AI accelerates code generation but increases the need for rigorous verification. The article compares solo vs. team workflows for reviewing AI-written code.
A guide to building an AI-powered automated code review system for Azure DevOps pull requests using OpenAI models via Microsoft Foundry.
A guide on improving communication in pull requests to enhance code reviews and project understanding.
Simon Willison critiques the trend of developers submitting untested, AI-generated code, arguing it shifts the burden of real work to reviewers.
Argues that software engineers must prove their code works through manual and automated testing, not just rely on AI tools and code reviews.
Martin Fowler's link blog covers mainframe modernization, AI code review challenges, and building disposable web apps with LLMs.
A guide to using a PowerShell script with Ollama and the Qwen 2.5 model to perform AI-assisted code reviews on PostgreSQL database migration scripts.
Explores the long-term impact of LLMs on software development, focusing on code validation and the balance between disposable and durable software.
Explores how AI-generated content challenges traditional work review heuristics and the need for new evaluation methods.
Learn how to use GitHub Copilot's #changes variable and other context tricks to analyze your git diffs and improve coding workflow.
A guide to managing complex Git workflows using stacked branches, focusing on techniques for handling common scenarios beyond basic commit changes.
An engineering manager reflects on the role's challenges, feeling accountable but not directly credited, and compares it to surfing.
A developer explains why generative AI coding tools don't increase their productivity, citing the time needed to review code and the responsibility for the final product.
![AI: did you check your work? [blog]](https://alldevblogs.blob.core.windows.net/thumbs/article-98b23f154f19-full-84e80bd4.webp)
A developer's cautionary tale about LLM inaccuracies in simple tasks, highlighting the need to verify AI-generated results.
A software engineer shares stories of taking initiative to meet colleagues and Alan Kay, leading to career opportunities and collaboration.
Explains why developers should split unrelated changes into separate pull requests for faster reviews, cleaner Git history, and better automation.
A senior developer shares eight key lessons on writing clean code, conducting effective reviews, and professional best practices from decades of experience.
A developer shares a custom Git script to improve the process of reviewing large Pull Requests by creating a dedicated review branch for local IDE navigation.
Explores how AI-powered tools automate and improve code reviews by identifying bugs, vulnerabilities, and inconsistencies to enhance software quality.
A developer explains why they limit AI use in programming, citing the difficulty of verifying AI-generated code without a proper mental model.
