DEV Community: Poorvi Shetty

EcoLens Pro: How I Turned an Abandoned Climate Prototype into an AI-Powered Sustainability Platform

Poorvi Shetty — Sun, 07 Jun 2026 11:44:46 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge

What I Built

EcoLens Pro – AI-Powered Climate Simulation Platform

EcoLens Pro is an AI-powered platform that helps users explore future climate scenarios through interactive simulations, predictive analytics, and sustainability visualizations.

The project began as an unfinished side project focused on climate data visualization. While the initial concept was promising, the application remained incomplete due to unfinished simulations, limited interactivity, and an unpolished user experience.

For the Finish-Up-A-Thon Challenge, I revisited the project and transformed it into a more complete platform by improving the architecture, completing simulation workflows, enhancing dashboards, and creating a more polished experience.

Key features include:

Interactive climate simulations
Sustainability impact visualizations
Predictive environmental analytics
AI-assisted insights
Responsive modern UI
Improved project structure and documentation

The goal of EcoLens Pro is to make climate data easier to understand and more engaging for students, researchers, and sustainability enthusiasts.

Demo

Live Demo

https://eco-lens-pro.vercel.app/

GitHub Repository

https://github.com/poorvishetty193/EcoLens-Pro

Architecture Overview

AI Workflow

Screenshot

The Comeback Story

I originally started EcoLens Pro as an experiment combining AI, climate data, and interactive visualization.

The idea was exciting, but the project eventually stalled.

The original version had:

Incomplete climate simulation logic
Basic dashboard prototypes
Limited user interaction
Inconsistent UI components
Minimal documentation
Experimental features without a clear structure

As the codebase grew, making changes became increasingly difficult, and the project was eventually abandoned.

The GitHub Finish-Up-A-Thon Challenge gave me the motivation to revisit the project and finally finish what I had started.

Instead of immediately adding new features, I focused on strengthening the foundation:

Refactored the codebase
Improved application architecture
Completed unfinished simulation modules
Enhanced dashboards and visualizations
Fixed bugs and edge cases
Added documentation
Improved responsiveness and usability

Much of the work involved cleaning up technical debt rather than building flashy features.

By the end of the challenge, EcoLens Pro had evolved from an abandoned prototype into a functional platform that demonstrates how AI can help users explore climate futures and sustainability decisions.

My Experience with GitHub Copilot

GitHub Copilot played a major role in helping me finish EcoLens Pro.

Rather than using Copilot only for code generation, I used it throughout the entire development process.

Understanding Old Code

Since the project had been inactive for a long time, Copilot helped me quickly understand parts of the codebase that I had not touched in months.

I used Copilot Chat to:

Review existing components
Understand data flows
Analyze API interactions
Identify refactoring opportunities

Refactoring and Cleanup

Copilot helped me:

Refactor repetitive code
Create reusable utility functions
Improve TypeScript types
Simplify component logic
Improve code consistency

Debugging

During development, I used Copilot to:

Investigate TypeScript errors
Debug API integration issues
Resolve state management problems
Identify edge cases

Documentation

Copilot also assisted with:

Writing README documentation
Generating code comments
Improving project organization

My Workflow

The workflow that worked best for me was:

Ask → Plan → Build → Test → Refine

Using Copilot as a development partner helped me maintain momentum and significantly reduced the time spent on repetitive tasks, allowing me to focus on completing the project.

Built With

React
TypeScript
Tailwind CSS
Node.js
Express.js
Python
AI / Machine Learning
GitHub Copilot
GitHub

Final Thoughts

The GitHub Finish-Up-A-Thon Challenge gave me the opportunity to revisit an unfinished idea and finally turn it into a project I am proud to share.

EcoLens Pro is no longer an abandoned prototype—it is now a functional platform that makes climate data more interactive, understandable, and accessible.

Most importantly, this challenge reminded me that unfinished projects do not have to stay unfinished.

The Most Important Part of Google I/O 2026 Wasn’t a Model — It Was the Infrastructure

Poorvi Shetty — Sun, 24 May 2026 16:47:55 +0000

This is a submission for the Google I/O Writing Challenge

Everyone keeps talking about the models.

Gemini Omni.

Gemini Flash.

AI video generation.

Smart glasses.

But after watching Google I/O 2026, I honestly don’t think the models were the most important part.

The real shift was infrastructure.

For the first time, it genuinely feels like Google is rebuilding its ecosystem around AI systems that can execute tasks, use tools, maintain memory, trigger workflows, and collaborate across systems.

And as someone who spends a lot of time experimenting with AI projects and multi-agent workflows, that realization hit hard.

Because the biggest problem with modern AI development is no longer intelligence.

It’s orchestration.

The Hardest Part of AI Isn’t the AI Anymore

A few years ago, getting good model outputs was the challenge.

Now, models are everywhere.

What actually becomes difficult is everything around them — execution environments, deployment, state management, scheduling, memory handling, backend coordination, and orchestration between systems.

At some point your “small AI side project” accidentally turns into distributed systems engineering 😭

And that’s exactly why Google I/O 2026 felt important to me.

Almost every major announcement pointed toward the same idea:

AI is becoming infrastructure instead of just a feature.

That shift matters much more than people realize.

Managed Agents Was the Biggest Signal

Out of everything announced, Managed Agents genuinely stood out the most.

Not because it looked flashy.

But because it quietly solved one of the biggest friction points developers run into while building advanced AI systems.

Instead of stitching together APIs, containers, execution layers, memory systems, deployment pipelines, schedulers, and orchestration logic manually, Google is trying to reduce all of that complexity into a much simpler workflow.

The idea that developers can spin up stateful AI systems with cloud execution, memory, workflows, deployment, and tool access without spending weeks configuring infrastructure is honestly huge.

Especially for students, indie developers, and hackathon builders.

Because now the gap between:

“I have an idea”

and

“I built something real”

is shrinking incredibly fast.

Why This Felt Weirdly Personal

For the past few months, I’ve been experimenting with AI systems where different components handle different responsibilities.

Some focused on research.

Some handled UI generation.

Some managed workflows and automation.

And one thing kept happening over and over again:

The intelligence wasn’t the hardest part.

Coordination was.

Getting systems to communicate properly.

Maintaining execution flow.

Passing context correctly.

Managing backend orchestration.

Deploying everything without accidentally breaking five other things 😭

So while watching Google I/O, I had this strange moment where everything suddenly felt familiar.

Not because I had built anything remotely close to Google’s scale obviously 😭

But because the architecture patterns felt incredibly similar.

It genuinely feels like the industry is moving toward ecosystems of specialized AI systems collaborating together instead of relying on one massive model to do everything.

And I think that shift is much bigger than people realize.

AI Is Slowly Becoming an Operating Layer

One thing I kept noticing throughout the keynote was how deeply AI is now being integrated into every layer of software development.

Not just chat interfaces.

Actual infrastructure.

Firebase evolving around AI workflows.

Gemini becoming more action-oriented.

AI-generated interfaces.

Workflow orchestration.

Agent execution systems.

Asynchronous task handling.

The important shift is this:

AI is moving from “assistant” to “participant.”

And honestly, I think that changes how software gets built forever.

Because once AI systems can execute tools, maintain memory, collaborate, trigger backend actions, schedule workflows, and adapt dynamically, they stop feeling like features.

They start feeling like operating systems.

But I Still Think Something Is Missing

As exciting as everything looked, I still think there’s one thing the industry hasn’t fully solved yet:

Genuine collaboration between AI systems.

Right now, most “multi-agent” systems still feel more like parallel task execution.

One agent writes code.

Another researches.

Another reviews outputs.

But real collaboration is messier than that.

Real collaboration involves disagreement, refinement, tradeoffs, discussion, and decision-making.

The really interesting future, in my opinion, is when AI systems can actively challenge each other’s reasoning to improve outcomes instead of simply working side-by-side.

That’s the part I’m most excited to see evolve.

Why Google I/O 2026 Actually Matters

I think this year’s event mattered because it made advanced AI development feel more accessible.

A few years ago, building systems like these required huge infrastructure, expensive compute, large engineering teams, and deep ML expertise.

Now students can prototype ideas in days.

Indie developers can experiment with architectures that previously only existed inside large research labs.

And honestly?

That’s probably the biggest shift of all.

Because innovation becomes far more interesting when more people can participate in it.

Final Thoughts

Google I/O 2026 didn’t just introduce new AI tools.

It revealed a future where AI systems actively participate in how software is designed, built, deployed, and experienced.

And for developers experimenting with AI right now, that future suddenly feels much closer than expected.

We’re slowly moving beyond AI that simply answers questions.

We’re entering the era of AI systems that actually do things alongside us.

And honestly?

I think we’re only seeing the beginning of it.