RESOURCES.md

🚀 GitPulse: GitHub Talent Finder — Technical Resources & Architecture

Welcome to GitPulse, a production-grade, AI-powered developer recruitment platform. This project combines high-performance asynchronous engineering with state-of-the-art Generative AI to analyze software engineering talent at scale.

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🛠️ Technology Stack

Layer	Technology	Purpose
Backend	Python 3.10 / FastAPI	High-concurrency asynchronous API engine.
AI Intelligence	Google Gemini 2.0 Flash	Deep profile analysis & repository architecture mapping.
Speed Engine	Groq (Llama-3.3-70B)	Blazing fast, streamed recruiter summaries (<200ms).
Persistence	DiskCache	High-performance file-based caching for sub-1ms repeat loads.
Frontend	HTML5 / Vanilla CSS / JS	Zero-dependency, lightweight Synthetix Dark UI.
Infrustructure	Docker & Docker Compose	Containerized for "One-Click" cloud deployment.

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🏗️ Core Architecture (Modular Monolith)

The application follows a Ready-for-Microservices structure:

• main.py: The central gateway and ASGI entry point.
• routers/: Self-contained service modules (Users, AI, Projects).
• core/: Shared singleton services for GitHub API communication and AI orchestration.
• templates/: High-fidelity UI templates with integrated Jinja2 server-side rendering.

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🔥 Key Intelligence Features

1. 3D Developer Persona: Analyzes public commit messages to detect if a developer is an Architect, Exterminator, Documenter, or Shipper.
2. Enterprise-Grade Scoring: Matches candidates against a specific Company Tech Stack using multi-vector AI evaluation.
3. Market Trends: Real-world salary and demand analytics based on live GitHub language activity.
4. JD Matcher: Analyzes Job Descriptions and cross-references them with the top 1% of GitHub talent in real-time.

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🚢 How to Run & Deploy

Option A: Local Development

1. Create a .env file with your keys: GITHUB_TOKEN, GOOGLE_API_KEY, GROQ_API_KEY.
2. Run with Uvicorn:

   uvicorn main:app --reload
   

Option B: Professional Docker Launch (Recommended)

Launch the entire stack with persistence and multi-worker optimization:

docker-compose up --build -d

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🎯 Performance Metrics

• Analysis Speed: AI summaries generated in ~150ms via Groq.
• Cache Hit Latency: <0.5ms (Instant reload for previously analyzed profiles).
• Image Size: Optimized <300MB Docker image using python:slim.

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Generated by Antigravity™ AI Engine • 2026