Developing with AI: Balancing Speed and Code Quality

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" summary

An experiment builds a complex Electron app with multiple backends and native integrations using Claude AI and ships fast until complexity rises. The team enforces no file over 500 lines, a readable architecture and absolute consistency (one pattern for data fetching not mixed fetch and react‑query). Code review focuses on the changed file list and sensitive areas while routine issues go to the SonarQube safety net, letting teams review decisions instead of every line.

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" chapters (20)

1. 0:00Introduction
2. 0:22How AI Coding Tools Changed Software Development in 2025
3. 1:24Why the IDE Matters Less in the Age of AI Coding Agents
4. 1:52AI Code Generation Creates a New Code Review Bottleneck
5. 2:45Building a Complex Application with Claude AI
6. 3:32Rapid Development Speed Until System Complexity Increases
7. 4:12Why Every AI Coding Session Feels Like a New Developer
8. 4:41Refactoring Reveals What AI Coding Assistants Really Need
9. 5:03Code Consistency Comes from the Existing Codebase
10. 5:20Treating AI Coding Agents Like New Team Members
11. 6:08Rule 1: Maintain Readable Software Architecture
12. 6:33Rule 2: Enforce Strict File Size and Structure Limits
13. 7:02Rule 3: Keep Coding Patterns Consistent Across the Codebase
14. 7:31Gradual Refactoring: Improving Codebases Step by Step
15. 8:21Why Code Review Still Matters in AI-Assisted Development
16. 8:35Using the Changed File List to Focus Reviews
17. 9:35Reviewing What Matters Instead of Every Line of Code
18. 10:06Letting SonarQube Catch Routine Code Quality Issues
19. 10:42Clean Architecture Creates a Virtuous Development Cycle
20. 11:10Three Practical Takeaways for Teams Starting with AI Coding

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