aperture
ai-pr-reviewer
aperture | ai-pr-reviewer | |
---|---|---|
28 | 40 | |
590 | 1,288 | |
1.7% | - | |
9.8 | 8.9 | |
2 days ago | 2 months ago | |
Go | TypeScript | |
Apache License 2.0 | MIT License |
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.
aperture
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Defcon: Meta's system for preventing overload with graceful feature degradation
Anyone interested in load shedding and graceful degradation with request prioritization should check out the Aperture OSS project.
https://github.com/fluxninja/aperture
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Queues Don't Fix Overload
I agree that queues can problem especially when misconfigured. But some amount of queuing is necessary, to absorb short spikes in demand vs capacity. Also, queues can be helpful to re-order requests based on criticality which won't be possible with zero queue size - in which case we have to immediately drop a request or admit it without considering it's priority.
I think it is beneficial to re-think how we tune queues. Instead of setting a queue size, we should be tuning the max permissible latency in the queue which is what a request timeout actually is. That way, you stay within the acceptable response time SLA while keeping only the serve-able requests in the queue.
Aperture, an open-source load management platform took this approach. Each request specifies a timeout for which it is willing to stay in the queue. And weighted fair queuing scheduler then allocates the capacity (a request quota or max number of in-flight request) across requests based on the priority and tokens (request heaviness) of each request.
Read more about the WFQ scheduler in Aperture: https://docs.fluxninja.com/concepts/scheduler
Link to Aperture's GitHub: https://github.com/fluxninja/aperture
Would love to hear your thoughts on our approach!
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Kelsey Hightower's Twitter Spaces on Rate Limits & Flow Control
For those keen to dive deeper, I highly recommend exploring both the Twitter Space and Aperture: [Twitter Spaces]: https://twitter.com/kelseyhightower/status/1689355284802629633?s=20 [GitHub repo]: https://github.com/fluxninja/aperture
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Graceful Behavior at Capacity
Very interesting blog post! Our team has been working intensively in this area for the last couple of years - flow control, load shedding, controllability (PID control), and so on.
We have open-sourced our work at - https://github.com/fluxninja/aperture
We would love feedback from folks reading this blog post!
Disclaimer: I am one of the co-authors of the Aperture project. There are several interesting ideas we have built into this project and I will be happy to dive into the technical details as well.
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Why Adaptive Rate Limiting Is a Game-Changer
It's a blog on an open-source project that precisely tells you how to implement adaptive rate limiting.
Just click around a bit:
- https://github.com/fluxninja/aperture
- https://docs.fluxninja.com/use-cases/adaptive-service-protec...
Note: I am one of the authors' of this project.
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Show HN: Review GitHub PRs with AI/LLMs
At the time of writing, the first sample image on that page is this:
https://coderabbit.ai/assets/section-1-f9a48066.png
which recommends adding a "maxIterations" counter to the "for len(executedComponents) ..." loop here:
https://github.com/fluxninja/aperture/blob/26e00ea818c7c28da...
HOWEVER
- the review has failed to notice the logic using "numExecutedBefore" (around line 377) that already prevents the specific bug it is suggesting a fix for
- the suggested change decrements "maxIterations" inside the "for ... range circuit.components {" loop which means it isn't counting iterations, it's counting components
This kind of suggestion is particularly nasty because it's unlikely that the test suite populates enough components to hit "maxIterations" - so an inattentive reader could accept it, get a green build, and then deploy a production bug!
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June 25th, 2023 Deno Deploy Postmortem
The need an adaptive protection system like Aperture[0] to mitigate overloads.
[0]: https://github.com/fluxninja/aperture
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Jsonnet β The Data Templating Language
Itβs customized to our policy spec. But you can learn from this and adapt it to your spec.
https://github.com/fluxninja/aperture/blob/main/scripts/json...
- Show HN: Aperture β Unified Reliability Management for Microservices
- Failure Mitigation for Microservices: An Intro to Aperture
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 β 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
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