2.5 → Groq (Llama 3) → OpenRouter. - Data-Driven Synthesis: We use LLMs strictly as a reasoning engine. We feed raw execution metrics, error logs, and historical bias data to generate structured, JSON-based learning curriculums.
- Structured Output: AI generates actionable plans, not just chat.
- Async Task Queue: Uses Celery & Redis for heavy background processing (report generation, batch stats syncing).
- Scalable Database: PostgreSQL with SQLModel (SQLAlchemy + Pydantic) for type-safe, high-performance data access.
- Modern Frontend: Next.js 16 (App Router) with TypeScript and TailwindCSS for a performant, type-safe UI.
graph TD
User[User] -->|Interact| Frontend[Next.js Frontend]
Frontend -->|REST / WebSocket| Backend[FastAPI Backend]
subgraph "Backend Infrastructure"
Backend -->|Persist| DB[(PostgreSQL)]
Backend -->|Cache/Queue| Redis[(Redis Cache)]
Backend -->|Dispatch| Celery[Celery Workers]
Celery -->|Process| Redis
end
subgraph "Intelligence Layer"
Backend -->|Contextual Prompting| AI[AI Service Orchestrator]
AI -->|Primary| Gemini[Google Gemini 2.5]
AI -->|Fallback| Groq[Groq Llama 3.3]
AI -->|Safety Net| OpenRouter
end
sequenceDiagram
participant U1 as Player 1
participant U2 as Player 2
participant Orch as Socket Orchestrator
participant MM as Matchmaking Service
participant DB as Database
U1->>MM: Join Queue (ELO 1200)
U2->>MM: Join Queue (ELO 1250)
MM->>MM: Match Found!
MM-->>U1: Session ID Created
MM-->>U2: Session ID Created
U1->>Orch: Connect WS (Session ID)
U2->>Orch: Connect WS (Session ID)
loop Real-Time Battle
Orch->>U1: Broadcast Question
Orch->>U2: Broadcast Question
U1->>Orch: Submit Code
Orch->>DB: Validate & Score
Orch->>U2: Notify Opponent Progress
end
Orch->>DB: Update ELO & Profile
Orch->>U1: Final Results
Orch->>U2: Final Results
| Component | Technology | Role |
|---|---|---|
| Core | Python 3.11 | High-performance, type-hinted application logic |
| API | FastAPI | Async REST API & WebSocket handling |
| ORM | SQLModel | Database modeling & interaction |
| Queue | Celery | Distributed task processing |
| Broker | Redis | Message broker & caching layer |
| AI | Gemini / Groq | Logic & Reasoning Engines |
| Component | Technology | Role |
|---|---|---|
| Framework | Next.js 16 | Server-Side Rendering & App Router |
| Language | TypeScript | Strict type safety |
| Styling | Tailwind CSS | Rapid UI development |
| State | Zustand / Hooks | Complex state management |
| Viz | Recharts | Data visualization for cognitive profiles |
NxtDevs/
├── backend/
│ ├── api/ # API Route handlers (Auth, LeetCode, Duels)
│ ├── engine/ # Core scoring, dueling, and orchestration logic
│ ├── models/ # Database schema (SQLModel)
│ ├── services/ # Business logic (AI, Matchmaking, Stats Sync)
│ ├── celery_app.py # Celery worker configuration
│ └── main.py # FastAPI entry point
├── frontend/
│ ├── app/ # Next.js App Router pages
│ ├── components/ # Reusable UI React components
│ └── lib/ # Shared utilities
└── docker-compose.yml # Container orchestration
To build and run the entire stack:
docker-compose up --build1. Backend
cd backend
python -m venv venv
source venv/bin/activate # or .\venv\Scripts\activate
pip install -r requirements.txt
uvicorn backend.main:app --reload2. Worker
celery -A backend.celery_app worker --loglevel=info3. Frontend
cd frontend
npm install
npm run devCopyright © 2026 NxtDevs. All Rights Reserved. Proprietary software.