In the fast-evolving landscape of AI-assisted development, it’s crucial to understand not just the capabilities of our tools, but also their limitations. Today, I want to share a raw, unfiltered experience of attempting to build a complete SaaS application using Bolt, and the valuable lessons learned when things didn’t go as planned.
The Initial Promise
In Part 1 of our journey, we successfully laid the groundwork for our SaaS application using Bolt. We implemented authentication, created user profiles, and everything looked promising. The foundation was solid, and our momentum was strong.
When Things Started to Break Down
As we ventured into more complex territory, several challenges began to emerge:
1. Database Integration Struggles
Our first major hurdle came with database initialization. Despite multiple attempts and improvements to error handling and logging, we kept running into connection issues. What should have been a straightforward setup turned into a time-consuming challenge.
2. Feature Implementation Complexity
While we managed to implement basic features like updating display names and creating a pricing page, each new feature began requiring more effort and troubleshooting. The development process became increasingly staggered and inconsistent.
3. Build and Deployment Issues
The real breaking point came during our attempt to deploy to Netlify. What worked in development (npm run dev) failed in production builds. We encountered multiple build errors that proved difficult to resolve through Bolt alone.
The Six-Hour Mark: A Critical Decision Point
After six hours of working with Bolt, it became clear that continuing down this path would be unsustainable. While Bolt had served us well for simpler tasks, it was struggling with:
- Maintaining consistency across the codebase
- Resolving complex build errors
- Handling production-level requirements
- Scaling with the growing complexity of our application
Key Learnings
This experience taught us several valuable lessons about AI-assisted development:
- Tool Appropriateness: Different tools serve different purposes. Bolt excels at simpler applications and is particularly useful for beginners, but may not be the best choice for complex SaaS applications.
- Recognition of Limitations: Understanding when a tool has reached its limits is crucial. This isn’t about the tool failing – it’s about knowing when to switch to more appropriate tools for the task at hand.
- Flexibility in Approach: Sometimes the best path forward involves changing direction. In our case, this meant transitioning to Cline, a VS Code extension that offers more direct control while maintaining AI assistance.
Moving Forward
This experience wasn’t a failure – it was a valuable learning opportunity. We successfully built our foundation with Bolt in Part 1, and now we’re making an informed decision to switch to tools better suited for our growing application’s needs.
What’s Next
In Part 3 of this series, we’ll explore how to rescue and continue our project using Cline. We’ll address the build issues we encountered and demonstrate how to maintain momentum while switching development tools.
The Takeaway
The key lesson here isn’t about Bolt’s limitations – it’s about understanding that different stages of development might require different tools. As developers, our strength lies not just in knowing how to use tools, but in recognizing when to switch them.
Remember: Knowing when to change direction is just as important as knowing how to move forward.
Stay tuned for Part 3, where we’ll continue our journey using Cline and demonstrate how to effectively transition between AI development tools while building a complex SaaS application.