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Speak, capture, type - my voice assistant works anywhere on your system

Hacker News

Speak, capture, type - my voice assistant works anywhere on your system

I built Vibevoice, a tool that lets you dictate text and run voice commands with screen context anywhere on your system. It works like this: For regular dictation, hold the right Ctrl key while speaking, then release to have your words typed automatically wherever your cursor is - perfect for coding, emails, or chat apps. The more interesting feature is the AI command mode: hold the Scroll Lock key, speak a prompt, and a local LLM responds based on both your words AND a screenshot of what you're looking at. The AI's response gets typed directly into your application as if you typed it yourself. Everything runs locally using Whisper for transcription and Ollama for the LLM (I recommend gemma3:27b for best results). No cloud services or API costs. The tool was inspired by Karpathy's "vibe coding" and builds upon Vlad's whisper-keyboard project. I extended it to work with local models and added the screenshot context feature, which makes the AI much more useful for everyday tasks like writing e-mails. Let me know what you think!

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, model, apps · Missing: mac, agents, macos
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 · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: llama, 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.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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