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) - 76/100
External PR Merge Rate: 92%
Response Time: 10h
First Timer Success: 80%
stenolabs/stenoai has a recorded first-timer success rate of 80.0%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in stenolabs/stenoai respond to incoming external pull requests in approximately 9.9 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
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 76.4 places stenolabs/stenoai in the S tier.
The external contributor pull request merge rate for stenolabs/stenoai is 91.5%, based on public PR activity from non-core contributors.
stenolabs/stenoai currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in stenolabs/stenoai. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 18:00 UTC (18:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 16:00 to 20:00 UTC (16:00 - 20:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~6 hours (96% approval). Large PRs (>500 lines) take ~47 hours (41% approval). Keep your diff compact for rapid review.
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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
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)
Goal Migrate the Python backend (simple_recorder.py + modules in src/) to Rust, enabling a cross-platform build that targets Windows alongside macOS. Why Windows is the most-requested platform we don't ship for — original Windows ask (#18) sat open since Feb 2026 Rust enables clean cross-compilation and avoids the Python+PyInstaller packaging fragility that has caused multiple shipping incidents (Intel Mac builds, hardened runtime issues) Smaller binary, faster startup, no Python interpreter dependency Easier to sign and notarize across platforms Scope The current backend is ~2.5k lines in simple_recorder.py plus modules: audio_recorder (sounddevice → cpal in Rust) transcriber (pywhispercpp → whisper-rs) summarizer (Ollama HTTP client → reqwest) ollama_manager (subprocess lifecycle) config (JSON-backed settings) folders, models The Electron frontend (app/main.js) shells out to a single CLI binary via spawn — that interface stays the same. The Rust binary just replaces the
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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, 91.5% were successfully merged into the primary branch.
stenolabs/stenoai has a recorded first-timer success rate of 80.0%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~9 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
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 76.4 places stenolabs/stenoai in the Elite tier.
The external pull request merge rate is 91.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, stenolabs/stenoai currently has 3 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
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.