🚀 Excited to launch my latest full-stack project: The Dual-Department AI SOC & Bug Bounty Command Center!
Are you tired of standard, boring security dashboards? I built a fully automated, gamified multi-agent platform that brings AI security operations to life using a 2D Pixel-Office simulation.
The system acts like a real-life virtual cybersecurity firm with 11 specialized autonomous agents, split into two distinct departments:
🛡️ 1. Cyber Crime Branch (Forensics & OSINT):
Automated EXIF metadata & GPS extraction from media files.
Email header hop tracing & SPF/DKIM verification.
Public username footprinting & threat intel reputation lookups.
🎯 2. Bug Bounty & AppSec Lab (Recon & Vulnerabilities):
Live subdomain asset mapping & JS endpoint extraction.
High-entropy regex scanning for exposed API keys & JWT secrets.
Deterministic security misconfiguration audits (CORS reflection, Clickjacking, & Origin IP leaks).
✨ Key Highlights:
Real-time 2D Visuals: Built with Next.js and Phaser.js v3, where character sprites walk to their assigned desks, work on live terminals, and take coffee breaks during idle states (synced via WebSockets at 60fps).
Zero False Positives: Powered by a LangGraph & FastAPI backend featuring a dedicated PoC Verifier Node that runs deterministic Python checks before compiling final reports.
Live Terminal Stream: Integrated xterm.js drawer to monitor agent stdout/stderr logs in real time.
🛠️ Tech Stack: Python 3.11, FastAPI, Async Socket.io, LangGraph, Next.js, Phaser.js v3, xterm.js.
Perfect for developers, security enthusiasts, or teams looking to integrate gamified multi-agent automation into their security workflows!
📂 Interested in getting the complete source code or setting this up? Check out the link below or drop a DM to grab your license! 👇
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sensei: AI code review that learns your team's style.
An open-source CLI (on PyPI) I built that learns how your team reviews code and posts inline, confidence-routed comments on GitLab merge requests: high-confidence issues get flagged, low-confidence ones stay quiet. Paired with mr-principle-review-agent (npm), which enforces team-specific coding principles on every MR. Together they save ~5 hours/week of manual review.
Stack: Python, LLM APIs, GitLab CI, agentic workflows.
Code: github.com/pulkit004/sensei
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FinSense - AI-Powered NPA Prevention Platform for Banking
AI-powered, post-disbursement loan health monitoring system built for SBI-scale banking use cases. Continuously monitors borrower financial behavior after loan disbursement, scores risk on a 0–100 scale across 4 tiers using XGBoost with SHAP-based explainability, and deploys a conversational AI agent to proactively engage at-risk borrowers 60–90 days before default — instead of only scoring creditworthiness at application time like traditional systems.
Built the FastAPI backend (loan, risk, agent, and dashboard routers), automated risk re-scoring via Celery + Redis, and real-time dashboard updates via WebSockets, with a React + Tailwind frontend for officer-facing risk visualization.
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