beehive-lab/jllm - Open Source PR Review Scorecard

AI inference engine for Java in Java. Fast.

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

External PR Merge Rate: 86%

Response Time: <1h

First Timer Success: 100%

Frequently Asked Questions

Is beehive-lab/jllm welcoming to first-time open-source contributors?

beehive-lab/jllm has a recorded first-timer success rate of 100.0%. 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 beehive-lab/jllm respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 66.4 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 66.4 places beehive-lab/jllm in the A tier.

What is the external contributor pull request merge rate for beehive-lab/jllm?

The external contributor pull request merge rate for beehive-lab/jllm is 85.7%, based on public PR activity from non-core contributors.

Are there Good First Issues available in beehive-lab/jllm?

beehive-lab/jllm does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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beehive-lab/jllm

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Active (30d)A•Welcoming(66/100)Java

AI inference engine for Java in Java. Fast.

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
7.5/10
Patience Score
8.2/10
Nitpick Rate
20%

Collaborative maintainer environment in beehive-lab/jllm. Reviews community pull requests with focus on project quality.

What Contributors Actually Say

Discussions in beehive-lab/jllm focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.

Hidden Friction Signals

Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.

Unwritten Rules

  • •1) Keep PR scope strictly aligned with the linked issue.
  • •2) Ensure local linters pass before opening a review.
  • •3) Maintain full test coverage for modified logic.

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
85.7%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
100.0%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
5 core
Small core review team
Diagnostic Health HUD
85.7%
Merge Gauge
100.0%
1st-Timer
Community Vibe94/100

Embed C-Rank Badge

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

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

Active Good First Issues (1)

View on GitHub

Description The langchain4j-gpu-llama3 module currently exposes classes from external dependencies through its public methods. Some exposure is required by LangChain4j’s model contracts: public ChatResponse doChat(ChatRequest request) public void doChat( ChatRequest request, StreamingChatResponseHandler handler ) These types come from langchain4j-core: ChatRequest ChatResponse StreamingChatResponseHandler This exposure is intentional and cannot be removed without departing from the LangChain4j model interfaces. The integration also exposes GPULlama implementation types: public Model getModel() public Sampler getSampler() These types come from the gpu-llama3 dependency: org.beehive.gpullama3.model.Model org.beehive.gpullama3.inference.sampler.Sampler Revapi reports all these classes as: java.class.externalClassExposedInAPI The current version handles this with targeted exceptions in langchain4j-gpu-llama3/revapi.json. This is appropriate for a non-breaking update, but it highlight

📅 Opened Jul 30, 2026💬 0 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for beehive-lab/jllm

When evaluating whether to contribute to beehive-lab/jllm, 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 beehive-lab/jllm acknowledge new external contributions in approximately <1 hour. Out of all submitted pull requests from non-core authors in the last 180-day window, 85.7% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to beehive-lab/jllm

01

Is beehive-lab/jllm welcoming to first-time open-source contributors?

beehive-lab/jllm has a recorded first-timer success rate of 100.0%. 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 66.4 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 66.4 places beehive-lab/jllm in the Welcoming tier.

04

What is the external contributor pull request merge rate for beehive-lab/jllm?

The external pull request merge rate is 85.7%. 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 beehive-lab/jllm?

beehive-lab/jllm does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for beehive-lab/jllm 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.