My Journey Through AI Coding Tools: From GitHub Copilot to Claude Code
The Evolution of AI-Powered Development
As a developer who has been experimenting with AI coding tools since their early days, I've had the opportunity to test and compare several major platforms. After using these tools extensively in real projects, I want to share my honest thoughts on what works, what doesn't, and where I think the future is heading.
The landscape of AI-assisted coding has evolved dramatically, and choosing the right tool can significantly impact your productivity. Here's my personal ranking and experience with four major players in this space.
4th Place: GitHub Copilot - The Disappointing Pioneer
I'll be honest - GitHub Copilot was one of the first AI coding tools I tried when it launched, and it left me underwhelmed. While it deserves credit for pioneering the space, the code quality was consistently poor in my experience.
The autocomplete suggestions often felt more like distractions than helpful assistance. Simple functions would be suggested with overcomplicated implementations, and the context awareness was limited. I found myself spending more time reviewing and correcting Copilot's suggestions than it saved me.
The Code Review Problem
What really turned me off was when our repository integrated Copilot's code review feature. Approximately 80% of the comments it generated were pure noise - flagging non-issues, missing actual problems, and providing generic feedback that added no value to our review process. Our team quickly disabled it.
Has it improved since then? Possibly. But my first impression was so negative that I haven't felt compelled to give it another serious try. In a crowded field of AI coding tools, first impressions matter, and Copilot didn't deliver when I needed it to.
3rd Place: Atlassian Rovo - The Promising Newcomer
I'll admit upfront - I haven't had hands-on experience with Atlassian Rovo yet, as it's relatively new to the market. However, from what I've seen and the strategic positioning, it has significant potential.
The Context Advantage
What excites me about Rovo is its integration with the broader Atlassian ecosystem. If it can seamlessly access Jira tickets, Confluence documentation, Bitbucket repositories, and other Atlassian tools, the context engineering required would be minimal compared to other solutions.
For teams already invested in the Atlassian ecosystem, this could be a game-changer. Instead of manually feeding context to your AI tool, Rovo could automatically understand:
- Current sprint objectives from Jira
- Architecture decisions documented in Confluence
- Historical code patterns from Bitbucket
- Team discussions and requirements
If executed well, this comprehensive context could make Rovo a formidable competitor in the AI coding space. I'm looking forward to testing it when I have the opportunity.
2nd Place: Cursor - The Polished AI IDE
Cursor has been my daily driver for over six months, and I have to say - it's genuinely impressive as an AI-integrated IDE. When I first started using it, the convenience was immediately apparent.
What Cursor Gets Right
The VSCode integration makes the transition seamless. I love how easy it is to invoke the AI interface, and the checkpoint feature is incredibly useful for tracking different approaches to a problem. Once Claude-4 integration was added, the coding capabilities became genuinely impressive.
The ability to easily include files, links, and images in your prompts is fantastic. Cursor automatically parses content and calls appropriate tools, making the context-sharing process smooth and intuitive. And the pricing is remarkably reasonable for what you get.
The Agent Mode Experience
Cursor now supports an AI agent mode that can work autonomously on tasks. While I appreciate this feature, I prefer to review changes before applying them - I want to maintain control over the direction of my codebase rather than letting the AI run completely free.
Pro Tip: MCP Integration
One trick that significantly improved my Cursor experience was installing MCP (Model Context Protocol) servers like Supabase and Context7. This allows the AI to access the latest documentation and significantly improves code quality, especially when working with rapidly evolving frameworks and APIs.
The Limitations
While Cursor is excellent as an AI IDE, I sometimes feel limited by its interface. For complex workflows or when I want to automate multiple steps, the GUI can feel constraining compared to programmatic approaches.
1st Place: Claude Code - The Power User's Dream
I've been using Claude Code since its first week of release, and despite using the same underlying Claude model as Cursor, it feels significantly more powerful and capable.
The CLI Advantage
Being a command-line tool, Claude Code offers unparalleled flexibility. I can integrate it into complex workflows, automate sequences of tasks, and even set up systems that could theoretically run with minimal human intervention (though I wouldn't recommend that for production code).
My GitHub Codespaces Workflow
I experimented with a particularly powerful setup using Claude Code in GitHub Codespaces. The combination was impressive - I could spin up environments, make complex changes across multiple files, run tests, and deploy changes, all through natural language instructions.
The theoretical potential is enormous. With the right workflow setup, you could automate significant portions of development tasks. I built what I consider a sophisticated workflow that could genuinely compete with tools like Devin in terms of autonomous capability.
The Reality Check: Cost and Quality
Here's the catch - I only used this workflow for a brief period before canceling it. The costs were substantial; I hit the $200 monthly limit multiple times. But more importantly, I realized that I don't have an endless stream of ideas that warrant this level of automation.
For serious projects (not toy applications), I prefer to carefully review and understand every change. The value of Claude Code isn't in replacing human judgment, but in dramatically accelerating the implementation of well-thought-out ideas.
The Devin Comparison
When properly configured, I believe Claude Code workflows can surpass what Devin offers. The flexibility of the CLI interface combined with Claude's reasoning capabilities creates a powerful combination that feels more like working with an extremely capable pair programmer than a simple autocomplete tool.
A Senior Developer's Perspective: AI Code vs. Junior Code
As a Senior SDE, I've noticed something interesting: reviewing AI-generated code feels remarkably similar to reviewing code from interns and junior developers. This isn't meant as a slight against either - it's actually a revealing insight into where AI currently stands and what we need to watch for.
Common Patterns I See
Both AI and junior developers tend to produce code that:
- Focuses on the happy path: The main functionality usually works, but edge cases are often overlooked
- Lacks defensive programming: Missing null checks, boundary validations, and error handling
- Has superficial test coverage: Tests exist but don't cover corner cases or failure scenarios
- Misses performance implications: Code works functionally but may not scale or handle large datasets efficiently
- Overlooks security considerations: Input sanitization, authentication checks, and data validation gaps
The Edge Case Problem
Perhaps the most striking similarity is how both AI and junior developers approach edge cases. They'll implement the core logic beautifully, but then miss scenarios like:
- What happens when the input is empty?
- How does this behave with extremely large datasets?
- What if the external API is down?
- How does this handle concurrent access?
- What about internationalization and different locales?
Why This Matters for Senior Developers
This observation has shaped how I work with AI tools. Just as I wouldn't ship junior code without thorough review, I apply the same scrutiny to AI-generated code. The review process becomes less about syntax and more about:
- Architectural decisions: Does this fit our system design?
- Edge case analysis: What could break this code in production?
- Maintainability: Will another developer understand this in six months?
- Performance implications: How will this scale?
- Security review: Are we introducing any vulnerabilities?
The Mentoring Analogy
Working with AI tools feels like mentoring an extremely fast learner who can implement anything you describe but lacks the experience to know what you didn't mention. The key is providing the right level of context and constraints, just as you would when guiding a junior developer.
This is why I believe senior developers won't be replaced by AI - our value increasingly lies in knowing what questions to ask, what problems to anticipate, and how to architect systems that won't break under real-world conditions.
The Bigger Picture: Implementation vs. Ideas
As I reflect on my experience with these tools, I keep coming back to a fundamental shift that's happening in software development. Implementation is becoming less important, while ideas, mindset, empathy, and communication are becoming everything.
The 70-30 Rule
In the past, maybe 70% of a developer's value came from implementation skills - knowing syntax, debugging, optimizing code. I believe that percentage will drop to around 30% in the near future. The remaining 70% will come from:
- Ideas: What should we build? What problems are worth solving?
- Mindset: How do we approach complex problems? What patterns should we apply?
- Empathy: Understanding user needs and team dynamics
- Communication: Explaining technical concepts, gathering requirements, collaborating effectively
The 100x Engineer Reality
AI development is accelerating at an incredible pace. Developers who embrace these tools and learn to work effectively with them aren't just getting a small productivity boost - they're becoming 100x engineers in specific domains.
But here's the key: it's not about the tools themselves. It's about understanding how to leverage AI to amplify your uniquely human capabilities rather than replace them.
Final Thoughts: Opportunity and Challenge
We're living through an exciting inflection point in software development. AI coding tools aren't just making us faster - they're changing what it means to be a developer.
The developers who thrive won't be those who resist AI or those who rely on it blindly. They'll be the ones who learn to dance with these tools, using them to eliminate tedious implementation work while focusing their human creativity on the problems that truly matter.
This is both an enormous opportunity and a significant challenge. The opportunity is to become dramatically more effective and work on more ambitious projects. The challenge is to continuously evolve our skills and focus on the areas where human intelligence remains irreplaceable.
The future belongs to developers who can bridge the gap between human insight and AI capability. Are you ready to build that bridge?