I like this quote on code review: "The idea that humans will reliably look through code to find the increasingly rare issues that automated tools miss seems like a fantasy." By Marc Brooker, AWS.
Developers say AI coding tools work—and that's precisely what worries them
Ars spoke to several software devs about AI and found enthusiasm tempered by unease.
Code Worth Writing - Ray Myers | SSW 2026
Vibe Coding Failures: Documented AI Code Incidents
A curated directory of real-world incidents where AI-generated code failed in production.
Devin Review: AI to Stop Slop | Cognition
As code generation gets easier, code review is the new bottleneck. That's why we're launching a new way to quickly review and understand complex PRs in our latest tool for codebase understanding - augmenting human attention with AI.
The Gap Through Which We Praise the Machine
My current theory of agentic programming: people are amazing at adapting the tools they're given and totally underestimate the extent to which they do it, and the amount of skill we build doing that is an incidental consequence of how badly the tools are designed.
AI vs human code gen report: AI code creates 1.7x more issues
We analyzed 470 open-source GitHub pull requests, using CodeRabbit’s structured issue taxonomy and found that AI generated code creates 1.7x more issues.
Measuring the Impact of Early-2025 AI on Experienced Open-Source...
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...
The End of Code Review: Coding Agents Supersede Human Inspection
Code review has been the primary quality gate in software development since Fagan formalised code inspection in 1976. For five decades, having a human examine and comment on a colleague's changes before merge has been a cornerstone practice at organisations of every size. Coding agents are large language model (LLM)-based autonomous systems capable of reading, writing, testing, and repairing software. We argue that coding agents have crossed a threshold of capability at which traditional human code review is no longer a necessary component of a software quality pipeline. Our argument rests on two claims: every stated goal of code review can be served by agents at lower cost and higher throughput; the naive integration in which agents write code and humans remain the mandatory reviewers is a dead end because it neither provides meaningful assurance nor scales with AI-assisted throughput.
Rachel Thomas on Twitter / X
"We argue for a reorientation from autonomous to human-centered coding agents: systems designed not only to complete tasks, but to collaborate effectively with people."@ZhiruoW @jyangballin @KLierethttps://t.co/ncWiAUxwbo 1/— Rachel Thomas (@math_rachel) October 20, 2026
The third golden age of software engineering – thanks to AI, with Grady Booch
The third golden age of software engineering – thanks to AI, with Grady Booch
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software engineering tasks, e.g., program understanding, code generation, software testing, and program repair. As CodeLLMs are being employed toward automating these tasks, one question that arises, especially in enterprise settings, is whether these coding assistants and the code LLMs that power them are ready for real-world projects and enterprise use cases, and how do they impact the existing software engineering process and user experience. In this paper we survey 57 developers from different domains and with varying software engineering skill about their experience with AI coding assistants and CodeLLMs. We also reviewed 35 user surveys on the usage, experience and expectations of professionals and students using AI coding assistants and CodeLLMs. Based on our study findings and analysis of existing surveys, we discuss the requirements for AI-powered coding assistants.
Where Did Software Go Wrong? | Jesse Li
Software is broken, but it's not because of NPM, startups, AI, or venture capitalists. A deep dive into how we think about and produce code, and how our software systems reflect the manic state of the modern world.
The 70% problem: Hard truths about AI-assisted coding
A field guide and why we need to rethink our expectations
The 70% problem: Hard truths about AI-assisted coding
A field guide and why we need to rethink our expectations