How I Let an AI Refactor My Whole Codebase (Using Gemini 3.5)
The article discusses how a developer utilized AI, specifically Gemini 3.5, to refactor a complex codebase into microservices. By creating an autonomous extraction worker named Flash, the developer was able to automate the process of mapping dependencies and generating clean pull requests. This approach significantly reduced the time and effort required to untangle a legacy monolith that had developed over five years of rapid development.
- ▪The developer faced challenges with a legacy monolith that had tightly coupled logic.
- ▪Using Gemini 3.5, the developer created an AI tool named Flash to automate code refactoring.
- ▪Flash can analyze large codebases and extract modules into isolated microservices efficiently.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3939498) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ahana Chatterjee Posted on May 21 How I Let an AI Refactor My Whole Codebase (Using Gemini 3.5) #webdev #ai #devchallenge #googleiochallenge Google I/O Writing Challenge Submission Every solo developer knows the dread of a Friday afternoon deployment. You push a minor update to a user profile component, and somehow your entire payment processing pipeline goes offline. That is the harsh reality of living with a legacy monolith.
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