hertz-ai/HARTOS - Open Source PR Review Scorecard

An AI-native OS. Models run on your own hardware, nodes federate peer-to-peer with no broker, and the API is OpenAI-compatible. Boots, has its own Wayland compositor, and runs on 8GB. Apache 2.0.

C-Rank Grade: A (Welcoming) - 66/100

External PR Merge Rate: 54%

Response Time: <1h

First Timer Success: 50%

Frequently Asked Questions

Is hertz-ai/HARTOS welcoming to first-time open-source contributors?

hertz-ai/HARTOS has a recorded first-timer success rate of 50.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in hertz-ai/HARTOS respond to incoming external pull requests in approximately 1.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 66.5 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 66.5 places hertz-ai/HARTOS in the A tier.

What is the external contributor pull request merge rate for hertz-ai/HARTOS?

The external contributor pull request merge rate for hertz-ai/HARTOS is 53.8%, based on public PR activity from non-core contributors.

Are there Good First Issues available in hertz-ai/HARTOS?

hertz-ai/HARTOS currently has 19 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

hertz-ai
hertz-ai/HARTOSAWelcoming50
GitHub
Back to Explorer
hertz-ai

hertz-ai/HARTOS

50
AWelcoming(66/100)Python

An AI-native OS. Models run on your own hardware, nodes federate peer-to-peer with no broker, and the API is OpenAI-compatible. Boots, has its own Wayland compositor, and runs on 8GB. Apache 2.0.

Compare
Jump to:

AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
6.8/10
Patience Score
7.6/10
Nitpick Rate
45%

Strict review standards in hertz-ai/HARTOS. Ensure PR scope matches issue requirements closely before requesting review.

Top PR Submission Do's

  • Ensure code complies with the project coding style
  • Keep PRs scoped to a single concern
  • Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • Do not submit PRs without linking an issue
  • Do not break existing tests without fixing them
  • Do not mix unrelated refactors in a single PR
Response Velocity
<1 hour
⚡ Fast reviewer response

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
53.8%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
50.0%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
3 core
Small core review team
Diagnostic Health HUD
53.8%
Merge Gauge
50.0%
1st-Timer
Community Vibe66/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for hertz-ai/HARTOS
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/hertz-ai/HARTOS)](https://getmerged.abhishekco.de/hertz-ai/HARTOS?utm_source=github&utm_medium=badge)

Active Good First Issues (19)

View on GitHub

This repository currently has 19 open good first issue tickets available for newcomers.

View all good first issues directly on GitHub

Looking for more Python beginner tasks?Explore Python GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

3 ratings required
Maintainer helpfulness
Review speed
Beginner friendliness

Contributor Compatibility & Review Speed Analysis for hertz-ai/HARTOS

When evaluating whether to contribute to hertz-ai/HARTOS, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of hertz-ai/HARTOS acknowledge new external contributions in approximately <1 hour. Out of all submitted pull requests from non-core authors in the last 180-day window, 53.8% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to hertz-ai/HARTOS

01

Is hertz-ai/HARTOS welcoming to first-time open-source contributors?

hertz-ai/HARTOS has a recorded first-timer success rate of 50.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~<1 hour. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 66.5 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 66.5 places hertz-ai/HARTOS in the Welcoming tier.

04

What is the external contributor pull request merge rate for hertz-ai/HARTOS?

The external pull request merge rate is 53.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in hertz-ai/HARTOS?

Yes, hertz-ai/HARTOS currently has 19 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for hertz-ai/HARTOS are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.