Yooz Labs
Sovereign Intelligence. Built for the skeptical.
Privacy-first AI that runs entirely on your devices. No cloud, no tracking, no compromises.
What we're building
We're the privacy infrastructure for the AI decade. Every Yooz product is designed for the 70% of consumers who don't trust cloud AI but lack consumer-grade alternatives.
- Yooz Engine — unified local AI service for macOS (STT, LLM, grammar, VAD, TTS).
- Yooz Whisper — voice keyboard for macOS.
- Yooz Notes — note-taking with private AI memory.
- Remi — Claude Code's distant friend. Secure peer-to-peer remote sessions for Claude Code (and soon Codex), with an iPhone app and local auto-approve.
- Yooz Vault — privacy hardware (home server).
- Universal AI Platform Layer — one API across Apple Core ML, Android ML Kit, Windows DirectML.
What lives on this Hugging Face org
The model weights — Apache 2.0, fully open source. The Yooz product code is source-available on GitHub under PolyForm Shield, but the weights stay open so the research community can build on them, audit them, and remix them.
Model categories
| Category | What it is |
|---|
| ASR | Speech-to-text checkpoints (Qwen3-ASR Swift port, Parakeet derivatives) |
| LLMs (Touchup) | Fine-tuned small LLMs that fix/clean speech-to-text transcripts |
| Distillations | Small students distilled from larger teachers for on-device inference |
| Adapters | LoRA / DoRA adapters published alongside their fused checkpoints |
All checkpoints document their lineage (base model + Hugging Face link), eval numbers (real benchmarks, not vibes), and Swift / Python usage snippets.
Why open weights?
The competitive moat in privacy-first AI lives in the product, not the weights:
- Multi-device orchestration (phone → PC → Vault) over WireGuard mesh.
- Universal platform abstraction across Apple, Android, Windows AI APIs.
- Private AI memory: encrypted, local, with permissioned cross-app context.
- Beautiful, consumer-grade UX.
The weights themselves should be open so the research community can audit privacy claims, reproduce evals, and build on top. Releases follow the standard "ship the artifact + the recipe to reproduce it."
Provenance
We never train on user data without opt-in. All training corpora and synthetic data sources are documented in the model card for each checkpoint. Where we fine-tune from a base model (Qwen, Gemma, etc.), the lineage is preserved and the upstream license is respected.
Source-available, not closed
The product code lives at
github.com/yooz-labs under PolyForm Shield. You can read it, fork it, and build on it for non-competing use cases. We chose this path because we want to stay community-aligned without enabling AWS-style "managed Yooz" competing services.
Get in touch
- Engineering & research: dev@yooz.info
- Bugs and feature requests: file on the relevant GitHub repo under yooz-labs
- Mailing list / news: coming soon
We're building the privacy infrastructure for the AI decade. Every decision prioritizes user sovereignty, data privacy, and beautiful simplicity.
