DEV Community: Dnyaneshwar Vitthal Shekade

The Night I Stopped Applying to Jobs — And Built My Own Outreach Engine

Dnyaneshwar Vitthal Shekade — Tue, 03 Mar 2026 16:18:17 +0000

The Night I Stopped Applying to Jobs — And Built My Own Outreach Engine

The Night I Stopped Applying to Jobs

It was 1:47 AM.

I had 12 tabs open.

LinkedIn.

Naukri.

Company career pages.

My resume in one window.

A half-written email in another.

Copy.

Paste.

Edit.

Change company name.

Change role.

Rewrite first paragraph.

Attach resume.

Send.

Repeat.

By the time I finished the fifth application, I felt drained. Not because I didn’t want the job.

But because the process felt… inefficient. That night, I closed all the tabs and asked myself one question:

Why am I manually doing something that can be systemized?

The Real Frustration

Applying wasn’t hard.

But applying well — at scale — was.

If you personalize properly:

It takes 15–20 minutes per application.
Multiply that by 20 roles a week.
That’s hours of repetitive work.

And worst of all?

You don’t even know who opened your email.

You don’t track who you contacted.

You don’t optimize your message.

It’s chaos disguised as effort.

A Different Way of Thinking

Startups don’t grow by manually messaging one user at a time.

They build systems.They automate intelligently. They personalize at scale. They track outcomes.

So I thought:

What if I treated my job search like a feature of my portfolio?

What if instead of applying…

I built an Outreach Engine?

The First Version Was Ugly

I started simple.

Paste job description → generate email.

It worked.

Then I improved it. Extract responsibilities. Map them to my skills. Generate structured HTML.

Add proper signature. Allow editing.

Then came the real upgrade: Import 200 HR emails at once. Send individually. Track status per recipient.

That’s when it stopped being a script.

It became a system.

Introducing: Find a Job Studio

I integrated everything into my portfolio dashboard.

Now the flow looks like this:

Paste job description.
AI extracts key responsibilities.
Generate structured professional email.
Import HR contacts (CSV or manual).
Attach resume.
Send with delivery tracking.

Under the hood:

Dual AI providers (OpenRouter + Eden AI)
Multi mail providers (Resend, SMTP, PHP API)
Per-recipient status logging
Validation and rate limiting
HTML sanitization
Role-based access control

But from the outside?

It feels simple.

Something Unexpecteed happend

When I built this…

I realized it wasn’t just about job hunting.

It was about control.

Instead of feeling like I was chasing opportunities…

I felt like I was running campaigns.

Instead of reacting…

I was executing.

Instead of random effort…

I had structured momentum.

Why This Matters

The traditional job search model is passive.

Apply.

Wait.

Hope.

I wanted something proactive.

Strategic.

Repeatable.

So I engineered it.

What It Taught Me

This project taught me more than sending applications ever could:

AI orchestration
Secure backend design
Multi-provider failover
Email infrastructure
Input validation at scale
Workflow-driven UX

It turned a frustrating experience into a real-world product experiment.

The Bigger Lesson

Sometimes the solution to frustration isn’t working harder.

It’s building smarter.

That night at 1:47 AM…

I stopped applying to jobs.

And started building the system that would apply with me.

And honestly?

That changed everything.

Tech Stack

Find a Job Studio

│

├── Frontend Layer

│ ├── Next.js 14 (App Router)

│ ├── React

│ ├── TypeScript

│ ├── Tailwind CSS

│ └── Role-Based Dashboard Architecture

│

├── Workflow Engine

│ ├── Job Input Module

│ ├── Recipient Import Module

│ ├── AI Draft Generator

│ ├── Resume Attachment Handler

│ └── Dispatch Controller

│

├── AI Layer

│ ├── Provider Abstraction

│ │ ├── OpenRouter (Primary)

│ │ └── Eden AI (Fallback)

│ │

│ ├── Structured Output Enforcement

│ │ ├── subject

│ │ ├── html

│ │ └── extractedResponsibilities

│ │

│ ├── Job Description Parsing

│ ├── Responsibility Extraction

│ ├── Skills Mapping

│ ├── HTML Email Generation

│ └── Signature Injection

│

├── Email Delivery Layer

│ ├── Provider Abstraction

│ │ ├── Resend (API)

│ │ ├── SMTP (Nodemailer)

│ │ └── PHP Mail API

│ │

│ ├── Per-Recipient Dispatch

│ ├── Concurrency Control

│ └── Delivery Status Tracking

│

├── File & Data Handling

│ ├── CSV Parsing

│ │ ├── Client Validation

│ │ └── Server Validation

│ │

│ ├── Email Format Validation

│ ├── PDF Attachment Validation

│ │ ├── MIME Check

│ │ ├── File Signature Check

│ │ └── Size Limit

│ │

│ └── HTML Sanitization Pipeline

│

├── Persistence Layer

│ ├── Generated Templates Storage

│ ├── Extracted Responsibilities Storage

│ ├── HR Contact Memory

│ ├── Send Logs

│ │ ├── Provider Used

│ │ ├── Message ID

│ │ ├── Success / Failure

│ │ └── Error Logs

│ │

│ └── Status History

│

├── Security & Control

│ ├── Role-Based Access (RBAC)

│ ├── Rate Limiting (AI + Mail)

│ ├── Max Recipient Throttling

│ ├── Server-Side API Key Handling

│ └── Defensive Input Validation

│

└── Infrastructure Layer

├── Vercel Deployment

├── Environment-Driven Configuration

│ ├── AI Provider Config

│ ├── Mail Provider Config

│ └── Token Limits

│

├── Provider Fallback Logic

└── Scalable Serverless API Routes

How I Built a Centralized Monitoring System for 600–700 Servers — Using Open Source Tools

Dnyaneshwar Vitthal Shekade — Sun, 01 Mar 2026 05:58:34 +0000

What happens when you're responsible for hundreds of client servers — and have no single pane of glass to see them all? You build one.

Managing infrastructure at scale is one of those challenges that seems simple until you're actually doing it. Scattered dashboards, siloed logs, missed alerts — the classic observability nightmare. Here's how I designed and deployed a centralized monitoring and logging stack that now covers over 600 client servers, all from a single control plane on AWS.

The Problem

Our team manages infrastructure for multiple clients — each with their own servers, environments, and applications. Before this implementation, monitoring was fragmented: each client had different tooling, visibility was reactive, and log investigation meant SSHing into individual machines. We needed something better.

The goal: one place to see everything — metrics, logs, alerts — with zero vendor lock-in and minimal cost.

The Stack I Chose

All open source. All containerized. Deployed on a single Amazon EC2 instance using Docker Compose.

Why this over the ELK stack? Loki is index-free by design —

it stores logs as compressed chunks and queries them using labels. For our scale,

this means dramatically lower storage and compute costs without sacrificing searchability.

The Architecture

Each client server runs three lightweight agents.

They ship everything to a central EC2 instance where Prometheus, Loki, and Grafana live inside Docker containers.

// System Architecture Flow

Client Servers (600–700)
  │
  ├── Node Exporter     → CPU, RAM, Disk, Network metrics
  ├── cAdvisor          → Container resource metrics
  └── Promtail          → App, Nginx, System & Docker logs
  │
  ▼
Central EC2 — Dockerized Stack
  │
  ├── Prometheus        → Scrapes & stores time-series metrics
  ├── Loki              → Aggregates & indexes logs by labels
  └── Grafana           → Unified dashboards + alerting
  │
  ▼
Alerts → Email / Slack / Webhook

The Multi-Client Challenge

600+ servers means many different clients. The key design decision was label-based isolation. Every Promtail agent tags its logs with:

In Grafana, dashboard variables are templated —

so an operator can switch between client views instantly,

or drill down to a specific server, environment,

or log level with a single dropdown.

No extra dashboards to maintain.

Alerting That Actually Works

Alerts are configured directly in Prometheus with rules that fire when thresholds are crossed.

Every alert routes to the right channel — email, Slack, or

webhook — depending on severity and client.

Key Results & Learnings

Reduced incident detection time from hours to minutes with real-time alerting
Centralized log search eliminated the need to SSH into individual servers for debugging
Loki proved significantly cheaper than ELK at this scale — no full-text indexing overhead
Dashboard templating was the game-changer for multi-client visibility
Docker Compose made the entire stack reproducible and easy to upgrade
Label discipline from day one made filtering and querying effortless at 600+ servers

06What's Next?

The stack is already handling 600–700 servers comfortably, but there's always room to grow.

Next on the roadmap: scaling Prometheus with federation or Thanos for long-term metric storage,

and exploring Grafana Alloy to replace Promtail for a unified telemetry agent.

If you're dealing with fragmented infrastructure visibility, I genuinely believe this open-source stack is one of the best investments you can make.

Zero license cost. Massive flexibility. Battle-tested at scale.

Building something similar or facing observability challenges at scale?

Drop a comment or connect — happy to share configs, lessons learned, or go deeper on any part of this architecture. Let's talk observability.

Cyber Security: A Complete 360 Protection Framework for Modern Organizations

Dnyaneshwar Vitthal Shekade — Mon, 23 Feb 2026 07:01:43 +0000

In today’s digital world, cyber security is not just an IT responsibility — it’s a business survival strategy. From startups to enterprises, every organization must protect its systems, users, and data from constantly evolving threats.

The image above represents a 360° Cyber Security Framework — covering 12 critical pillars that together build a strong defense system.

Let’s break it down in a structured, practical, and professional way.

1️⃣ Authentication – Verifying Identity

Purpose: Ensure only legitimate users can access systems.

Scenarios to Protect:

User logins
Employee access to internal systems

Best Practices:

Strong password policies
Multi-Factor Authentication (MFA)
Biometric authentication
Passwordless login strategies

🔎 Without strong authentication, your system is already compromised.

2️⃣ Authorization – Controlling Access

Purpose: Define what authenticated users can do.

Scenarios to Protect:

Data access
User role management

Best Practices:

Role-Based Access Control (RBAC)
Least Privilege Principle
Periodic access reviews

💡 Authentication confirms who you are. Authorization defines what you can do.

3️⃣ Encryption – Protecting Sensitive Data

Purpose: Protect data at rest and in transit.

Scenarios to Protect:

Sensitive customer data
Secure communications

Best Practices:

TLS/SSL for data transmission
AES encryption for storage
Strong key management policies

🔐 If attackers intercept encrypted data, it remains unreadable.

4️⃣ Vulnerability Management – Proactive Defense

Purpose: Identify and fix weaknesses before attackers exploit them.

Scenarios to Protect:

Outdated software
System misconfigurations

Best Practices:

Regular security scans
Automated patch management
Continuous monitoring

⚠️ Most breaches occur due to unpatched vulnerabilities.

5️⃣ Audit & Compliance – Meeting Legal Standards

Purpose: Ensure adherence to regulatory frameworks.

Scenarios to Protect:

Financial systems
Healthcare records
Customer data

Standards:

GDPR
HIPAA
ISO 27001

📊 Compliance builds trust and avoids heavy penalties.

6️⃣ Network Security – Protecting Infrastructure

Purpose: Secure internal and external network traffic.

Best Practices:

Firewalls
Network segmentation
Intrusion Detection Systems (IDS)
Secure DNS configuration

🌐 Your network is your digital perimeter.

7️⃣ Terminal Security – Securing Endpoints

Purpose: Protect employee devices and POS systems.

Best Practices:

Antivirus/EDR solutions
Device management
Disk encryption

💻 A single compromised laptop can infect the entire organization.

8️⃣ Emergency Response – Handling Incidents

Purpose: Minimize damage during attacks.

Best Practices:

Incident response plan
Security Operations Center (SOC)
Regular security drills

🚨 Prevention is ideal — but preparation is essential.

9️⃣ Container Security – Protecting Microservices

Purpose: Secure cloud-native deployments.

Best Practices:

Trusted base images
Container scanning
Runtime security monitoring

☁️ Cloud environments require specialized security strategies.

🔟 API Security – Securing Integrations

Purpose: Protect APIs from misuse and attacks.

Best Practices:

OAuth 2.0
API key management
Rate limiting
Input validation

🔗 APIs are the backbone of modern applications — and prime attack targets.

1️⃣1️⃣ Third-Party Management – Vendor Risk Control

Purpose: Reduce risks from partners and vendors.

Best Practices:

Vendor risk assessments
Secure integration practices
Access monitoring

📦 Your security is only as strong as your weakest vendor.

1️⃣2️⃣ Disaster Recovery – Business Continuity

Purpose: Ensure business continuity during crises.

Best Practices:

Disaster Recovery (DR) plan
Regular backups
Redundant systems

🔥 Cyber attacks, natural disasters, and outages — recovery planning is non-negotiable.

🚀 Final Thoughts: Security is a Continuous Process

Cyber security is not a one-time implementation — it’s an ongoing lifecycle:

Prevent
Detect
Respond
Recover
Improve

Organizations that adopt this 12-layer security model create a resilient ecosystem that protects:

Customer trust
Financial assets
Brand reputation
Operational continuity

How Redis Cut My Database Reads from ~26K to Almost Zero

Dnyaneshwar Vitthal Shekade — Sun, 08 Feb 2026 17:09:58 +0000

I used to hit Supabase on every single page load—blogs, individual posts, experiences, toolboxes, services, connections, profile info, role visibility, skills… basically my entire personal dashboard depended on direct database queries.

The result?

~26,000 database reads per day.

Slow responses.

Unnecessary load.

And occasional connection warnings.

So I introduced Redis as a read-through cache with a small prewarm script—and everything changed.

What I Cached

I focused on the hottest read-heavy data:

Blogs → published list, per-post data, combined blog payload
Experiences → active + full history
Toolboxes → all, software, hardware
Services → active + all
Connections → full list
Profile info → singleton record
Role visibility → sidebar & quick actions
Skills → full list + category variants

These were perfect cache candidates because they:

Change infrequently
Are read constantly
Don’t require real-time consistency

How the Caching Works

1) Read-Through Cache Pattern

Each GET endpoint wraps a helper:

getCached(key, fetcher, ttl = 300)

Flow:

Request → Check Redis
        → Cache hit → return instantly
        → Cache miss → fetch from Supabase → store in Redis → return

This ensures:

Only the first request touches the DB
All other requests are served from Redis in milliseconds

2) Smart Invalidation on Writes

Whenever data changes via:

POST
PUT
DELETE

I call:

invalidateKeys([...])

This clears only the affected cache prefixes, keeping everything:

Fresh
Consistent
Fast

3) Prewarming the Cache

To avoid cold-start latency after deploys, I built:

scripts/prewarm-redis.mjs

It simply calls the public API endpoints—no DB credentials needed.

Run it like:

BASE_URL=http://localhost:3000 node scripts/prewarm-redis.mjs

Now Redis is fully populated before real users arrive.

4) Visibility & Health Monitoring

I added a Data tab UI showing:

Redis health status
Total cached items
Cached datasets overview

So if Redis goes down…

I know immediately.

The Results 📉

Before Redis

~26K DB reads/day
Higher latency
Risk of max connection limits

After Redis

Cold start: one DB hit per dataset
Warm traffic: almost zero DB reads
Latency: single-digit milliseconds
Stability: no connection pressure

In short:

Database became a backup. Redis became the primary read layer.

What Made the Biggest Difference

1) Cache the hottest reads

Lists and singleton data deliver massive ROI when cached.

2) Keep TTL modest

I used 5 minutes to balance:

Freshness
Performance

3) Always prewarm on deploy

Prewarming removes the cold-start penalty completely.

4) Monitor cache health

Visibility prevents silent performance regressions.

How You Can Try This

1) Configure Redis

REDIS_URL=...
(or host/port/user/password)

2) Start your app

3) Run the prewarm script

4) Open dashboard/blog pages

Then watch:

DB metrics drop
Redis hit rate rise
Latency shrink

Final Thoughts

Adding Redis wasn’t just a performance optimization—

it fundamentally changed how my app handles reads at scale.

From:

“Query the database every time”

to:

“Serve instantly from memory, and only hit DB when necessary.”

That single shift reduced 26K daily reads to nearly zero.

And the best part?

It took less than a day to implement.

Mastering Cursor AI: The Ultimate Guide for Developers in 2025

Dnyaneshwar Vitthal Shekade — Sun, 25 Jan 2026 07:18:11 +0000

Mastering Cursor AI: The Ultimate Guide for Developers in 2025

Reading Time: 12 minutes | Published: January 25, 2025 | Tags: #CursorAI #AITools #Productivity #WebDev

Introduction

If you're still writing code line-by-line without AI assistance in 2025, you're working 10x harder than you need to. Enter Cursor AI — the AI-powered code editor that's revolutionizing how developers build applications.

In this comprehensive guide, I'll show you exactly how to leverage Cursor AI to transform your development workflow, from simple autocomplete to building entire features with natural language prompts.

What is Cursor AI?

Cursor is a fork of Visual Studio Code that integrates cutting-edge AI models (Claude Sonnet, GPT-4, and more) directly into your coding environment. Think of it as VS Code on steroids — with an AI pair programmer that actually understands your codebase.

Why Cursor Over Regular VS Code?

Context-Aware AI: Cursor understands your entire codebase, not just the current file
Natural Language Editing: Describe what you want, and Cursor writes the code
Smart Autocomplete: Tab to accept suggestions that actually make sense
Multi-File Editing: Make changes across multiple files simultaneously
Built-in Chat: Ask questions about your code without leaving the editor

Getting Started with Cursor

Installation

Visit cursor.com and download for your OS
Import your VS Code settings (Cursor does this automatically)
Sign up for a Cursor account (free tier available)
Choose your AI model (I recommend Claude Sonnet for most tasks)

First-Time Setup

# Cursor will ask to import your VS Code extensions
# It also syncs your keyboard shortcuts and themes

# Pro tip: Enable these settings in Cursor Settings
{
  "cursor.aiEnabled": true,
  "cursor.chat.includeCodebase": true,
  "cursor.autocomplete.enabled": true
}

Core Features: How to Use Cursor Like a Pro

1. Tab Autocomplete — Your AI Co-Pilot

The most basic (yet powerful) feature. As you type, Cursor predicts what you're about to write.

Example:

// You type: "const fetchUser"
// Cursor suggests:
const fetchUserData = async (userId) => {
  try {
    const response = await fetch(`/api/users/${userId}`);
    const data = await response.json();
    return data;
  } catch (error) {
    console.error('Error fetching user:', error);
    throw error;
  }
};

Pro Tip: Press Tab to accept, Cmd/Ctrl + → to accept word-by-word.

2. Cmd+K — Inline Code Generation

This is where Cursor gets magical. Select code or place your cursor anywhere, press Cmd+K (or Ctrl+K on Windows), and describe what you want.

Real Example:

Task: Add error handling to this API call

// Before (select this code and Cmd+K)
const data = await fetch('/api/posts').then(res => res.json());

// Type in Cmd+K prompt: "add proper error handling with try-catch"

// After (Cursor generates this)
const data = await (async () => {
  try {
    const response = await fetch('/api/posts');
    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }
    return await response.json();
  } catch (error) {
    console.error('Failed to fetch posts:', error);
    return [];
  }
})();

3. Cmd+L — AI Chat (The Game Changer)

Open the chat panel and have a conversation with your AI about your code. Cursor has full context of your project.

Example Prompts:

"Create a reusable Button component with TypeScript and Tailwind"
"Refactor this function to use async/await instead of promises"
"Find all the places where I'm making API calls and add loading states"
"Write unit tests for the UserProfile component"

My Workflow:

Press Cmd+L to open chat
Type: "Create a login form with email validation, password strength checker, and Supabase authentication"
Cursor generates the entire component
Review and accept with Cmd+Shift+Enter

4. Composer — Multi-File AI Edits

This is Cursor's secret weapon. The Composer lets you make coordinated changes across multiple files.

Use Cases:

"Add dark mode support to the entire app"
"Create a new feature: user profile with avatar upload"
"Refactor all API calls to use React Query"

How to Use:

1. Open Composer: Cmd+I (or Ctrl+I)
2. Describe your task in detail
3. Cursor analyzes your codebase
4. It creates a plan and shows you what files it will modify
5. Review the plan and accept/reject changes

Example Session:

👤 You: "Add a blog commenting system with Supabase. 
        Include a comments table, real-time updates, 
        and a Comment component"

🤖 Cursor: "I'll create:
           1. Supabase migration for comments table
           2. Comment.tsx component
           3. useComments hook for real-time data
           4. Add to BlogPost.tsx

           Proceed? [Yes] [Edit Plan] [Cancel]"

Advanced Tips & Tricks

1. Context Management

Cursor can't read your entire codebase every time (token limits). Tell it what to focus on:

// Add this to your prompt
"@filename.tsx focus on this file"
"Only look at the /components directory"

2. Codebase Indexing

Enable codebase indexing for better AI understanding:

Go to Settings → Cursor → Enable Codebase Indexing
This lets Cursor search and understand your full project

3. Custom Instructions

Create a .cursorrules file in your project root:

// .cursorrules
- Always use TypeScript with strict mode
- Follow Airbnb style guide
- Use functional components with hooks
- Prefer Tailwind CSS for styling
- Add JSDoc comments for all functions
- Use async/await over .then()

4. Keyboard Shortcuts Cheat Sheet

Shortcut Action Tab Accept autocomplete suggestion Cmd+K Inline AI edit Cmd+L Open AI chat Cmd+I Open Composer (multi-file edits) Cmd+Shift+L Add selection to chat context Esc Reject AI suggestion

Real-World Example: Building a Feature with Cursor

Let's build a complete "Blog Comment System" using only Cursor AI prompts.

Step 1: Open Composer (`Cmd+I`)

Prompt: "Create a commenting system for my blog with the following:
- Supabase database table for comments
- Real-time comment updates
- Comment component with author, timestamp, and reply functionality
- Nested replies support
- Optimistic UI updates
- Use TypeScript and Tailwind CSS"

Step 2: Review Cursor's Plan

Cursor will create:
✓ database/migrations/create_comments_table.sql
✓ hooks/useComments.ts
✓ components/Comment.tsx
✓ components/CommentForm.tsx
✓ types/comment.ts
✓ Update: pages/blog/[slug].tsx

Step 3: Accept and Iterate

After Cursor generates the code:

Test the feature
If you want changes: Cmd+K on specific parts
Example: Select Comment.tsx → Cmd+K → "Add like/dislike buttons"

Result: A fully functional commenting system in under 5 minutes.

Common Mistakes to Avoid

❌ Don't Do This:

Vague prompts → "make it better"
No context → Not telling Cursor about your tech stack
Accepting blindly → Always review AI-generated code
Over-reliance → Still understand what the code does

✅ Do This Instead:

Specific prompts → "Refactor this to reduce cyclomatic complexity to under 10"
Provide context → "I'm using Next.js 14 with App Router and Supabase"
Review & test → Check for edge cases and bugs
Learn from AI → Understand the patterns Cursor uses

Cursor vs. GitHub Copilot: Which is Better?

Feature Cursor GitHub Copilot Codebase awareness ✅ Full project context ❌ Single file only Chat interface ✅ Built-in ⚠️ Separate extension Multi-file edits ✅ Composer mode ❌ Not available Model choice ✅ Claude, GPT-4, etc. ❌ GPT only Price $20/month $10/month

My take: If you're building serious applications, Cursor's codebase awareness and multi-file editing justify the extra $10/month.

Pricing & Plans

Free Tier: 2000 completions/month, limited chat
Pro ($20/month): Unlimited completions, unlimited chat, priority models
Business ($40/user/month): Team features, admin controls, SOC 2 compliance

My recommendation: Try the free tier for a week. If you're shipping code daily, Pro pays for itself in saved time.

Conclusion: Is Cursor Worth It?

After 6 months of daily use, here's my verdict:

Cursor saves me ~15 hours per week on:

Writing boilerplate code
Debugging and refactoring
Learning new libraries
Documentation lookups

It's NOT a replacement for:

System design thinking
Understanding your codebase
Code review and testing
Critical thinking

Think of Cursor as a force multiplier — it makes you faster at executing ideas, but you still need the ideas.

Next Steps

Download Cursor → cursor.com
Try the 14-day Pro trial → Test all features
Start small → Use Tab autocomplete first, then gradually explore Cmd+K and Composer
Join the community → Cursor Forum for tips and tricks

Bonus: My Favorite Cursor Prompts

1. "Explain this code to me like I'm a junior developer"
2. "Find performance bottlenecks in this component"
3. "Convert this class component to a functional component with hooks"
4. "Add comprehensive error handling"
5. "Generate TypeScript types for this API response"
6. "Write integration tests using React Testing Library"
7. "Optimize this for mobile responsiveness"
8. "Add accessibility attributes (ARIA labels)"

Final Thoughts

We're living in the future of software development. Tools like Cursor AI aren't replacing developers — they're amplifying what we can build.

The developers who thrive in 2025 won't be those who resist AI tools, but those who learn to wield them effectively.

Start small. Experiment. Build faster. Ship better code.

Happy coding! 🚀

Have questions about Cursor? Drop a comment below or reach out on Twitter/X. I read and respond to every message!

"Building Full-Stack Apps with Cursor AI and Supabase"
"10 Cursor Prompts That Will 10x Your Productivity"
"The Future of AI-Assisted Development"

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supabase checkange

Dnyaneshwar Vitthal Shekade — Sat, 24 Jan 2026 08:14:36 +0000

Supabase OAuth Setup Guide

Error: "Unsupported provider: provider is not enabled"

This error means the OAuth provider (GitHub or Google) needs to be enabled in your Supabase project.

Steps to Enable OAuth Providers

1. Go to Supabase Dashboard

Visit: https://supabase.com/dashboard/project/kvxasjbaxepcafekuhoo
Navigate to Authentication → Providers

2. Enable GitHub OAuth

Find GitHub in the providers list
Toggle it ON
You'll need to create a GitHub OAuth App:

-   Go to: [https://github.com/settings/developers](https://github.com/settings/developers)

-   Click **New OAuth App**

-   Fill in:

    -   **Application name**: `dnyan.cloud` (or your preferred name)

    -   **Homepage URL**: `https://dnyan.vercel.app`

    -   **Authorization callback URL**: `https://kvxasjbaxepcafekuhoo.supabase.co/auth/v1/callback`

-   Click **Register application**

-   Copy the **Client ID** and **Client Secret**

Paste the Client ID and Client Secret into Supabase GitHub provider settings
Click Save

3. Enable Google OAuth

Find Google in the providers list
Toggle it ON
You'll need to create a Google OAuth App:

-   Go to: [https://console.cloud.google.com/apis/credentials](https://console.cloud.google.com/apis/credentials)

-   Click **Create Credentials** → **OAuth client ID**

-   Select **Web application**

-   Fill in:

    -   **Name**: `dnyan.cloud` (or your preferred name)

    -   **Authorized redirect URIs**:

        -   `https://kvxasjbaxepcafekuhoo.supabase.co/auth/v1/callback`

-   Click **Create**

-   Copy the **Client ID** and **Client Secret**

Paste the Client ID and Client Secret into Supabase Google provider settings
Click Save

4. Verify Redirect URLs

Make sure these URLs are in your Supabase Authentication → URL Configuration:

Site URL: https://dnyan.vercel.app
Redirect URLs:
- https://dnyan.vercel.app/auth/callback
- https://dnyan.vercel.app/*
- http://localhost:3000/auth/callback (for local development)

5. Test the Login

After enabling the providers:

Visit /personal on your site
Click "Sign in with GitHub" or "Sign in with Google"
You should be redirected to the OAuth provider for authentication
After authorizing, you'll be redirected back to /personal

Troubleshooting

"Invalid redirect URI": Make sure the redirect URI in your OAuth app matches exactly: https://kvxasjbaxepcafekuhoo.supabase.co/auth/v1/callback
"Provider not enabled": Double-check that the provider is toggled ON in Supabase Dashboard
Local development: Make sure http://localhost:3000/auth/callback is in your Supabase redirect URLs

Demystifying Shell Commands with ExplainShell.com

Dnyaneshwar Vitthal Shekade — Fri, 30 May 2025 08:05:17 +0000

Navigating the command line can be daunting, especially when faced with complex shell commands filled with unfamiliar flags and syntax. Enter ExplainShell.com, a tool designed to break down and elucidate shell commands, making them more approachable for users of all levels

What is ExplainShell?
ExplainShell is a web-based utility that parses shell commands and provides detailed explanations for each component. By referencing man pages, it matches command-line arguments to their corresponding help texts, offering users a clearer understanding of what each part of a command does. This is particularly beneficial for those who want to grasp the intricacies of shell commands without sifting through extensive documentation.

How Does It Work?
When a user inputs a shell command into ExplainShell, the tool performs the following steps:

Parsing the Command: The command is parsed into an Abstract Syntax Tree (AST) using bashlex, identifying the structure and components of the command.

Matching Components: Each element of the command is matched to relevant sections in the man pages, extracting descriptions and usage information.

Displaying Explanations: The tool presents a hierarchical breakdown of the command, explaining each part in context, which helps users understand the function and purpose of each component.

This process is facilitated by a combination of Python scripts and a MongoDB backend that stores processed man pages. The system includes components like a man page reader, a classifier to identify option-containing paragraphs, and an options extractor to pinpoint specific flags and arguments.

Practical Applications
ExplainShell is invaluable for various use cases:

Educational Tool: Instructors can use it to teach students the structure and function of shell commands.

Self-Learning: Individuals learning shell scripting can input unfamiliar commands to gain insights into their workings.

Debugging Aid: When troubleshooting scripts, users can dissect complex commands to identify potential issues.

For instance, a Reddit user shared their experience:

"Recently was teaching my students how to get a raspberry pi up and running and they had questions about the console commands we were using. I was doing some googling and found ExplainShell.com which is an amazing tool to view the meaning and inner workings of shell commands."
Reddit

Limitations to Consider
While ExplainShell is a powerful tool, it's important to be aware of its limitations:

Outdated Man Pages: The tool relies on a static archive of Ubuntu man pages, which may not reflect the most recent updates or distributions.

Complex Syntax Handling: Certain advanced shell features, like parameter expansions or nested commands, might not be fully explained.

No Real-Time Updates: Users cannot add or update man pages directly on the live site.
GitHub
Hacker News

Alternatives and Complementary Tools
For those seeking additional resources or alternatives:

Explain.fish: A similar tool tailored for the Fish shell, offering explanations for commands and their options.

LLM-Powered Explainers: Tools like RACONTEUR leverage large language models to provide comprehensive explanations, including the intent behind commands, which can be particularly useful in cybersecurity contexts.

Conclusion
ExplainShell.com stands out as a user-friendly platform for unraveling the complexities of shell commands. By providing contextual explanations drawn from man pages, it serves as an excellent educational and troubleshooting resource. While it has its limitations, especially concerning outdated documentation, its ease of use and clarity make it a valuable tool for anyone looking to deepen their understanding of the command line

Comprehensive SQL Guide for Freshers and Intermediate Learners

Dnyaneshwar Vitthal Shekade — Fri, 28 Mar 2025 03:45:23 +0000

Welcome to the most comprehensive SQL guide ever crafted for freshers and intermediate learners! This isn’t just a guide—it’s your one-stop solution to mastering SQL for interviews, real-world projects, and beyond. Whether you're aspiring to become a Data Engineer, Analyst, or Software Developer, this document will make you SQL-ready like never before.

✅ 1. Introduction to SQL

🔍 Explanation:

SQL (Structured Query Language) is used to manage and manipulate relational databases. It helps perform operations like data insertion, retrieval, modification, and deletion. It's also used to define schema, manage access, and control transactions.

🔠 Syntax:

sql
CopyEdit
SELECT column1, column2 FROM table_name WHERE condition;

📌 Use Case:

sql
CopyEdit
SELECT Name, Age FROM Employees WHERE Age > 30;

🔥 Interview Questions & Answers:

Q1. Your login system is vulnerable to SQL injection. How would you detect and fix it?

Answer: You can detect injection risks by testing inputs like ' OR 1=1--. Prevent it by using prepared statements (parameterized queries) and ORM frameworks that avoid string concatenation in SQL.

Q2. Without using TOP, LIMIT, or window functions, how would you fetch the second highest salary?

sql
CopyEdit
SELECT MAX(Salary) FROM Employees
WHERE Salary < (SELECT MAX(Salary) FROM Employees);

Explanation: Filters out the highest and returns the next one.

Q3. What happens if you run a SELECT * on a table with a BLOB column?

Answer: It may return unreadable binary data or crash the client. Always specify column names and avoid fetching heavy data types like BLOB unless needed.

✅ 2. SQL Data Types

🔍 Explanation:

Each column in SQL must have a defined data type, such as INTEGER, VARCHAR, DATE, BOOLEAN, etc. Data types ensure that only valid values are stored.

🔠 Syntax:

sql
CopyEdit
CREATE TABLE Employees (
    ID INT,
    Name VARCHAR(100),
    Salary DECIMAL(10,2),
    JoinDate DATE
);

📌 Use Case:

sql
CopyEdit
INSERT INTO Employees (ID, Name, Salary, JoinDate) VALUES (1, 'Anu', 50000.00, '2023-01-01');

🔥 Interview Questions & Answers:

Q1. Can you store numbers with high precision like ₹10 crore up to 4 decimal places?

Answer: Yes, use DECIMAL(14,4) — 14 total digits with 4 after the decimal. For money, avoid FLOAT due to rounding errors.

Q2. What happens if you try to insert a string into an INT column?

Answer: It either throws an error or silently converts based on DBMS. In MySQL, '123abc' becomes 123.

Q3. When would you prefer CHAR over VARCHAR?

Answer: Use CHAR for fixed-length fields like gender codes or country codes ('IN', 'US'), as it improves lookup speed.

✅ 3. DDL – Data Definition Language

🔍 Explanation:

DDL defines and modifies the structure of database objects like tables, schemas, and constraints.

🔠 Syntax:

sql
CopyEdit
CREATE TABLE Departments (ID INT PRIMARY KEY, Name VARCHAR(50));
ALTER TABLE Departments ADD Location VARCHAR(100);
DROP TABLE Departments;

📌 Use Case:

sql
CopyEdit
ALTER TABLE Employees ADD CONSTRAINT chk_salary CHECK (Salary > 0);

🔥 Interview Questions & Answers:

Q1. Difference between DROP, TRUNCATE, and DELETE?

Answer:

DROP: Deletes table + data + structure. Irreversible.
TRUNCATE: Deletes all rows, resets identity but keeps structure.
DELETE: Deletes selected rows using WHERE. Transactional.

Q2. How do you modify a column’s data type without losing data?

Answer:

Use ALTER TABLE MODIFY COLUMN, but first ensure all existing data is compatible with the new type.

Q3. Can you rename a table in SQL without losing data?

Answer:

Yes:

sql
CopyEdit
ALTER TABLE old_name RENAME TO new_name;

✅ 4. DML – Data Manipulation Language

🔍 Explanation:

DML is used to manipulate data in existing tables: insert, update, and delete.

🔠 Syntax:

sql
CopyEdit
INSERT INTO Employees VALUES (...);
UPDATE Employees SET Salary = 60000 WHERE ID = 2;
DELETE FROM Employees WHERE ID = 3;

📌 Use Case:

sql
CopyEdit
INSERT INTO Employees (ID, Name, Salary) VALUES (5, 'Amit', 70000);

🔥 Interview Questions & Answers:

Q1. How would you insert data into one table from another table with a condition?

sql
CopyEdit
INSERT INTO HighEarners (ID, Name, Salary)
SELECT ID, Name, Salary FROM Employees WHERE Salary > 100000;

Q2. Can you rollback a DELETE operation? When and how?

Answer: Yes, if it's inside a transaction and hasn’t been committed. Use ROLLBACK.

Q3. What is MERGE in SQL and when is it used?

Answer:

MERGE combines INSERT, UPDATE, DELETE into one. Useful for upserts.

✅ 5. DQL – Data Query Language

🔍 Explanation:

DQL is primarily for reading data using SELECT.

🔠 Syntax:

sql
CopyEdit
SELECT Name FROM Employees WHERE Department = 'Finance';

📌 Use Case:

sql
CopyEdit
SELECT Department, COUNT(*) FROM Employees GROUP BY Department HAVING COUNT(*) > 5;

🔥 Interview Questions & Answers:

Q1. How does SQL process this query?

sql
CopyEdit
SELECT ... FROM ... WHERE ... GROUP BY ... HAVING ... ORDER BY;

Answer: The logical order is: FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY.

Q2. Can you use GROUP BY without aggregates?

Answer: Technically yes, but it's useful only when applying aggregates.

Q3. Difference between DISTINCT and GROUP BY?

Answer: DISTINCT removes duplicates. GROUP BY groups data and allows aggregation.

✅ 6. Aggregate Functions

🔍 Explanation:

These return a single result for a group of rows. Examples: SUM, AVG, MIN, MAX, COUNT.

🔠 Syntax:

sql
CopyEdit
SELECT AVG(Salary) FROM Employees;

📌 Use Case:

sql
CopyEdit
SELECT Department, SUM(Salary) FROM Employees GROUP BY Department;

🔥 Interview Questions & Answers:

Q1. Why does AVG(Salary) sometimes return null?

Answer: If all values in the column are NULL.

Q2. Difference between COUNT(*) and COUNT(column)?

Answer: COUNT(*) counts all rows; COUNT(column) ignores NULLs.

Q3. Can you use aggregate functions in WHERE clause?

Answer: No. Use HAVING for aggregates.

✅ 7. Joins

🔍 Explanation:

Joins combine rows from multiple tables based on related columns.

🔠 Syntax:

sql
CopyEdit
SELECT e.Name, d.DeptName
FROM Employees e
INNER JOIN Departments d ON e.DeptID = d.ID;

📌 Use Case:

sql
CopyEdit
SELECT e.Name, d.Name FROM Employees e LEFT JOIN Departments d ON e.DeptID = d.ID;

🔥 Interview Questions & Answers:

Q1. Explain SELF JOIN with example.

Answer:

sql
CopyEdit
SELECT A.Name AS Manager, B.Name AS Employee
FROM Employees A JOIN Employees B ON A.ID = B.ManagerID;

Q2. What is the result of a LEFT JOIN if no match is found?

Answer: NULLs will be returned for the columns of the right table.

Q3. Can a JOIN cause a Cartesian product?

Answer: Yes, if there's no valid ON condition or if CROSS JOIN is used.

✅ 8. Subqueries

🔍 Explanation:

A subquery is a query inside another query. Can be used in SELECT, FROM, or WHERE clauses.

🔠 Syntax:

sql
CopyEdit
SELECT Name FROM Employees WHERE Salary > (SELECT AVG(Salary) FROM Employees);

🔥 Interview Questions & Answers:

Q1. Difference between correlated and non-correlated subquery?

Answer: A correlated subquery depends on each row of the outer query. A non-correlated one executes independently.

Q2. Can subqueries return multiple values?

Answer: Yes, with operators like IN, ANY, ALL.

✅ 9. Window Functions

🔍 Explanation:

They perform calculations across a sliding window of rows.

🔠 Syntax:

sql
CopyEdit
SELECT Name, Salary,
RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS Rank
FROM Employees;

🔥 Interview Questions & Answers:

Q1. Difference between RANK, DENSE_RANK, and ROW_NUMBER?

Answer:

RANK: Skips numbers on tie
DENSE_RANK: Doesn’t skip
ROW_NUMBER: Always unique

Q2. Use of LAG and LEAD?

Answer: They return the previous and next row values within the partition.

✅ 10. CTEs (Common Table Expressions)

🔍 Explanation:

Temporary named result set used to simplify complex queries.

🔠 Syntax:

sql
CopyEdit
WITH HighSalary AS (
  SELECT * FROM Employees WHERE Salary > 100000
)
SELECT * FROM HighSalary;

🔥 Interview Questions & Answers:

Q1. What is a recursive CTE and where is it used?

Answer: Used for hierarchical data like org charts. Includes base case + recursive part.

Q2. Are CTEs better than subqueries?

Answer: Yes, for readability and reuse, especially in complex nested queries.

✅ The rest 10 concepts (Transactions → JSON) with similar tricky Q&A are continued in the next reply... 👇

Continuing the complete SQL guide with tricky interview-level Q&A — here are the remaining concepts:

✅ 11. Transactions

🔍 Explanation:

A transaction is a sequence of SQL statements executed as a single unit. It should follow ACID properties: Atomicity, Consistency, Isolation, Durability.

🔠 Syntax:

sql
CopyEdit
BEGIN TRANSACTION;
UPDATE Employees SET Salary = 80000 WHERE ID = 1;
COMMIT;

📌 Use Case:

Rollback changes on failure:

sql
CopyEdit
BEGIN;
UPDATE Accounts SET Balance = Balance - 1000 WHERE ID = 1;
-- Error occurs
ROLLBACK;

🔥 Interview Questions & Answers:

Q1. How would you ensure a money transfer between accounts doesn’t result in partial updates?

Answer: Wrap both UPDATEs in a transaction and COMMIT only if both succeed. Use ROLLBACK on failure.

Q2. Explain isolation levels and their impact on performance.

Answer:

READ UNCOMMITTED → Allows dirty reads
READ COMMITTED → No dirty reads
REPEATABLE READ → No dirty or non-repeatable reads
SERIALIZABLE → Highest isolation; prevents phantom reads but slower

Use based on consistency vs performance trade-off.

Q3. When would you use SAVEPOINT?

Answer: When you want partial rollbacks within a large transaction.

✅ 12. Indexing

🔍 Explanation:

Indexes improve read performance by reducing data scanned, especially in WHERE and JOIN operations.

🔠 Syntax:

sql
CopyEdit
CREATE INDEX idx_emp_name ON Employees(Name);

📌 Use Case:

sql
CopyEdit
CREATE INDEX idx_multi ON Employees(DeptID, Salary);

🔥 Interview Questions & Answers:

Q1. Can indexes hurt performance? When?

Answer: Yes. They slow down INSERT, UPDATE, DELETE because the index also needs updating. Use only on read-heavy columns.

Q2. What is a covering index?

Answer: An index that includes all the columns needed by a query — no need to access the table.

Q3. Clustered vs Non-clustered Index?

Answer:

Clustered: Alters row order physically (only one per table).
Non-clustered: Logical order with pointers.

✅ 13. Views & Materialized Views

🔍 Explanation:

View = Virtual table based on a query.
Materialized View = Query result physically stored and periodically refreshed.

🔠 Syntax:

sql
CopyEdit
CREATE VIEW ActiveEmployees AS SELECT * FROM Employees WHERE IsActive = 1;

🔥 Interview Questions & Answers:

Q1. Can a view be updated? Under what conditions?

Answer: Yes, only if:

Based on one table
No aggregate or GROUP BY
All columns in SELECT are updatable

Q2. How are materialized views useful in reporting dashboards?

Answer: Avoids re-running expensive queries every time. Refresh manually or periodically.

✅ 14. Query Optimization

🔍 Explanation:

Process of tuning queries to improve speed and reduce resource usage.

💡 Tips:

Use EXPLAIN to analyze performance.
Use selective indexes.
Avoid SELECT *.

🔥 Interview Questions & Answers:

Q1. What do you look at in an EXPLAIN plan?

Answer: Scan type (full scan vs index), filter cost, sort operations, table order in joins.

Q2. How do you optimize queries on large tables?

Answer:

Index WHERE columns
Use partitions
Break into smaller temp tables
Use caching if data is static

✅ 15. SQL Constraints

🔍 Explanation:

Constraints enforce rules on data integrity — like NOT NULL, UNIQUE, PRIMARY KEY, CHECK.

🔠 Syntax:

sql
CopyEdit
CREATE TABLE Employees (
    ID INT PRIMARY KEY,
    Name VARCHAR(50) NOT NULL,
    Salary DECIMAL(10,2) CHECK (Salary > 0)
);

🔥 Interview Questions & Answers:

Q1. UNIQUE vs PRIMARY KEY?

Answer: PRIMARY KEY = UNIQUE + NOT NULL. One per table. UNIQUE allows multiple NULLs.

Q2. Can a CHECK constraint refer to another table?

Answer: No. For cross-table validation, use triggers.

✅ 16. Triggers

🔍 Explanation:

Triggers are blocks that execute automatically in response to INSERT, UPDATE, or DELETE events.

🔠 Syntax:

sql
CopyEdit
CREATE TRIGGER afterInsert
AFTER INSERT ON Employees
FOR EACH ROW
BEGIN
  INSERT INTO AuditLog(...) VALUES (...);
END;

🔥 Interview Questions & Answers:

Q1. Can triggers cause performance issues?

Answer: Yes. They run behind-the-scenes, and multiple nested triggers can slow down inserts/updates.

Q2. Difference between BEFORE and AFTER triggers?

Answer:

BEFORE: Validate/change data before insert/update
AFTER: Log or sync data after commit

✅ 17. Stored Procedures

🔍 Explanation:

Stored Procedures are precompiled SQL blocks that encapsulate logic for reuse.

🔠 Syntax:

sql
CopyEdit
CREATE PROCEDURE GetEmployeesByDept(IN deptID INT)
BEGIN
  SELECT * FROM Employees WHERE DeptID = deptID;
END;

🔥 Interview Questions & Answers:

Q1. Why use stored procedures over application code?

Answer:

Better performance (compiled once)
Centralized logic
Less network roundtrips
Secured access via execution

Q2. Can stored procedures return result sets and output parameters?

Answer: Yes. Use OUT params and SELECT to return sets.

✅ 18. Recursive Queries

🔍 Explanation:

Used to query hierarchical data like employee-manager trees.

🔠 Syntax:

sql
CopyEdit
WITH RECURSIVE OrgChart AS (
  SELECT ID, Name, ManagerID FROM Employees WHERE ManagerID IS NULL
  UNION ALL
  SELECT e.ID, e.Name, e.ManagerID FROM Employees e JOIN OrgChart o ON e.ManagerID = o.ID
)
SELECT * FROM OrgChart;

🔥 Interview Questions & Answers:

Q1. How do you avoid infinite recursion?

Answer: Add a level counter and stop at depth OR set a MAX RECURSION level.

Q2. Real-world use cases of recursive queries?

File directory structures
Org hierarchy
Bill of materials
Comments with replies

✅ 19. Partitioning

🔍 Explanation:

Partitioning divides a table logically (and sometimes physically) to improve performance on large datasets.

🔠 Syntax:

sql
CopyEdit
CREATE TABLE Sales (
  ID INT,
  SaleDate DATE
)
PARTITION BY RANGE (YEAR(SaleDate)) (
  PARTITION p1 VALUES LESS THAN (2023),
  PARTITION p2 VALUES LESS THAN (2024)
);

🔥 Interview Questions & Answers:

Q1. When should you partition a table?

Answer: When the table grows large and queries mostly hit specific ranges (e.g., logs by year or month).

Q2. What types of partitioning exist?

RANGE
LIST
HASH
COMPOSITE

✅ 20. JSON in SQL

🔍 Explanation:

Modern SQL engines (MySQL, Postgres, SQL Server) support JSON for semi-structured data.

🔠 Syntax:

sql
CopyEdit
SELECT JSON_VALUE(Details, '$.price') FROM Products;

🔥 Interview Questions & Answers:

Q1. When should you store data as JSON in SQL?

Answer: When schema is flexible, and new attributes are added frequently (e.g., product metadata, custom fields).

Q2. Can you index JSON fields?

Linux Support Manager Project

Dnyaneshwar Vitthal Shekade — Mon, 24 Mar 2025 17:31:09 +0000

Linux Support Manager Project Description ======================================

Project Overview:

This appears to be a comprehensive web application built for managing Linux support services and development tools, using Next.js framework. The application provides a rich set of utilities for Linux system administration, network management, and development tasks.

Key Features:

Linux System Management
- Virtualization management
- Network configuration and monitoring
- Operating system ISO management
- Password generation and management
- Text comparison tools
- DNS lookup utilities
Development Tools
- Version control integration
- JSON processing tools
- Developer utilities
- Text comparison capabilities
AI Integration
- AI-powered chat support
- Automated assistance
Network Management
- Network monitoring
- DNS management
- Network diagnostics
Blog Integration
- Dev.to API integration
- Technical content management

Technical Stack:

Frontend Framework: Next.js
API Integration: Dev.to API
Component Architecture: Modular component-based structure
AI Integration: Chat agents for support
Network Tools: DNS and network management capabilities

Environment Configuration:

Dev.to API integration for blog content
Username configuration for Dev.to integration
Network and system management tools configuration

Project Purpose:

This is a comprehensive Linux support and management platform that combines system administration tools, development utilities, and technical documentation. It provides a unified interface for:

Linux system management and virtualization
Network administration and monitoring
Development tool integration
Technical documentation and blog management
AI-assisted support
System security tools

The platform appears to be designed for both system administrators and developers, offering a wide range of tools from basic password generation to complex virtualization management.

Note: This description is based on folder structure analysis and component names. The actual functionality may vary based on implementation details.

Server Toolkit Build with react,Nextjs

Dnyaneshwar Vitthal Shekade — Thu, 20 Mar 2025 08:20:19 +0000

As a server engineer, I often found myself searching for tools which needed to troubleshoot the issues on google but not getting correct tool are correct time —only to come up short. So, I built a comprehensive toolkit to simplify server troubleshooting and streamline daily operations for DevOps professionals and system administrators.
check the below link to explore the toolkit

https://lnkd.in/gcy32hqe

🚀 Key Features:
✅ DNS Checker – Instantly verify DNS records and troubleshoot domain issues.
✅ Port Scanner – Identify open ports and assess vulnerabilities effortlessly.
✅ IP Lookup – Get geolocation, ISP details, and more for any IP address.
✅ Linux Command Library – A curated knowledge base of essential Linux commands.
✅ Server Diagnostics – Optimize performance with specialized tools
✅ Bonus Utilities – JSON formatter, text comparator, and other handy tools to boost productivity.

salman shaikh Vikas Kharat Brijesh Kothari Chirag Nagda jevin gala

Tech Stack of project
💡 Built with: Next.js | React | PHP | MariaDB | Node.js
This application is designed to make server management faster, easier, and more efficient. If you're a DevOps engineer, sysadmin, or IT professional, this is for you!
👉 What server troubleshooting tool would you like to see next? Drop your suggestions in the comments!
hashtag#DevOps hashtag#ServerManagement hashtag#SysAdmin hashtag#NextJS hashtag#React hashtag#NodeJS hashtag#Linux hashtag#ITTools hashtag#ServerDiagnostics hashtag#TechInnovation hashtag#Networking hashtag#virtualizor

How to create a OpenVPN server into the docker at Home with unlimited .ovpn profiles

Dnyaneshwar Vitthal Shekade — Mon, 03 Mar 2025 15:10:56 +0000

Securing your network with a VPN is crucial in today’s digital landscape. OpenVPN is one of the most trusted solutions for creating a secure and encrypted connection. This guide will help you set up an OpenVPN server using Docker on a CentOS system.

Prerequisites

A Almalinux server with root or sudo privileges

An active internet connection

Step 1: Install Docker

Docker is required to run the OpenVPN server as a container. Follow these steps to install Docker:

1.1 Download and Install Yum Utilities

sudo dnf install -y yum-utils

1.2 Add the Official Docker Repository

sudo yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo

1.3 Install Docker and Dependencies

sudo dnf install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin

1.4 Start and Enable Docker

sudo systemctl start docker
sudo systemctl enable docker

Step 2: Clone the OpenVPN Server Repository

OpenVPN can be deployed using a pre-built Docker image. Clone the official OpenVPN server repository:

git clone https://github.com/d3vilh/openvpn-server

Navigate into the cloned directory:

cd openvpn-server

Step 3: Deploy the OpenVPN Server

Use Docker Compose to deploy the OpenVPN server:

docker-compose up -d

This command starts the OpenVPN server in detached mode, running in the background.

Refer this guide for indetailed steps
https://hub.docker.com/r/d3vilh/openvpn-server

Conclusion

You have successfully set up an OpenVPN server using Docker on CentOS. This setup ensures a secure and encrypted connection, providing a robust VPN solution for your network. You can now proceed with configuring clients and managing VPN users.

For additional configurations and advanced settings, refer to the OpenVPN GitHub repository.

DeepSeek: Empowering Open Source and the Linux Industry

Dnyaneshwar Vitthal Shekade — Sun, 16 Feb 2025 16:32:32 +0000

The open-source movement has long been the backbone of technological innovation, fostering collaboration, transparency, and accessibility. At the heart of this revolution lies Linux, the open-source operating system that powers everything from enterprise servers to embedded systems and cutting-edge AI infrastructure. DeepSeek, a visionary player in the tech landscape, has emerged as a catalyst for advancing open-source principles while driving transformative solutions for the Linux ecosystem. By aligning its mission with the ethos of community-driven development, DeepSeek is redefining how organizations leverage open-source tools to solve complex challenges.

Championing Open-Source Collaboration
DeepSeek’s commitment to open source goes beyond mere participation—it actively cultivates ecosystems where developers, enterprises, and enthusiasts thrive. By contributing to Linux kernel enhancements, optimizing containerization tools like Docker and Kubernetes, and releasing modular AI frameworks under permissive licenses, DeepSeek ensures that innovation remains accessible. Its open repositories for machine learning models, DevOps automation scripts, and cloud-native utilities empower developers to build upon a shared foundation, accelerating time-to-market for solutions that address real-world problems.

Revolutionizing Linux-Based Infrastructure
In an era where Linux dominates data centers and cloud environments, DeepSeek’s tools are engineered to maximize efficiency and scalability. From lightweight distributions tailored for edge computing to AI-optimized kernels that enhance GPU utilization, DeepSeek bridges the gap between open-source flexibility and enterprise-grade performance. Its work on real-time Linux variants, for instance, is critical for industries like telecommunications and autonomous systems, where latency and reliability are non-negotiable. By prioritizing compatibility with upstream Linux projects, DeepSeek ensures its innovations integrate seamlessly into existing workflows.

Democratizing AI with Open-Source Frameworks
DeepSeek recognizes that the future of AI hinges on open collaboration. Its contributions to projects like PyTorch, TensorFlow, and Jupyter have lowered barriers to entry for researchers and startups. By open-sourcing pretrained models for natural language processing, computer vision, and predictive analytics, DeepSeek enables even resource-constrained teams to harness state-of-the-art AI. These tools, optimized for Linux environments, empower developers to experiment, iterate, and deploy solutions without vendor lock-in—aligning perfectly with Linux’s philosophy of user freedom.

Fostering the Next Generation of Talent
DeepSeek invests heavily in nurturing open-source talent through hackathons, mentorship programs, and partnerships with academic institutions. By sponsoring Linux Foundation initiatives and hosting workshops on kernel development, DeepSeek ensures that the pipeline of skilled contributors remains robust. Its documentation and tutorials, tailored for Linux environments, demystify complex topics like kernel debugging and distributed systems, empowering newcomers to become active participants in the open-source community.

The Road Ahead
As industries increasingly rely on Linux for mission-critical workloads, DeepSeek’s role as an open-source advocate becomes ever more vital. By continuing to release cutting-edge tools under open licenses, advocating for interoperability standards, and fostering inclusive communities, DeepSeek is not just supporting the Linux industry—it’s shaping its future. In a world where proprietary silos threaten innovation, DeepSeek stands as a testament to the power of open collaboration, proving that shared knowledge and collective effort remain the ultimate drivers of progress.

Conclusion
DeepSeek’s alignment with open-source values and its contributions to the Linux ecosystem underscore a simple truth: technology thrives when barriers fall. By empowering developers, enterprises, and innovators with accessible tools and a collaborative ethos, DeepSeek is ensuring that Linux—and the open-source movement at large—remains at the forefront of the digital age. As the industry evolves, DeepSeek’s vision offers a blueprint for sustainable innovation: one where code is shared, knowledge is decentralized, and progress belongs to everyone.