Forem: Komal Harshita

Deploying My Portfolio with Kuberns AI — Easier Than I Expected

Komal Harshita — Mon, 02 Mar 2026 10:12:33 +0000

I’ll be honest.

Deploying projects is usually the part I delay the most.
Not because I don’t finish building them — but because hosting, setup, and infrastructure always feel overwhelming.

This time, I decided to try deploying my portfolio using Kuberns AI’s one-click deployment.

And it was… surprisingly simple.

My Portfolio Is Now Live

Here’s the link:
👉 https://portfolio-main-73f0916.kuberns.cloud/

Seeing it live felt good — especially knowing I didn’t struggle with complicated configuration to get there.

What the Process Looked Like

It was basically:

Connect my code
Click deploy
Wait a few minutes
Get a live URL

No server setup.
No manual infrastructure configuration.
No confusing steps.

For someone who’s still learning and not deeply into DevOps, that made a big difference.

The Dashboard Experience

The Kuberns dashboard was straightforward.
I could clearly see:

Deployment status
Resource usage
My live URL
Build history

Everything was organized in one place, and I didn’t feel lost navigating it.

How AI Helped

What stood out most was how smooth everything felt.

Instead of worrying about technical setup, I could focus on improving my portfolio itself — refining content, fixing UI details, and making sure everything worked properly.

The AI-driven deployment removed that “deployment fear” barrier.

Why This Matters (At Least For Me)

As students and early developers, we build a lot.
But we don’t always ship.

Making deployment easier makes it more likely that we actually publish our work instead of leaving it on localhost.

If you’re building projects and want a simpler way to go live, you can check it out here:
👉 https://kuberns.com

This experience reminded me that sometimes the hardest part isn’t coding — it’s putting your work out there.

Now that my portfolio is live, I’m more motivated to keep building and keep shipping.

From Prompts to Pipelines: What I Learned About Building Real AI Agents

Komal Harshita — Thu, 04 Dec 2025 05:06:43 +0000

From Prompts to Pipelines: What I Learned About Building Real AI Agents

When I joined the AI Agents Intensive, I thought “agents” were nothing but just smart chatbots. And by the end, I was building ProdigyFlow — a full multi-agent pipeline that loads raw data, cleans it, analyzes it, creates charts, and finally produces an automated report.

This course completely changed how I think about AI.

The biggest shift for me was understanding that agents are not one big model — they’re a team. Each agent has a job, its own tools, and a clear role in the pipeline.
In ProdigyFlow, we built:

a Cleaning Agent to fix messy data
an Analysis Agent to derive insights
a Visualization Agent to generate graphs
and a Main File to make them work together smoothly

Seeing these parts collaborate felt like building a small company inside Python.

There were a lot of behind-the-scenes struggles too:

API keys broke so many times we memorized our .env file 😭
GitHub workflows failed at 2 AM for reasons we did not understand
We learned how important logs, error handling, and reproducible runs really are
Python libraries (Pandas, Matplotlib, tqdm) became our superpowers

But every challenge made the final system more real.

This project also became a true team win. My teammate Priyamvadha and I figured things out together — fixing bugs, testing agents, and redesigning the pipeline more than once.

Here are the project links if you want to explore:
🔗 GitHub: https://github.com/komalharshita/prodigyflow
🔗 Kaggle Submission: https://www.kaggle.com/competitions/agents-intensive-capstone-project/writeups/ProdigyFlow
🔗 My LinkedIn: https://www.linkedin.com/in/komalharshita/

Huge thanks to Google, Kaggle, and the mentors for creating this opportunity. I started this course writing simple prompts, and finished by building an end-to-end intelligent pipeline.