AI Code Review | Greptile | Merge 4X Faster, Catch 3X More Bugs

AI Code Reviews | CodeRabbit | Try for Free.

AI-first pull request reviewer with context-aware feedback, line-by-line code suggestions, and real-time chat.

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.

Socialize the Plan

On AI and code review.

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.

Git AI - Track AI Code all the way to production

Cross-agent observability from prompt to production. Track AI-generated code from Cursor, Claude Code, GitHub Copilot, Gemini, and more through the entire SDLC.

Cut 40% of Code Review Time with AI — No IDE Change Required

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New AI Flaw Reporting System Fills Crucial Security Gap | CMU Software Engineering Institute

Flaw Reporting for AI (FLARE-AI) allows developers and security researchers to submit artificial intelligence flaws for formal, coordinated disclosure.

Google opens Deep Review AI tool to help researchers catch manuscript errors

Google Research on Oct. 14 released Deep Review, an AI system designed to flag mathematical errors, logical gaps and unsupported claims in technical documents. The public launch follows pilots that provided feedback on more than 15,000 manuscripts, according to Google. Speaking at Google’s Research@ event in Mountain View, California, Rajesh Jayaram, a research scientist at…

Agent Skills

AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...

Cursor: AI coding agent

Built to make you extraordinarily productive, Cursor is the best AI coding agent.

AI reviewers are here — we are not ready

Nature - Artificial intelligence promises rapid and polite feedback on papers — but we must first review the reviewer.

SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?

Large Language Model (LLM) agents have been widely adopted in modern software development workflows. SWE-bench [13] and related works [23, 24, 22, 25, 15] establish the task of issue resolution as a de-facto standard for assessing their capability and usefulness. In this setting, an agent is given an entire codebase, a task description (e.g., a bug report or feature request) in natural language and is instructed to produce a code patch that resolves the issue and passes the repository’s test suite. These benchmarks have been instrumental in demonstrating both the substantial potential and the persistent limitations of current models as SWE agents.

AI Tools Accelerates Coding, but Not Overall Software Delivery, GitLab Research Finds

GitLab's 2026 AI Accountability Report highlights an AI Paradox: although 78% of developers say they code faster, overall software delivery has not accelerated due to downstream testing and review bottlenecks and new challenges for enterprise governance and traceability.

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.

Sung Kim (@sungkim.bsky.social)

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.