Wordbird: free dictation for Mac running Nvidia Parakeet locally

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Dear HN, Wordbird is an open-source voice dictation app for macOS, powered by Nvidia Parakeet running locally on Apple Silicon via MLX. You can teach it project-specific terms like brand names or frameworks using a project-level WORDBIRD.md file. Run it via `uvx wordbird`. Press a hotkey, speak, and your words are transcribed and pasted into whatever app is focused. A small LLM, also running locally, post-processes the transcription to fix errors. If you use apps like VS Code, Zed, Terminal.app or iTerm, Wordbird will know which directory you're in, look up a project-specific WORDBIRD.md file, and if it exists, use it for post-processing the transcription. With the VS Code extension this even works via SSH. My friend Till Hoffmann wrote this because he was "tired of yet another subscription", and standard dictation just gets too many technical terms wrong. I'm posting this here on his behalf because he's not as active on HN. I've really fallen in love with Wordbird, already made some small PRs, and I hope that many here will find it equally useful. Really eager to hear feedback (I'll make sure Till monitors this thread as well)!

Visit product View on Hacker NewsMar 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

97%97% 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: para · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released

50%50% 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, para · Missing: mobile apps, ios, personal

36%36% 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

34%34% 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, active · Missing: arr, mrr, revenue

18%18% 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.

Incorrect prediction on native model

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