Meta Data Operations (DevOps)

Building XR + AI at Meta
Data scientist •Software Engineer J• Meta Strategist • Interdigital at Facebook, Instagram and YouTube

[Meta Data Operations (DevOps) @AiMetaEngr]

@_wett__ Alright kindly send a dm for better assistance

[Lian Lim | Dashboard & AI Automation Expert @dashboardlim]

i built a Meta ads dashboard that showed a client where 2.2M clicks went

most brands never see that drop-off because their reporting can't show it

the funnel view changed how they buy media:
→ clicks to link clicks: 56% drop
→ link clicks to page views: 22% drop
→ page views to leads: 92% drop

each stage is a different problem with a different fix

creative problem, landing page problem, offer problem

one funnel view tells you which one is bleeding

the rest of the build:
→ CPM, CPC, CPL tracked live across every campaign
→ 117.5M impressions mapped by region
→ device split that revealed 96% of leads come from mobile
→ campaign table ranked by spend and CTR

$3M in spend, one screen, owned by the client forever

if you want the funnel view on your ad account

Comment “DASHBOARD” and I’ll dm you a guide on how to setup your Meta Ads Intelligence Dashboard

[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

[Facebook @facebook]

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

[Facebook @facebook]

We’re aware that some people are having trouble accessing our products. We’re working to get things back to normal as quickly as possible, and we apologize for any inconvenience.

[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

[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]

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]

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.

[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]

To spend more test-time reasoning without drastically increasing latency, we can scale the number of parallel agents that collaborate to solve hard problems. While standard test-time scaling has a single agent think for longer, scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

[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]

5/ https://t.co/mtEUo3Appi

[AI at Meta @AIatMeta]

🔜

[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.

🧵👇

[Jean-Rémi King @JeanRemiKing]

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

Explore how we decode sentences from non-invasive brain recordings: https://t.co/IdR6gK2hcd

Links:
📄v1 Nature Neuro: https://t.co/wnRjc9W9gI
📑v2 Meta preprint: https://t.co/oSfLOQFcvg
💻Code: https://t.co/Xbe0XWfWQL
📊Data: https://t.co/SCBbs4AhTg
📝Blog: https://t.co/15RvsAaXlH
🧵Thread: https://t.co/d8FJrVyDut

[AI at Meta @AIatMeta]

Check out some cool ways the community has been putting Muse Spark to work (and play) 🧵👇

1/ https://t.co/SAaOe6sO7O

[AI at Meta @AIatMeta]

Big congrats to our SAM 3D team for receiving a Best Paper Honorable Mention at #CVPR26! This prestigious recognition underscores their incredible work pushing the boundaries of computer vision.

Read the paper here: https://t.co/jr59xlzwlX

[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