AI code review tools

Linux kernel maintainer Greg Kroah-Hartman has given a talk examining what large language models mean for software security. Discussion centres on how AI-generated code affects vulnerability review, maintainer workload and trust in open-source supply chains, with developers debating whether LLM tools add risk or help auditors find bugs faster.

A Hacker News discussion asks whether anyone is actually producing good code with AI coding agents, drawing engagement from developers weighing in on their real-world experience with tools like code-generating assistants. The thread taps into ongoing debate over whether AI-written code is reliable enough for production use or still requires heavy human review.

A MakeUseOf comparison put three leading AI coding tools — Anthropic's Claude Code, OpenAI's Codex, and Google's Antigravity — to the test by having each build the same Android app, with all three finishing in under 30 minutes. The piece names which tool the reviewer would choose to use again, adding to ongoing debate among developers over which AI coding assistant delivers the best results.

System76, the company behind the Pop!_OS Linux distribution, has banned AI-generated code across many of its COSMIC codebases, the desktop environment underpinning the operating system. The decision bars contributions written by AI tools from large parts of the project, with developers expected to write and review code themselves. The move is drawing attention and debate among open-source developers weighing code quality, licensing and trust issues around AI-assisted programming.

A developer has published findings that CodeRabbit, a popular AI-powered code review tool, produces excessive noise when reviewing real pull requests. On a public benchmark of 50 genuine pull requests, the tool flagged far more issues than necessary, and a modification reduced its review noise by roughly a third. The work has sparked discussion among developers about whether AI review tools create too many low-value comments that slow teams down.

Former Google CEO Eric Schmidt said the best programmers no longer write code themselves, pointing to a shift toward directing AI tools that do the coding instead. His comments highlight how artificial intelligence is reshaping software engineering, with experienced developers increasingly acting as reviewers and architects of machine-generated code rather than typing it out line by line.

A new blog post by Andrew Moffat argues that changes generated by AI should be evaluated and designed to minimize the cognitive load they place on developers reviewing them. The piece discusses how programmers can structure AI-assisted modifications so they are easier to understand and verify. It is being shared among software developers discussing the practical challenges of integrating AI tools into everyday coding workflows.

A common workflow flaw is drawing attention: when a reviewer approves a change, an AI coding agent can make further edits while the task still displays as approved. The problem arises because approval is attached to the task rather than to a specific delivery. Commenters argue a solid review process should clearly define what was approved, at which point, and whether later edits require re-review.

Discussion is growing around whether open source software projects can keep pace with AI tools that generate code far faster than human developers. The concern centres on how volunteer-driven communities, which maintain much of the world's critical software infrastructure, will absorb or compete with automated code production while still ensuring quality, security and proper review.