JetBrains Junie: Is It Better Than Other AI Coding Assistants?

My Journey with Junie: A Game-Changer for Developers #

Over the past three weeks, I’ve been extensively using JetBrains Junie, and I’m genuinely impressed with how it has transformed my development workflow. In this short period, I’ve successfully implemented three significant features with remarkable ease. Based on my experience, Junie offers capabilities that go well beyond what GitHub Copilot provides.

The AI Coding Assistant Landscape #

I’ve been using GitHub Copilot in JetBrains Rider for about a year now. It’s undeniably a helpful tool - whenever I get stuck, I can ask Copilot for assistance. I’ve also experimented with various other LLM models; Sonnet 3.7’s thinking mode has been particularly effective at generating quality code.

However, Copilot has a significant limitation: it doesn’t directly modify your codebase. Instead, you need to manually copy-paste and implement all the suggested changes one by one. This creates a rather cumbersome user experience. In the early days, even the copy-paste functionality was problematic, with text sometimes being uncopyable, making the overall UX quite frustrating. Fortunately, these copy-paste issues have been resolved in recent months.

Alternative Solutions: Cursor #

I’ve also tried Cursor, which offers a more streamlined experience. With Cursor, changes happen automatically, and developers can accept or reject them as needed. This makes the coding process more efficient and intuitive.

Enter Junie: A Different Approach #

Junie takes a unique approach compared to other AI coding assistants. When I describe a new feature or make a request, Junie:

1. Creates a step-by-step implementation plan
2. Executes each step methodically
3. Makes all necessary file changes
4. Provides a comprehensive view of all modifications at the end

This structured approach has significantly improved my productivity and code quality.

Real-World Success Stories with Junie #

In just the past few days, I’ve completed three substantial features that demonstrate Junie’s effectiveness:

1. Swaggerific Loader Animation (GitHub PR #18)
   • This feature had been non-functional for about a year
   • With Junie’s assistance, I completed the implementation in approximately 40 minutes
2. Shortcut Settings UI (GitHub PR #19)
   • While not 100% complete, the UI now successfully displays defined shortcuts
   • Implementation time: roughly 1 hour
3. Proxy Settings UI (GitHub PR #20)
   • Developed a fully functional implementation
   • Total development time: 1-2 hours

After completing these features, I opened pull requests and leveraged additional AI tools like CodeRabbit and SonarQube to scan the new code and minimize potential issues.

The Future of Software Development #

This new AI-assisted workflow has dramatically increased my development speed and efficiency. I’m convinced that AI tools like Junie will continue to accelerate software delivery timelines far beyond what we currently consider possible.

The combination of AI-powered code generation, structured implementation approaches, and automated code quality checks creates a powerful development ecosystem that allows developers to focus on solving complex business problems rather than getting bogged down in implementation details.

For developers looking to enhance their productivity, I highly recommend giving Junie a try. The structured approach and seamless code integration capabilities make it stand out among the growing field of AI coding assistants.

References:

• Guide