stenolabs/stenoai - Open Source PR Review Scorecard

Steno is the highly secure privacy-first AI notepad & notetaker for all your confidential conversations. On Windows & MacOS. For government and defence sectors.

C-Rank Grade: S (Elite) - 72/100

External PR Merge Rate: 88%

Response Time: 10h

First Timer Success: 67%

Frequently Asked Questions

Is stenolabs/stenoai welcoming to first-time open-source contributors?

stenolabs/stenoai has a recorded first-timer success rate of 66.7%. Repositories ranked S 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 stenolabs/stenoai respond to incoming external pull requests in approximately 9.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 71.5 C-Rank™ score (S 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 71.5 places stenolabs/stenoai in the S tier.

What is the external contributor pull request merge rate for stenolabs/stenoai?

The external contributor pull request merge rate for stenolabs/stenoai is 88.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in stenolabs/stenoai?

stenolabs/stenoai currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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stenolabs/stenoai

1,267
InactiveS•Elite(72/100)Python

Steno is the highly secure privacy-first AI notepad & notetaker for all your confidential conversations. On Windows & MacOS. For government and defence sectors.

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AI Maintainer Review Guidelines

Review Persona

Welcoming Community Builder

Warmth Score
8.3/10
Patience Score
8.4/10
Nitpick Rate
40%

Highly welcoming maintainers in stenolabs/stenoai. Prompt code reviews with positive guidance for new contributors.

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
9 hours
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
88.0%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
66.7%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
13 core
Highly collaborative maintainer core
Diagnostic Health HUD
88.0%
Merge Gauge
66.7%
1st-Timer
Community Vibe78/100

Embed C-Rank Badge

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

GetMerged C-Rank badge for stenolabs/stenoai
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Active Good First Issues (3)

View on GitHub

RFC: UI localization (i18n) - multi-language support, built to extend Problem Steno's interface is English-only. Every user-facing string is hardcoded - across the ~67 React .tsx files in the renderer and the Electron-native chrome in app/main.js (app menu, tray menu, system notifications, native dialogs). Non-English users get an English UI even when their meeting content (transcript, summary) is already in their own language. There's clear latent demand: past spoken-language requests (#45, #70/#71/#72) show a non-English user base, and German is a concrete first ask from the community. Scope guard - this RFC is UI chrome only. The content language (summaries, reports) is a separate, already-solved concern: output_language in src/config.py + src/report_store.py, plus transcript language detection (#283). This proposal does not touch any of that. Reusing the name get-language is off-limits here - it already means transcription/output language (app/preload.js, app/main.js). Proposal Int

📅 Opened Jul 14, 2026💬 1 comment
Quality: 65/100Contribute

NoteLicense and capability clarification (2026-07-14): FunASR is a toolkit, not a single checkpoint. The FunASR and SenseVoice repository source code is MIT; model weights follow each model card. SenseVoiceSmall supports Chinese, Cantonese, English, Japanese, and Korean, and its weights use the linked FunASR Model Open Source License Agreement. Fun-ASR-Nano-2512 is Apache-2.0. Language coverage, punctuation, and performance depend on the selected model and runtime configuration. Hi! Great AI meeting transcription tool! Would you consider FunASR or SenseVoice? Why for meeting notes? 5-10x faster — SenseVoice-Small: ~70ms/10s Built-in punctuation — Readable transcripts Speaker diarization — CAM++ built-in Streaming — Real-time transcription Fully local — Privacy-friendly 50+ languages, Apache 2.0 References FunASR: https://github.com/modelscope/FunASR (17K+ stars) SenseVoice: https://github.com/FunAudioLLM/SenseVoice (8K+ stars)

📅 Opened Jun 5, 2026💬 2 comments
Quality: 45/100Contribute

Windows alpha builds already ship from the Python backend, so a Rust rewrite is no longer a prerequisite for Windows support. This issue is an RFC to decide whether a backend rewrite has a measurable benefit worth its migration cost. It does not commit to an implementation. An evaluation should define a specific target, such as packaged size or startup time, and compare a small prototype with the current backend. It must account for macOS MLX transcription, Windows/Linux ONNX transcription, audio capture, existing file formats, and the CLI contract with Electron. Smaller binaries, faster startup, and simpler packaging remain hypotheses to test. Windows release hardening can continue independently on the existing backend.

📅 Opened Apr 28, 2026💬 1 comment
Quality: 70/100Contribute
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Contributor Compatibility & Review Speed Analysis for stenolabs/stenoai

When evaluating whether to contribute to stenolabs/stenoai, 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 stenolabs/stenoai acknowledge new external contributions in approximately 9 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 88.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to stenolabs/stenoai

01

Is stenolabs/stenoai welcoming to first-time open-source contributors?

stenolabs/stenoai has a recorded first-timer success rate of 66.7%. Repositories ranked Elite 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 ~9 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 71.5 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 71.5 places stenolabs/stenoai in the Elite tier.

04

What is the external contributor pull request merge rate for stenolabs/stenoai?

The external pull request merge rate is 88.0%. 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 stenolabs/stenoai?

Yes, stenolabs/stenoai currently has 3 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 stenolabs/stenoai 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.