Hey HN, we’re building mrge (https://www.mrge.io/home), an AI code review platform to help teams merge code faster with fewer bugs. Our early users include Better Auth, Cal.com, and n8n—teams that handle a lot of PRs every day.

Here’s a demo video: https://www.youtube.com/watch?v=pglEoiv0BgY

We (Allis and Paul) are engineers who faced this problem when we worked together at our last startup. Code review quickly became our biggest bottleneck—especially as we started using AI to code more. We had more PRs to review, subtle AI-written bugs slipped through unnoticed, and we (humans) increasingly found ourselves rubber-stamping PRs without deeply understanding the changes.

We’re building mrge to help solve that. Here’s how it works:

1. Connect your GitHub repo via our Github app in two clicks (and optionally download our desktop app). Gitlab support is on the roadmap!

2. AI Review: When you open a PR, our AI reviews your changes directly in an ephemeral and secure container. It has context into not just that PR, but your whole codebase, so it can pick up patterns and leave comments directly on changed lines. Once the review is done, the sandbox is torn down and your code deleted – we don’t store it for obvious reasons.

3. Human-friendly review workflow: Jump into our web app (it’s like Linear but for PRs). Changes are grouped logically (not alphabetically), with important diffs highlighted, visualized, and ready for faster human review.

The AI reviewer works a bit like Cursor in the sense that it navigates your codebase using the same tools a developer would—like jumping to definitions or grepping through code.

But a big challenge was that, unlike Cursor, mrge doesn’t run in your local IDE or editor. We had to recreate something similar entirely in the cloud.

Whenever you open a PR, mrge clones your repository and checks out your branch in a secure and isolated temporary sandbox. We provision this sandbox with shell access and a Language Server Protocol (LSP) server. The AI reviewer then reviews your code, navigating the codebase exactly as a human reviewer would—using shell commands and common editor features like "go to definition" or "find references". When the review finishes, we immediately tear down the sandbox and delete the code—we don’t want to permanently store it for obvious reasons.

We know cloud-based review isn't for everyone, especially if security or compliance requires local deployments. But a cloud approach lets us run SOTA AI models without local GPU setups, and provide a consistent, single AI review per PR for an entire team.

The platform itself focuses entirely on making human code reviews easier. A big inspiration came from productivity-focused apps like Linear or Superhuman, products that show just how much thoughtful design can impact everyday workflows. We wanted to bring that same feeling into code review.

That’s one reason we built a desktop app. It allowed us to deliver a more polished experience, complete with keyboard shortcuts and a snappy interface.

Beyond performance, the main thing we care about is making it easier for humans to read and understand code. For example, traditional review tools sort changed files alphabetically—which forces reviewers to figure out the order in which they should review changes. In mrge, files are automatically grouped and ordered based on logical connections, letting reviewers immediately jump in.

We think the future of coding isn’t about AI replacing humans—it’s about giving us better tools to quickly understand high-level changes, abstracting more and more of the code itself. As code volume continues to increase, this shift is going to become increasingly important.

You can sign up now (https://www.mrge.io/home). mrge is currently free while we're still early. Our plan for later is to charge closed-source projects on a per-seat basis, and to continue giving mrge away for free to open source ones.

We’re very actively building and would love your honest feedback!

Been using this for https://github.com/cartography-cncf/cartography and am very happy, thanks for building this.

Automated review tools like this are especially important for an open source project because you have to maintain a quality bar to keep yourself sane but if you're too picky then no one from the community will want to contribute. AI tools are like linters and have no feelings, so they will give the feedback that you as a reviewer may have been hesitant to give, and that's awesome.

Oh, and on the product itself, I think it's super cool that it comes up with rules on its own to check for based on conventions and patterns that you've enforced over time. E.g. we use it to make sure that all function calls that pull from an upstream API are decorated with our standard error handler.

This is an awesome direction. Few thoughts:

It would be awesome if the custom rules were generalized on the fly from ongoing reviewer conversations. Imaging two devs quibble about line length in a PR, and in a future PR, the AI reminds about this convention.

Would this work seamlessly with AI Engineers like Devin? I imagine so.

This will be very handy for solo devs as well, even those who don't use Coding CoPilots could benefit from an AI reviewer, if it does not waste their time.

Maybe there can be multiple AI models review the PR at the same time, and over time, we promote the ones whose feedback is accepted more.

Threw a random PR at it… of the 11 issues it flagged, only 1 was appropriate, and that one was also caught by pylint :(

(mixture of 400 lines of C and 100 lines of Python)

It also didn't flag the one SNAFU that really broke things (which to be fair wasn't caught by human review either, it showed in an ASAN fault in tests)

There are a few of these already. Is this a land grab play, i.e. with investment get the big accounts then all the compliance ticks then dominate?

AI or conventional bots for PRs are neat though. Where I work we have loads of them checking all sorts of criteria. Most are rules based. E.g. someone from this list must review if this folder changes. Kinda annoying when getting the PR in but overall great for quality control. We are using an LLM AI for commenting on potential issues too. (Sorry I don't have any influence to help them to consider yours)

It looks like graphite.dev has pivoted into this space too. Which is annoying, because I'm interested in graphite.dev's core non-AI product. Which appears to be stagnating from my perspective -- they still don't have gitlab support after several years.

Looks interesting. I’m a bit confused about how it knows the codebase and the custom rules interface. I generally have coding standards docs in the repo. Can it simply be made aware of those docs instead of requiring me to maintain two sets of instructions (one written one for humans, and one in the mrge interface for AI)? I could imagine that without being highly aware of a team’s standards, the usefulness of its review would be pretty poor. Getting general “best practices” type stuff wouldn’t be helpful.

I see on your website that you claim the subprocessors are SOC2 type 2 certified, but it doesn't appear that you claim anything about your SOC2 status (in progress, certified, not interested). I mention this because I would suspect the breach risk is not that OpenAI gets popped but rather that a place which gathers continuously updated mirrors of source code does. The sandbox idea only protects the projects from one another, not from a malicious actor injecting some bad dep into your supply chain

It's been useful at our company. My only gripe is I'd like to run it locally. I don't want the feedback after I open a PR.

I've been evaluating AI code review vendors for my org. We've trialed a couple so far. For me, taking the workflow out of GitHub is a deal breaker. I'm trying to speed things along, not upend my whole team's workflow. What's your take on that?

Looked at it, but as a security person, I have to recommend against it as it requires permissions to act on behalf of repository maintainers. That is asking for trouble, and represents a backdoor into every project that signs up for it.

This looks like a cool solve for this problem. Some of the other tools I tried didn't seem to contextualize the app, so the comments were surface level and trite.

I'm on Bitbucket so will have to wait :)

Huge fan of this direction. Code review is one of those critical bottlenecks that hasn’t seen much UX innovation in years; excited to see a tool rethink it from first principles. The logical grouping of diffs and the ephemeral sandbox approach both sound super thoughtful. Also love the idea of making the review experience feel more like linear. I will suggest that my dev team gives it a try!

How does this work for large monorepos?

If the repo is several GB, will you clone the whole thing for every review?

This looks really cool. We've been using Graphite for a long time now, and been pretty happy with it. It was a huge step up from base Github review workflows, and the AI reviewer does point out real issues from time to time.

I watched your demo vid and the two things that stuck out to me were the summarizing of changes, grouping of file changes by concept, and the diagram generation. Graphite does generate summaries of PRs if you ask it to, but it's an extra step that replaces the user authored PR description. I see that you have stacked diff support too.

I probably don't want to spend the time/energy to migrate my team off Graphite anytime soon, but would be interested in evaluating mrge. Is the billing per reviewer of PRs or by author of PRs? And how long is the free trial? I'm always reluctant to sign up for limited time free trials because I don't know if I'll actually have time to commit to assessing the tool in that time window.

Happy mrge user here - congrats on the launch! It’s encouraged our team to do more stacked PRs and made every review a bit nicer

happy user here—our team moved from coderabbit to mrge, and everyone seems to love how much more useful the AI comments are

The call to action button says "Get Started for Free", while the pricing page lists $20/month.

Clicking the get started button immediately wants me to sign up with github.

Could you explain on the pricing page (or just to me) what the 'free' is? I'm assuming a trial of 1 month or 1 PR?

I'm somewhat hesitant to add any AI tooling to my workflows, however this is one of the use cases that makes sense to me. I'm definitely interested in trying it out, I just think its odd that this isn't explained anywhere I could find.

I got Claude Desktop to perform code review using mcp in Jetbrains IDE. I don’t know why would you prefer a cloud based pipeline with separate CR tool to it. This way I can extend the review process the way I want for example adding feature specs etc. There are other issues with llm based CR but I think mcp (or similar protocol) is the way to go.

> We know cloud-based review isn't for everyone, especially if security or compliance requires local deployments. But a cloud approach lets us run SOTA AI models without local GPU setups, and provide a consistent, single AI review per PR for an entire team.

I feel like that’s being overlooked here a bit too briefly. Is your target market not primarily larger teams who are most likely to have some security and privacy concerns?

I guess is there something on the roadmap to maybe offer something later ?

This is good. PR review has been completed neglected basically from day 0.

Did some self-research on Reddit about why (https://www.reddit.com/r/github/comments/1gtxqy6/comment/lxv...)

This is super well done - love the approach with cloud-based LSP and the focus on making reviews actually faster for humans.

I love this idea. We experimented with building an AI coding agent that we showed to a small set of users and the most common feedback was confusion over what exactly the agent did. And so, I think that something like this can solve that problem, especially as AI performs increasingly complicated edits.

Heyo your launch video is unlisted on youtube. Maybe intentional, but you might benefit from having it be public :)

I wanted to check this out, so I installed the GitHub app on my account, with access to all my personal repos. However when I went looking for one of my repos (auscompgeek/sphinxify) I couldn't find it. It looks like I can only see the first 100 repos in the dashboard? I have a lot of forks under my account…

Honest initial reaction to your pitch: > Cursor for code review

Isn't cursor already the "cursor for code review?"

Great that AI seemingly revives the stalled PR / review space. I just hope that human and local workflows will not be an afterthought or even made harder by these tools. Its also a great chance for stacked PRs and jujutsu to shake up the market.

I've tried something similar in the past. The concept is cool, but so far the solutions I've seen are not so useful in terms of comments quality and ability to catch bugs.

Hope this is the right time, as this would be a huge time-saver for me

I was wondering if it has information about previous commits with deleted code? Sometimes we make a change and later realize that the previous code worked better, would mrge be able to understand that?

the biggest issue i've had for things like this is that ai doesn't understand context very well, anything that is beyond a context window creates hallucinations and it starts making up things that may exist in one location but it tries to apply it to a completely unrelated scenario, would be curious if this does understand the connected pieces appropriately and catches things that break those connections, otherwise it's just another linter?

from the video: devs spend 30% to 50% of time on code reviews, with AI writing code, it's only going to get worse.

So, buy this AI tool (Merge) to review code written by another AI tool?

Instead of a Code review tool, why not have it instead as a static analyzer? Overall, the whole process will take much less time.

Would be great to have support for GitLab also (have a project there that I would love to try this on and I can't switch it to GitHub)

in the demo video i see that you can apply a recommended code change with one click. how do you make sure that the code still works after the AI changes?

also, i tried some other ai review tools before. one big issue was always that they are too nice and even miss obvious bad changes. did you encounter these problems? did you mitigate this via prompting techniques or finetuning?

As far as I can see, this doesn't directly integrate with github (we currently use coderabbit on github)? Is it on your timeline?

Excellent product, congrats on the launch guys!

Any plans to support github enterprise on different URLs? Would love to give this a try with my team.

I've used CodeRabbit for Code Review. It does pretty cool work.

How different it is from that?

Is that the four letter domain PG recently tweeted about? Congrats!

> subtle AI-written bugs slipped through unnoticed, and we (humans) increasingly found ourselves rubber-stamping PRs without deeply understanding the changes.

I'm not even going to add to this.

would love to use this but def need soc2 to justify it to our security folks

If you are looking for an alternative that can also chat with you in Slack, create PRs, edit/create/search tickets and Linear, search the web and more, check out codegen.com

Any plans for supporting Gitlab?

This looks interesting!

This looks incredible!

Congrats on the launch. Another happy user here. (Caught a really sneaky issue too!)

why not GitHub Copilot?

Looks awesome!

One personal niggle: "Code Review For The AI Era". I hate when people say era in relation to AI because it reminds me of Google's tasteless Gemini era thing.