cubic is an AI-powered code review platform that automatically reviews PRs and gives human reviewers superpowers. It’s tool of choice for fast-moving teams like cal.com and n8n.
This is the 2nd launch from cubic. View more
cubic 2.0
Launched this week
Over the past few months, we've been completely rebuilding cubic's AI review engine. Today we're excited to announce cubic 2.0, the most accurate AI code reviewer available.
cubic helps teams read, trust, and merge AI-generated code in real repos. It is optimized for accuracy and low noise, and it goes beyond PR comments with a CLI, AI docs, and PR description updates.
Used by 100+ orgs including Cal.com, n8n, Granola, and Linux Foundation projects.
Free
Launch Team
CodeRabbit
Disco.dev
Golf
cubic
Hey Hunters, I’m Paul, the founder of cubic.
If you’ve tried AI code review tools before, you’ve probably seen both failure modes:
1. they miss the important stuff
2. they comment so much that you stop reading
We built cubic because review is now the bottleneck. AI made it easy to produce code. It did not make it easy to trust a big diff in a complex repo.
Over the last few months we’ve been iterating hard on the engine, and the change is big enough that we’re calling it cubic 2.0. It’s faster, more accurate, and noticeably less noisy than it was a few months ago.
The other thing we learned is that “a GitHub bot that comments on PRs” is not enough anymore. Review is a workflow, not a feature, so we built the pieces around it too:
- incremental checks on every push
- PR descriptions that stay accurate
- wiki docs that stay in sync
- `cubic.yaml` for config-as-code
- and a CLI so you can run review before you push
If you try it, I’d love blunt feedback:
- What did it catch that you actually cared about?
- What should it stop commenting on?
I’ll be here in the comments!
cubic
@thekidsprinkles Thanks!
Upvoted! We face the same struggle at Dashform—the real pain isn't syntax, but those subtle logic hallucinations that look correct at a glance.
A small question, Does Cubic specifically target those 'confident but wrong' errors beyond just style checks?
Rooting for you guys! Happy to support fellow teams pushing the boundaries of AI dev tools.
cubic
@openaigpt5 Hey Ethan, thanks for the support.
> Does Cubic specifically target those 'confident but wrong' errors beyond just style checks?
yes! by default it's designed to not catch a lot of styling issues. You can configure it to do that by increasing the sensitivity, but our customers tend to be teams who prefer focusing on more serious issues
@paul_sangle_ferriere1 Thanks for the reply, Paul! We are launching Dashform with that same 'low-noise' focus. later this year, hoping we can count on your support then!
Framing review as a workflow, not just a PR bot, really resonates. Curious which piece ends up being most valuable in practice. The incremental checks, the CLI, or the config-as-code?
The focus on accuracy over noise makes sense—most AI reviewers I've seen lean too far in one direction. I'm curious how cubic handles codebases with mixed AI-generated and human-written code. Does it adjust review depth based on the origin of the code, or treat all changes uniformly?
Wow, cubic looks amazing! The updated AI review engine sounds like a game changer. How does it handle reviewing auto-generated code, specifically, to avoid reinforcing potential biases? Super keen to try this out!
Cool project. And I can see how you could easily extend it: add the ability to run full technical audits. That is, you connect Git, it audits the entire project and generates a report with recommendations divided into three groups: critical, standard, and minor. If this report is high quality, I think you’ll have a huge number of clients!