When you’re a solo founder, nobody reviews your pull requests. You push code, you merge code, and you move on. For CoupleFi, I do the design, engineering, product strategy, and marketing. All of it. Most days I’m bouncing between Figma and VS Code, between customer research and deployment configs. It’s a lot of context-switching, and it means I don’t have a colleague catching the thing I missed at 11 pm.

I’ve wanted a second pair of eyes on my code for a while. Not enough to pay someone. Not at this stage. But enough to try something. So when I came across CodeRabbit, an AI-powered pull request review tool, I figured it was worth an experiment. I tested it across about a dozen PRs during a free trial, with minimal configuration and no extra context beyond the repository itself.

Here’s what I found.

What I was hoping for

I already have Prettier handling formatting and ESLint catching common mistakes. Those tools are mature and reliable. They’ve long since solved the easy stuff.

What I wanted was the harder feedback, the kind you get from a thoughtful colleague who’s been reading your code. Things like: “This component is doing too much.” Or: “You’re duplicating this pattern from another module.” The kind of review that requires understanding intent, not just syntax.

In short, I was hoping for architectural thinking. I knew that was a high bar, but it felt like the right one.

Setup was painless

I’ll give CodeRabbit credit here. It’s a GitHub app, and getting it running took a few clicks. No config files, no YAML, no fuss. It started commenting on my PRs right away. If nothing else, the onboarding experience is genuinely good.

The summaries were the real value

The PR summaries surprised me. Every time CodeRabbit reviewed a PR, it generated a clear, readable summary of what the changes did and categorized them as features or chores. As someone who constantly jumps in and out of the codebase, this was helpful for re-orienting. Sometimes I’ll spend a full day on an administrative problem, then a week on design, then drop back into code. I’d come back to a PR after a few days away, read the summary, and remember exactly where I left off.

I wasn’t going to write those summaries for myself. That alone made the tool worth trying.

But the summaries also had a trust problem. Sometimes they described changes I didn’t make. Classic hallucination: confident, well-written, and wrong. It happened enough that I couldn’t fully rely on them, which undercut the value. If I have to fact-check the summary anyway, I’m back to doing the work myself.

The code feedback didn’t go deep enough

Across roughly 12 PRs, I implemented one suggestion from CodeRabbit. One. And honestly, I probably would have caught it on my own if I hadn’t been tired when I wrote the code.

Most of the code feedback was surface-level. The kind of thing a linter would flag, or something I’d already considered and intentionally skipped. A few specific issues stood out:

• Comments were verbose. The useful feedback got buried under walls of text. (There’s apparently a setting for this, but I was testing the defaults.)
• No in-line annotations. Everything came as a single comment on the PR, not attached to specific lines of code. That’s a big gap compared to how humans review code.
• It generated sequence diagrams for each PR. I’ve never used sequence diagrams professionally, and these didn’t change that. They were technically accurate but not helpful for a solo engineer who already knows the flow.
• Every PR comment ended with a little poem. Cute the first time, invisible by the third.

After about 10 PRs, I started skipping CodeRabbit’s comments entirely. It just wasn’t catching anything I hadn’t already thought about.

I don’t think that makes it a bad tool. It just wasn’t built for my situation: an experienced developer working solo on a codebase they know inside out.

What I actually need vs. what AI can do today

Here’s the bigger picture. What I need from a code reviewer is someone who thinks about architecture. Is this the right abstraction? Am I coupling things that should be separate? Should this logic live in a hook or a utility? Those are the questions that matter when you’re building alone, because they’re the ones you’re most likely to get wrong without outside perspective.

Current AI review tools are in what I’d call a “summarize and suggest” phase. They can describe what your code does and offer surface-level improvements. They’re not yet in an “architect” phase, where they reason about your codebase’s structure and push back on your design decisions.

There’s a broader conversation happening about whether AI actually makes developers more productive. The research I’ve seen is mixed at best. And a lot of the wins AI promises in coding (consistent formatting, catching common mistakes, boilerplate generation) were already solved by purpose-built tools years ago. The hard wins, like architectural review and design feedback, are where AI could genuinely change things. But we’re not there yet.

When it might work better

I want to be fair. There are contexts where I think CodeRabbit, or tools like it, could be more useful:

• On teams where consistent PR summaries help everyone stay aligned across multiple engineers and projects.
• In codebases built with languages that have weaker linting and formatting ecosystems.
• With more configuration investment: giving the tool a style guide, establishing patterns, and tuning the feedback. I didn’t do any of that.
• As a triage layer for high-volume teams that need to prioritize which PRs get deep human review.

I also think giving the AI more context (letting it read your dependency lock files, linter configs, and established patterns) could meaningfully improve what it catches. I hope that’s where these tools are headed.

Where I landed

I’m not using CodeRabbit anymore, but I’m glad I tried it. The PR summaries taught me something about my own workflow: I need better documentation of my thinking, not just my code. Whether that comes from an AI tool, more intentional commit messages, or architectural decision records is still an open question.

AI code review will get better. The gap between “summarize what I wrote” and “challenge how I think” is closing. But it’s not closed yet. For now, as a solo founder, I’m still my own best reviewer. Which is both the problem and the point.