AI code reviewers

Kernel maintainer Greg Kroah-Hartman discusses software security in an era when large language models are increasingly used to write code. The talk, shared as a video, examines how AI-generated contributions affect the Linux kernel's review process and the challenges of maintaining security standards as LLM-assisted development spreads. Readers are weighing how maintainers can vet code at scale when machine-written patches grow in volume.

A question posed to Hacker News asks whether anyone is producing good code with AI coding agents. The discussion invites developers to share real-world experiences with tools like automated coding assistants, probing whether the output is genuinely production-quality or still requires heavy human review. Responses so far are mixed, reflecting ongoing debate in the software community about the practical value of AI-assisted programming.

Engineer Alexey Indeev has written that he no longer reviews code produced by his AI agents, and describes the alternative workflow he uses instead in a Substack essay. The piece has drawn modest attention on Hacker News, where developers are weighing whether traditional code review still makes sense as more work is delegated to autonomous coding agents.

A new project called oh-my-agent has been presented on the German Ubuntu users forum, describing how existing code agents can be used to carry out automated project reviews. The thread, published under the Linux and open source sections, explains how developers can apply the tooling to check their current codebases. Responses so far appear limited, with the discussion still in an early stage.

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.

DoorDash has moved its AI engineering agents from developers' laptops to Flux, a cloud-based platform that now handles around 130,000 automated tasks a month, including roughly 25,000 weekly code reviews. The system runs agents in isolated micro virtual machines and cloud sandboxes designed to keep autonomous code operations secure. The scale of deployment is drawing attention as one of the larger corporate examples of AI agents doing routine software engineering work.

A software engineer writing on Substack says he no longer manually reviews the code produced by his AI coding agents, and has adopted a different approach instead. The piece is drawing attention among developers debating how much oversight AI-generated code needs, as teams increasingly rely on agents to ship production changes with limited human inspection.

SpaceX has reportedly completed a $60 billion acquisition of Cursor, the AI coding company, in what would fold enterprise AI software into a business better known for rockets and Starlink satellite internet. Reports frame the deal as adding a new AI revenue stream alongside Starlink's connectivity business. Details on regulatory review and how Cursor fits into SpaceX's structure remain limited, so the full scope of the deal is still unclear.

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.

An engineer has shared five lessons from running Anthropic's Claude Code as a cloud agent every hour on a large production monorepo. The write-up covers validating review comments, preventing the agent from getting stuck in loops, fixing a 403 error, and reducing token consumption. The post is drawing attention from developers interested in using AI coding agents for automated code review in real production environments.

The open-source firmware project Coreboot has released version 26.09, adding support for the Framework Laptop 12 and introducing a new policy on AI-generated comments in code reviews. The release matters to users who want open firmware alternatives to vendor BIOS on recent hardware, particularly the modular laptop maker's latest device.

A new kind of coding challenge is making the rounds: instead of writing code, developers are asked to review a pull request containing three deliberately planted bugs and find them. The exercise inverts the usual interview format, testing code review and reading skills rather than implementation. Programmers are debating it as a fairer, more realistic test of everyday engineering ability.

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.

An experiment tested whether AI reviewer models can catch cheating in code changes when agents review agents, an assumption behind automated software pipelines. Across 77 diffs containing deliberately planted cheats, three reviewer models caught every exotic trick but approved one case where an assertion was quietly made unfalsifiable, meaning the test could never fail. The finding raises doubts about relying on AI review alone to guarantee code quality where automated testing falls short.

MIT and Sakana AI have introduced a new framework that uses a large language model as an automated judge to evaluate the output of self-improving coding agents. The approach is designed to significantly reduce evaluation costs, which typically require expensive human review or heavyweight testing as AI coding systems iterate and improve themselves.