Streaming transcription is something I’m working on. The main challenge so far has been accuracy. Streaming models, especially cloud ones, often drop enough quality that the tradeoff isn’t always worth it. Local models look more promising, so streaming will likely land there first.

On multimodal input, the UX you’re prototyping where you switch between dictating and typing while composing is interesting. I haven’t really seen that approach before.

The direction I took is a bit different. Instead of mixing modalities mid-composition, dictation becomes context-aware during post-processing. Selected/Copied text or surrounding field content can be inserted into the post-processing prompt so the spoken input is interpreted relative to what’s already on screen.

The main differences are transcription quality and what happens after the transcript is generated.

Utter uses GPT-4o Transcribe by default for cloud transcription, and in my experience it’s best in class. The gap is most obvious on names, niche terminology, and technical vocabulary. I use it a lot for prompting coding agents, and I've found Apple’s built-in dictation and most other apps don't come close in terms of accuracy.

It also adds a custom post-processing step. So instead of ending up with a raw transcript, you can record a long, messy voice note and have it turned into a clean, structured markdown notes.

If you want to test the accuracy difference yourself, try dictating this with both Apple dictation and ChatGPT web (uses same model) and compare the output:

“My FastAPI service uses Pydantic, Celery, Redis, and SQLAlchemy, but the async worker is deadlocking when a background task retries after a Postgres connection pool timeout.”