ai-pr-reviewer
ontology
| ai-pr-reviewer | ontology | |
|---|---|---|
| 40 | 1 | |
| 1,288 | 11 | |
| - | - | |
| 8.9 | 9.4 | |
| 4 months ago | 13 days ago | |
| TypeScript | Makefile | |
| MIT License | Creative Commons Attribution Share Alike 4.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ai-pr-reviewer
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How CodeRabbit AI is Revolutionizing Coding with Intelligent Automation
According to their official site, CodeRabbit is an AI-based code reviewer and summarizer for GitHub pull requests, utilizing OpenAI's gpt-3.5-turbo and gpt-4 models. It is designed to be used as a GitHub Action and can be configured to run on every pull request and review comments.
- CodeRabbit – The AI-First Code Reviewer
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Mastering Code Review skills using AI tools
After completing your review, employ automated AI-based code review tools such as CodeRabbit. These tools can quickly analyze code for common issues, style inconsistencies, and potential bugs, providing an immediate second opinion on the PR.
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How we managed GPT-4 API cost at scale
Since its inception, CodeRabbit has experienced steady growth in its user base, comprising developers and organizations. Installed on thousands of repositories, CodeRabbit reviews several thousand pull requests (PRs) daily.
- Show HN: AI driven code reviewer (Free for OSS)
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[P] Link related issues in PR automatically
No need to link issues in a PR now, the CodeRabbit AI code review bot can find the relevant issues and linking those with the PR.
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Ask HN: Dear startup founders, what have you developed in-house?
FluxNinja [0] founder here. I developed an in-house AI-based code review tool [1] that CodeRabbit is now commercializing [2].
I did it because of the increasing frustration due to the time-consuming, manual code review process. We tried several techniques to improve velocity - e.g., stacked pull requests, but the AI tool helped the most.
[0]: https://www.fluxninja.com
[1]: https://github.com/coderabbitai/ai-pr-reviewer
[2]: https://coderabbit.ai
- CodeRabbit: AI based MR reviewer
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CodeRabbit(AI Powered Code Reviewer) is now available for GitLab Merge Requests
Our Base Prompts are open-sourced and have gained decent traction. Please check out us - https://github.com/coderabbitai/ai-pr-reviewer
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Recursively Summarizing Enables Long-Term Dialogue Memory in LLMs
We have been doing this at CodeRabbit[0] for incrementally reviewing PRs and allowing conversations in the context of code changes, giving the impression that the bot has much more context than it has.
For each commit, we summarize diff for each file. Then we create a summary of summaries which is incrementally updated as further commits are made on a pull request. This summary of summaries is saved, hidden inside a comment on a pull request, and is used while reviewing each file and answering the user's queries.
Some of our code is in the open source. Here is the link to the relevant prompt for recursive summarization - https://github.com/coderabbitai/ai-pr-reviewer/blob/main/src...
[0]: coderabbit.ai
ontology
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[P] Link related issues in PR automatically
Here's an example of linking related issues for okp4/ontology/pull/220 (linked automatically by CodeRabbit PR review bot).
What are some alternatives?
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mask-json-field-transform
Funnel-Transformer
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