AI at Meta ✓
@AIatMeta · joined 29 Aug 2018
Together with the AI community, we are pushing the boundaries of what’s possible through open science to create a more connected world.
854 647Followers
367Following
2 770Posts total
21.3MViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
@finkd just unveiled Muse Realtime Avatar, our real-time embodiment technology that turns Muse Realtime Voice into expressive, interactive avatars.
Muse Realtime Avatar enables an entirely new range of interactions in @Muse, starting with real-time conversations.
🧵👇 https://t.c
14.4K views · 153 likes
· 15 reposts · 12 replies
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2:01
Muse is now available on Mac 💻.
Your personal agent can get things done for you directly on your computer (all with your explicit permission).
- Organize your downloads folder
- Find a file you’ve lost track of
- Summarize your messages and notes
…more coming soon.
Try https
195.5K views · 1.3K likes
· 109 reposts · 128 replies
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0:05
Today we launched https://t.co/wrM0KhfXGS, a personal AI agent, and published a deep dive on how we built safety into its system.
An agent that gets to know you over time necessarily holds a lot of context about you. That's the source of its usefulness and the reason we built ht
120.6K views · 1.2K likes
· 128 reposts · 140 replies
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1:00
Introducing Muse, a personal agent that gets things done for you, powered by Muse Spark 1.3.
Get an inside look at how we built Muse and what it can do for you: https://t.co/qzFgoNAUSV https://t.co/7skLRgWCaI
553.1K views · 3.1K likes
· 325 reposts · 225 replies
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2:36
Introducing Muse, your personal AI agent from Meta that gets things done across every part of life.
Download the Muse app and get started: https://t.co/KBjYWfshGo https://t.co/1exp56xj93
7.5M views · 12K likes
· 1.1K reposts · 1.3K replies
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1:22
As a test of our progress to advance the frontier of AI research, in June we entered the next generation of our autonomous AI research system, AIRA₃, in a live Kaggle competition run by NVIDIA to fine-tune a 30B Nemotron model. The challenge was to teach the model to reason https
316K views · 2K likes
· 217 reposts · 118 replies
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0:09
Muse Spark 1.3 with max reasoning is now available on Muse Code and Meta Model API 👇
60.1K views · 791 likes
· 45 reposts · 62 replies
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1/ we just publicly released Muse Spark 1.3 max!
we see significantly stronger coding and agentic performance on muse spark 1.3 max, so would strongly recommend trying it out even if you've already tried muse spark 1.3 high or muse spark 1.3 xhigh. https://t.co/U5PvZ8CiN1
329.3K views · 2K likes
· 168 reposts · 142 replies
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0:52
0:10
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We’re excited to release Muse Spark 1.3 with improved performance on agentic and coding tasks, and a focus on real-world usability.
Key capabilities:
→ Sustains longer-horizon work across multiple workflows in a single thread
→ More actively collaborates with users: it asks http
423.6K views · 3.1K likes
· 295 reposts · 146 replies
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Introducing Muse Voice Transcribe, the first real-time audio perception model from Meta Superintelligence Labs.
Muse Voice Transcribe delivers real-time streaming ASR, diarization with 20+ speakers, and endpointing. It’s multilingual with seamless code-switching and improves htt
112.5K views · 1.5K likes
· 182 reposts · 72 replies
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@AIatMeta 5.6 sol worse that 5.5 on human eval looks very suspicious?
359 views · 2 likes
· 0 reposts · 0 replies
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Here are the generated artifacts of Muse Spark 1.1 and 1.2 on the Plywood Whale: https://t.co/q5dATHjPGZ https://t.co/Qb8vZISu4V
8.2K views · 23 likes
· 2 reposts · 1 replies
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Here are the generated artifacts of Muse Spark 1.1 and 1.2 on the Cat Mesh: https://t.co/loY5g46x1V https://t.co/1O9AaD7urh
11.3K views · 65 likes
· 6 reposts · 4 replies
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We’re sharing some of our design principles for WildArtifactBench: https://t.co/PgFwydsc4s
11.8K views · 55 likes
· 5 reposts · 8 replies
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Today we’re also previewing WildArtifactBench, an internal evaluation framework designed to assess agents on complex, real-world tasks across diverse deliverable formats.
By using win rates and Elo scores from human and agentic preference judges rather than strict ground-truth h
151.6K views · 634 likes
· 59 reposts · 92 replies
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Muse Spark 1.2 supports a broad range of multimodal tasks, from turning visuals into working code to translating perception into physical action. It also brings robust audio-visual understanding to enable video-heavy workflows common in real-world enterprise use.
Today, we’re ht
53.2K views · 528 likes
· 75 reposts · 46 replies
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@AIatMeta I thought open-source contributions were gone for good, it's amazing to be proven wrong by the successor to Llama!
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· 0 reposts · 1 replies
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🔗 Download Muse Glimmer on @huggingface: https://t.co/s7Lzb8MqCG
🔗 Read the technical blog: https://t.co/X6htFnhRbc
🔗 Find resources: https://t.co/Gt3aCdWFwU
83.4K views · 572 likes
· 75 reposts · 13 replies
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For a local agent to be practical, generation latency must be low enough to maintain workflow continuity.
To run Muse Glimmer on consumer hardware without degrading quality, we used quantization to shrink the language model to under 20GB and a lightweight DFlash drafter model to
50.7K views · 457 likes
· 25 reposts · 7 replies
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Muse Glimmer can complete multi-step agentic tasks end-to-end from a single natural language prompt.
In this demo, it autonomously discovers a local Home Assistant instance via network tool calls, queries device APIs, writes a responsive HTML/CSS/JS dashboard from scratch, and h
52.6K views · 428 likes
· 34 reposts · 9 replies
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Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on htt
1.4M views · 8.3K likes
· 1.1K reposts · 381 replies
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@AIatMeta Perfect score on a physics olympiad is impressive, but structured exams are still far from real world research challenges let's see how this translates to actual scientific discovery.
#Cybernorse #MetaAI #ArtificialIntelligence #AIBenchmarks
4 views · 3 likes
· 1 reposts · 0 replies
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@AIatMeta What in the AI generated certificate is that
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@AIatMeta The top three student contestants watching a server rack roll up to the podium to claim its medal. https://t.co/IRjZNWA0gN
2.5K views · 19 likes
· 0 reposts · 0 replies
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To demonstrate Meta AI's advanced reasoning and multimodal capabilities, we submitted a model to participate in the Asian Physics Olympiad’s theoretical exam. We’re happy to share that our model achieved a perfect score of 30/30, tying with the top 3 student contestants.
We http
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· 65 reposts · 47 replies
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@AIatMeta 500 hrs of brain scans to predict what makes us click... zuck's building the ultimate engagement algo from inside our heads. wild times https://t.co/drxYpxUxL9
55.3K views · 1.2K likes
· 29 reposts · 6 replies
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Without any retraining, TRIBE v2 can reliably predict the brain responses of individuals it has never seen before, achieving a nearly 2-3x improvement over previous methods for both movies and audiobooks
We’re releasing the model, codebase, paper, and demo to help researchers h
210.2K views · 1K likes
· 116 reposts · 36 replies
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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
7M views · 16K likes
· 2.5K reposts · 727 replies
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0:29
@AIatMeta @mo_baioumy How much VRAM do those two MacBooks have? 128 GB each?
1.2K views · 4 likes
· 0 reposts · 0 replies
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เทียบกับบัญชีขนาดเดียวกัน
22 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม แสดงต่อคนมากกว่าบัญชีขนาดเดียวกัน.
ยอดดูมัธยฐาน74 332บัญชีนี้5 983ค่ามัธยฐานของ 100K–1M
การเข้าถึง, %8.70%บัญชีนี้1.49%ค่ามัธยฐานของ 100K–1M
การมีส่วนร่วม, %0.78%บัญชีนี้0.93%ค่ามัธยฐานของ 100K–1M
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 100K–1M | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 74 332 | 5 983 | 12.4× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 8.70% | 1.49% | 5.84× |
| อัตราการมีส่วนร่วม | 0.78% | 0.93% | 0.84× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
11.3K20 Aug
8.2K
359
112.5K1 Sep
423.6K2 Sep
329.3K4 Sep
60.1K
316K5 Sep
7.5M8 Sep
553.1K
120.6K
195.5K17 Sep
65.3K
14.4K24 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.68%20 Aug
0.36%
0.56%
1.57%1 Sep
0.87%2 Sep
0.73%4 Sep
1.51%
0.76%5 Sep
0.21%8 Sep
0.69%
1.28%
0.80%17 Sep
0.94%
1.29%24 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes59.6%57 385 in total
Reposts6.9%6 688 in total
Replies3.9%3 799 in total
Quotes4.1%3 911 in total
Bookmarks25.5%24 550 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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