Launch HN: Hyprnote (YC S25) – An open-source AI meeting notetaker

I'm always amazed at these relatively tiny projects that "launch" with a "customers" list that reads like they've spent 10 years doing hard outbound enterprise sales: Google, Intel, Apple, Amazon, Deloitte, IBM, Ford, Meta, Uber, Tencent, etc.

Congrats on the launch. I never understood why an AI meeting notetaker needed sota LLMs and subscriptions (talking about literally all the other notetakers) - thanks for making it local first. I use a locally patched up whisperx + qwen3:1.7 + nomic embed (ofcourse with a swift script that picks up the audio buffer from microphone) and it works just fine. Rarely i create next steps / sop from the transcript - i use gemini 2.5 and export it as pdf. I’ll give Hyprnote a try soon.

I hope, since it’s opensource, you are thinking about exposing api / hooks for downstream tasks.

Nice!

Would be great if you could include in your launch message how you plan to monetize this. Everybody likes open source software and local-first is excellent too, but if you mention YC too then everybody also knows that there is no free lunch, so what's coming down the line would be good to know before deciding whether to give it a shot or just move on.

I just tried to build on Linux and it keeps panicking because it requires dozen(s) of API keys. I was not expecting that from local first software.

How are you balancing accuracy vs. time-to-word-on-live-transcript? Is this something you're actively balancing, or can allow an end user to tune?

I find myself often using otter.ai - because while it's inferior to Whisper in many ways, and anything but on-device, it's able to show words on the live transcript with minimal delay, rather than waiting for a moment of silence or for a multi-second buffer to fill. That's vital if I'm using my live transcription both to drive async summarization/notes and for my operational use in the same call, to let me speed-read to catch up to a question that was just posed to me while I was multitasking (or doing research for a prior question!)

It sometimes boggles me that we consider the latency of keypress-to-character-on-screen to be sacrosanct, but are fine with waiting for a phrase or paragraph or even an entire conversation to be complete before visualizing its transcription. Being able to control this would be incredible.

Do you intend to reach feature parity with something like MacWhisper? I'd love to switch to something open source, but automated meeting detection, push to transcribe (with custom rewrite actions) are two features I've learned to love, beside basic transcript. I also enjoy the automatic transcription from an audio, video or a even a YouTube link.

But because MacWhisper does not store transcripts or do much with them (other than giving you export options), there are some missed opportunities: I'd love to be able to add project tags to transcripts, so that any new transcript is summarized with the context of all previous transcript summaries that share the same tag. Thinking about it maybe I should build a Logseq extension to do that myself as I store all my meeting summaries there anyway.

Speaker detection is not great in MacWhisper (at least in my context where I work mostly with non native English speakers), so that would be a good differentiation too.

Looks great & kudos for making it local-first & open-source, much appreciated!

From a business perspective, and as someone looking also into the open-source model to launch tools, I'd be interested though how you expect revenue to be generated?

Is it solely relying on the audience segment that doesn't know how to hook up the API manually to use the open-source version? How do you calculate this, since pushing it via open-source/github you would think that most people exposed to it are technical enough to just run it from source.

Hello, I am a manager at a tech firm. Came across your product, and feeling this could be a thing I was looking for these days - recently got banned of some notetakers. Impressive demo video btw!

First of all I wanna try this on my personal device. How can I implement your product in my devices?

I also want to discuss some points (on-device, self-hosting, open-source, etc.) to persuade our compliance team, as well

I just downloaded on mac M4 pro mini. I installed the apple silicon version and try to launch it and it fails. No error message or anything. Just the icon keep bouncing on the dock. I assumed it needs some privacy and screen recording and audio permissions and explicitly gave them, however still just jumps on the dock and the app does not open. (OS, mac sequoia 15.5)

Looks really cool - I noticed Enterprise has smart consent management?

The thing I think some enterprise customers are worried about in this space is that in many jurisdictions you legally need to disclose recording - having a bot join the call can do that disclosure - but users hate the bot and it takes up too much visibility on many of these calls.

Would love to learn more about your approach there

I was talking about this a week ago. One person wanted to make a pdf tutorial of how to use a software. I asked him to record himself in teams and share his screen and have AI take notes. It will create a fabulous summary with snapshots of everything he is going over.

Congrats on the launch! I'm very bullish on how powerful <10B-param models are becoming, so the on-device angle is cool (and great for your bottom line too, as it's cheaper for you to run).

Something that I think is interesting about AI note taking products is focus. How does it choose what's important vs what isn't? The better it is at distinguishing the signal from the noise, the more powerful it is. I wonder if there is an in-context learning angle here where you can update the model weights (either directly or via LoRA) as you get to know the user better. And, of course, everything stays private and on-device.