DEV Community: Ankit Rattan

Interview E01 : From DSA to System Thinking – Rapido

Ankit Rattan — Sun, 05 Apr 2026 10:44:56 +0000

From this post, I’m starting a series where I’ll be sharing my interview preparation journey and real interview experiences across companies.

This was my interview experience for the Product Engineering role at Rapido.

I recently had the opportunity to interview with Rapido after successfully clearing their online assessment. Although I couldn’t make it to the final round, the experience was extremely valuable and filled with great learning.

Online Assessment (OA)

The OA consisted of a decent set of Data Structures and Algorithms (DSA) questions. The difficulty level was moderate, requiring a solid understanding of core concepts and problem-solving skills. It was a good filter to assess both speed and accuracy.

Interview Process

The interview started on a very positive and friendly note. The interviewer began with a general introduction, followed by questions about my technical stack and the projects I had worked on.

Before diving deep into my projects, I was asked a question based on a Fenwick Tree (Binary Indexed Tree). This tested my understanding of advanced data structures and how efficiently I could apply them in problem-solving.

After that, the discussion moved toward computer science fundamentals, where I was asked questions related to core concepts. This part evaluated my theoretical understanding and clarity of basics.

Project Discussion & Development Focus

The most interesting part of the interview was the deep dive into development and projects. The interviewer focused heavily on:

My internship projects
Design decisions in my hackathon projects
How I approached problem-solving in real-world scenarios

We had a detailed discussion on how these projects could be improved further. The conversation included insights on:

Database design and optimization
REST API design principles
Scalability and better architectural choices

It felt more like a collaborative discussion rather than a strict Q&A session, which made it highly engaging and insightful.

Key Takeaways-

Even though I didn’t progress to the final round, the interview helped me understand:

The importance of strong fundamentals (both DSA and CS concepts)
How deeply companies evaluate real-world project experience
The need to think beyond implementation and focus on design, scalability, and optimization

Overall, it was a great learning experience, and I’m grateful for the opportunity.

Kafka vs. RabbitMQ: The Post Office vs. The Time Machine

Ankit Rattan — Sun, 22 Mar 2026 09:48:58 +0000

Every developer eventually hits a wall. Your standard REST APIs are timing out, your database is locked, and you realize synchronous requests just can't handle the scale anymore.
You need to go asynchronous. You need a message queue.
You do a quick search, and two massive names pop up: Kafka and RabbitMQ.
People often treat them like they are the same thing, just competing brands. But guys... they are fundamentally different beasts. Choosing the wrong one is like bringing a tractor to a Formula 1 race.
Well, Why this....?
Because understanding the difference between a Message Broker and an Event Streaming Platform is the real deal for modern backend architecture.

Let's break it down simply:

RabbitMQ is the Post Office. It takes a letter (message), looks at the address (routing key), and guarantees it gets delivered to the right mailbox (queue). Once the receiver reads it, the letter is destroyed.

Kafka is a Time Machine. It writes everything down in a massive, append-only log. It doesn't care who reads it. And once you read a message, it doesn't disappear. You can go back in time and replay the events from yesterday.

RabbitMQ (Smart Broker, Dumb Consumer): The broker does all the heavy lifting. It handles complex routing logic. It’s perfect for task queues. For example: A user signs up -> RabbitMQ routes a task to the email service to send a welcome email. The email is sent, the message is deleted. Clean, simple, traditional microservices.

Kafka (Dumb Broker, Smart Consumer):
Kafka just ingests data as fast as humanly possible and stores it on disk. The consumers have to keep track of what they've read (using offsets).
Imagine you are building a system to optimize delivery riders based on real-time store demand. You are getting blasted with thousands of live location pings, order creations, and weather updates every single second. You don't want those messages deleted after one service reads them! Your real-time dashboard needs to read them, but your machine learning models also need to ingest that exact same data pipeline to update the rider rostering algorithm.
That massive, high-throughput, multi-consumer data stream? That’s Kafka territory.

Don't use Kafka just because it's a shiny buzzword.
Use RabbitMQ if: You need complex routing, you are triggering specific actions (like sending notifications or processing a background video render), and you want the message gone once it's processed.

Use Kafka if: You are doing data engineering, building real-time analytics, aggregating logs, or need to replay past events.

Trust the Process

Ankit Rattan — Sun, 08 Mar 2026 13:40:51 +0000

This is a submission for the 2026 WeCoded Challenge: Echoes of Experience

My tech journey didn't start with a grand master plan. As a student at NIT Delhi, my initial foray into the software world began exactly how you'd expect: piecing together basic projects, struggling with seemingly endless bugs, and celebrating when a simple script finally ran without throwing an error.

But the real turning point happened when I stepped into the hackathon arena.

I threw myself into building under pressure, but it wasn't an overnight success story. I participated in more than six hackathons, ensuring I had a valid, working submission every single time. My trajectory was a slow, deliberate climb. I went from just being happy to submit, to cracking the Top 50. I kept refining my craft—diving deeper into AI and building complex projects eventually pushed me into the Top 10.

That hackathon grit paved the way for my professional career. My first real break was at Cabeasy, where I learned how to translate theoretical knowledge into practical, fast-paced execution. That invaluable experience acted as a launchpad, opening the door to an incredible opportunity at Microsoft. There, I was exposed to engineering at a massive scale and learned what it takes to build robust, impactful tech.

Beyond the code and the companies, one of the most fulfilling parts of this journey has been growing alongside a developer community of over 5,000 members. Helping others navigate their own paths, debug their code, and land their first roles reminds me of exactly where I started.

To anyone feeling stuck on a basic project or trying to break out of the bottom tier of a hackathon: keep building. Your echoes of experience will eventually become the blueprint for your success.

It’s been a wild ride, and there are many more to go!

2026 is different!!

Ankit Rattan — Sun, 22 Feb 2026 06:52:15 +0000

MERN is Just the Alphabet: Why Full-Stack Isn't Enough in 2026

Ankit Rattan ・ Feb 22

MERN is Just the Alphabet: Why Full-Stack Isn't Enough in 2026

Ankit Rattan — Sun, 22 Feb 2026 05:14:28 +0000

If you go on GitHub right now, you will find a million E-commerce clones, To-Do lists, and Social Media dashboards. A few years ago, building a complete MERN (MongoDB, Express, React, Node) stack app was the ultimate flex. It got you the interview. It got you the job.

But we are in 2026 now, guys. The game has leveled up. Building a standard full-stack CRUD app isn't the finish line anymore—it’s just the starting line.

Well, Why this....?
Because MERN is the base now. It’s literally the ABCD of web development.
Knowing how to set up a database, write a REST API, and connect it to a frontend is expected. It’s muscle memory. If you submit a project that just takes user input and saves it to a database, recruiters aren't going to be impressed.

A project without smart integrations and a real-world use case is just... boring. It doesn't highlight your ability to solve actual problems.

The shift is moving from "storing data" to "understanding data."
Your backend shouldn't just be a dumb pipe that passes JSON from the database to the client. You need to be integrating with Agents.

You have to rewrite how you think about backend controllers. Instead of a controller that just runs a find() query, you need controllers that handle dynamic, non-deterministic AI responses. How do you parse an agent's output? How do you handle a hallucinated JSON structure? That is real agenda.

You shouldn't just rely on closed APIs. Tapping into open-source intelligence, pulling real-time data, and feeding it to local or open-source models is where the magic happens.

You aren't just building UI components; you are orchestrating intelligence. You are taking a standard full-stack skeleton and giving it a brain. When you plug an Agentic workflow into a clean Next.js frontend, the smoothness and the features you can offer are just on another level.

The Golden Rule: It MUST Be a Useful Application
Here is the most important thing, guys: Integrating all this tech with no real application is NOT a good project.

I’ve seen portfolios where people staple 5 different AI models, a vector database, and LangChain into a single app... but the app just summarizes random Wikipedia articles. Nobody cares about that!

The tech stack doesn't matter if the product is useless.
A simple full-stack app that uses one intelligent agent to solve a very specific, real-world problem (like automating a tedious workflow for your college club, or parsing messy invoices into clean CSVs) will always beat a highly complex app that does nothing useful.

Have an E-commerce clone? Add an agentic support bot that actually reads the product database to answer questions.

Have a standard dashboard? Use open-source intelligence to generate predictive insights on that data, rather than just displaying it in a chart.

So, what's your plan then ...?
The days of getting hired just for knowing how to center a div and write a POST request are fading. The infrastructure is too good now to settle for basic apps.

Are you going to keep writing the alphabet, or are you going to start writing the story?

Check this one!

Ankit Rattan — Sat, 07 Feb 2026 13:11:50 +0000

TTS is on Next Level: My First Experience with ElevenLabs!

Ankit Rattan ・ Feb 7

#ai #discuss #programming #agentaichallenge

TTS is on Next Level: My First Experience with ElevenLabs!

Ankit Rattan — Sat, 07 Feb 2026 13:11:25 +0000

We used to joke about "robot voices." You know, that monotonous, soulless sound that screamed "I am a computer." Well, I just spent the weekend playing with ElevenLabs, and let me tell you... the joke is over. The voice isn't just "good." It’s indistinguishable from a human.
I’ve used TTS (Text-to-Speech) APIs before for simple projects, but this was my first time trying to build a fully Agentic Voice Bot. I went in expecting a steep learning curve and high latency. Instead? I got one of the smoothest developer experiences of my life.

Well, Why this....?
Because "Voice" is the new "Chat." We conquered text with LLMs in 2024. Now, in 2026, we are conquering audio. I wanted to see if I could build a bot that didn't just "read text" but actually represented a product. I didn't want a generic assistant; I wanted a domain expert. And that’s where ElevenLabs blew my mind. It’s not just an API that converts string to audio. It’s a full Conversational AI stack.

This is why I love this tool!
It’s the Knowledge Base feature. This is the game-changer, guys. Usually, to make a voice bot "smart," you have to build a complex RAG pipeline, fetch the text, send it to an LLM, and then send it to the TTS. Latency nightmare, right?

With ElevenLabs, I just:
Uploaded my files (PDFs, Docs).
Pointed it to a URL Endpoint (My product's documentation).

And boom. The bot wasn't just sounding human; it became an expert on my specific data. It answers questions about my product with the confidence of a senior engineer, in a voice that pauses, breathes, and intonates perfectly.

Usually, tools this powerful are gated behind massive enterprise contracts. But ElevenLabs gives you 10,000 credits for free. I thought I’d burn through that in 10 minutes. But honestly? It was more than sufficient to build a working prototype, test the latency, and actually experience the power of their model. You can build a full POC (Proof of Concept). That is how you win developers.

If you are building an AI agent, stop limiting it to text. Go to ElevenLabs, grab those free credits.
Don't just generate static audio.
Connect it to a Knowledge Base.
Feel the smoothness.

So, what's your plan then ...?
The barrier to entry for building "Jarvis-level" voice assistants just dropped to zero. The tech is smooth, the integration is easy, and the result is scary good. Are you going to keep your AI silent, or are you going to give it a voice?

Story of T-Apex: Zero Ideas, 24 Hours, and One Crazy Pivot to the Top 10.

Ankit Rattan — Sat, 10 Jan 2026 16:00:00 +0000

We’ve been here before. Last to last year, we walked into HackCBS full of confidence, and we walked out with nothing. Not even a mention. It stung. So, when HackCBS 8.0 rolled around this November, we honestly went in with zero expectations. Actually, it was worse than that—we went in with zero ideas. We thought, "Here we go again, probably going to be worse than the last one."
But man, were we wrong...

It was a 24-hour hackathon, and for the first two hours, we (Team-Apex) didn't write a single line of code. We just sat there, staring at a whiteboard, planning the flow. The panic started to set in, but then we hit a rhythm. The whole night became a blur of VS Code, endless debugging, and enough caffeine to kill a horse.

The Turning Point There was this specific moment around 3 AM. We were stuck on a logic problem, and suddenly we had 2-3 mentors swarming around our desk. They weren't just nodding; they were arguing, brainstorming, and genuinely excited. That’s when it hit us: We aren't just building a normal app. We are building something unique. That validation fueled us more than the coffee ever could.

We tackled a problem every developer knows: The gap between a designer's sketch and a developer's code. Designers draw beautiful interfaces, and developers spend days fighting with CSS to make it look right. We wanted to fix that.

Why it’s the real deal:
It Sees Context: It doesn't just see boxes. It uses Vision to read your text. If you sketch "Nike", it fetches the real Nike logo. If you sketch a food delivery app, it fetches food images.

No Templates: It writes code from scratch using Tailwind CSS or more.

Natural Language Refinement: Don't like the color? Just type "Make it dark mode" or "Make it more professional," and the AI rewrites the code instantly.

Look, we didn't win the Top 3. But out of hundreds of teams, landing in the Top 10 Nationally is a massive win for us. From walking in with no idea to walking out with a working GenAI product, the journey was insane.

If you are hesitant about joining a hackathon because you think you aren't "ready" or don't have an idea: Go anyway. The pressure cooker environment forces you to learn faster than any tutorial ever will. We learned more about Multimodal AI in those 24 hours than we did in the last 6 months.

Hackathons are really important, guys. The learning, the networking, and yes, even the debugging at 4 AM—it’s all worth it.

Unlocking the "Dark Data"

Ankit Rattan — Fri, 09 Jan 2026 08:22:31 +0000

If you've been coding for a while, you know the old mantra: "Data is the new oil." We've heard it a million times. But let's be honest, for the last decade, most companies were just storing that oil in massive tanks (Data Lakes).

But 2026? The game has changed completely. It’s not enough to just have data anymore. You need to ignite it. We are seeing a massive shift where Data provides the direction, but AI provides the velocity.

Well, Why this....?
Because static dashboards are dead. In the old days (like... 2023), if a CEO wanted to know "Why did sales drop in Q3?", a Data Analyst had to write SQL queries, build a Tableau dashboard, and spend a week cleaning CSVs. That is too slow.

The modern stack is different.
The Data is the context. It’s your logs, your customer support.
The AI is the reasoning engine. It’s the layer that sits on top, reads all that data in seconds, and tells you why it matters.
Without Data, AI is just a hallucinating chatterbox. Without AI, Data is just expensive storage fees. Together? That’s the magic.

Unlocking the "Dark Data" :)
This is the part that blows my mind. Before this AI boom, companies only really used about 20% of their data—the structured stuff in SQL tables. The other 80%? The PDFs, the Slack messages, the recorded Zoom calls? That was "Dark Data." It was unsearchable and unusable.
AI has unlocked that 80%. Using Vector Databases and LLMs, we can now "query" a PDF just like we query a SQL table. We have effectively turned the entire company's knowledge base into one giant API.

This is why I love this shift!
It democratizes the codebase and the business logic. You don't need to be a Python expert or a SQL master to get answers anymore. You just need to ask. I love it because it forces us engineers to stop worrying about writing the perfect query and start worrying about the Data Pipeline.

The focus has shifted from "How do I retrieve this?" to "How do I ensure the AI has the right context?" It’s a faster and smarter way to build.

How RAG Works...

Ankit Rattan — Sat, 03 Jan 2026 09:49:41 +0000

If you've been following the AI space, you've definitely heard the buzzword "RAG" (Retrieval-Augmented Generation). It sounds complex, but honestly? It’s the real deal for making AI actually useful in the real world.

Imagine you are taking a really hard exam.

Case A (Standard LLM): You have to answer purely from memory. You studied the textbook 2 years ago. You might remember a lot, but you'll probably forget the specific numbers, or worse, you'll make them up just to sound smart (Hallucination).

Case B (RAG): You are allowed to take the textbook into the exam with you. When a question comes up, you look up the specific page, read the exact paragraph, and then write your answer.

That is RAG. It’s simply giving the AI an "Open Book" test instead of a memory test. It bridges the gap between the AI's frozen training data and your live, real-time data.

It really just breaks down into three simple steps:

Retrieval (The Search): When you ask a question (e.g., "What is my company's leave policy?"), the system doesn't send that straight to the LLM. First, it searches your private database (PDFs, docs, emails) to find the relevant paragraphs.

Augmentation (The Context): This is the cool part. The system takes your question AND the paragraphs it found, and pastes them together.

Prompt: "Using these notes [paste notes here], answer this question: What is the leave policy?"

Generation (The Answer): The LLM (like GPT-4 or Claude) reads the notes you gave it and generates a perfect answer based only on that data.

It solves the two biggest problems we have with AI: Trust and Recency. Because the AI is citing its sources (the documents it retrieved), it stops making things up. It’s no longer guessing; it’s summarizing. And the best part? You don't have to spend millions "retraining" the model every time you update a document. You just update the document in your database, and boom—the AI knows it instantly.

RAG is the "Hello World" of AI Engineering. It’s the first step out of being a "User" and into being a "Builder."

Software Developer to AI Engineer: The Change is Real!

Ankit Rattan — Fri, 02 Jan 2026 12:22:52 +0000

If the last few years were about discovering AI, 2026 is officially the year of engineering it. The landscape is shifting fast, guys. It’s no longer just about writing clean functions or managing state in React. The role of the "Software Developer" is evolving right before our eyes into something bigger, messier, and way more exciting: The AI Engineer.

It’s not just a fancy title change on LinkedIn—it’s the real deal.

Well, Why this....?
For the longest time, coding was deterministic. Input A always led to Output B. But now? We are moving from writing logic to managing probability.

I realized this shift recently when I stopped just "calling APIs" and started trying to make them behave reliably. The difference between a Junior Dev and an AI Engineer today isn't just knowing Python—it's knowing how to glue a probabilistic model to a deterministic system without everything breaking apart.

The stack has changed. Yeah, I know, we just got used to the old one. But if you want to stay relevant, you need to look at the new primitives. It's not just about SQL databases anymore; it's about:

Vector Databases: Understanding embeddings is the new "understanding SQL."

Orchestration: Tools like LangChain or LangGraph aren't optional. They are the middleware of the future.

Eval Driven Development: You can't just write a unit test assert true. You need to evaluate context.

This is why I love this shift!
It brings the "engineering" back into AI. For a while, it felt like Data Scientists had all the fun. But now? The industry realized that a great model is useless if it can't be integrated into a real product. That’s where we come in. Software engineers are used to handling errors, managing latency, and building scalable architectures. AI Agents need exactly that. They need guardrails, they need memory, and they need structure.

Here's my suggestion -> Don't just be a "Prompt Engineer." That’s surface level. Dive into the architecture. Understand how RAG actually retrieves data, and how an Agent decides which tool to use.

So, what's your plan then ...?
The transition from Software Dev to AI Engineer isn't about throwing away your old skills. It's about upgrading them. Your experience with APIs, system design, and debugging is the bedrock. But the tools are new, and the pace is insane. The change is real, and it’s happening now. Are you going to watch it happen, or are you going to build it?

DEV Community: Ankit Rattan

Interview E01 : From DSA to System Thinking – Rapido

Kafka vs. RabbitMQ: The Post Office vs. The Time Machine

Trust the Process

2026 is different!!

MERN is Just the Alphabet: Why Full-Stack Isn't Enough in 2026

Ankit Rattan ・ Feb 22

MERN is Just the Alphabet: Why Full-Stack Isn't Enough in 2026

Check this one!

TTS is on Next Level: My First Experience with ElevenLabs!

Ankit Rattan ・ Feb 7

TTS is on Next Level: My First Experience with ElevenLabs!

Story of T-Apex: Zero Ideas, 24 Hours, and One Crazy Pivot to the Top 10.

Unlocking the "Dark Data"

How RAG Works...

Software Developer to AI Engineer: The Change is Real!

How’s your 2025? :)

From Consumer to Builder: My 2025

Ankit Rattan ・ Dec 30 '25