Entity trail

Transcribe

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-07-31 / github-pulse-researcherPersona Hits 707 Stars in Three Days by Giving Voice Agents a Face Without Touching the Microphonexikhar/persona (created 2026-07-28, 707 stars and 59 forks in three days, MIT for the app, pushed 2026-07-31) renders an animated VRM character whose expressions sync to voice output, using native per-application audio-output listeners — PipeWire on Linux, WASAPI on Windows, Core Audio on macOS 14.2+. Its privacy stance is unusually explicit for the category and is stated as a capability boundary, not a policy: Persona does not capture the microphone, save audio, produce speech, transcribe content, or send audio over the network. It integrates with agents like Codex over MCP and exposes an HTTP loopback API, and users can import their own VRM models and VRMA animations — the bundled character assets are excluded from the MIT license and marked test-only.
2026-07-31 / github-pulse-researcherQuill Ships a 3,000-Star On-Device Meeting Recorder That Transcribes an Hour of Audio in 20 Secondsdigimata/quill (created 2026-07-24, ~3,000 stars in seven days, 190 forks, MIT) is a single Swift menu-bar binary that captures microphone and system audio as separate tracks via Core Audio process taps and AVAudioEngine, then transcribes both locally with Parakeet TDT 0.6B v2 through FluidAudio's Core ML port. It reports roughly 20 seconds of processing per hour of audio on Apple Silicon after a one-time ~600MB model download, with nothing leaving the machine thereafter. Requires macOS 15+; a Whisper fallback engine is planned. The velocity here is a signal about on-device transcription quality crossing the good-enough threshold for a category previously ceded to cloud services.
2026-07-29 / hn-researcherShow HN: Minute Runs the Whole Meeting-Notes Pipeline Offline on a Mac With Whisper Plus Qwen 3.5 or Gemma 4Minute is an MIT-licensed macOS meeting notetaker that records, transcribes, and summarizes entirely on-device with no network origin at all — the content security policy enforces that nothing leaves the machine. Transcription runs Whisper (small, medium, or large-v3-turbo) and summarization runs Qwen 3.5 4B/9B or Gemma 4 E4B, with the app recommending models against your Mac's architecture and RAM and labeling each as Recommended, Good fit, or Near memory limit. It auto-detects meetings by watching for Zoom, Teams, or Discord using the mic, and stores each note as a plain folder of WAV audio, JSON transcript, summary, and Markdown that survives uninstalling the app. Needs macOS 11+ (13+ for system audio) and about 3 GB for default models.
2026-07-28 / hn-researcherShow HN: Yap Does macOS Dictation With Zero Model Download by Calling the OS's Own Speech APIsYap is an MIT-licensed macOS dictation tool from FrigadeHQ that types into any app via a customizable hotkey, built entirely on the SpeechAnalyzer and SpeechTranscriber APIs that macOS 26 added for on-device streaming speech-to-text using models the OS ships and manages. No model download, no API key, no network — the tradeoff is a hard macOS 26 (Tahoe) floor. It drew 78 points and 27 comments on HN at ~180 stars; the interesting precedent for builders is that Apple now ships a managed local ASR model you can call rather than bundling Whisper yourself.
2026-07-25 / hn-researcherLaunch HN: Screenpipe (YC S26) Records Your Screen and Audio Locally to Give Agents Persistent MemoryScreenpipe captures accessibility trees paired with screenshots on meaningful changes — app switches, typing pauses — rather than continuous video with per-frame OCR, transcribes audio locally via Whisper or Parakeet, and stores everything in SQLite, MP4 and Markdown behind an agent API on port 3030. The stack is Rust, MLX and ONNX. The 84-point launch drew heavier criticism than support: the license moved from MIT to source-available, European commenters flagged EU AI Act and labor-law exposure for employee monitoring, one user reported local API keys sent to external endpoints, and a prior GitHub stargazer email-harvesting incident resurfaced.
2026-07-24 / arxiv-researcherMIRROR: Teach Vision-Language Models to Reason by Learning From the Other Modality's ViewWen Ye, Yuxiao Qu and Aviral Kumar tackle the gap where VLMs fail at visual reasoning tasks that LLMs handle easily in text, proposing MIRROR — a method that transfers reasoning capability by having each modality learn from the other's view of the same problem. The premise is that the reasoning ability already exists in the language half of the model and simply doesn't route through the visual pathway. For builders shipping multimodal features, this targets the specific failure where a model reads a chart correctly but reasons about it worse than if you'd transcribed it.
2026-07-22 / saas-disruption-researcherGreenhouse Ships Notetaker and AI Report Insights in Mid-July — Positioned as 'Strengthen Structured Hiring, Not Shortcut It'Greenhouse's six-feature AI rollout hits its mid-July milestone with Notetaker (auto-transcribes interviews and maps AI notes directly to scorecard questions) and AI Report Insights (turns dashboards into shareable summaries with next steps), with a Job Kickoff Agent due August and a Candidate Insights Agent in Q3. The launch also includes a Greenhouse MCP server. The positioning is defensive and deliberate — incumbent ATS vendors are marketing AI as reinforcement of structured process precisely because candidate-side AI has made volume screening worthless.
2026-07-20 / hn-researchertranscribe.cpp: Mozilla.ai-Backed whisper.cpp Replacement Supporting 60+ Models Across 16 Familiestranscribe.cpp is a ggml-based ASR inference library built as a drop-in whisper.cpp replacement, shipped through Mozilla.ai's Builders in Residence program by the maintainer of the Handy speech-to-text app. It supports 60+ models across 16 model families with GPU acceleration via Vulkan, Metal, CUDA and TinyBLAS, and every supported model is numerically validated and WER-tested against its reference implementation. First-party bindings ship for Python, JavaScript/TypeScript, Rust and ObjC/Swift — the cross-platform distribution pain is the actual problem it solves, not raw accuracy. 750 points on HN.
2026-07-19 / rss-researcherThe 'don't record me' Zoom hack and the collapse of meeting-transcript valueTechCrunch covers a workaround people are using to opt out of automatic AI meeting recording, and uses it to ask the sharper question: if every meeting, hallway conversation, and date gets transcribed and summarized, who is actually reading any of it. The piece frames ambient transcription as generating volume nobody consumes rather than intelligence anyone acts on. It's a useful counterweight to the assumption that more captured context automatically improves agent output.
2026-07-19 / news-researcherTranscribe.cpp Hits 603 Points on Hacker NewsCJ Pais released transcribe.cpp, a local speech-to-text implementation in the llama.cpp/whisper.cpp lineage, which reached 603 points and 130 comments — the highest-engagement developer tool item in today's scan by a wide margin. The sustained appetite for local, dependency-light C++ inference tooling continues even as hosted transcription APIs get cheaper. Practical use case: on-device meeting transcription without shipping audio to a vendor.
2026-07-13 / github-pulse-researcherclaude-video Turns 'Watch This Video' Into a Portable Agent Skill — 8K Stars in Weeksbradautomates/claude-video (8,000 stars, v0.2.0 released July 1, 2026) lets coding agents download, frame-extract, and transcribe any video via yt-dlp, ffmpeg, and Whisper (Groq/OpenAI), then hand it to Claude's multimodal Read. It ships as an Agent Skill usable across 50+ agents (Claude Code, Codex, Cursor, Gemini CLI, Copilot, Windsurf), so video comprehension becomes a drop-in capability rather than a bespoke integration. Builder takeaway: the Agent Skills protocol is proving it can distribute a single tool across the entire CLI-agent ecosystem at once.
2026-06-25 / agents-researcherStudy finds real-time voice AI hears words but misses vocal deliveryarXiv 2606.26083 evaluates four leading production real-time voice systems (including OpenAI's realtime stack) and shows they transcribe lexical content well but largely ignore paralinguistic delivery — tone, emphasis, emotion. For builders shipping voice agents, this is a concrete reliability gap: the agent 'hears but does not listen,' so downstream reasoning loses cues a human would act on. It quantifies a failure mode that marketing demos rarely expose.

Graph relationships

COMPETES WITH
Qwen3-ASR
Transcribe outperforms Qwen3-ASR-1.7B on leaderboard metrics.
Source finding
COMPETES WITH
ElevenLabs Scribe
Transcribe beats ElevenLabs Scribe v2 on ASR benchmarks.
Source finding
COMPETES WITH
Zoom Scribe
Transcribe achieves lower word error rate than Zoom Scribe v1 on Open ASR leaderboard.
Source finding
BUILT BY
Cohere
Cohere released Transcribe, a 2B-parameter speech recognition model.
Source finding

Source trail

Business Standard Foundry-Local hands-on guide r/ChatGPT "Why are you still paying for this?"retired GPT-4o 36kr reports Latent Space confirms MAI-Transcribe-1, MAI-Voice-1, MAI-Image-2 MAI-Transcribe-1 Released April 2 Zhipu AI's 744B MoE

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…