anarlog

m audio, so you don't need to invite bots. It generates a summary based on the notes you take. Everything runs on local AI models by default, using Whisper and HyprLLM. HyprLLM is our proof-of-concept model fine-tuned from Qwen3 1.7B. We learned that summarizing meetings is a very nuanced task and that a model's raw intelligence (or weight) doesn't matter THAT much. We'll release more details on evaluation and training once we finish the 2nd iteration of the model (still not that good we can make it a lot better). Whisper inference: https://github.com/fastrepl/hyprnote/blob/main/crates/whispe... AEC inference: https://github.com/fastrepl/hyprnote/blob/main/crates/aec/sr... LLM inference: https://github.com/fastrepl/hyprnote/blob/main/crates/llama/... We also learned that for some folks, having full data controllability was as important as privacy. So we support custom endpoints, allowing users to bring in their company's internal LLM. For teams that need integrations, collaboration, or admin controls, we're working on an optional server component that can be self-hosted. Lastly, we're exploring ways to make Hyprnote work like VSCode, so you can install extensions and build your own workflows around your meetings. We believe privacy-first tools, powered by local models, are going to unlock the next wave of real-world AI apps. We're here and looking forward to your comments!