EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 14·5 min read
Stop sending me huge PRs; a rant
TL;DR
Large pull requests (PRs) are overwhelming for reviewers, making code comprehension difficult and time-consuming. Smaller, manageable PRs enhance the review process and improve code quality.
✦ Why It Matters
Prioritize smaller pull requests to enhance code review efficiency and maintain quality.
Key Takeaways
- Limit pull requests to manageable sizes for easier review.
- Use clear variable names to reduce the need for comments.
- Avoid relying solely on AI for understanding large code changes.
- Encourage a culture of smaller, incremental code contributions.
Full Summary
Reviewing large pull requests, sometimes exceeding a thousand lines, is becoming a significant burden for software maintainers. The author argues that while AI tools can expedite coding, they often lead to larger, less digestible code changes that complicate the review process.
The rationale behind small PRs is to create manageable pieces of work that are easier to understand and review, rather than to deliver complete features in one go. The author also criticizes excessive comments on well-named variables, suggesting that clear naming should suffice.
Furthermore, the reliance on AI for code comprehension is questioned, as it may not effectively address the challenges posed by large PRs. Ultimately, the author calls for a return to smaller PRs to facilitate better code reviews and maintain quality.