Best of Code Review — July 2025

1. 1
   Video

   The Coding Gopher·42w

   99% of Developers Don't Get Git Rebase

   Git rebase is a powerful command that rewrites commit history by replaying commits from one branch onto another, creating a linear history instead of merge commits. Unlike git merge which preserves branching structure, rebase transplants commits as if they were created on top of the target branch. The process involves finding the common ancestor, temporarily storing unique commits, and reapplying them with new hashes. Interactive rebase allows developers to squash, reorder, edit, or drop commits for cleaner history. However, rebasing shared branches is dangerous as it rewrites history and can disrupt collaborators. Best practices include only rebasing local feature branches and using clear commit messages for better code review workflows.

   173

   14
2. 2
   Article

   The Polymathic Engineer·42w

   What to Look for in Code Reviews

   A comprehensive guide covering the essential aspects of effective code reviews, prioritized by importance. Covers design considerations as the most critical factor, followed by functionality review, complexity assessment, test quality evaluation, naming and documentation standards, and style consistency. Emphasizes the importance of structured approaches over random scanning, focusing on catching bugs, improving code quality, and facilitating team learning through systematic review practices.

   136

   5
3. 3
   Article

   System Design Newsletter·42w

   How to Do Code Review

   Code reviews are essential for maintaining code quality as teams grow, but they can create bottlenecks and conflicts without proper guidelines. Effective code reviews require both authors and reviewers to follow best practices: authors should keep changes small, self-review first, and provide clear descriptions, while reviewers should respond within 24 hours, focus on constructive feedback, and approve when code is good enough rather than perfect. The typical workflow involves creating pull requests, running automated checks through CI, conducting human reviews, making updates based on feedback, and deploying approved changes. AI tools like CodeRabbit can complement human reviewers by handling routine checks and providing consistent feedback, ultimately improving developer velocity while maintaining code quality.

   80
4. 4
   Article

   Graphite.dev·42w

   How I got Claude to write code I could actually ship

   Claude Code can generate thousands of lines of code quickly, but large AI-generated PRs are difficult to review safely. The solution is teaching Claude to create stacked pull requests using Graphite's gt CLI, breaking complex features into smaller, focused, reviewable chunks. By adding specific instructions about stacking workflows and requiring Claude to plan its approach before coding, developers can make AI-generated code more manageable and shippable. The GT MCP tool helps align Claude's internal planning with actual PR structure.

   48

   2
5. 5
   Article

   Lobsters·44w

   Two Simple Rules to Fix Code Reviews

   Two fundamental rules can dramatically improve code review effectiveness: minimize response time to prevent context loss and momentum disruption for authors, and always include explicit 'because' clauses in comments to clearly communicate the reasoning behind feedback. Quick response doesn't mean quick approval, but rather prompt engagement with the change. Senior engineers play a crucial role in establishing positive code review culture through their behavior, as others will follow their example.

   47

   1
6. 6
   Article

   Product Hunt·43w

   Palmier: Agents that write prod-ready code. Automatically.

   Palmier is an autonomous AI software engineering assistant that automatically handles development tasks like writing code, fixing bugs, reviewing pull requests, and triaging issues. It integrates with GitHub and Slack through event-driven triggers, working asynchronously with full codebase context to reduce manual developer work and accelerate shipping cycles.

   23
7. 7
   Article

   HashiCorp·44w

   AI is making developers faster, but at a cost

   Google's 2024 DORA report reveals that AI coding tools increase code review speed by 3.1% and quality by 3.4%, but cause a 7.2% reduction in delivery stability. The instability stems from AI's limited understanding of broader system context, reinforcement of outdated patterns, and security vulnerabilities like hardcoded secrets. Organizations can mitigate these issues through platform engineering practices including secure infrastructure modules, centralized secrets management, unified visibility systems, and golden images with pre-approved workflows.

   21

   4
8. 8
   Article

   Programming Digest·43w

   cursor for code review

   Cubic is an AI-powered code review platform that automates pull request reviews by identifying bugs, antipatterns, and tech debt. It integrates with GitHub, provides one-click fixes, supports custom coding standards, and offers features like intelligent file ordering and context-aware chat. The platform emphasizes security with SOC 2 compliance and offers tiered pricing from free to enterprise plans.

   20

   1
9. 9
   Article

   FREEK.DEV·45w

   Continuous AI in software engineering

   Continuous AI involves automatically integrating AI tools into software development workflows to streamline processes like pull request reviews, issue labeling, and generating daily project summaries. This approach aims to enhance productivity by embedding AI assistance seamlessly into existing development practices rather than requiring manual intervention.

   11
10. 10
    Article

    Community Picks·46w

    Writing Code Was Never The Bottleneck

    LLMs have made code generation faster and cheaper, but the real bottlenecks in software development remain unchanged: code reviews, knowledge transfer, testing, debugging, and team coordination. While AI tools can quickly produce working code, understanding, verifying, and maintaining that code requires the same human effort as before. The challenge has shifted from writing code to ensuring quality and shared understanding as teams process larger volumes of generated code, making careful review and thoughtful design more critical than ever.

    10

    4