The Merge: Why Building AI Chat Is Harder Than It Looks
A conversation with Assistant UI founder Simon Farshid about the hidden work behind AI chat interfaces, product judgment, and his coding-agent loop with CodeRabbit.
Stay updated with the latest developments in Artificial Intelligence
A conversation with Assistant UI founder Simon Farshid about the hidden work behind AI chat interfaces, product judgment, and his coding-agent loop with CodeRabbit.
CodeRabbit is committing more than $10 million of its actual direct cost to open source over the next year through cash sponsorships, free Review and Security, and broader agentic support across the software development lifecycle.
Install the CodeRabbit plugin for Cursor and run focused code reviews from the same Agent session where you build.
More changes are flowing through pull requests than ever thanks to AI-generated code, but a human still needs to decide what ships.
CodeRabbit Security traces suspected vulnerabilities across the codebase, evaluates reachability and the conditions required for exploitation against code evidence, and proposes reviewable fixes in the pull request workflow.
CodeRabbit raised a $143 million Series C at a $1.5 billion valuation and is introducing Agentic Change Management, the control layer for software changes created by humans and agents.
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
Reviewing every agent-generated change is becoming impossible. The next bottleneck is deciding what deserves attention, rebuilding context, and keeping code safe after merge.
Code is now plentiful, but judgment about what deserves to merge is not. Better models sharpen what agents write, but they don't make that call.
Explore CodeRabbit’s Claude Opus 5 benchmarks for code review: actionable comments, coverage, nitpicks, token use, and where the model fits.
Coding agents can expand change volume faster than teams can understand it. Review needs a shared path from intent to system behavior to code so people can keep shaping the system.
Post-Merge Actions use pull request context to handle changelogs, documentation, tickets, and other work that should happen after merge.
Compare GPT-5.6 Sol and Terra for coding agents and code review. Explore CodeRabbit’s benchmark results, follow-through, review quality, and cost trade-offs.
Monorepos keep shared code in sync but make every bad PR everyone's problem. Here's when to commit to one, when polyrepo wins, and how to review at scale.
Compare Claude Sonnet 5 with Sonnet 4.6 and flagship models using hands-on coding work, CodeRabbit review results, and practical cost trade-offs.
Loop engineering allows you to step away completely by designing a system that operates autonomously, removing the human from the loop entirely.
How Ayush Sridhar CalHacks Hackathon project turned into an SWE internship with CodeRabbit
Showing 35 of 35 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from CodeRabbit Blog
Join 15,000+ AI enthusiasts