DEV Community: Aryaman M Singha

OpsMind AI: The Incident Memory Engine for Cloud Operations

Aryaman M Singha — Mon, 29 Jun 2026 20:41:40 +0000

🚨 The Problem: "Didn't we fix this last month?"

If you work in DevOps, Site Reliability Engineering (SRE), or Cloud Operations, you've probably experienced the frustration of déjà vu. A critical service goes down, pagers start ringing, and after hours of debugging, someone suddenly remembers:

"Didn't this exact database deadlock happen a few months ago? How did we fix it?"

The reality is that operational knowledge is fragile. It gets buried in Jira tickets, lost in Slack conversations, scattered across documentation, or exists only in the minds of experienced engineers. As a result, teams repeatedly spend valuable time rediscovering solutions to problems they've already solved.

🧠 The Solution: OpsMind AI

OpsMind AI is an AI-powered DevOps Incident Response and Organizational Memory Platform designed to preserve engineering knowledge and accelerate incident resolution.

Rather than functioning as a traditional ticketing system, OpsMind AI becomes your engineering team's permanent memory engine. It continuously learns from every incident, connects current outages with historical resolutions, and helps engineers resolve issues faster using AI-powered insights.

✨ Key Features

🧬 Incident DNA Engine

Every incident is analyzed and fingerprinted using:

Error signatures
Infrastructure components
CPU & Memory metrics
Application behavior
Service dependencies

This creates a unique "Incident DNA" that allows future incidents to be matched intelligently.

🤖 AI Root Cause Analysis (RCA)

When a new incident occurs, OpsMind AI automatically:

Detects similar historical incidents
Generates a detailed Root Cause Analysis
Suggests proven resolution steps
Estimates confidence scores for recommendations

⚔️ AI War Room

A collaborative environment where engineers and AI agents work together in real time.

Features include:

AI-assisted debugging
Incident timeline
Shared investigation notes
Intelligent recommendations
Faster MTTR (Mean Time To Resolution)

📚 Self-Building Knowledge Base

Every successfully resolved incident automatically becomes organizational knowledge.

The platform continuously builds a searchable knowledge base so future engineers never have to solve the same problem twice.

🛠 Tech Stack

Frontend

Next.js (App Router)
React
Tailwind CSS
shadcn/ui

Backend

Next.js API Routes
Drizzle ORM

Database

Amazon Aurora PostgreSQL

AI

OpenAI GPT-4o

Authentication

Clerk

Cloud Infrastructure

AWS
Amazon S3
Vercel

☁ Why Amazon Aurora PostgreSQL?

OpsMind AI stores mission-critical operational intelligence.

To provide high availability, scalability, and enterprise-grade reliability, we selected Amazon Aurora PostgreSQL.

Using Drizzle ORM, we built a relational schema capable of storing:

Incident fingerprints
Error signatures
Deployment history
Infrastructure metadata
AI-generated RCA reports
Similarity scores

Example schema:

export const incidentDna = pgTable("incident_dna", {
  id: serial("id").primaryKey(),
  incidentId: varchar("incident_id", { length: 20 }).notNull(),
  fingerprint: varchar("fingerprint", { length: 50 }).notNull(),
  errorSignatures: text("error_signatures").array().default([]),
  cpuUsage: real("cpu_usage").default(0),
  similarityScore: real("similarity_score"),
  createdAt: timestamp("created_at").defaultNow(),
});

Aurora PostgreSQL's compatibility with PostgreSQL allowed us to efficiently query historical incident patterns and perform similarity matching that powers the Incident DNA Engine.

🚀 Future Roadmap

OpsMind AI is only getting started.

Upcoming features include:

Datadog integration
PagerDuty integration
Prometheus integration
Grafana integration
Loki log ingestion
Kubernetes event monitoring
Real-time webhook processing
Automated incident creation
AI-powered preventive recommendations
Multi-cloud support (AWS, Azure, GCP)

Our vision is to build an AI-first DevOps platform that dramatically reduces Mean Time To Resolution (MTTR) while preserving valuable operational knowledge for engineering teams.

🌐 Live Demo

https://opsmind-ai-rouge.vercel.app/

💻 GitHub Repository

https://github.com/Aryamnsls/opsmind-ai

Built with ❤️ to redefine DevOps operations in the AI era—transforming every incident into lasting organizational intelligence.

Neon OSS Starter Kit Challenge

Aryaman M Singha — Sun, 25 Aug 2024 14:45:42 +0000

This is a submission for the Neon Open Source Starter Kit Challenge : Ultimate Starter Kit

My Kit

Link to Kit

Your Journey

Chat Application Using Next.Js

Aryaman M Singha — Mon, 15 Apr 2024 05:03:03 +0000

_To create a chat application using Next.js, you'll need to combine Next.js with other technologies like web sockets for real-time communication. Here's a general outline of steps you can follow:

Step 1: Set up a Next.js Project

Install Next.js: Make sure you have Node.js installed. You can create a new Next.js project using npx create-next-app.

   npx create-next-app my-chat-app

Navigate into your project directory:

   cd my-chat-app

Step 2: Implement a Real-time Server

Choose a Real-time Server: You can use libraries like socket.io for real-time communication between clients and the server.

   npm install socket.io

Set up a WebSocket Server: Create a WebSocket server in a custom API route in your Next.js project.
- Create a new API route in pages/api directory, e.g., pages/api/chat.js.
- Set up your WebSocket server inside this API route.
- Use socket.io to handle WebSocket connections and events.

Step 3: Build Chat Components

Create Chat Components: Build components for displaying messages, input field for sending messages, and user interface.
- Use React components within your Next.js pages to render the chat interface.
- Manage state using React hooks or a state management library like Redux.

Step 4: Connect to the WebSocket Server

Connect from the Client: In your chat components, establish a connection to your WebSocket server.
- Use socket.io-client to connect from the client side.

   npm install socket.io-client

Handle Chat Events: Define event listeners for receiving and sending messages via WebSocket.
- When a user sends a message, emit an event to the server.
- Listen for incoming messages and update the chat interface accordingly.

Step 5: Testing and Deployment

Test Locally: Run your Next.js application locally to test the chat functionality.

   npm run dev

Deploy: Deploy your Next.js application to a hosting platform like Vercel or Netlify.
- Ensure your WebSocket server is deployed and accessible.
- Configure any necessary environment variables for deployment.

Example Implementation

Here's a simplified example of how you might implement a chat application using Next.js and socket.io:

// pages/api/chat.js

import { Server } from "socket.io";

export default async function handler(req, res) {
  if (!res.socket.server.io) {
    const io = new Server(res.socket.server);
    io.on("connection", (socket) => {
      console.log("Client connected");

      socket.on("message", (data) => {
        console.log("Message received:", data);
        io.emit("message", data); // Broadcast the message to all connected clients
      });

      socket.on("disconnect", () => {
        console.log("Client disconnected");
      });
    });
    res.socket.server.io = io;
  }

  res.end();
}

// pages/index.js (or any chat component)

import React, { useState, useEffect } from "react";
import io from "socket.io-client";

export default function Home() {
  const [messages, setMessages] = useState([]);
  const [inputMessage, setInputMessage] = useState("");
  const socket = io(); // Connect to the WebSocket server

  useEffect(() => {
    socket.on("message", (data) => {
      setMessages((prevMessages) => [...prevMessages, data]);
    });
    return () => {
      socket.disconnect();
    };
  }, [socket]);

  const sendMessage = () => {
    if (inputMessage.trim() !== "") {
      socket.emit("message", inputMessage);
      setInputMessage("");
    }
  };

  return (
    <div>
      <ul>
        {messages.map((message, index) => (
          <li key={index}>{message}</li>
        ))}
      </ul>
      <input
        type="text"
        value={inputMessage}
        onChange={(e) => setInputMessage(e.target.value)}
      />
      <button onClick={sendMessage}>Send</button>
    </div>
  );
}

This is a basic outline to get you started. Depending on your requirements, you might need to add more features like user authentication, message persistence, or error handling. Make sure to handle edge cases and optimize your application for performance.