Using AI to write better code more slowly

Developers are increasingly using AI not just to generate code quickly but to write better, more reliable code at a slower pace, emphasizing thorough review and bug detection.

Recent discussions on Hacker News reveal a shift in how AI tools are being employed in software development. Instead of focusing solely on rapid code production, developers are leveraging AI to conduct detailed reviews, find subtle bugs, and improve code quality through slower, more deliberate workflows. This approach involves using multiple AI agents to scrutinize pull requests, identify critical bugs, and guide developers in fixing issues before merging.

One developer shared their workflow, which involves running several AI models—such as Claude, Codex, and Cursor Bugbot—to detect bugs, prioritize them, and then fix critical issues before proceeding. This method often uncovers pre-existing bugs, leading to more stable code and better understanding of complex architectures. The process may slow down the development cycle but results in higher-quality code and a more thorough understanding of the system.

Why It Matters

This approach challenges the common perception that AI’s primary benefit is rapid code generation. Instead, it suggests that AI can enhance code quality when used for careful review and bug detection, promoting more sustainable and maintainable software development practices. For organizations, this could mean fewer bugs, more robust codebases, and a shift toward quality over speed.

Background

Historically, AI in coding has been associated with speeding up development—often at the expense of code quality. Recent discussions indicate a paradigm shift where developers now see AI as a tool for meticulous review. This change is partly driven by the recognition that AI models are effective at bug detection and that slowing down to thoroughly vet code can lead to better long-term results. The idea aligns with broader industry debates about sustainable coding practices and reducing technical debt.

“You can use AI just as effectively to write high-quality code more slowly. It’s about thorough review, bug detection, and making the code better for the next developer.”

— Hacker News user

“Using multiple AI agents to scrutinize PRs helps find tons of bugs with near-zero false positives, leading to more stable and understandable code.”

— Developer sharing workflow

What Remains Unclear

It is still unclear how widespread this slower approach will become and whether organizations will adopt it at scale. The long-term impacts on productivity and project timelines remain to be seen, and some developers remain skeptical about slowing down in fast-paced environments.

What’s Next

Further experimentation and case studies are expected to evaluate the effectiveness of slow, AI-assisted development workflows. Industry adoption may increase if these methods prove to reduce bugs and technical debt without significantly delaying releases.

Key Questions

Can AI reliably find all bugs in a codebase?

AI models are effective at detecting many types of bugs, especially subtle or overlooked issues, but they are not infallible. Human oversight remains important.

Does slowing down development reduce overall productivity?

While it may decrease raw lines of code produced, this approach can lead to fewer bugs and less rework, potentially saving time in the long run.

Is this slower, quality-focused approach suitable for all projects?

It depends on project requirements. Critical systems may benefit more from thorough review, while rapid prototyping might still prioritize speed.

Will this method replace traditional fast-paced coding workflows?

Currently, it appears more as a complementary approach rather than a wholesale replacement, especially for high-stakes or complex systems.

Source: Hacker News