From Idea to Working Application in 24 Hours: An Engineer's Experience with AI-Assisted Development

Nilesh Indore — Sun, 11 Oct 2026 11:43:43 +0000

After more than 15 years in IT, from assembling PCs and managing Linux infrastructure to building and implementing production infrastructure, I've always enjoyed exploring how technology can solve real problems.

Over the years, I've worked across infrastructure, cloud, automation, and DevOps. I've also had application ideas that I wanted to bring to life but never prioritized because of the time and programming effort involved.

Over the last 24 hours, I decided to revisit one of those ideas.

The result? Bharat Markets — a stock market index monitoring demo built using Go, TypeScript, and GitHub Copilot.

The Idea: A Simple Stock Market Index

A few years ago, I developed a similar application using Python. I wanted to revisit the concept and rebuild it using a different technology stack.

The idea was straightforward:

Generate share prices within configurable minimum and maximum price ranges.
Calculate an overall index value based on individual share prices.
Display index performance through a continuously updating graph.
Provide an admin panel to configure price ranges for individual shares.

The objective was to build a working application that brings these components together.

1. Live Index Monitoring Dashboard

The dashboard displays the current index value, its change, session high and low, and a live-updating graph.

The graph makes it easy to observe how the index value changes over time as individual share prices fluctuate.

Here is the dashboard in action:

Figure 1: Live index monitoring dashboard showing the calculated index value, performance, session range, and price graph.

2. Administrative Configuration Panel

A monitoring dashboard is only one part of the application. I also wanted a simple way to control how individual share prices are generated.

The admin panel allows an authorized user to configure the minimum and maximum price ranges for each share.

These settings are saved to the database and picked up by the share-price services on their next update.

Here is the configuration interface:

Figure 2: Administrative interface for configuring minimum and maximum price ranges for individual shares.

3. The Technology Stack

For this version, I used:

Go: Backend services and application logic.
TypeScript: Frontend application.
GitHub Copilot: AI-assisted development throughout implementation.
Database: Postgres database for persisting share-price configuration.
Cache Database Redis database for live stream graph
Live graph: Visualizing index performance as new values arrive.

The interesting part wasn't simply using a new programming language. It was bringing the backend, frontend, data persistence, configuration interface, and live visualization together into a working application.

4. My Experience Building with GitHub Copilot

I used GitHub Copilot to help translate my requirements into implementation.

Rather than spending all my time working through programming syntax and implementation details, I could focus more on defining the expected behavior, understanding how the components should interact, reviewing the generated code, and testing the results.

There was still engineering work involved: guiding the implementation, resolving issues, checking behavior, and validating the application.

AI didn't remove those responsibilities. It helped me move through the development process more efficiently.

Coming from an infrastructure and DevOps background, I found this particularly interesting. The same problem-solving mindset I use when designing infrastructure can also help when building applications with AI assistance.

5. The Biggest Takeaway

For a long time, two constraints influenced which ideas I pursued: the time available and the effort required to implement them.

I could understand a problem, visualize a solution, and think through how its components should work together. Turning that idea into a working application, however, required a significant investment of time.

AI-assisted development is changing that equation.

The opportunity is not just to write code faster. It's to expand the range of ideas we can realistically explore and implement.

For experienced engineers, this opens up interesting possibilities. We can experiment with unfamiliar technologies, validate concepts, build prototypes, and explore solutions that previously might have remained on our to-do lists.

That doesn't make programming fundamentals, architecture, testing, or engineering judgment less important. If anything, they remain essential for evaluating AI-generated code and deciding whether the resulting solution is fit for its intended purpose.

The real advantage comes from combining engineering experience with AI capabilities.

6. What's Next for Bharat Markets?

This is currently a demonstration project, not a production trading platform or a source of real-time exchange data.

There is plenty of room to extend it. Some areas I'd like to explore include:

Containerizing the application and deploying it on Kubernetes.
Implementing CI/CD and automated testing.
Adding application monitoring, metrics, and observability.
Exploring deployment automation and cloud-native operational practices.

These would provide an opportunity to explore the application beyond development and into the areas I work with most: infrastructure, automation, reliability, and platform engineering.

Final Thoughts

After 15+ years in IT, I believe one of the most valuable professional habits is staying curious and continuing to build.

Technology evolves, tools change, and new ways of working emerge. Our experience gives us a foundation, but we must remain willing to explore and adapt.

This project was a reminder that an idea doesn't have to remain an idea simply because implementation seems time-consuming or outside our immediate comfort zone.

Think through the problem. Define the solution. Use the tools available. Review the results. Keep building.

I'm looking forward to sharing more of my experiments and engineering projects as I continue exploring Platform Engineering, cloud-native technologies, automation, and AI-assisted development.

What have you built recently using AI coding assistants? I'd be interested to hear about your experience.

DEV Community: Nilesh Indore