It's Not Just Vibe Coding, Something Beyond That...

Preface: The Knowledge Drought

I used to have tech debates. Real ones. In person. Before COVID. Before the layoff trends made everyone paranoid.

It is not like a whiteboard meeting mostly, just while having coffee, at least sometimes in workshops, arguing about design patterns, sharing battle stories from production incidents, and making each other aware of new technologies. Someone would say "Hey, I discovered this new library, tech and everyone would benefit. Knowledge flowed freely because we felt secure.

Then something shifted.

Job security became a concern because of Frequent Layoffs and Ramp downs. AI Term is used as defense by some tech giants. But the actual problem is sharing knowledge become less. Guarded. Nowadays, everybody is cautious about what they share, especially around the worst kind of people - the ones who extract from us, not enlighten us. You know the type. They'll pick your brain for hours, take credit for your ideas, and never contribute back. They're knowledge vampires.

So we stopped sharing as much. The hallway conversations dried up. The unprepared and unplanned pairing sessions disappeared. The tech community I loved started feeling more like a competition than a collaboration.

But then something unexpected happened. Knowledge started coming to me in a different way. And it changed everything.

How It Started: The Evolution from Copilot to Cursor

In the beginning, there was GitHub Copilot in VS Code. It was like having a assistant developer who could autocomplete functions and write basic unit tests. Helpful? Sure. Revolutionary? Not really. It was just fancy autocomplete that saved me some typing.

Then I tried Windsurf. Some basic vibe coding - you know, that feeling when you're just playing around, seeing what AI can do, not really building anything serious. It was fun, but I wasn't trusting it with real work.

Claude Code came next, and I was impressed. There was something different about how it understood context. But I was still treating these tools as toys, not tools.

Kiro? Yeah, that sucked. Moving on.

Then something changed when I discovered Antigravity. It did something meaningful. Not just autocomplete, not just "write me a function" - it helped me solve real problems. It understood architecture. It could refactor intelligently.

But Cursor... Cursor became my trustworthy buddy.

Not just for vibe coding. For real work.

The Moment Everything Changed

Let me tell you about two moments when I realized this was different.

Moment 1: Databricks

I was working on setting up a local Databricks development environment. If you've ever worked with Databricks, you know the pain - spinning up clusters costs money, waiting for them to initialize wastes time, and testing simple Python transformations requires a full cloud setup.

I told Cursor: "I need a local setup that mimics Databricks - Spark, Jupyter, workflows with Airflow, a data lake with MinIO, SQL warehouse, and dashboards."

What happened next blew my mind.

It didn't just give me docker-compose with Spark. It built me a complete ecosystem:

Apache Spark for compute
Jupyter for notebooks
Apache Airflow for workflows (replacing Databricks workflows)
MinIO with Delta Lake for storage
PostgreSQL as a SQL warehouse
Redash for dashboards

And awesome thing is, it made the code portable. The same Python code that ran locally would run in Databricks. No two codebases. No "local version" vs "production version."

That's when I stopped thinking of Cursor as a coding assistant and started treating it as a development partner.

Moment 2: Go Lang Migration

The second moment was even more wild. I had a Node.js serverless application - AWS Lambda functions, CDK infrastructure, the whole stack. I wondered: "What if we rewrote this in Go for better performance?"

So I asked Cursor: "Rewrite this entire codebase from JavaScript to Go. Both the Lambda functions AND the CDK infrastructure."

I expected to babysit it. I expected bugs. I expected to spend weeks fixing things.

Instead, Cursor:

Analyzed the JavaScript architecture
Created equivalent Go packages with proper structure
Migrated all Lambda functions maintaining the same logic
Rewrote the CDK stack in Go
Preserved all the AWS resource configurations
Maintained the same API contracts

Was it perfect? No. Did I review every line? Absolutely. But what would have taken me 2-3 months took 2 weeks. And most of that time was me understanding Go better, not fixing AI mistakes.

That's when I knew: this isn't just autocomplete with extra steps. This is something beyond that.

What I Actually Mean: Unleashing Delivery Capacity

Let me be clear: I haven't stopped using my brain. I haven't become dependent on AI.

What AI-based editors and LLMs did was unleash my delivery capability and let me focus on what I actually want to accomplish.

Here's what I mean:

Before AI (The Old Way)

Let's say I needed to figure out the best programming language for a specific project. I had to consider:

Performance characteristics
Observability (logging, monitoring, tracing)
Security best practices
Reliability patterns (retries, circuit breakers)
Scalability concerns (horizontal vs vertical)
Automation possibilities (CI/CD, IaC)
Documentation standards
Failure isolation strategies
Versioning approaches
Microservice patterns

To get answers, I had to:

Consult language experts (if I could find them and then if they had time)
Go through documentation (scattered across various sites)
Read blog posts (half of them outdated)
Browse Stack Overflow (where half the answers are outdated too)
Experiment myself (spending days on dead ends)

This was a long, slow process. Progress was slow because gathering information took forever.

After AI (The New Way)

Now? I can have conversations like this:

"I need to build a real-time data processing pipeline. Compare using Node.js with Kafka vs Go with Kafka vs Python with Databricks. Consider: performance at 10K messages/sec, team learning curve (we know JavaScript), cloud costs on AWS, observability tools available, and ability to handle backpressure. Give me an architecture recommendation with pros/cons."

And I get a comprehensive analysis in minutes, not weeks.

But here's what's even more powerful: it's not just one language or one technology.

The 2-Month Review of 6 Years

Over the past 2 months, I went through ALL the applications my team built in the past 6 years. Let me list them:

An 8-9 year old client's material testing platform
- Legacy React 16 with class components
- Terraform infrastructure
- Multiple Jenkins files for different environments
- Complex Groovy scripts
A 5-year-old capacity management system -
- React 16, needed upgrade to 19.2
- Redux Saga (needed migration to Redux Toolkit)
- Outdated UI libraries
- No modern testing framework
Serverless data pipeline for Order Management -
- Serverless Framework configuration
- Performance bottlenecks
- No code optimization
Recent React web appl -
- No comprehensive documentation
- No unit tests
- No E2E tests
- Needed UI/UX improvements

For each application, I worked with Cursor to:

1. Deep Documentation

Not just "here's how to run it" docs, but:

Complete architecture diagrams
API specifications
Data flow documentation
Developer guides
Deployment guides
Troubleshooting guides

Time saved: What would take 3-4 weeks per application took 2-3 days.

2. Modernization Analysis

For each app, I analyzed:

Library updates (what's deprecated, what's the modern alternative)
React class components → functional components with hooks
Testing strategy (unit, integration, E2E)
Performance optimization opportunities
Security vulnerabilities
Architecture improvements

Time saved: What would take 2 months of research took 1 week.

3. Migration Planning

Created detailed migration plans:

Phase-by-phase approach
Risk assessment matrices
Resource allocation
Timeline estimates
Rollback strategies

Time saved: What would take 1 month took 2 days.

4. Infrastructure Evolution

Serverless → AWS CDK
Terraform → AWS CDK
Multiple Jenkins files → Single parameterized Jenkins file
Environment configuration cleanup

Time saved: What would take 2 months took 2 weeks.

What This Gave Me (And My Team)

This review provided benefits on three levels:

1. Knowledge to me and the team

We now understand our own systems better
We have comprehensive documentation for onboarding
We can make informed decisions about tech debt

2. Performance benefits to application end-users

Identified bottlenecks (connection pooling, batch processing)
Optimized React rendering especially with (memoization, code splitting)
Improved error handling and retry logic across all apps

3. Cost benefits to our clients

Reduced AWS Lambda cold starts
Optimized DynamoDB queries (saved significant RCU/WCU)
Better resource allocation
Identified unused resources

The math is simple: 6 years of applications, reviewed and improved in 2 months. That's not vibe coding. That's a force multiplier.

The Full Technology Spectrum

Let me show you the breadth of technologies I worked with, learned, and implemented using AI assistance:

Frontend Modernization

React 16 → React 19: Migration strategies, hooks, concurrent features
Class components → Functional components: Complete refactoring patterns
Redux Saga → Redux Toolkit: State management modernization
Webpack → Vite: Build tool optimization
Enzyme → React Testing Library: Modern testing approaches
Custom UI → Tailwind and Internal Design System: Component library migration

Backend & Infrastructure

Serverless Framework → AWS CDK (JavaScript): Infrastructure as Code evolution
Terraform → AWS CDK: Complete infrastructure migration
Node.js optimization: Connection pooling, async patterns, error handling
Go Lang implementation: Complete rewrites for performance
AWS Lambda best practices: Cold starts, memory optimization, concurrency

Data Engineering

Databricks workflows: Local development setup, Delta Lake, PySpark
Apache Spark: Local cluster setup, job optimization
Apache Airflow: Workflow orchestration, DAG design
MinIO + Delta Lake: Local data lake implementation
PostgreSQL → SQL warehousing: Schema design, query optimization

CI/CD & DevOps

Jenkins modernization: Multiple files → single parameterized pipeline
Multi-environment strategies: dev, stage, prod, preprod configuration
Docker containerization: Local development environments
Infrastructure monitoring: AWS CloudWatch, custom metrics

API Development

REST API design: OpenAPI specs, Bruno collections
API Gateway optimization: Request validation, response mapping

Testing & Quality

Unit testing: Vitest, Jest, React Testing Library
E2E testing: Cucumber, Nightwatch.js, behavior-driven development
Test coverage: Comprehensive test strategies
Code quality: ESLint, Prettier, SonarQube integration

Security & Monitoring

AWS resource monitoring: Automation in Certificate expiry, Secrets Manager changes monitoring
Cost optimization: Resource analysis, recommendations
Security best practices: IAM policies, encryption, secret management
Observability: Logging, monitoring, alerting strategies

Team Analysis & Process

Code review analysis: Team contribution patterns
AI-assisted code detection: Understanding adoption patterns
Coding standards: Establishing consistency across microservices
Developer productivity: Measuring improvement metrics

Specific Project Highlights

Here are some concrete examples:

Databricks Local Development

The Problem: Developers writing Python in VS Code, testing in Databricks cloud, waiting for clusters, wasting money.

The Solution: Built a complete local Spark environment:

Local Development Stack:
├── Apache Spark (compute engine)
├── Jupyter Notebooks (development interface)
├── Apache Airflow (workflow orchestration)
├── MinIO + Delta Lake (data lake)
├── PostgreSQL (SQL warehouse)
└── Redash (dashboards)

The Impact:

Zero Databricks cluster costs during development
Instant feedback loop (no waiting for cluster startup)
Same code works locally and in production
Team learned data engineering fundamentals

Legacy App Entire Stack Migration

The Challenge: 8-9 year old application with:

Terraform infrastructure
5+ different Jenkins files (one per environment)
Complex Groovy scripts
No documentation

What We Did:

Documentation: Created complete architecture docs, API specs, database schemas
Terraform → CDK: Migrated all infrastructure to AWS CDK with JavaScript
Jenkins consolidation: 7 files → 1 parameterized Jenkins file
Comparison & verification: Ensured 100% feature parity
React Upgrade: From 16.8 to 19.2 everying with hooks and redux tookit

The Result:

Infrastructure as actual code (not declarative)
Single source of truth for deployments
Reduced Jenkins complexity by 80%
Full documentation for future developers

Legacy React Application

The Situation: 5-year-old React app, stable but ancient:

React 16.14
Class-based components
Outdated dependencies
No design system

The Transformation Plan:

Dependencies analysis: 40+ libraries needed updates
React 19 upgrade strategy
Class → Hooks migration plan (component-by-component)
Redux Saga → Redux Toolkit migration
UI component replacement with Nike EDS

The Documentation: Created 20+ detailed docs including:

Modernization roadmap
Risk assessment matrices
Week-by-week execution plan
Code change requirements
Testing strategy

Go Migration

The Experiment: Can we improve performance by switching from Node.js to Go?

The Approach:

Keep existing Node.js code
Create parallel Go implementation
Rewrite Lambda functions in Go
Rewrite CDK infrastructure in Go
Compare performance

The Learning:

AI handled the translation logic
I reviewed Go Lang’s Magic and it needs for presence
Performance benchmarks showed 40% improvement
But team learning curve was steep
Decision: stay with Node.js, apply optimization patterns learned from Go

Why Not Before, But Now?

You might be wondering: "If these applications needed this work for years, why didn't you do it before?"

Fair question. Let me be honest about why.

The Reality of Backlogs

Some of these tasks were sitting in our backlogs for years:

"Modernize React application to latest version" - Backlog for 3 years
"Create comprehensive documentation" - Always a "nice to have," never a priority
"Migrate from Terraform to CDK" - Discussed multiple times, never started
"Add unit and E2E tests" - In the backlog since the app launched
"Analyze and optimize performance" - Everyone knew we needed it, no one had time

Others weren't even in the backlog. They were just thoughts:

"Should we rewrite this in Go?"
"Can we run Databricks locally?"
"What if we consolidated all these Jenkins files?"

These stayed thoughts because even proposing them felt impossible.

Why We Couldn't Do It Before

Multiple factors blocked us:

1. Team Priorities

We were busy building new features
Business wanted new capabilities, not refactoring
"It's working, why touch it?" was the common response
Official Tech debt always lost to new feature requests

2. Budget Constraints

Each modernization would take months
Management saw it as "spending money to rebuild what already works"
ROI was hard to justify
Cost of doing it > cost of leaving it alone (in the short term)

3. Resource Allocation

Our team was already stretched thin
We couldn't spare 2-3 developers for 6 months just to modernize one app
Hiring external consultants? Too expensive
Taking developers off feature work? Not an option

4. Risk Aversion

"If it ain't broke, don't fix it"
Fear of introducing bugs into production systems
Concerns about downtime during migration
What if we make it worse?

5. Knowledge Gaps

Some team members didn't know the latest React patterns
No one was an expert in Go
Data engineering was something we recently adopted
Learning curve felt too steep while delivering features

6. Time This was the biggest factor. To properly:

Document an 8-year-old application: 3-4 weeks
Analyze modernization options: 2-3 weeks
Create migration plan: 2-3 weeks
Migrate Terraform to CDK: 2-3 months
Update React 16 to 19: 3-4 months
Add comprehensive tests: 2-3 months

For one application. We had six.

The math was brutal: 18-24 months of work with a dedicated team.

We didn't have 18-24 months. We didn't have a dedicated team. We had ongoing features development, production issues, and business pressures.

So these tasks stayed in the backlog. Year after year.

Then Agentic AI Emerged

When Cursor, with Claude Sonnet and Opas became mature enough, something changed. Not because AI could "do it for me," but because AI could amplify my capability. I'm not highlighting GPT 5.2 or another. GPT never made me feel like to believe it as like Claude connected with me. Gemini 3.X is doing good but haven’t tried it at enterprise level. Antigravity and Gemini gave me different perspective, it helped me to train a Machine learning model using tensorflow.js and I was able to do some experiments with it. that is subject for another blog post. coming back here I started using cursor just like other editors like windsurf and vscode,when it got introduced, and then I didn’t touched it for a while, then clients enforced it and eventually became an essential enterprise tool. Coming Back to story,

The Game Changer: Time compression.

What took 3-4 weeks now took 2-3 days. What took 2-3 months now took 2 weeks.

Suddenly, that 18-24 month project became a 2-month exploration.

The Turning Point: "What If We Just Try?"

I had a realization:

"These tasks are never going to be a priority. But what if they don't need to be? What if I can make progress on them WITHOUT taking resources from the main roadmap?"

So I started experimenting:

Nights and weekends initially
Spare time between meetings
"Dead time" while waiting for builds or deployments
Instead of Insta and Youtube Feeds, I'd work with Cursor on documentation

The hope was simple: Maybe I can make enough progress to show value. Maybe then it becomes easier to justify.

The Promising Results

Within the first 2 weeks, I had:

Complete documentation for one application
Modernization analysis for another
A working local Databricks setup
Infrastructure migration plan

I demonstrated what is progress in weekly meeting

"Wait, you started, we will be doing in all other applications!!!"

Suddenly, these "impossible" tasks looked feasible. Not easy, but possible.

More importantly, the ROI calculation changed:

Before: "Spend 6 months to modernize = not worth it"
After: "Spend 2 weeks to modernize = worth considering"

Risk changed:

Before: "Big bang migration over months" = risky
After: "Incremental changes I can review carefully" = manageable

Budget conversations changed too:

Before: "We need 2 FTEs for 6 months" = denied
After: do you think it will change? Nothing changed :D

Why It Worked This Time

Three key factors made the difference:

1. Speed Without Sacrificing Quality

AI generated the initial draft
I reviewed and refined everything
Quality remained high, but time dropped 10x

2. Learning While Building

I didn't need to be a Go expert to explore Go migration
I didn't need to master Databricks before setting up local dev
AI helped me learn while I built

3. Low Risk Experimentation

I could try things without committing the team
Failed experiments cost days, not months
Easy to pivot when something didn't work

The Domino Effect

Once I had results for one application, everything changed:

Week 1-2: Documentation for first app

Team: "This is actually useful"

Week 3-4: Modernization plan for second app

Management: "Show us more"

Week 5-6: Working local Databricks environment

Team: "This will save us $2500/month"

Week 7-8: Infrastructure migration completed

Management: "Can you do this for other apps?"

Each success made the next project easier to justify. The momentum built.

By month 2, I wasn't fighting for time anymore. People from different teams ask for feasibility in their application.

"What else can we modernize? What other tech debt can we address?"

The Reality Check

Don't get me wrong - I'm not saying AI magically solved everything. I'm saying:

AI changed the economics of tech debt.

Before:

Tech debt was expensive to address
ROI was questionable
Teams avoided it

After:

Tech debt became cheaper to address
ROI became obvious
Teams could tackle it

The work that was economically impossible became economically feasible.

The tasks that were "someday maybe" became "we can do this now."

The backlogs that kept growing started shrinking.

That's the real transformation.

Not that AI writes perfect code (it doesn't). Not that you don't need skills (you do).
Not that everything is easy (it isn't).

But those things which were impossible due to time and budget are now possible.

And that changes everything.

How AI Actually Changes Coding

Let me break down what AI did for my workflow:

1. Automation of Repeated and Usual Tasks

AI handles the boring stuff:

Writing boilerplate code
Converting between formats (Postman → Bruno, YAML → JSON)
Generating test scaffolds
Creating documentation templates
Implementing repetitive patterns
Updating dependency versions

This reduces cognitive load. I'm not wasting mental energy on boilerplate. I can focus on the actual problems.

2. Shift to Higher-Level Work

I spend my time on:

Architecture decisions: Should we use Lambda or ECS? Event-driven or REST?
Creative problem-solving: How do we handle this edge case? What's the failure mode?
Defining requirements: What does "success" actually look like here?
Strategic thinking: Is this the right technology for the next 5 years?

The tactical execution? AI handles most of it.

3. "Agentic Coding" - The New Developer Role

I've become something like a Tech Lead for an AI team member:

My responsibilities now:

Provide clear instructions: "Create a CDK stack with Lambda, API Gateway, and DynamoDB"
Review critically: Check for security issues, performance problems, edge cases
Take responsibility: I own the final product, not the AI
Course correct: "This approach won't scale, try using SQS for buffering instead"
Validate: Test everything, ensure it works as expected

What changed:

I write less code
I read more code
I think more about architecture
I focus more on the "why" than the "how"

4. Increased Focus on What Matters

By removing tedious tasks, I can focus on:

Learning: I learned Go, data engineering, infrastructure patterns - all in 2 months
Discovery: Found performance issues across 6 years of applications
Documentation: Created comprehensive docs for entire systems
Team growth: Shared knowledge, established standards, mentored others

The AI doesn't make me lazy. It makes me more desired.

The New Developer Role

So what's my job now? Let me explain with analogies:

Editor/Director (instead of just Writer)

I don't type every line of code anymore. I direct the story:

"This component should handle user authentication"
"Add error boundaries here"
"Optimize this database query"
"Refactor this for testability"

I'm steering the ship, not rowing it.

Architect/Problem Solver (instead of just Coder)

I focus on:

Designing systems that scale
Solving unique business challenges
Making technology choices
Planning migration strategies

The implementation details? That's increasingly handled by AI.

Reviewer/Refiner (instead of just Builder)

Critical review becomes the most important skill:

When AI generates code, I check:

Security: Are secrets exposed? SQL injection possible?
Performance: Will this scale? Any N+1 queries?
Maintainability: Can the team understand this in 6 months?
Correctness: Does it handle edge cases? Error states?
Best practices: Following team standards? Using right patterns?

I review, test, and validate before anything goes live.

But Let's Be Real: What I Still Do Manually

I'm not blindly accepting AI suggestions. Here's what I ALWAYS do manually:

1. Review Every Line

Before any code goes to deployment, I read it. All of it. I don't just copy-paste.

Questions I ask:

Do I understand what this does?
Is there a simpler way?
What happens if this fails?
Are there hidden assumptions?

2. Test Everything

I don't trust AI testing:

Unit tests? I run them and verify coverage
Integration tests? I test the actual integrations
E2E tests? I manually verify user flows
Load tests? I check performance under realistic conditions

The AI can generate tests, but I verify they're actually testing the right things.

3. Security Reviews

I manually check:

IAM policies (are they too permissive?)
Secret management (are we exposing credentials?)
API authentication (are endpoints properly secured?)
Input validation (can this be exploited?)

AI might follow best practices, but security is my responsibility.

4. Architecture Decisions

AI can suggest approaches, but I decide:

Should we use microservices or a monolith?
Is this pattern right for our team?
What are the long-term maintenance implications?
Does this align with client's and industrial standards?

These decisions require business context that AI doesn't have.

The Reality: AI as a Learning Accelerator

Here's something I didn't expect: AI made me learn faster.

Learning New Technologies

When I needed to learn Go:

AI explained concepts (goroutines, channels, interfaces)
Showed me appropriate Go code
Helped me compare with JavaScript patterns I knew
Generated practice examples

But I still had to understand it. The AI couldn't learn for me.

Understanding Legacy Code

When reviewing old applications:

AI helped explain complex code
Generated documentation from code
Identified patterns and anti-patterns
Suggested refactoring approaches

But I still had to make judgment calls about what to keep and what to change.

Exploring New Domains

Data engineering was new to me:

AI taught me Spark concepts
Explained Delta Lake architecture
Showed me Airflow DAG patterns
Helped with PySpark syntax

But I still had to design the pipelines and understand the data flows.

What This Means for the Industry

Based on my experience, here's what I think is happening:

1. The Bar is Rising

With AI assistance:

"Good enough" code isn't enough anymore
You need to think architecturally
Understanding becomes more important than memorization
Cross-technology skills become achievable

Junior developers can output senior-level code (with proper review). Senior developers can be 10x more productive.

2. Solo Developers Can Do Team-Level Work

I reviewed and improved 6 applications in 2 months. That's normally a team effort over a year.

This means:

Small teams can punch above their weight
Individual contributors can have massive impact
Companies need fewer developers? (Maybe, but they need BETTER ones)

3. The Nature of "Skill" Changes

Old skills:

Memorizing syntax
Knowing every framework detail
Typing fast

New skills:

Asking the right questions
Critical code review
Architecture and design thinking
Understanding business context
Knowing what to ask AI (and what not to)

4. Documentation Becomes Critical

With AI, I could generate comprehensive docs for all 6 applications. This means:

No excuse for poor documentation anymore
Onboarding new developers is faster
Knowledge isn't trapped in senior developers' heads
Teams can move faster

5. Learning Becomes Exponential

I learned:

Data engineering (Spark, Databricks, Airflow)
Infrastructure as Code (CDK in depth)
Go programming language
Advanced React patterns (React 19)
Security best practices across AWS

In 2 months.

That's not because I'm brilliant. It's because AI removes the friction from learning.

My Controversial Take

Some people worry that AI will replace developers. I don't think that's the right framing.

AI is replacing the "code monkey" role.

If your job is:

Converting requirements into basic CRUD operations
Writing boilerplate
Following patterns without thinking
Implementing obvious solutions

Then yeah, you might be in trouble.

But if your value is:

Understanding the problem deeply
Designing elegant solutions
Making smart technology choices
Reviewing code critically
Mentoring others
Thinking about edge cases
Understanding business context

Then AI makes you more valuable, not less.

The Trust Factor: Why Cursor Became My "Buddy"

Let me explain why I call Cursor my "trustworthy buddy" and not just a "tool":

It Understands Context

When I'm working on a CDK stack and ask about Lambda configuration, it knows:

What other Lambdas exist in the stack
The naming conventions I'm using
The environment structure I've set up
The patterns I've established

It's not just answering questions in isolation. It's working with me on my project.

It Learns My Style

After a few interactions:

It matches my coding style
Uses my preferred patterns
Follows my file organization
Maintains consistency across the codebase

I don't have to constantly re-explain my preferences. Seriously this attracted me frequently, because it provides results in my own style, like what I was thinking will be the output. How could that happen!

It Admits Limitations

When Cursor doesn't know something, it says so. When I asked it to access Client's internal documentation, it said:

"I need actual access to the documentation to create an accurate collection."

I asked to access a website documentation, when it could not access it, it assumed few things by itself and generated something which when I cross verify, it admits that is its mistake.

Why couldn’t Cursor do? It is not limited to, but my client’s network is limited, which I am aware of and expected sometime so no big deal for me

Conclusion: The Future is Agentic

I am not sharing this as advice for "Agentic Coding". It is my experience and it is my Take! After 2 months of intense work with AI assistance, here's what I believe:

AI doesn't replace developers. It replaces the tedious parts of development.

The future developer is:

More architect than coder
More reviewer than writer
More director than implementer
More strategic than tactical

But also:

More responsible (you own what ships)
More knowledgeable (you need to evaluate AI suggestions)
More careful (review is critical)
More ambitious (you can tackle bigger problems)

It's not just "vibe coding" where you:

Like Typing a prompt, Get some code, Hope it works and shipping it

It's "agentic coding" where you:

Define the problem clearly
Guide the implementation
Review critically
Test thoroughly
Take responsibility
Learn continuously

The AI is my pair programmer, but I'm the senior developer in this pair.

And honestly? This is the most productive, most creative, most impactful period of my career. Not because I'm typing faster, but because I'm thinking bigger.

I reviewed 6 years of applications in 2 months. I learned data engineering, Go, advanced infrastructure patterns, and modern React - all while improving existing applications and creating comprehensive documentation.

That's not vibe coding.

That's something beyond that.

Final Thoughts: An Invitation

If you're still treating AI as "just autocomplete," you're missing out.

If you're afraid AI will replace you, shift your focus from writing code to solving problems.

If you're drowning in tech debt, AI can help you understand it and create a plan to fix it.

If you're overwhelmed by new technologies, AI can be your learning partner.

The knowledge drought is over. Not because people are sharing more (they're not), but because AI has become the colleague who's always willing to explain, teach, and collaborate.

The worst people who extract from us without enlightening us? They're still out there. But now, I don't need them as much.

Because I have a trustworthy buddy who's always ready to help me learn, build, and grow.

And that's changed everything.