JB Blocker
Hi! I vibecoded this with my daughter. It uses the SpeechAnalyzer framework in iOS 26, so all speech-to-text processing happens entirely on-device. One interesting thing we noticed is that real-time transcription is noticeably faster in English than in other languages. My guess is that the English model benefits from more training data compared to Spanish, French, German, etc. It works slightly better with earphones, but it’s surprisingly good even if you just point your phone away from you and speak in a normal voice. The transcription runs a few seconds behind, but it can handle continuous…
JB Blocker launched on January 21, 2016 with 7 votes. The site was not reachable when checked in September 2026.
What it does
In the maker’s words, at launch
Hi! I vibecoded this with my daughter. It uses the SpeechAnalyzer framework in iOS 26, so all speech-to-text processing happens entirely on-device. One interesting thing we noticed is that real-time transcription is noticeably faster in English than in other languages. My guess is that the English model benefits from…
Does a similar job
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Yap – OSS on-device voice dictation for macOS with no model to downloadJul 2026 · github.com · ▲104Hey HN! I wanted to share this OSS project I've been working on. It's called Yap and its a small menu-bar app for macOS that does voice to text for any input. You'll set a hotkey, press it, talk, press it again, and the text gets pasted into whatever field you were in. Everything runs locally and never leaves your computer. Fully OSS and MIT licensed. With macOS 26, Apple recently added two new APIs, SpeechAnalyzer and SpeechTranscriber, that do streaming on-device speech to text using models the OS ships and manages. So the app ships no model of its own and loads nothing before the first…
DictatoFeb 2026 · dicta.to · ▲93I built a macOS dictation app where everything runs on-device. Transcription, auto-correct, translation. No audio or text leaves your machine. It ships with 4 transcription engines you can swap between: WhisperKit (99 languages), NVIDIA Parakeet TDT 0.6B (25 European languages, fastest of the bunch), Qwen3-ASR 0.6B (30 languages), and Apple Speech on macOS 26+. They all run through CoreML/Metal. Whisper is the most versatile, Parakeet wins on raw latency for European languages, Qwen3 does better with CJK. I went with a protocol-based architecture so you pick the engine that fits your…
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