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The value of know-how in the age of AI

A developer recounts a debugging session where an AI assistant failed to fix a Tailwind CSS issue, leading them to discover the root cause through a GitHub issue thread. The experience highlights the continued importance of human knowledge and community forums in the AI age, as they serve as the source material for AI models and enable engineers to guide LLMs effectively.

read2 min views1 publishedAug 28, 2026

Recently, I struggled with a bug in one of my programs that did not work as expected. The issue made me feel frustrated because I had done everything correctly based on my knowledge at the time. Then I went through a whole debugging session to get it to work. In this blog, I will talk about the debugging session, how I solved the issue, which tools helped me, and how to avoid this issue in the future.

When I started debugging, the first thing I tried was to have AI fix the issue, but it did not work as I wanted. The problem I had was with Tailwind classes on one of my elements; when I used Claude, it converted the Tailwind classes to pure CSS for React Native, and it worked correctly. Yet that was not the solution I wanted; I did not want to have glue code here and there in the codebase and was willing to follow the guideline we had for using Tailwind.

This issue made me go back to the old methods and check online for a solution till I stumbled upon this issue. I not only read the solution but also learned an important lesson that I almost forgot about: third-party elements and classes and how Tailwind works with them. After learning this, I tried to ask the model again about the issue with a direct hint about third-party classes, and it gave me the right solution.

Without people contributing to this issue and sharing their experiences, I would either be lost or just go with whatever AI gave me.

I strongly believe that this kind of issue and online forums are still important, even when AI can provide solutions fast, cause it's the source AI will use to feed itself and learn from. I believe that we now have a strong burden on engineers to learn and understand what underlies the technology they use to move fast in the AI age and to lead LLMs effectively.

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