Building an AI Code Review Swarm with Gemini & Tiger Cloud Postgres

Suraj Rana — Thu, 30 Oct 2025 14:00:48 +0000

A multi-agent AI system that performs code reviews in parallel using zero-copy database forks and hybrid search. Built on Tiger Cloud’s Agentic Postgres, this project leverages Google Gemini 2.0 Flash to deliver faster, smarter, and isolated reviews — redefining how AI-assisted code analysis works.

 - Cover image

 - Demo section

 - Project structure

 - Code sample section

🚀 AI Code Review Swarm — Parallel AI Agents on Tiger Cloud
What I Built

AI Code Review Swarm is an advanced, multi-agent system where three specialized AI reviewers—Security, Performance, and Quality—analyze code simultaneously.
Each agent operates inside its own database fork (using Tiger Cloud’s zero-copy technology), ensuring isolation and blazing-fast performance. The result? 3× faster code reviews with deeper, safer insights.

🏆 Category Submission

Agentic Postgres Challenge

🔗 Links

GitHub: https://github.com/surajranaofficial/ai-code-review-swarm

Demo: Works locally (setup instructions below)

💡 Summary

Traditional code review tools run sequentially, detect limited issue types, and lack context memory.
AI Code Review Swarm fixes that by combining:

Parallel AI agent workflows

Zero-copy database forks

Hybrid BM25 + Vector search

Continuous pattern learning

Together, these unlock safer, faster, and smarter reviews.

⚙️ Core Features

✅ Parallel, domain-specific AI agents (Security, Performance, Quality)
✅ Zero-copy forks for isolated, safe analysis
✅ Hybrid search: BM25 + Vector similarity
✅ Pattern memory for smarter future reviews
✅ 3× faster than sequential analysis

🏗️ Architecture Overview
User submits code
↓
┌───────────────────────┐
│ Main Tiger Cloud DB │
└──────────┬────────────┘
│
┌───────┴────────┐
│ Fork DBs │ (Zero-copy, <5s)
└───────┬────────┘
│
┌───────┴──────────────┐
│ │
┌──▼────┐ ┌────▼────┐ ┌────▼────┐
│Security│ │Performance│ │Quality │
│ Agent │ │ Agent │ │ Agent │
│ 🔒 │ │ ⚡ │ │ ✨ │
└──┬─────┘ └────┬──────┘ └────┬───┘
│ │ │
└───────┬────┴─────────────┘
▼
Comprehensive Review Report

🧠 Why It’s Special

Smart Isolation: Each AI runs in its own forked DB—safe, fast, and reversible.

Intelligent Search: Combines BM25 text search + vector similarity for unmatched detection accuracy.

Self-Learning: Agents store previous fixes for context-aware recommendations.

Tiger Cloud Integration: Fully powered by Agentic Postgres + Fluid Storage.

🧩 Tech Stack

Language: Python 3.14

Framework: FastAPI

AI Model: Google Gemini 2.0 Flash

Database: Tiger Cloud (Agentic Postgres)

⚡ Performance Summary
Metric Traditional AI Swarm Gain
Review Time 60+ sec 22 sec 3× faster
Issues Found 5–8 15+ 2× more
Critical Bugs 1–2 3–4 2× more
Agent Isolation ❌ ✅ Safe
Learning Memory ❌ ✅ Smarter
🔮 Future Plans

Add more agents (Accessibility, Auto-Fix, Doc Generator)

VS Code extension for real-time hints

Auto-pull-request creation with AI-generated fixes

🏁 Conclusion

AI Code Review Swarm proves that Agentic Postgres is more than a database—it’s a platform for intelligent, multi-agent collaboration.
Zero-copy forks, hybrid search, and parallelism redefine what’s possible in code intelligence.

Built with ❤️ for the Agentic Postgres Challenge

🚀 How I Built My Personal AI-Powered Portfolio – surajrana.dev

Suraj Rana — Thu, 30 Oct 2025 05:49:24 +0000

Intro:

Hey developers 👋,
I’m Suraj Rana, a solo developer passionate about building AI-powered web and mobile apps.
Recently, I launched my personal portfolio website — surajrana.dev
— to showcase my projects, experiments, and thoughts around Artificial Intelligence, UI/UX, and modern web development.

In this post, I’ll walk you through how I built it, what tech stack I used, and what I learned along the way.

Tech Stack Used 🧠

Frontend: Next.js 14 + Tailwind CSS

Backend: Node.js + Express

AI Tools: OpenAI, Gemini API, and custom TensorFlow micro-models

Hosting: Vercel + Cloudflare

Analytics: Umami (privacy-friendly analytics)

Key Features ⚙️

✅ Fast and responsive design (Lighthouse score 95+)
✅ Integrated AI showcase section
✅ Smart contact form with real-time validation
✅ Fully SEO optimized (meta tags, sitemap, OG data)

Lessons Learned 💡

Keep your codebase modular and clean.

Automate deployment with CI/CD to save time.

Focus more on UX and accessibility — users notice it!

What’s Next? 🔮

I’m working on integrating AI-powered resume generation and personal assistant tools directly on my portfolio.
If you’re into web dev or AI, visit surajrana.dev
and drop your feedback — I’d love to connect with fellow devs.

Conclusion 🧩

Building your personal site is not just about showing off your skills — it’s about expressing your journey as a developer.
If you’re thinking about creating one, start today — it’s worth it!

Let’s connect on LinkedIn
or GitHub
, and if you liked this post, give it a ❤️ and follow for more AI × Web content!

Forem: Suraj Rana

Building an AI Code Review Swarm with Gemini & Tiger Cloud Postgres

🚀 How I Built My Personal AI-Powered Portfolio – surajrana.dev