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Comment by sksxihve | Hacker News Reader

Are people really willing to commit code that was only reviewed by an AI? I personally wouldn't trust that for anything that is customer/revenue impacting. Obvious bugs and defects aren't all that hard to catch in normal code reviews but subtle race conditions/deadlocks/memory errors can be very tricky, do you have examples where it shows it can catch those?

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This assumes that human reviews also catch these which is DEFINITELY not the case either.

As long as you have good pipelines, linters, a careful suite of tests at different levels like unit, integration, e2e and if you can test things in an acceptable like environment then human code reviews offer very very little benefit…

Your never going to catch 100% of issues, human or AI review, but I've found that in code reviews a lot of the benefit is when people ask questions about the code being reviewed and have a discussion on it.

Is the AI tool going to ask why something was implemented in a way that might not match the requirement specs? Is it even going to know what the requirements are for the code or is it going to rubber stamp a review because the code looks reasonable?

If you think human code reviews offer very very little benefit then you probably aren't doing them right.

PR reviewing isn't really about finding bugs because the tests should be doing that - but are the tests good enough? Is the approach sound and aligned with the architecture? And does anyone else understand it apart from the author?

Great points about code review reliability. LlamaPReview is designed to be a complementary tool for senior developers, not a replacement for human review. Here's our approach:

1. It helps save senior developers' time by handling routine checks and providing initial insights 2. It analyzes the entire codebase context to provide more meaningful reviews 3. It's particularly useful for identifying patterns and relationships across the codebase

The goal is to make human reviewers more efficient, allowing them to focus on complex architectural decisions and critical business logic. We've seen positive results from both open-source and commercial projects using this approach.

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