Introduction
This week, I tackled one of the most challenging issues I’ve encountered in my journey with ChatCraft. Initially, I felt overwhelmed and unsure of what the maintainers expected from me. The uncertainty paralyzed me for a while, but a thoughtful comment from one of the maintainers provided a much-needed perspective:
"If you can see a path forward that builds on what we have, I'd start with that, and the evolution of this code to work outside of hooks (which is ideally what we want to get to), can happen in follow-ups, by you or someone else.”
This encouragement helped me refocus. It reminded me that I didn’t need to solve everything perfectly in one go - progress could come incrementally. I finally felt comfortable diving back into the issue.
Pivoting My Approach
To overcome my initial overwhelm, I decided to approach the problem differently. Instead of struggling to work on ChatCraft in isolation, I began using ChatCraft itself to assist me in my work.
This meta approach - leveraging the tool to improve the tool - helped me slowly get familiar with its functionality and codebase. It was a pivotal shift that made all the difference.
Here’s why this strategy worked:
Interactive Understanding: By using the tool as intended, I gained insights into how its features connected with the code.
Incremental Learning: Instead of trying to understand everything at once, I tackled one feature or function at a time.
Confidence Building: Seeing the tool work in action boosted my confidence and gave me concrete examples to analyze.
Breaking the Logic Free from Hooks
After understanding the maintainers' comments more clearly, I developed a plan to decouple the logic for fetching AI models from hooks. Previously, this functionality was tightly bound to hooks, limiting its scalability and flexibility. I realized that by refactoring the logic, I could make it reusable outside hooks, paving the way for a more modular architecture.
Key Achievements:
Leverage Existing Functions: Instead of rewriting everything from scratch, I reused and adapted existing functions, ensuring consistency and reducing redundancy.
Custom AI Service Module: I built a new module to handle AI model fetching. This centralized logic allows both the normal text functionality and image generation to share the same infrastructure.
Reduced Hardcoding: The previous implementation hardcoded the model for image generation and relied heavily on hooks for text functionality. My refactor replaced these with the new AI service module, making the code DRY (Don’t Repeat Yourself) and easier to maintain.
What’s Next?
With the core functionality refactored, the next steps for my PR are:
Update Existing Code: Transition all current features to use the new AI service module, ensuring consistency across the project.
Optimize for Scalability: Ensure the code is well-ordered and future-proof, making it easier for others to add new AI models or functionalities without significant rework.
Unify Image and Text Logic: Adapt the image generation feature to use the new logic, eliminating its dependency on hardcoded models.
Once these updates are complete, I’ll be ready to release my changes and gather feedback from the maintainers. Their insights will be crucial to refining my implementation further and ensuring it aligns with the project’s goals.
Reflection
This week taught me the importance of:
Seeking Clarity: Don’t hesitate to ask for clarification or take time to understand the maintainers’ expectations. It can save hours of frustration.
Incremental Progress: You don’t need to solve everything at once. Small, meaningful improvements can lead to significant breakthroughs.
Leaning on Tools: Don’t shy away from using the tools at your disposal, even if it feels unconventional.
Looking forward to wrapping up this work and sharing the final results in my next blog. Stay tuned to see how the maintainers react and what lessons emerge from their feedback!