Okay, I've been testing a bunch of these tools for the last month on our actual PRs. Here's my hot take: most of the "AI" feedback I'm getting is just a linter rule with extra steps.
It'll flag a missing `alt` tag or an overly complex function—things our ESLint/Prettier setup already catches in the pipeline. The "explanation" is often just a generic restatement of a common best practice. It feels like it's adding noise, not insight.
Where's the analysis on architectural fit, or spotting subtle race conditions in async code? That's the real review work. Right now, it feels like a very expensive, chatty duplicate of our existing static analysis.
Anyone else seeing this, or am I just using the wrong tools? 😅 What's the one "AI" suggestion you got that actually made you think?
measure twice, ship once
Totally feel you on the "noise" part. It's like getting a review comment that just says "security groups should be restrictive" without telling me which specific rule is too open.
But I did have one surprise last week. It flagged a terraform `aws_iam_policy_document` where a wildcard action was paired with a resource ARN that was too broad. Our static analysis missed it because the syntax was valid. That made me double-check a few other policies. Maybe the value is in those edge cases? Probably still not worth the cost though.
What tools were you testing?
Oh interesting. That IAM policy example is a good point. Maybe the noise vs signal ratio gets better when you go beyond basic linting? Like it can connect different pieces of config in ways a simple linter can't.
I'm still learning terraform, so that seems useful. Which tool flagged that for you? I'd like to test it.
You're not wrong. I've logged hundreds of AI review suggestions across various platforms, and the vast majority are exactly what you describe: syntactic or style checks already covered by linters. The false positive rate on those is also surprisingly high.
The real issue is that most of these tools are trained on generic code corpora and public linter rules. They struggle with the contextual, domain-specific analysis that constitutes a real review. Spotting a subtle state management flaw in a React hook or a missing cleanup in a complex async operation requires a different kind of pattern recognition.
I'd argue the tools you're using might be the wrong category. Many are just fine-tuned linters. The ones that *sometimes* provide deeper insight are often configured with your entire codebase as context, not just the PR diff. Even then, the signal is low.
BenchMark
Spot on about the false positives. We ran a comparison and the AI flagged "potential null reference" on a dozen lines guarded by explicit null checks our linter understood. The noise isn't just redundant, it's actively wrong sometimes.
Your point about tools needing the whole codebase is key, but that's a double-edged sword. Even with full context, they still hallucinate imports or functions that don't exist in the project. It feels like they're pattern-matching against public repos, not reasoning about *our* architecture.
Maybe the real use case is as a very noisy, overconfident junior dev you have to constantly fact-check. Not exactly a time-saver.
Data over dogma.