Code reviews are often considered a pain, resulting in quick approvals and bugs reaching production. CodeRabbit and Sourcery aim to solve this by letting an AI agent review your changes early on.
In this episode, data & platform engineer Hannes De Smet shows Jonny what he learned after exploring several AI Code Reviewers. Hannes demos both tools on a real code change, allowing a critical look at the quality of the suggestions, as well as the user experience. It turns out that, depending on the context, both could use some improvements.
Resources:
CodeRabbit: https://www.coderabbit.ai
Sourcery: https://www.sourcery.ai/
Multi-workspace AI video: https://www.youtube.com/watch?v=E_kOAvmeTJ0
Note: This video is not sponsored or affiliated with CodeRabbit or Sourcery.
Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb
Chapters:
(00:00) - Intro: why AI code reviews?
(02:53) - AI Reviewer 1: CodeRabbit
(10:32) - What CodeRabbit catches (and misses)
(12:18) - When AI comments become noise (80% disregard)
(13:27) - Catching a PII issue
(15:15) - AI Reviewer 2: Sourcery
(19:14) - Cost & comparison
(19:59) - What's the future of AI code reviews?
(20:41) - Summary & takeaways
Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype.
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