Coffee, Code, and Junie: My Journey to 10x Productivity with JetBrains AI

Source: Dev.to

What I Built with Junie

ScrIM Coach

An esports performance‑analysis tool that transforms raw League of Legends match data into actionable coaching insights.

• Data source: GRID Esports API
• Features:
  • Executive summary of professional‑team performance
  • Detailed analysis of strategic patterns, execution gaps, and draft weaknesses
  • Practice‑planning recommendations
  • Printable action plans for coaches

The project is licensed under the MIT license.

• Source code:
• Live demo:

My Favourite Part – Brave Mode

In Brave Mode I give Junie full control to modify the project and its files. I simply provide a prompt, take a long sip of coffee, and let Junie do the heavy lifting.

Prompt: “Refactor this to follow clean architecture with proper separation of concerns.”

Junie restructures 15 files simultaneously, and I return to a codebase that is suddenly… beautiful. The best part? Nothing broke—tests still passed and the UI still rendered.

Important Contribution – UI Enhancement

Coming from a backend and Android background, pixel‑perfect UI was never my strong suit. With Junie I can:

1. Draft a UI by describing the desired look.
2. Attach a screenshot of the current (or broken) UI and the target design.
3. Let Junie generate or adjust the code until the UI matches the mockup.

The result is a polished, responsive interface without me having to become a front‑end specialist.

Impressive Refactoring

I originally built the MVP with a haphazard architecture, ignoring SOLID principles and clean‑code standards. With a single prompt, Junie refactored the entire codebase to adhere to Clean Architecture and SOLID—and nothing broke. Adding new features afterward became far easier and more scalable.

UI Magic for Backend Developers (Recap)

• Provide a screenshot of the problematic UI.
• Describe the desired outcome.
• Junie automatically generates the necessary HTML/CSS/JS (or Jetpack Compose, etc.) to achieve pixel‑perfect results.

The Flow‑State Achievement

My workflow became smoother, and I entered a genuine flow state throughout the hackathon. Adding features or enhancing functionality was just a prompt away, and Junie’s built‑in debug mode produced working code automatically.

Most Misused Capability – Version Control

I also experimented with Junie’s ability to handle Git operations:

• Automatic commits with clear messages.
• Pushes to the remote repository.
• Squashing multiple commits into a single one.

Junie obeyed like a diligent servant—though I quickly learned to use these powers responsibly.

Wish It Was There

I wish changes could be deployed live the moment I modify the codebase, because “legends debug in production.” If IntelliJ offered MCP server support (continuous deployment from the IDE), it would be just a prompt away. The absence of this feature left me a little disappointed.

Closing Thoughts

If you’re like me—comfortable in JetBrains’ ecosystem but curious about AI assistance—take the leap. The hackathon may be over, but the learning isn’t. Junie showed me that the future of development isn’t about replacing developers; it’s about amplifying our abilities and helping us reach that elusive 10× developer state.

Amplifying our humanity through technology.