If you use Aqua Voice, WisprFlow or SuperWhisper, you know the drill: talk to a wall, hit stop, pray the AI didn't hallucinate.

Nobody does live transcription because it completely breaks standard AI models. We spent months figuring out local agreement, audio buffering, and aggressive real-time self-correction just so you can finally edit at the speed of thought.

Live transcription is not final transcription, but faster. It is a trust problem.

When someone is still speaking, the model is decoding partial audio: clipped phonemes, silence, background noise, half-finished words, and sentences that may still change direction.

I've watched a lot of people try voice dictation, get a good demo, and then quietly go back to their keyboard within a week. I did it myself for years.

For me the breaking point was always the cleanup. I'd talk for thirty seconds, get a transcript that was mostly right, and then spend the next minute fixing capitalization, adding punctuation, deleting the "um, no wait, actually" parts, and reformatting it into something I'd actually send. By the time I was done editing, typing would have been faster. So the dictation never stuck.

We built Juno because voice input on Mac still feels strangely unfinished.

Most dictation tools expect you to speak perfectly, wait, and then clean up the mess afterwards. But real speech is not like that. You pause, restart, correct yourself, say wait, no, mention product names, jump between ideas, and still need the final text to look like something you would actually send.

Juno is our attempt at a different kind of voice tool.

Press a key. Talk naturally. Juno turns the thought into clean text in the app you were already using.

The standard speech-to-text metric is Word Error Rate, or WER.

But users don t feel percentages.

A transcript can be 97% accurate and still feel like garbage if the missing 3% is a client s name, a critical date, a URL, or broken formatting.

For voice input, the real metric is much more binary: