Meta AI & Data Devs(ops)

Building VR + Al at Meta
Data scientist •Software Al Engineer • Meta Strategist • Interdigital at instagram and facebook

[Meta Newsroom @MetaNewsroom]

Today we’re introducing Muse Spark, our most powerful model yet, giving you a faster and smarter Meta AI.

Muse Spark currently powers the Meta AI app and website and will be rolling out to @whatsapp, @Instagram, @facebook, @messenger, and AI glasses in the coming weeks.

https://t.co/WHTipuuAmj

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

✔️

[Meta Newsroom @MetaNewsroom]

Today we’re introducing Muse Spark, our most powerful model yet, giving you a faster and smarter Meta AI.

Muse Spark currently powers the Meta AI app and website and will be rolling out to @whatsapp, @Instagram, @facebook, @messenger, and AI glasses in the coming weeks.

https://t.co/WHTipuuAmj

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

We’re happy to announce 2 releases today:
- 🧠Brain2qwerty v1 is published
- 🚀 Brain2Qwerty v2 is now publicly released

[AI at Meta @AIatMeta]

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

🧵👇

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

Muse Ai

[AI at Meta @AIatMeta]

To build personal superintelligence, our model’s capabilities should scale predictably and efficiently. Below, we share how we study and track Muse Spark’s scaling properties along three axes: pretraining, reinforcement learning, and test-time reasoning. 🧵👇

Let’s start with pretraining. Over the last 9 months, we rebuilt our pretraining stack with improvements to model architecture, optimization, and data curation, enabling us to increase the capability we can extract from every unit of compute. To rigorously evaluate our new recipe, we fit a scaling law to a series of small models and compare the training FLOPs required to hit a specific level of performance.

The results: we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick, making Muse Spark significantly more efficient than the leading base models available for comparison.

[AI at Meta @AIatMeta]

Muse Spark is built from the ground up to integrate visual information across domains and tools. It achieves strong performance on visual STEM questions, entity recognition, and localization, enabling interactive experiences like troubleshooting your home appliances with dynamic annotations.

[AI at Meta @AIatMeta]

Personal superintelligence will help people learn about their health. We collaborated with 1,000+ physicians to curate training data that enables more factual and comprehensive responses. It can generate interactive displays that unpack and explain health information such as the nutritional content of various foods or muscles activated during exercise.

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

🖊️

[AI at Meta @AIatMeta]

We’re releasing SAM 3.1: a drop-in update to SAM 3 that introduces object multiplexing to significantly improve video processing efficiency without sacrificing accuracy.

We’re sharing this update with the community to help make high-performance applications feasible on smaller, more accessible hardware.

🔗 Model Checkpoint: https://t.co/KgW0zZQ0QT
🔗 Codebase: https://t.co/Ks61vfokB0

[AI at Meta @AIatMeta]

We’re releasing SAM 3.1: a drop-in update to SAM 3 that introduces object multiplexing to significantly improve video processing efficiency without sacrificing accuracy.

We’re sharing this update with the community to help make high-performance applications feasible on smaller, more accessible hardware.

🔗 Model Checkpoint: https://t.co/KgW0zZQ0QT
🔗 Codebase: https://t.co/Ks61vfokB0

[AI at Meta @AIatMeta]

The core innovation in SAM 3.1 is object multiplexing, allowing the model to track up to 16 objects in a single forward pass. Previously, each object required its own dedicated pass, but with multiplexing, SAM 3.1 processes all tracked objects together, eliminating redundant computation and memory bottlenecks.

This approach doubles the processing speed for videos with a medium number of objects, increasing throughput from 16 to 32 frames per second on a single H100 GPU.

[AI at Meta @AIatMeta]

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound.

Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks.

Try the demo and learn more here: https://t.co/VkMd1YpQWI

[Facebook @facebook]

Watch…kinda learn…then SERVE with beauty hacks from Uniquely TyAna and friends. 🔥❤️‍

[AI at Meta @AIatMeta]

We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.

By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.

The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume

[Facebook @facebook]

We’ve launched new updates to Facebook Reels 🤩:

✔ Extend your reels to 90 seconds
✔ Easily share ready-made reels from Memories
✔ Use our new visual beat technology to sync videos with your favorite songs with Grooves
✔ Save time and get inspiration from Templates

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

We’re introducing new updates to @WhatsApp group chats, including improvements to polls, (@)all mentions to get the whole chat’s attention, and more.

[WhatsApp @WhatsApp]

Guest Calling is here 💻 invite anyone to a WhatsApp call with a call link, even if they don't have WhatsApp

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

We’re hoping Threads can be great space for public conversations, and we’re very focused on the creator communities that already enjoy Instagram.

[Kalshi @Kalshi]

JUST IN: Mark Zuckerberg says Threads has surpassed X in monthly active users

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

Personal superintelligence will help people learn about their health. We collaborated with 1,000+ physicians to curate training data that enables more factual and comprehensive responses

[AI at Meta @AIatMeta]

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound.

Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks.

Try the demo and learn more here: https://t.co/VkMd1YpQWI

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

From optimizing codebases to creative writing

[AI at Meta @AIatMeta]

We trained Brain2Qwerty v2 on ~22,000 sentences from 9 volunteers, each recorded for 10 hours wearing an MEG device while typing.

By using end-to-end deep learning on raw brain signals from MEG devices and fine-tuning LLMs, the system effectively bridges the gap between noisy neural data and coherent language.

The results are promising:
- Avg word accuracy of 61% across participants
- 78% word accuracy and 50%+ of sentences decoded with ≤ 1 word error for the top-performing participant
- Performance scales log-linearly with data volume

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

Started as a ring. Now Meta AI is its own thing!

[Alexandr Wang @alexandr_wang]

1/ muse for small businesses 📊

we found lots of people using muse to run their small business. plumbing businesses, grocery stores, farms, restaurants, and shops.

today we're launching a bunch of connectors to make that easier!

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

Gemini 4 Argon can turn messy data into clean, visual narratives in just minutes 🪐

[Meta @Meta]

Imagine controlling your devices with a subtle hand or finger gesture. Our cutting-edge research turns intent and muscle signals into seamless computer control. This breakthrough wrist technology is redefining how we interact with computers—intuitive, precise, and ready for the future.

[Meta AI & Data Devs(ops) @MetaVrTechEngr]

Personal Intelligence is now rolling out for Gemini on Android Auto and in the coming months to cars with Google built-in, so you can get even better hands-free help on the road.

[Meta @Meta]

We go together like AI and glasses—is that not an expression? It will be. Check out all the news from Connect 2025.