Best of Code Review — September 2025
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The Coding Sloth·36w
AI Makes Programming Fun
Programming with AI tools can make development more enjoyable by automating boring tasks like learning new codebases, writing PR summaries, and handling code reviews. The author demonstrates using AI to contribute to VS Code's open source project, showing how AI can help understand large codebases, generate code, and streamline workflows. While AI tools like code completion and automated agents can speed up development and reduce tedious work, they're not perfect and require careful use to avoid over-reliance and potential errors.
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Hacker News·34w
How to stop functional programming
A satirical piece that demonstrates the absurdity of banning functional programming practices in the workplace. Through code examples, it shows how attempting to avoid pure functions and functional concepts leads to unnecessarily complex and awkward solutions, highlighting the inherent value of functional programming principles even when explicitly prohibited.
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The Daily WTF·36w
The Getter Setter Getter
A Java code example demonstrates poor naming conventions where a getter method calls a setter method, neither of which actually gets or sets values. The real issue is converting a Java DTO object to a Map<String,String>, which indicates discomfort with object-oriented programming and creates unnecessary complexity when Java already provides transparent serialization capabilities.
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SD Times·35w
Microsoft shares Insiders preview of Visual Studio 2026
Microsoft launched the Insiders preview for Visual Studio 2026, featuring deeper AI integration that suggests tests and maintains code consistency, significant performance improvements across all IDE operations, a refreshed UI with better iconography and themes, and backward compatibility with Visual Studio 2022 extensions. The new Insiders Channel will receive monthly updates replacing the previous Preview Channel.
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8th Light·36w
AI-assisted Coding is Not Doing My Dishes and Laundry
AI-assisted coding tools like Cursor and ChatGPT struggle with the mundane tasks developers hoped they would handle. While AI excels at creative coding tasks, it consistently fails at precise, structured work like project setup and data processing without significant developer oversight. The author argues that AI requires extensive scaffolding and review processes that often take longer than doing the work manually, challenging the promise that AI would free developers from tedious chores.
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The PayEng Playbook·35w
How To Review Code That Deals With Money
A systematic 4-step approach to reviewing code in financial systems: start with test files to understand context, verify alignment with issue requirements, mentally solve the problem yourself, then compare the actual implementation against your approach. This method helps catch errors early, provides deeper understanding, and is especially critical for payment systems where mistakes can be costly.
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Lobsters·33wLinus Torvalds and the Supposedly “Garbage Code”
A detailed analysis of Linus Torvalds' criticism of a RISC-V kernel contribution, examining the technical merits of helper functions versus explicit bit manipulation code. The author argues that proper helper functions with type casting and bit masking are safer and more maintainable than inline expressions, demonstrating correct implementations in both C macros and C++ constexpr functions.
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Reinier·37w
Cursor AI Agents Work Like 10 Developers (Cursor VP Live Demo)
Lee Robinson, VP of Developer Experience at Cursor, demonstrates practical techniques for maximizing productivity with AI coding tools. The demo covers prompt structuring, custom commands, and using AI agents for bug fixes and code reviews, showing how AI makes development more accessible while improving code quality.
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The Art of Simplicity·36w
The prompt as documentation: Should AI-generated code include its origin story?
Explores whether developers should include original AI prompts as comments in generated code. Examines benefits like reproducibility, intent preservation, and debugging aid against drawbacks including code clutter, maintenance burden, and version control complexity. Raises questions about the long-term value and practicality of documenting AI prompts in codebases.
