Home / Companies / Convex / Blog / Post Details
Content Deep Dive

Comparing 9 Code Review Tools - Which is Best?

Blog post from Convex

Post Details
Company
Date Published
Author
Mike Cann
Word Count
14,775
Company Posts That Month
6
Language
English
Hacker News Points
-
Post removed?
No
Summary

A developer evaluated nine AI code-review tools on ten deliberately designed pull requests in a React, Vite, and Convex-based Trello clone, testing security, performance, data-modeling, authorization, and Convex-specific correctness issues such as missing membership checks, unbounded queries and arrays, improper use of indexes, stale aggregates, and optimistic concurrency conflicts. All tools were tested with default settings, scored for correctly identifying primary issues, useful additional findings, and false positives, with grading cross-checked by AI agents and selected manual tests. Qodo ranked first with 32/40 and GitHub Copilot closely followed at 31/40, while Cubic stood out for avoiding false positives and correctly recognizing that nested Convex database calls are not traditional N+1 problems; CodeRabbit and Greptile placed in the middle. CodeAnt AI had mixed results, while Sourcery, Macroscope, and Graphite AI scored poorly, with Graphite producing almost no review comments. The comparison found that many bots handled general authorization issues reasonably well but often lacked an understanding of Convex-specific behavior, particularly internal functions, collocated database and compute execution, document-size limits, and framework guidance stored in repository rules files. Despite Qodo’s strongest raw score, its dashboard and account experience were criticized, while Copilot was identified as the most practical day-to-day choice because of its GitHub integration, solid performance, and ease of use.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
AI Coding Assistant 13 1,565 481 159 +31%
Serverless 3 1,341 270 110 +29%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.