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3.67M views
Today is the start of a new era of natively multimodal AI innovation.
Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality.
Llama 4 Scout
• 17B-active-parameter model
2.27M views
🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date.
Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potential of this line of research to usher in
📊 Post performance
🔥 Top post: Introducing Aria Gen 2, next generation glasses that we hope wil · 3.77M views
📊 Activity & format
Recent posts
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We’re excited to introduce Muse Spark 1.1, a significant upgrade from the first Muse Spark model we released earlier this year.
Along with this release, we are launching a public preview of the new Meta Model API where developers can acce…
Today is the start of a new era of natively multimodal AI innovation.
Today, we’re introducing the first Llama 4 models: Llama 4 Scout and Llama 4 Maverick — our most advanced models yet and the best in their class for multimodality.
Ll…
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🎥 Today we’re premiering Meta Movie Gen: the most advanced media foundation models to-date.
Developed by AI research teams at Meta, Movie Gen delivers state-of-the-art results across a range of capabilities. We’re excited for the potentia…
📣 Introducing Llama 3.2: Lightweight models for edge devices, vision models and more!
What’s new?
• Llama 3.2 1B & 3B models deliver state-of-the-art capabilities for their class for several on-device use cases — with support for @Arm…
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Starting today, open source is leading the way. Introducing Llama 3.1: Our most capable models yet.
Today we’re releasing a collection of new Llama 3.1 models including our long awaited 405B. These models deliver improved reasoning capabi…
Today we’re announcing Meta LLM Compiler, a family of models built on Meta Code Llama with additional code optimization and compiler capabilities. These models can emulate the compiler, predict optimal passes for code size, and disassemble…
💡 Facts
🕵️ Fake follower check
Estimated- Est. 85% real, active audience · Low fake-follower risk.
- Engagement (~1.4× of followers engage each post) is around typical for X.
- Organic base — far more followers than accounts it follows.
- Verified account.
- Established account (7+ years old).
- 0.87% engagement — below the ~14.4% typical for this size.
Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
📸 Gallery
🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
Matthew Mercer
18%
TAPI岡🧋フリーランスイラストレーター
18%
Vivek Ranjan Agnihotri
16%
Suresh Prabhu
16%
西野七瀬&STAFF
13%
fitzy
13%
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