[FEATURE] Add on-device macOS model support for AI Enhancement
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 45/100
Research direction
Start by reading the app’s AI Enhancement section and compare the proposed workflow with Yap’s on-device transcript clean-up. Identify how the built-in macOS model could be offered as an option without downloading a separate model. Done means users can choose on-device cleanup as a fast, privacy-friendly alternative; the issue does not name files or tests, so locate those before estimating the implementation.
Written by the indexing model from the issue text.
Description
Problem
I’d love it if you could add an option in the AI Enhancement section for using the on-device macOS LLM.
I’ve been testing Yap (https://github.com/FrigadeHQ/yap), which is very fast because it relies entirely on on-device models. However, it doesn’t offer other models such as Parakeet v3, which is significantly better than SpeechAnalyzer in my experience.
One particularly nice feature of Yap is transcript clean-up using the embedded macOS models. It requires no additional download, which is a big advantage compared with downloading (end executing) the 3.77 GB fluid-1 model, and it works quite well in the end.
Proposed solution
Could you please consider adding something similar? An option to use the built-in macOS LLM for transcript clean-up would provide a fast, privacy-friendly alternative without requiring users to download and run a large AI model.
In any case, BIG congratulations on developing this app! I really enjoy using it every day, Keep up the great work, you’re helping so many people type faster and better! :)
Thanks!!!
Alternatives considered
No response
- Dominant language
- Swift
- Stars
- 11.9k
- Forks
- 863
- Avg merge
- 1d 16h
- Merged PRs (30d)
- 26
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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