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Kartavyasonar/README.md

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👨‍💻 About Me

I build infrastructure that scales, fails gracefully, and can be observed.

My engineering focus is on:

  • High-throughput backend systems
  • Concurrent Go services
  • Distributed systems
  • Kubernetes and cloud-native infrastructure
  • API infrastructure
  • AI agent security
  • Observability and reliability engineering

I care about correctness, clean system boundaries, measurable performance, and systems that survive real load.

🔭 Currently Building

InvokeCordon

An open-source security gateway for MCP servers, written in Go.

It scans MCP tools for security risks and acts as a runtime JSON-RPC policy gateway between AI agents and MCP servers.

🌱 Open Source

Active contributor to Kubernetes with work around rootless namespace testing, kube-proxy testing, Prow CI, and Kubernetes API behavior.


🛡️ InvokeCordon

What if an MCP tool description itself contains a malicious instruction?

InvokeCordon is a security scanner and runtime policy gateway designed to protect AI agents from:

  • Tool poisoning
  • Prompt injection
  • Over-privileged tools
  • Dangerous tool arguments
  • Secret leakage
  • Unsafe tool execution

Architecture

                    ┌───────────────────┐
                    │     AI AGENT      │
                    └─────────┬─────────┘
                              │
                         JSON-RPC
                              │
                              ▼
                    ┌───────────────────┐
                    │     InvokeCordon      │
                    │   Security Proxy  │
                    └─────────┬─────────┘
                              │
              ┌───────────────┼───────────────┐
              │               │               │
              ▼               ▼               ▼
        ┌──────────┐    ┌────────────┐   ┌────────────┐
        │  Scan    │    │  Policy    │   │  Secrets   │
        │  Tools   │    │  Engine    │   │ Redaction  │
        └──────────┘    └────────────┘   └────────────┘
                              │
                              ▼
                    ┌───────────────────┐
                    │    Audit Log      │
                    │     SHA-256       │
                    └─────────┬─────────┘
                              │
                              ▼
                    ┌───────────────────┐
                    │    MCP SERVER     │
                    └───────────────────┘

Core Capabilities

Capability What it does
🔍 Tool Scanner Grades MCP tool inventories from 0–100
🛡️ Policy Engine Allow / deny / monitor tools/call
🔐 Secret Redaction Recursively removes sensitive values
🧾 Audit Logging Concurrent-safe JSONL logging
#️⃣ SHA-256 Avoids storing raw request payloads
📊 Prometheus Runtime metrics at /metrics
🚨 Security Checks Path traversal, shell injection, cloud metadata
⚡ Go Lightweight concurrent proxy

Repository → github.com/Kartavyasonar/InvokeCordon


☸️ Kubernetes & Open Source

Added RunInUserNS() to re-execute test binaries inside unprivileged Linux user and network namespaces, enabling rootless kube-proxy nftables testing.

Area: sig-network · Focus: Linux namespaces · Rootless Kubernetes

Added Prow presubmit CI jobs gated by custom build tags for namespace isolation tests.

Area: sig-testing · Focus: CI/CD · Kubernetes Test Infrastructure

Documented API behavior around ConfigMap.BinaryData propagation and validated the behavior with Kubernetes API reviewers.

Area: Kubernetes API · Focus: ConfigMap behavior


🚀 Engineering Work

⚡ PulseAPI - Distributed API Gateway

Stack

Node.js Redis Kafka PostgreSQL Prometheus Grafana k6

Production-grade API gateway designed for high-concurrency traffic.

Engineering

  • Redis Lua scripts for atomic token bucket rate limiting
  • Sliding-window rate limiting
  • Distributed circuit breaker state
  • Kafka-based asynchronous logging
  • PostgreSQL batch writes
  • Automatic Grafana provisioning
  • Prometheus latency metrics
  • Event-loop monitoring

Load Test

Virtual Users       : 200
Throughput          : 207 req/s
p99 Latency         : 881 ms
Error Rate          : 0.0%
Kafka Batch         : 500 records/sec

🧠 GhostMind - Self-Improving Research Agent

Stack

Python FastAPI FAISS NetworkX GraphRAG

LLM-powered research system that learns from retrieval failures using episodic memory and TD learning without retraining model weights.

Results

Response confidence
~60%  ─────────────────►  ~83%

Hallucination rate
~40%  ─────────────────►  ~17%

Controlled sessions
57

🤖 Multi-Agent Code Review Platform

Python LangGraph ChromaDB AST Radon SSE

Four specialized agents covering:

🐛 Bug
🔐 Security
⚡ Performance
✨ Quality

Uses Python AST for function-level chunking, Radon for static complexity analysis, LangGraph for orchestration, and SSE for real-time progress streaming.


☁️ FaaS Performance Benchmarking

Kubernetes K3s OpenFaaS JMeter Azure Functions

Benchmarked managed serverless infrastructure against self-hosted OpenFaaS on K3s.

Focus areas:

  • Cold-start latency
  • Throughput
  • Concurrent execution
  • Error rates
  • Scaling predictability

🧰 Tech Stack

Languages

Go Python TypeScript SQL Bash C++

Backend

FastAPI Node.js gRPC REST JSON-RPC WebSockets

Infrastructure

Kubernetes Docker Linux Helm GitHub Actions nginx

Data & Messaging

PostgreSQL Redis Kafka MongoDB SQLite

Observability

Prometheus Grafana OpenTelemetry k6

AI & Retrieval

LangGraph FAISS ChromaDB HuggingFace NetworkX GraphRAG


📊 GitHub Snapshot


🔥 Contribution Streak


📈 GitHub Activity

Your GitHub profile already provides the authoritative contribution graph.


🏆 Open Source Impact


📌 Featured Projects

🛡️ InvokeCordon

MCP Security Gateway

Go · JSON-RPC · YAML Policies · Prometheus

Security layer for AI agents and MCP servers.

View Repository →

⚡ PulseAPI

Distributed API Gateway

Node.js · Redis · Kafka · PostgreSQL

High-concurrency API infrastructure with observability and resilience.

View Repository →

🧠 GhostMind

Self-Improving Research Agent

Python · FAISS · NetworkX · GraphRAG

Retrieval system using episodic memory and adaptive strategies.

🤖 Multi-Agent Code Review

AI Code Analysis Platform

Python · LangGraph · ChromaDB · AST

Specialized agents for bugs, security, quality and performance.


💻 Problem Solving


🧩 Engineering Principles

┌──────────────────────────────────────────────────────┐
│                                                      │
│   Correctness       >       Cleverness               │
│                                                      │
│   Observability     >       Guesswork                │
│                                                      │
│   Measured Speed    >       Assumptions              │
│                                                      │
│   Simple Boundaries >       Complexity               │
│                                                      │
│   Secure Defaults   >       Hope                    │
│                                                      │
└──────────────────────────────────────────────────────┘

🎯 Current Focus


📫 Connect


Build systems that scale. Secure systems that matter.

Pinned Loading

  1. InvokeCordon InvokeCordon Public

    Security scanner and runtime policy gateway for MCP servers

    Go 1

  2. GhostMind GhostMind Public

    Python 1

  3. AI-CODE-REVIEW AI-CODE-REVIEW Public

    Python 1

  4. PulseAPI PulseAPI Public

    JavaScript 1 1