Introducing @Muse, the personal agent that understands your goals and works 24/7 to get things done for you. Your Muse gets its own private computer in the cloud to do work for you. We've built new technology so the Muse Secure VM keeps your content safe.  We want everyone to have access to personal superintelligence, so we're providing Muse for free for up to 100 million tokens per week.

Muse Voice Transcribe is MSL's first real-time audio perception model -- rolling out today. SOTA in streaming speech-to-text, it handles speaker diarization, and endpointing natively in a single model.

The model decides when to listen. It waits a little longer on hard words and commits faster on easy ones, using adaptive delay to predict each token and increase accuracy. With adaptive delay, the model is near the pareto front on speed-accuracy tradeoff.

It holds up on messy, real audio too -- trained across 70+ languages (with 25 validated at launch), handles mid-sentence code-switching, and manages hour-long sessions with 20+ speakers. You can bias it toward names and domain terms as well.

Already powering dictation in the Meta desktop app and voice input in Muse Code. Live now on the Meta model API with a zero-data-retention tier. https://research.meta.ai/blog/introducing-muse-voice-transcribe

Sessions can share context with inter-session messaging. When two are working on things that affect each other, they share state directly, instead of the developer copying it over manually.

Workflows can break out a task across multiple focused agents, carry the intermediate work between stages, and return one result at the end. Good for engineering work that's too complex for a single agent.

Muse Code is out of beta and now built to handle bigger, more complex engineering tasks. Developers can get started with one command today: curl -fsSL https://dev.meta.ai/install.sh | bash