marshalla
I tried setting up a script so Claude could automatically call agy, but it kept hitting timeouts, and letting Claude control that tends to burn my usage too quickly anyway (and if I'm not copy-pasting into different terminals, where's my value add in all this?).
Argon hallucinates, so I rarely use it for impl, but I've actually had better results with it than Opus for SwiftUI and AppKit work.
I try not to run more than three sessions at once, or I get anxiety about having to keep all the session caches fresh and end up typing silly things into some of them every 30-45 min so they don't expire or auto-compact.
I do think Parakeet is pretty amazing, though, so some of these product announcements have limited interest to me, especially since it's cloud and costs per hour, even if it's a low cost.
Parakeet is trained on text that has had ITN done on it, so it prints digits, which messes up window merges. I'm training that out of it: https://github.com/MimicScribe/parakeet-spoken-form
It's just about done. I've got a custom version of Parakeet Ultra I'm training now that does digits better.
On a more serious note, I think on-device object/face recognition search is a great idea. Having the cta button on the site go to Polar is a little unexpected. It'd be nice to be able to try the app without going through the hoops of running it in a Docker container, if that's the same app or system?
Applications built using deterministic code to work might just not be a thing people need to pay for anymore.
Maybe work relating to training ML can still have value? The compute cost and time involved in it add to the moat.
Key takeaways: People don't like subscriptions for on-device apps, and people really don't like telemetry.
Long story short: I got a batching model to behave more like a streaming model, ~2s updates, and it runs on the ANE so it’s efficient annnd it’s more accurate than all feasible alternatives annnd it only took me like a month. Annnd this is a meeting transcription / notetaker app for macOS.
I used a bot to help me generate precise language around the numbers for this post. Forgive me.
Dear AlmostHadYa, I got live previews working with a batch ASR model
The noises confuse the ASR models, I presume.
Someone could still record audio and manually transcribe it or take notes with a pen and pencil... just sayin'. Some of this just comes down to having to trust the other party.
I do take some issue with my voice almost certainly being used to train models when using certain services.
I like the idea of having a completely headless auto recording system. I was against it initially over privacy concerns but with completely on-device its a better idea. I might snag the concept
Speaker identification is a huge trick to do on device. I've masked by sentence and capture embeddings within that mask to get things more accurate.
If you try it that would be super cool and it's basically free rn while I figure out how to market.
I'm testing obsidian integration rn. It's currently available through mcp but that kind of defeats the offline transcription thing
Ollama just exposes models via an OpenAI compatible endpoint though (I'm pretty sure), so adding that standard is probably a good idea. The prompts are a bit tuned for Gemini. I'd have to test how much that matters.
I just tried Hedy, same concept, also a great tool. It's todos are nice.
MimicScribe works well with any Apple silicon Mac so it'll feel snappy on an M1 with 8GB of RAM even. It uses Apple's on-device ML accelerator, the ANE. https://mimicscribe.app/docs/performance
Show HN: On-device transcriber that's 97% accurate at identifying speakers
I’ve spent the last seven months building a tool I wish I’d had in my previous roles. MimicScribe is a macOS menu bar app that fits the "AI notetaker" category. It has accurate on-device speaker identification (a first possibly?), real-time meeting talking points for discovery calls, and a fully keyboard- and voice-driven interface.I believe the accuracy of the speaker ID system is its biggest...