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Looped Whisper (FOSS) – Voice transcription menubar app for macOS

Hacker News

Looped Whisper (FOSS) – Voice transcription menubar app for macOS

I built a free, open-source (MIT) macOS menu-bar app that runs Whisper models locally to assist with dictation. There is also the option to use an LLM (BYOK). You hold a global hotkey, speak, and the text gets pasted at your cursor similar to other similar/popular apps. Some details: - Transcription runs locally via WhisperKit (CoreML), so it works offline once the model is downloaded. BYO model — tiny through large-v3, auto-downloaded and cached. The base model has been working surprisingly well for me with gpt-5.4-mini as the transformation model. - Push-to-talk (hold) and start/stop toggle hotkeys, both configurable. - fn/Globe key supported. - Realtime mode for live captions as you speak. - Optional LLM cleanup to fix typos/punctuation — point it at Anthropic or any OpenAI-compatible endpoint, key stored in the macOS Keychain. - A vocabulary feature to bias recognition toward your names, jargon, and identifiers (handy for dictating code). - This works as a menu-bar agent, no Dock icon, optional launch at login. I built this because I wanted local dictation that I could fully own and that handled dev jargon, without a subscription. Superwhisper/Wispr Flow is a more polished and full-featured - this is just a hackable, open source version.

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Actual performance

2points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, agent · Missing: agents, claude, apple
99%99% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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