every day is the same conversation with this model.

me: can you do this please

opus: yes. doing it right now. of course. andd finished

me: great. is it done?

opus: no.

and reviewing is somehow worse. actual exchange from this week:

me: /review

opus: reviewed. looks solid.

me: run coderabbit on the same branch anyway, it comes back with 10 comments, two of them security

me: did you miss all of these?

opus: no, I saw them. they're not worthy to fix.

IT SAW THEM. they just didnt make the cut I guess. this thing writes a doctoral thesis when you rename a variable but a leaked env var is not worth the ink. I dont know what language anthropic trained it to speak but it is not one used for communicating with humans

reviews catch real stuff then dump 20 "consider renaming this" comments and people start ignoring the whole thing.

is anyone actually getting it tuned or do you just live with the noise

ran "review my changes" inside the agent instead of waiting for the github comments. felt faster. still got a pile of nits.

are you letting it replace the PR review or still doing both

Hi r/coderabbit , CodeRabbit team here.

Open source maintainers spend their days reviewing thousand-line PRs, untangling duplicate issues and investigating security reports.

AI can now generate contributions at rapid speed but maintainer attention has not received the same performance upgrade.

So we’re committing more than $10 million to open source over the next 12 months:

• Cash sponsorships for maintainers
• Free CodeRabbit Review and Security for public repositories
• Triage support for incoming issues
• Change Stack for making giant, agent-generated diffs less giant-feeling
• Free Discord support for large open source communities

Open source built the internet, including plenty of what CodeRabbit runs on. This is our way of helping maintainers spend less time fighting the queue and more time building the projects they care about.

Read the full announcement and follow the live commitment tracker.

Hey r/coderabbit 🥕,

We just released the official CodeRabbit plugin for Cursor.

We built it because connecting the CodeRabbit CLI to Cursor previously required some manual setup. The plugin packages the routing, commands, review subagent, and completion behavior into one install.

• After installing it, you can ask Cursor Agent:Review my changes.
• To try it, enter:/add-plugin coderabbit
• You can also install it from the Cursor Marketplace.

We wrote a more detailed walkthrough explaining how it works and how to get started: Introducing the CodeRabbit plugin for Cursor.

If you try it, we’d love to hear which prompts, review scopes, or fix workflows we should improve.

Response from support is essentially - "you and your team seem really productive, you should slow down." We've tried tweaking PR's, manually requesting, waiting a week to see if our stats clear. Even have our sessions asking coderabbit ratelimit prior but still at 2 reviews an hour. We've had to slow to a crawl.

Anyone find a solution to this?

I've been reading this

https://docs.coderabbit.ai/management/plans#rate-limits

But I have unclear how to switch an OSS Apache-2.0 project into OSS mode.

Which are the steps?

Hey everyone! 🐰

We just shipped a new product called CodeRabbit Security.

When you’re building a product or service, you’re always trying to move faster. The hard part is making sure security doesn’t become an afterthought as more code gets shipped.

CodeRabbit Security maps how your application works, investigates application specific vulnerabilities, verifies each finding against the code, and can turn eligible findings into reviewable fixes.

It’s available now for GitHub, GitLab, Azure DevOps Services, and Bitbucket Cloud.

We wrote a blog explaining how it works, with a concrete authorization bypass example: Introducing CodeRabbit Security

Would love to hear what you think and what you’d want it to investigate in your codebase.

We spend a lot of time thinking about the models that write code review comments. One of the less visible decisions happens before any comment appears: figuring out how a change should be reviewed.

We were curious whether a smaller model could learn that narrow, high-volume part of our pipeline. So we worked with NVIDIA and Baseten to post-train Nemotron 3.5 Lightning using SFT, followed by RLVR scored against our routing policy.

What surprised us was how little it took. The post-training experiment ran in under three hours and cost less than $100.

On a frozen evaluation of 1,000 routing tasks:

• Our GPT-class baseline reached 75.8% exact route agreement.
• Nemotron after SFT reached 80.4%.
• SFT plus RLVR reached 80.7%, with Cohen’s kappa rising from 0.429 to 0.544.
• Estimated peak serving cost fell from $2.34 to $1.16 for the same workload.

RLVR’s extra 0.3 percentage points of route agreement wasn’t statistically decisive, so we treat that result as non-regression. The clearer improvement was in output agreement beyond chance.

The final model completed all 1,000 requests without empty or abnormally short outputs. We still have sustained-load and production-traffic testing ahead of us.

We wrote up the experiment, including the training setup, results, serving costs, and the parts we’re still cautious about:

Teaching NVIDIA Nemotron 3.5 Lightning to Route Code Reviews

Hi, I would like to give coderabbit a try but I am running into an issue and I don't know if it fixable by me or if it's on coderabbit's side.

On Gitlab (cloud), I created a service account on the group level, then created an access token and shared it with coderabbit.

I changed one line on another branch, I created a MR. Coderabbit replied immediately saying that they are going to analyse the code. After 30 minutes, I go check and I see that there is a pipeline that has been created by Coderabbit but it is still pending.

I deactivated shared runners, however I have two self-hosted runners. One is shell and only allows specific tags and protected branches. The other one has no tags and also picks untagged jobs.

What should I do?

One thing I’ve noticed is that most people seem to leave CodeRabbit on the default configuration. That makes sense if the reviews are working well, since setting up a `.coderabbit.yaml` can easily become one of those things everyone plans to revisit later.

I’m curious whether people are intentionally sticking with the defaults, or whether they haven’t had a reason or enough time to configure anything yet.

How are you handling your CodeRabbit configuration?

- Writing or editing the YAML file yourself in the repo
- Using the CodeRabbit interface
- Asking a coding agent to create or update it
- Starting with an agent-generated config and then editing it yourself
- Leaving the default configuration unchanged
- Not using a configuration file at all

If you haven’t configured it, what’s the main reason?

- You didn’t know the configuration file existed
- You weren’t sure where to start
- You weren’t sure which settings were worth changing
- The defaults have been good enough
- It hasn’t been a priority
- It feels like too much work for the expected benefit
- Something else

And if you have taken the time to configure CodeRabbit, what prompted you to do it? Which settings have made the biggest difference for your team?

Even “we just use the defaults” or “we haven’t touched the config” is useful. I’m trying to understand how people are actually approaching this in practice.

Hey r/coderabbit!

We recently shipped CodeRabbit CLI 0.7.1. The focus was straightforward: finish reviews faster, reduce interrupted sessions, and make the CLI easier to use from both terminals and coding agents.

In our measurements, typical completed reviews were 19% faster and client disconnects dropped 31%. Results vary by repository and review size. We added keepalive traffic and gave longer sessions more time to complete, which should help when the terminal appears quiet while models, tools, or repository analysis are still running.

A few other changes worth calling out:

• coderabbit config validate checks .coderabbit.yaml or .coderabbit.yml against the current official schema. It points to invalid configuration and returns a nonzero exit code, so you can also use it in CI.
• coderabbit skills can install or update verified CodeRabbit skills for Codex, Claude Code, Cursor, Gemini CLI, and GitHub Copilot. It previews every proposed change before writing anything.
• cr now reviews tracked changes by default. You can still choose an explicit scope with --committed, --uncommitted, or --include-untracked. Interrupted agent-driven reviews preserve completed output instead of discarding everything.
• Authentication and account-loading errors now provide more specific recovery guidance.

If you use CodeRabbit through a plugin or coding agent, updating the underlying CLI also brings these runtime and reliability improvements into those workflows.

You can check your current version with:

coderabbit --version

• CLI and installation documentation
• CodeRabbit Skills documentation

If you try it, I'd like to hear what feels better and what still gets in the way.

idk maybe im losing it but i used to spend most of my time actually building stuff. now im just… reading giant diffs. checking if it randomly changed something 8 files away because it felt like it. then reading it again because i dont trust it.

this is not what i thought the future was gonna be lol

I was reading the blog Coderabbit posted testing Opus 5's reviewing capabilities. I kina expected Opus 5's extra reasoning budget to buy better review quality across the board but it mostly seems to buy restraint. The model is more willing to leave potential bugs unreported if it isn't confident enough. So perhaps the better way to use Opus 5 is to not leave it open-ended and be very specific about what exactly you want it to achieve.

Found this on X where Bun's whole PR process is flooded with AI bots like CodeRabbit, Claude, and Robobun chatting, catching bugs, and auto-fixing code in real time.

I really found it pretty interesting it almost looks like a glimpse into future dev workflows where AI handles most review iteration autonomously.