Nitin

Full Stack Developer
React • Node.js • REST APIs
Let's talk about tech and development

[Nitin @nitin_coder]

Me debugging a production issue:

CPU: “I’m fine.”
Memory: “All good.”
Database: “Queries are fast.”
Logs: “Nothing unusual.”

Users: “THE APP IS DOWN.”

Backend development is just 90% detective work and 10% pretending you know what’s happening. 💀

[Nitin @nitin_coder]

AI writes 500 lines of code in 30 seconds.

Me: “This is the future.”

The code fails one edge case.

Me, 3 hours later, reading the entire codebase:

“I was faster before the future.” 😭

[Nitin @nitin_coder]

Production debugging is basically detective work.

CPU: “Not me.”

Memory: “Not me.”

Database: “Queries are fast.”

Logs: “Everything looks normal.”

Users: “THE APP IS DOWN.”

Welcome to backend engineering. 💀

[Nitin @nitin_coder]

You don't need to know every framework.

You need to understand fundamentals deeply enough to learn new frameworks quickly.

HTTP, databases, concurrency, networking, security, testing, and debugging will outlive many of today's favorite libraries.

Learn the concepts. Tools will change.

[Nitin @nitin_coder]

Job searching as a developer is a strange engineering problem.

Inputs: applications, projects, networking, interview practice.

Output: hopefully, an offer.

The feedback loop is terrible, the latency is unpredictable, and sometimes the system returns no useful error message. 💀

[Nitin @nitin_coder]

One underrated way to learn software engineering:

Explain a concept as if you're teaching another developer.

If you can't explain why a database pool gets exhausted, why React re-renders, or why retries create duplicate payments, you probably have more to explore.

Teaching exposes gaps that tutorials can hide.

[Nitin @nitin_coder]

Companies want developers who can build production systems.

Interviews sometimes test whether you can reverse a linked list on a whiteboard while someone watches.

Both skills can matter.

But I wish more interviews included debugging a real API, investigating a slow query, or designing a reliable workflow.

[Nitin @nitin_coder]

A portfolio with 10 basic CRUD projects may be less convincing than one project that solves a real problem.

Build something that demonstrates:
- Authentication and authorization
- Database design
- Error handling
- Testing
- Deployment
- Real business logic

Don't just show that you can code.

Show that you can engineer.

[Nitin @nitin_coder]

Being a full-stack developer means:

You fix a CSS bug.

Accidentally break an API.

Fix the API.

Break authentication.

Fix authentication.

Then discover the database migration never ran. 💀

[Nitin @nitin_coder]

TypeScript can catch many mistakes before runtime.

But it won't save you from:
- Incorrect business logic
- Invalid external API data
- Race conditions
- Bad database queries

Types are a safety net, not a replacement for runtime validation and good engineering.

[Nitin @nitin_coder]

Node.js can handle thousands of concurrent connections.

But that doesn't mean one process can efficiently execute thousands of CPU-heavy tasks simultaneously.

I/O concurrency and CPU parallelism are different problems.

Learn the event loop, worker threads, and queues before throwing more servers at the problem.

[Nitin @nitin_coder]

Frontend says the API is slow.

Backend says the database is fast.

Database says the query takes 5 ms.

Network traces reveal the frontend is making 14 requests for one screen.

Sometimes performance optimization starts by asking:

“Why are we doing this much work?”

[Nitin @nitin_coder]

Adding `useMemo` everywhere doesn't make a React app fast.

It adds complexity and has its own cost.

First identify the expensive calculation or unnecessary re-render.

Then optimize the actual bottleneck.

Measure first. Memoize second.

[Nitin @nitin_coder]

Me before AI: “I need to understand the documentation.”

Me with AI: “I need to understand why the AI ignored the documentation.” 😭

[Nitin @nitin_coder]

In the AI era, typing code faster may become less valuable.

Knowing what to build, how to validate it, how to secure it, and how to debug it in production will still matter.

The advantage isn't just using AI.

It's knowing enough to challenge its output.

[Nitin @nitin_coder]

A demo AI agent looks impressive when everything goes right.

A production AI agent must handle:
- Tool failures
- Invalid outputs
- Retries and duplicate actions
- Permissions
- Timeouts and cost limits

The hard part isn't making an agent work once.

It's making it fail safely.

[Nitin @nitin_coder]

If AI can generate a complete CRUD application in minutes, what should junior developers focus on learning?

A. Syntax and fundamentals
B. Debugging and testing
C. System design and databases
D. Building real products with AI

My take: fundamentals still matter because someone has to recognize when the generated solution is wrong.

[Nitin @nitin_coder]

AI reduced my coding time from 2 hours to 20 minutes.

Then I spent 90 minutes understanding the code it generated.

AI is incredibly useful.

But generating code and owning the consequences of that code are two very different skills.

[Nitin @nitin_coder]

Would you rather build a monolith that handles 100,000 requests per second reliably or 20 microservices that struggle at 10,000?

Architecture isn't a competition to use the most technologies.

It's about meeting requirements with the least unnecessary complexity.

Agree or disagree?