Inspiration

As developers and learners, we noticed a gap between real-world coding experience and online learning resources. Many platforms teach concepts in isolation, without connecting them to actual projects or measurable progress. We wanted to bridge this gap by creating an AI-powered platform that analyzes real GitHub repositories and provides actionable learning insights.

What it does

MANTIS helps developers and learners by:

Analyzing GitHub repositories for code quality, documentation, and maintainability

Generating personalized, job-ready learning roadmaps

Providing AI-driven code evaluation and mentoring

Tracking skill progression and learning analytics over time

Producing AI-generated developer profiles and resume signals

How we built it

Backend: Python, Flask, SQLite

AI Layer: ERNIE 4.5 models via Novita AI

Features:

GitHub repo analysis → structured JSON scores

Learning roadmap generation → progressive skill-based items

AI code evaluation → feedback, improved code, and scoring

Data Flow: Repo download → file parsing → ERNIE analysis → structured feedback

We focused on structured AI reasoning instead of just generating text, ensuring accurate, evidence-based feedback.

Challenges we ran into

Handling large repositories and ensuring models could reason over multiple files

Designing strict JSON contracts to guarantee reliable AI outputs

Integrating multiple ERNIE models for different tasks while keeping the system efficient

Providing meaningful, actionable learning insights rather than generic feedback

Accomplishments that we're proud of

Successfully built a platform that analyzes real GitHub repositories end-to-end

Implemented multi-model ERNIE reasoning for feature-specific tasks

Created job-ready learning roadmaps based on actual repository content

Delivered AI-powered code evaluation with structured scoring

Turned GitHub activity into measurable learning intelligence

What we learned

How to use multimodal AI models (ERNIE 4.5) effectively in real applications

Importance of structured prompting and JSON contracts to avoid AI hallucinations

Challenges of scaling AI analysis for full repositories versus individual code snippets

How to translate AI insights into actionable learning and skill progression

What's next for MANTIS

Add auto PR review assistance for real-time code improvement

Implement team dashboards for classrooms and collaborative learning

Enable multi-agent curriculum planning for advanced personalized roadmaps

Generate resume-ready PDFs from AI-analyzed repositories

Deploy cloud version with CI/CD integration for seamless updates

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