Monday I'm sending out invites to https://tish.bshk.app/?utm_source=x&utm_medium=organic_social&utm_campaign=tish_alpha_2026_05_31&utm_content=en_post
If you're interested, you can sign up as an alpha tester by sending me your email, or subscribe for updates via the link.
Alpha testers get the PRO version free during the alpha test and a 50% discount once it ends.
Runs automatically the moment the call ends. Not happy with the result? One Reanalyze button.
Under the hood: VAD in parallel on raw + processed audio, SED for hygiene, LUFS from preview — single pass over the files.
Just rolled out Call Insights in Tish.
After every call you get what's in the screenshot: Talk/Listen, NC effectiveness in dB, noise floor, speech hygiene (lip smacks, chewing, background events).
Currently polishing things up before an App Store release. If you work with audio and want to give it a try — reach out, I'd love the feedback.
#audio #podcasting #macos #indiedev #audioprocessing #noisereduction #solovey #buildinpublic
Solovey fixes that in a couple of clicks: drop in a file, the app removes noise, levels out the volume — and gives you a clean result. Everything runs locally on your Mac, no cloud uploads.
Speed-wise: a 10-minute recording processes in about 3 minutes on a MacBook Pro.
Building Solovey — a macOS app that cleans up audio recordings
You know the deal: you recorded a podcast, interview, or lecture — and the audio has background noise, hum, and volume jumping all over the place.
**11/**
Want to adapt this for your domain?
The approach works for any specialized translation:
• Legal
• Medical
• Technical
• Gaming
Build a dictionary. Fine-tune a small model. Beat the giants.
**10/**
Key lessons:
1. Remove what you don't need
2. Domain dictionaries > model size
3. 16-bit LoRA >> 4-bit QLoRA
4. Measure everything
5. Iterate relentlessly
