AI code generation

Microsoft has released MXC, a sandboxed code execution system, on GitHub. The tool lets developers run untrusted code in isolated environments, a growing need as AI agents and automated pipelines increasingly execute generated code. Developers are weighing in on its design and how it compares with existing sandboxing options.

Commenters are arguing that research on AI productivity in software development has focused on the wrong question. The claim is that for AI-assisted coding to deliver real gains, another language model should review most of the code an AI generates, with humans acting mainly as judges and moderators rather than line-by-line reviewers.

Linux creator Linus Torvalds says he "really likes using AI", a notable stance as the Linux kernel community debates how to handle LLM-generated code and content in contributions. The comments come amid discussions over disclosure rules for AI-assisted patches, with maintainers weighing quality and accountability concerns against growing use of AI coding tools. Torvalds' pragmatic view contrasts with stricter proposals from parts of the developer community.

JetBrains has published a developer diary describing how it built a retrieval-augmented generation pipeline for semantic code search, sharing field notes and practical lessons from the process. Developers are discussing the technical trade-offs of applying RAG to codebases, a topic of growing interest as AI-assisted coding tools become mainstream in software engineering workflows.

New research reported by Ars Technica finds that AI coding agents do generate more code, but software output overall does not increase. The study concludes that productivity gains are absorbed by a human review bottleneck, as developers must still verify, test and integrate machine-written code. Readers are debating whether review capacity, not generation, is now the limiting factor in software development.