DEV Community: Anushka

How I Started Using GitHub to Document My Projects

Anushka — Wed, 02 Sep 2026 03:12:43 +0000

For a long time, I thought GitHub was something you were supposed to understand before you started using it.

Commands, repositories, commits, pushing code, errors...

Honestly, when I first started using it, a lot of it was confusing.

I only started using GitHub seriously toward the end of my 2nd year of B.Tech.

And my first proper repository was for a project I was actually building:

👉 AI Civic Issue Mapper

That was when GitHub stopped feeling like something I should learn and started becoming something I actually needed to use.

🚀 My First Repository

AI Civic Issue Mapper was my first proper GitHub repository.

As I worked on the project, I started putting almost everything there:

Source code
Project structure
README documentation
Features
Tech stack
Screenshots
Deployment information
Roadmap
Known limitations

It wasn't perfect from the beginning.

I was learning while building.

And that made the repository much more meaningful to me than simply uploading a finished project.

😵 Learning GitHub Wasn't Easy

One of the biggest problems I faced initially was simply understanding what was happening.

I'd run a command and get an error.

Then I'd wonder:

“Okay... what did I do wrong?” 😭

Sometimes I didn't even understand what the command was supposed to do in the first place.

I had to gradually understand things like:

What a repository actually is
How to push code
How commits work
How changes are tracked
How to update a project
How to structure a README
How GitHub connects with deployment

I learned a lot of this through Claude and ChatGPT, especially when I got stuck on errors or didn't understand a command.

But the biggest learning came from actually using GitHub while working on real projects.

💻 Now I'm Using GitHub for More Than Just Code

Today, I use GitHub for almost everything related to my projects.

I use it to:

Store my code
Document projects
Track changes
Showcase my work
Collaborate with others
Connect projects with deployment
Keep my work organized
Have something I can eventually show recruiters

And I'm still learning.

I don't consider myself a Git or GitHub expert.

There are still things I don't understand immediately, and I still run into errors.

But now, instead of being completely lost when something goes wrong, I can usually figure out where to start.

🤖 My Projects Are Growing Too

AI Civic Issue Mapper was my first proper repository, but it didn't stay my only project.

I currently have three projects on GitHub.

Two of them involve AI, while another is a project I'm working on with a friend based on her idea.

That has also helped me understand something important:

GitHub isn't just about having a huge number of repositories.

It's about having a place where your actual work exists and grows.

🌱 What I’ve Realized

When I first started, I thought I needed to become good at GitHub before I could properly use it.

Now I think the opposite is closer to the truth.

You learn GitHub by using GitHub.

You make mistakes.

You get errors.

You search for what they mean.

You fix them.

And eventually, commands that once looked confusing start becoming normal.

I'm still somewhere in that process.

🎯 My Biggest Takeaway

If you're a student who hasn't started using GitHub yet, don't wait until you have 10 or 20 projects.

You don't need a huge portfolio.

You don't need to know every Git command.

You don't even need to be an expert.

Start documenting what you're already building.

Even one small project is enough to start.

For me, that first project was AI Civic Issue Mapper.

And looking back, I'm glad I started when I did.

Because now GitHub isn't just a place where my code sits.

It's becoming a record of what I'm learning and building.

How I'm Managing College, GATE Preparation, Projects and Learning AI

Anushka — Sat, 29 Aug 2026 02:56:27 +0000

One thing I've slowly realized during my B.Tech journey is that learning Computer Science is not difficult only because of the subjects. Sometimes, the bigger challenge is figuring out how to manage everything at the same time.

Right now, I'm trying to balance college, GATE preparation, AI, projects, and the usual college work. And honestly, I don't always manage it perfectly.

🎓 College Comes First

My college syllabus is actually helping me decide what to focus on.

Since subjects like DAA and DBMS are already part of my B.Tech syllabus, I'm preparing them for GATE alongside my regular studies. This way, the same topic serves two purposes instead of feeling like completely separate preparation.

I'm also studying Engineering Mathematics because I know it is important for GATE and requires consistent practice.

🎯 Preparing for GATE

I started my GATE preparation around the end of July.

I'm treating GATE 2027 as a trial attempt, while 2028 is my main target. I decided this because I don't want to rush through the syllabus just to say that I completed it.

For now, I'm focusing on building my concepts properly.

I'm using Gate Smashers for DAA and DBMS and Gate Wallah for Mathematics.

My current approach is simple:

Learn the concept
Make notes
Practice questions
Revise
Move forward

It sounds simple, but following it consistently is the difficult part.

🤖 Learning AI and Building Projects

Alongside college and GATE, I don't want to stop working on AI.

I've realized that watching lectures alone doesn't give me the same understanding as actually trying to build something.

That's why I've been working on projects such as my AI Civic Issue Mapper, where I got to work with technologies like Python, Flask, MySQL and APIs.

Building a project also showed me that things don't always work the way they do in tutorials. There are bugs, deployment problems, confusing errors and plenty of moments where I have to search, experiment and try again.

But that's also where I feel I'm learning the most.

⏰ The Biggest Problem: Time Management

This is probably the part I'm still struggling with the most.

There are days when I have enough time but don't use it properly. On other days, college work takes up most of my time and I can't give GATE preparation or projects the amount of time I planned.

So I'm learning that having a huge daily timetable isn't necessarily the answer.

Instead, I'm trying to decide what is actually important for that day and complete that first.

Some days that might be GATE preparation.

Some days it might be college work.

And sometimes it might simply be working on a project.

🌱 I'm Still Figuring It Out

I don't have a perfect productivity system, and I don't think I need one right now.

I'm still a student trying to figure out how to balance everything while learning what actually works for me.

For me, the goal isn't to become perfect at managing college, GATE, AI and projects overnight.

It's to keep moving forward without completely giving up on any of them.

And I think that's what this phase of my B.Tech journey is really about — learning not just Computer Science, but also learning how I learn best.

Building My First AI Project: What Went Right and What Went Wrong

Anushka — Thu, 27 Aug 2026 16:51:05 +0000

After learning different concepts in Computer Science and AI, I wanted to build something that was more than just another college assignment.

That is how I started working on my first major project — AI Civic Issue Mapper.

The idea was simple: what if people could report everyday civic problems like garbage dumping, potholes, water leakage, or broken streetlights using photos and location information, and the complaints could then be tracked properly?

That simple idea turned into a much bigger project than I initially expected.

💡 The Idea

The main goal of the project was to create a platform where citizens could report civic issues and track what happened to their complaints.

A user can report an issue by providing information such as:

📸 A photo of the problem
📍 Location/GPS information
📝 Description
⚠️ Urgency level

The system then helps organize the complaint, route it to the appropriate department, detect possible duplicate reports, and track its status.

I also wanted the project to focus on transparency, so there is a public page where people can see complaint-related statistics without needing to log in.

🛠️ What I Used

For the project, I worked with technologies that I was already learning as a Computer Science student:

Python
Flask
MySQL
HTML, CSS & JavaScript
Leaflet.js for maps
Google OAuth for login
Brevo API for emails
Render for deployment

One of the biggest things I learned was that knowing individual technologies is very different from actually connecting them into one working application.

😅 What Went Right

The most satisfying part was seeing the project actually work.

I was able to build features such as:

User registration and login
Google login
Issue reporting with images and location
Automatic department routing
Duplicate complaint detection
Admin dashboard
Complaint status tracking
Resolution proof uploads
SLA tracking
Public transparency page
Complaint tracking without login
Responsive pages for different screen sizes

Eventually, I also deployed the application so that it could be accessed online.

That moment felt very different from simply running a project on localhost.

🤯 What Went Wrong

Of course, everything did not go smoothly.

One of the biggest lessons was that building a project involves a lot more debugging than coding from tutorials.

I had to deal with problems involving:

Connecting the Flask backend with MySQL
Managing environment variables
Authentication
Email delivery
Deployment
Database changes
Handling errors properly
Making pages responsive
Testing different parts of the application

Sometimes I would fix one problem and accidentally create another one somewhere else.

There were also times when something worked perfectly on my local machine but behaved differently after deployment.

That was probably one of the most frustrating parts.

🧠 What I Learned

This project taught me something that tutorials cannot fully teach you.

You don't really understand a technology until you have to use it to solve an actual problem.

I learned how different parts of a software project depend on each other.

For example, adding a simple feature could require changes to the frontend, Flask routes, database structure, validation, and sometimes deployment configuration.

I also learned that building a project is not about writing thousands of lines of code.

It is about understanding the problem, breaking it into smaller pieces, testing those pieces, and being patient when things don't work.

🤖 What About the "AI"?

Interestingly, the project is called AI Civic Issue Mapper, but the AI image-classification part is still something I want to improve.

The current version focuses more on the complete civic-reporting system, while AI-based image classification is part of the future roadmap.

I actually think this is an important lesson too.

A project does not have to be "100% complete" before you can learn from it or share it.

There is always another feature, another improvement, or another problem to solve.

🌱 What's Next?

The project is still evolving.

Some of the things I want to work on next include:

🤖 AI-based image classification
🗺️ A complete multi-complaint map
🌡️ Geo-spatial heatmaps
Better testing
Further improvements to the user experience

I also want to continue improving my understanding of AI and eventually integrate what I am learning into projects like this.

❤️ Final Thoughts

Building my first major project made me realize that learning Computer Science is not just about completing a syllabus.

It is about taking what you learn and trying to build something with it.

There were bugs, confusing errors, failed attempts, and plenty of moments where I had no idea what I was doing.

But there was also something much more valuable — the feeling of finally seeing an idea turn into a working application.

And honestly, I think that is one of the best ways to learn.

This is only my first major project, and I still have a lot to learn.

If you are also a student who is thinking about building your first project, my biggest advice would be:

Start before you feel ready. You will learn a lot along the way.

🔗 Project: : https://github.com/Anushka190921/ai-civic-issue-mapper

🌐 Live Demo: : https://ai-civic-issue-mapper.onrender.com/

How I'm Preparing for GATE 2027 Alongside My B.Tech

Anushka — Tue, 25 Aug 2026 13:45:32 +0000

Preparing for GATE was not something I had planned from the very beginning of my B.Tech.

As I started thinking more seriously about my career, I realized that coming from a tier-3 college means I sometimes need to look beyond the opportunities available directly through my college. For me, GATE felt like one of those opportunities that could open doors to many different possibilities in the future.

I also know that I started preparing relatively late for the 2027 attempt. Because of that, I'm being realistic about what I can achieve in the time I have.

For me, GATE 2027 is going to be my first attempt and a learning experience, while GATE 2028 is my main target.

That doesn't mean I want to take 2027 lightly. I want to use it to understand the actual exam environment, experience what preparing for GATE is really like, identify my weak areas, and learn what works for me.

In a way, 2027 is going to help me prepare better for 2028.

When I Actually Started

I started preparing toward the end of July 2026.

By then, I knew that there wasn't a lot of time left for GATE 2027. Instead of trying to rush through the entire syllabus just to say that I had completed it, I decided to focus on building my fundamentals properly.

I want to use this attempt to understand the exam and the preparation process while gradually building a stronger foundation for my main attempt in 2028.

Where I Started

I decided to begin with DAA, DBMS, and Mathematics.

There was a practical reason behind this choice.

DAA and DBMS are already part of my B.Tech syllabus, so preparing them for GATE can also help me with my university studies. I thought it would make more sense to work on subjects that are useful for both instead of treating college and GATE preparation as completely separate things.

I also decided to start Mathematics because it is an important part of GATE preparation and I know that building a strong foundation in Mathematics takes time.

So, rather than trying to study everything at once, I'm starting with these subjects and will gradually move forward.

The Resources I'm Using

For DAA and DBMS, I'm currently using Gate Smashers.

For Mathematics, I'm using Gate Wallah.

These are the resources I'm following at the moment. I'm still figuring out what works best for me, so I don't want to keep switching between resources every time I find a new playlist or recommendation.

For now, my approach is simple:

Choose a resource → understand the concepts → make notes → practice questions → revise.

I know I'll probably change my strategy as I progress, and that's okay.

The Biggest Challenge: Time Management

The biggest challenge I'm facing right now is time management.

Preparing for GATE alongside B.Tech isn't always easy. There are college classes, assignments, exams, projects, and other things I'm trying to learn at the same time.

Some days I manage to study properly.

On other days, I don't get nearly as much time as I had planned.

And honestly, that's something I'm still trying to improve.

I've realized that making a perfect timetable is easy. Following it consistently is the difficult part.

Because 2027 is my first attempt, I don't want to put unnecessary pressure on myself to study everything perfectly in a limited amount of time. At the same time, I don't want to use that as an excuse to stop trying.

So I'm trying to make the most of the time I actually have.

What I Want to Learn From GATE 2027

Since GATE 2028 is my main target, I want to use the 2027 attempt for more than just getting a score.

I want to understand what the actual exam feels like.

I want to know how well my preparation translates into solving questions under pressure.

I want to identify the subjects and topics where I struggle.

And most importantly, I want to understand what I need to change before my main attempt.

I think having this experience will make my 2028 preparation much more focused.

What I'm Trying to Do Differently

One thing I'm learning already is that starting matters more than waiting for the perfect time.

I could keep telling myself that I'll start when college becomes less busy, when I have more time, or when I have a perfect study schedule.

But there will probably always be something going on.

So for now, I'm trying to work with the time I actually have.

I'm not expecting myself to finish everything immediately. I want to build my concepts, practice questions, solve PYQs as I progress, and understand where I need to improve.

The goal is to make 2027 useful, even if 2028 is the attempt I'm preparing for more seriously.

This Is Only the Beginning

I don't know exactly how my GATE 2027 attempt will turn out.

Maybe I'll cover less of the syllabus than I planned. Maybe I'll discover that some subjects need much more work than I expected. Maybe the actual exam experience will teach me things that I could never learn from preparation alone.

And that's okay.

I'm treating GATE 2027 as my first learning experience and GATE 2028 as my main target.

For now, I just want to start, stay consistent, learn from my mistakes, and keep improving.

I'll continue documenting this journey here—what I'm studying, what works for me, what doesn't, the mistakes I make, and the things I learn along the way.

This is the beginning of my GATE journey, and I'll be documenting it as I go.

What I Wish Someone Had Told Me About Learning Computer Science

Anushka — Sat, 22 Aug 2026 08:02:57 +0000

When I started my B.Tech in Computer Science, I thought learning Computer Science would mostly mean attending classes, studying for exams, and getting better at programming.

But the more I started exploring it, the more I realized that studying Computer Science and actually learning it are two different things.

I'm still figuring things out myself, but after my first few years of college, there are some things I really wish someone had told me earlier.

1. You Don't Have to Learn Everything at Once

There are so many things to learn in Computer Science—C++, Java, Python, DSA, DBMS, Web Development, Machine Learning, AI, and much more.

When you see everything together, it's easy to feel like you're already behind.

I've felt that way too.

But I've slowly realized that trying to learn everything at the same time only makes things more confusing. It's better to focus on one thing, understand it properly, practice it, and then move forward.

You don't need to know everything just because someone else does.

2. Watching Tutorials Isn't the Same as Learning

This is something I had to learn the hard way.

It's easy to watch a programming tutorial, understand everything while it's being explained, and feel like you've learned something.

Then you close the video and try to write the code yourself.

Suddenly, it's a completely different story. 😭

That's when I realized that understanding something and being able to use it yourself are two different things.

So now, whenever possible, I try to practice what I'm learning instead of only watching lectures.

Making mistakes while coding is part of the learning process.

3. Don't Ignore the Basics

Sometimes advanced topics look much more exciting.

Learning AI sounds more interesting than learning Python fundamentals. Building a neural network sounds more exciting than understanding basic programming concepts.

But eventually, the basics catch up with you.

I've realized that having a strong foundation makes advanced topics much easier to understand.

So I'm trying to focus on programming fundamentals, DSA, mathematics, and core Computer Science subjects instead of constantly rushing toward the next advanced topic.

4. Projects Teach You Differently

There is something different about actually building something.

When you're following a tutorial, someone else has already solved most of the problems for you.

When you're building your own project, you suddenly have questions like:

Why isn't this working?

Why am I getting this error?

How should I solve this problem?

And honestly, those frustrating moments can teach you a lot.

I've experienced this myself while working on my AI projects. They haven't always worked perfectly, but every problem has taught me something new.

A project doesn't have to be huge. Even a small project that you actually understand can teach you more than simply completing another tutorial.

5. You Don't Have to Be Ready Before Starting

I used to think I needed to learn more before starting a project, writing a blog, or sharing something publicly.

But if I keep waiting until I feel completely ready, I'll probably keep waiting forever.

There will always be something else to learn.

So I'm trying to follow a simple approach:

Start → Learn → Make mistakes → Improve → Repeat.

That's also one of the reasons I started writing these blogs.

I'm learning in public instead of waiting until I become an expert.

6. Stop Comparing Your Journey With Someone Else's

This is probably one of the easiest traps to fall into as a student.

You see someone who started programming earlier. Someone else already has several projects. Another person has an internship.

And suddenly you feel like you're behind.

I've felt that way too.

But everyone's starting point is different.

Instead of constantly asking:

"Why am I not where they are?"

I've started asking:

"Am I further ahead than I was a few months ago?"

That question is much more useful.

7. Consistency Matters More Than Motivation

There are days when you feel extremely motivated.

You make a huge study plan, save ten courses, and decide you're going to change everything from tomorrow.

And then a few days later, the motivation disappears. 😭

I've done this more times than I'd like to admit.

That's why I'm slowly trying to focus less on motivation and more on consistency.

Even if I learn something small on a particular day, it's still progress.

I'm Still Learning

I don't want this article to sound like I've figured everything out.

I haven't.

I'm still learning Computer Science. I'm still struggling with concepts, trying to become more consistent, and figuring out which areas interest me the most.

But that's exactly why I'm writing about it.

These aren't lessons from someone who has already reached the finish line. They're lessons from a student who is still somewhere in the middle of the journey.

And maybe that's what makes them useful.

If you're also learning Computer Science, I hope something here helps you avoid a mistake I made or simply reminds you that you don't have to have everything figured out right now.

We're all learning.

One concept, one project, and one mistake at a time. 💜

What's something you wish someone had told you when you started learning Computer Science?

I'd love to hear it in the comments.

How I'm Learning AI in Public: My Roadmap

Anushka — Sun, 16 Aug 2026 18:30:46 +0000

When I decided that I wanted to seriously start learning Artificial Intelligence, I quickly realized that one of the hardest parts wasn't finding resources.

It was figuring out where to start.

There are countless courses, YouTube playlists, roadmaps, tools, frameworks, and technologies to learn. Every time I looked at what other people were doing, I felt like there was something else I should be learning.

So instead of trying to learn everything at once, I decided to create a roadmap for myself.

This isn't a roadmap written by an AI expert or someone who has already mastered everything.

It's simply the roadmap I'm following as a B.Tech Computer Science (Artificial Intelligence) student who is still learning.

And I'm sharing it publicly because I want to document what works, what doesn't, and how my understanding changes along the way.

Why I Decided to Learn AI Seriously

I'm studying Computer Science with Artificial Intelligence, so AI has naturally become one of the areas I want to explore deeply.

But for a long time, I didn't really know how to approach it.

I knew that AI was important. I knew that Machine Learning, Deep Learning, and other AI technologies were becoming increasingly relevant.

But knowing that something is important and actually learning it are two completely different things.

After spending a lot of my first and second year without doing as much as I wanted, I realized that I couldn't keep waiting for the "right time" to begin.

I had to start somewhere.

So I decided to stop worrying about learning everything at once and focus on building my foundation first.

Step 1: Strengthening My Programming Foundation

Before jumping deeply into Machine Learning, I want to become more comfortable with programming.

Python is one of the main languages I'm using for my AI journey because of how widely it is used in data science and Machine Learning.

Alongside Python, I'm also learning C++ for Data Structures and Algorithms and working with Java for my college studies.

My goal isn't to become an expert in multiple languages.

It's to understand programming concepts well enough that the language itself doesn't become a barrier when I'm trying to solve a problem.

Step 2: Learning the Mathematics Behind AI

This is one part of AI that I initially underestimated.

Machine Learning isn't just about calling a library and training a model.

There is mathematics behind many of the concepts.

So I'm working on strengthening areas such as:

Linear Algebra
Matrices and Determinants
Probability
Statistics
Basic Calculus and mathematical concepts used in ML

I'm learning these alongside my regular studies instead of trying to finish all the mathematics first.

Step 3: Data Handling

Once the programming and mathematical foundations become stronger, I want to become comfortable working with data.

This includes learning tools such as:

NumPy
Pandas
Data visualization
Data cleaning
Data preprocessing

I think this stage is especially important because real-world Machine Learning isn't just about training models.

A large part of the work is understanding and preparing the data.

Step 4: Machine Learning

This is where I want to start understanding how Machine Learning actually works.

Instead of only learning how to use ML libraries, I want to understand the concepts behind the algorithms.

Some of the areas I plan to cover include:

Supervised Learning
Unsupervised Learning
Regression
Classification
Clustering
Model evaluation
Feature engineering
Overfitting and underfitting
Model optimization

My goal is to be able to look at a problem and understand why a particular Machine Learning approach might be useful, rather than simply following a tutorial.

Step 5: Deep Learning

After building a solid Machine Learning foundation, I want to move toward Deep Learning.

This is where topics such as neural networks, CNNs, and other deep learning architectures come into the picture.

I've already had some exposure to Deep Learning through projects, but I want to understand it more systematically rather than only using pre-trained models.

Step 6: Learning Through Projects

This is probably the most important part of my roadmap.

I don't want my learning journey to become:

Watch a course → make notes → watch another course → forget everything.

I want to build things.

Even if my first projects aren't perfect, I believe that actually trying to solve a problem teaches me things that watching tutorials cannot.

One of the projects I've worked on is an AI-based civic issue mapper, where AI can be used to classify civic problems from images and help map them.

Projects like this help me understand how different concepts come together—from data and models to backend development and real-world applications.

Step 7: Learning in Public

This is where this blog comes in.

Instead of keeping everything private until I feel "good enough," I want to document my progress while I'm learning.

I'll share:

Things I'm learning
Concepts I find difficult
Projects I'm building
Mistakes I make
Resources that actually help me
Lessons I learn along the way

I'm hoping that this will also keep me accountable.

When you know that you're documenting your journey, it becomes a little harder to keep saying, "I'll start tomorrow."

I'm Not Following a Perfect Roadmap

One thing I've already realized is that learning doesn't always happen in a straight line.

Sometimes I'll have to go back and revise something.

Sometimes I'll spend days understanding a concept that looked simple at first.

Sometimes I'll change the order in which I'm learning things.

And that's okay.

This roadmap is a guide, not a strict schedule.

I don't want to rush through topics just so I can say I've completed them.

I'd rather understand fewer concepts properly than collect dozens of certificates without actually knowing how to use what I learned.

What I Want to Achieve

I don't expect to become an AI expert overnight.

My current goal is much simpler.

I want to become technically stronger, build meaningful projects, understand the fundamentals properly, and gradually become ready for internships and future opportunities.

And most importantly, I want to look back after a year and know that I actually made progress.

What's Next?

This is just the beginning of my AI learning journey.

I'll probably make mistakes. I'll probably change this roadmap. I'll probably discover new areas that I want to explore.

But this time, I don't want to wait until I know everything before I begin.

I'm going to learn, build, share, and improve along the way.

And if you're also learning AI or Computer Science, maybe we can figure it out together.

This is my roadmap for now.

Let's see where it takes me.

Things I Wish I Knew Before Starting My B.Tech in Computer Science

Anushka — Thu, 13 Aug 2026 17:43:37 +0000

If I could go back to my first day of B.Tech, there are quite a few things I would tell myself.

Not about which subjects to study or how to score better in exams, but about everything beyond the classroom that I didn't understand at the time.

When I started college, I thought I had plenty of time to figure everything out. Like many students, I was excited about college life—making friends, attending classes, enjoying campus life, and creating memories.

And honestly, that's how my first year went.

Then came my second year.

There were classes, exams, assignments, and, along with them, plenty of distractions. I spent a lot of time on my phone, kept postponing things, and constantly told myself that I would start learning "later."

At that point, I didn't realize how quickly the semesters were passing.

Eventually, I looked around and realized something that honestly scared me a little.

The competition in the tech industry was getting tougher, while I felt like I hadn't done enough to prepare myself.

That was when I started asking myself:

"What am I actually doing with my time?"

It wasn't a very comfortable realization, but it was an important one.

I finally understood that college wasn't just about attending classes and passing exams. There was so much more I needed to learn, explore, and build on my own.

So, instead of waiting for the "perfect time" to start, I decided to begin with whatever I could.

And that's when my learning journey really started.

Looking back now, there are several things I wish I had understood much earlier. These are not lessons from someone who has everything figured out—they are lessons I'm learning from my own experience as a B.Tech student.

Welcome to My AI & Computer Science Journey

Anushka — Sat, 01 Aug 2026 14:18:37 +0000

Hello and welcome! 👋

Hello to everyone out there reading my very first blog.

First of all, thank you for taking the time to read my very first blog. It truly means a lot to me.

My name is Anushka, and I'm a pre-final year B.Tech Computer Science (Artificial Intelligence) student.

I started this blog to document my learning journey and share everything I learn along the way. My hope is that it makes the journey a little easier for other students who are walking a similar path.

How it all started

Like many students, when I entered university as a first-year student, I was excited about college life. I thought it would be all about making friends, enjoying campus life, attending classes, and creating memories.

And honestly, that's exactly how my first year went.

Then came my second year.

Along with classes and exams, there were always distractions. I spent a lot of time on my phone, kept postponing things, and told myself that I would start learning "later."

One day, I looked around and realized something.

The competition in the tech industry had become incredibly high, and I felt like I hadn't done anything meaningful to prepare myself.

That realization hit me hard.

The turning point

Instead of continuing to delay, I decided it was finally time to start learning.

I began my journey during my summer break.

I still remember asking myself,

"What am I actually doing?"

That simple question made me realize that waiting for the perfect moment wasn't going to help.

The best time to start was now.

What's next?

This is just the beginning of my journey.

In the coming articles, I'll be sharing what I learn about Artificial Intelligence, Computer Science, programming, projects, and everything in between.

I'm not an expert.

I'm simply a student who's learning in public, one step at a time.

If you're on a similar journey, I hope we can learn and grow together.

Thank you for reading my first blog. ❤️