发布节奏
最近 12 周每周中位浏览
最近 12 周发布时间
颜色 = 中位浏览爆款 vs 日常
爆款 = 浏览前 10%| 指标 | 爆款 | 日常 |
|---|---|---|
| 帖子 | 2 | 17 |
| 带媒体 | 100% | 82.4% |
| 回复 | 0% | 0% |
| 平均字数 | 355 | 409 |
| 中位浏览 | 50.4万 | 9,465 |
打法建议
只陈述相关性- 互动最好的时段是 03:00 前后(北京时间),中位浏览 35.4万。
- 一周里 周三 的中位浏览最高。
- 形式上 纯文字 的中位浏览更高(9,873 vs 6.2万)。
- 爆款里带媒体占 100%、回复占 0%;日常分别是 82.4%、0%。
- 当前节奏约 0.3 条/天,日均浏览 10.2万。
内容结构
按本站分析的分类(近 120 天)- 模型6
- 产业5
- 产品4
- 论文3
形式与语言
帖子占比- 视频11
- 图片5
- 纯文字3
回复占比 0% · 引用 21.1% · 带媒体 84.2%
互动最高的帖子
按浏览排序- 1
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://introducing.muse.ai/
- 2
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 clarifying questions, flags when it's stuck, confirms before consequential actions → Better calibrated on its own limits instead of hallucinating outcomes → ~20% fewer tool calls and ~25% fewer tokens vs. Muse Spark 1.2 in internal comparisons
- 3
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 better — all competitors had access to the same information and were graded externally on a private test set. AIRA₃ placed 8th out of ~4,000 teams to win Gold, outperforming human competitors who had access to the same frontier tools. We believe this is a reliable signal that AIRA₃ can improve a targeted capability of an AI model at a level similar to human experts.
- 4
In the past 6 months... - Muse Spark - Muse Image - Muse Video - Muse Spark 1.1 - Meta Model API - Muse Glimmer - Muse Spark 1.2 - Muse Code - Muse Spark 1.3 - Muse ...and we’re just getting started. 🚀
- 5
Today we launched http://muse.ai, 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 Muse to be secure, safe, and private. Read the full deep dive here: http://security.muse.ai/
- 6
@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. 🧵👇 #MetaConnect
- 7
@Muse for Mac, rolling out today. 👇
- 8
Muse Spark 1.3 with max reasoning is now available on Muse Code and Meta Model API 👇
- 9
We’re excited to introduce the Muse Wonder series with Muse Wonder and Muse Wonder Full, our most capable models yet. Both are frontier models, built to deliver state-of-the-art performance for personal and professional agentic tasks. Muse Wonder is available today via the Meta Model API and Muse Code. Muse Wonder Full is available today for Muse Code subscribers. Muse Wonder will also soon power experiences in our personal agent product, Muse, rolling out first to subscribers.
- 10
Muse Wonder and Muse Wonder Full deliver SOTA performance on personal and professional agentic tasks. Learn more about the models on our researcher blog: https://go.meta.me/musewonder
- 11
From rebuilding our foundation to pushing the frontier in 14 months 🚀
- 12
We trained our Muse foundational models, including Muse Wonder, at Prometheus, our gigawatt-scale supercluster in New Albany, Ohio. Across a supercluster with hundreds of thousands of GPUs, even one underperforming host can slow an entire training run. We engineered Prometheus for resilience at scale, achieving 85%+ goodput on pretraining, 90%+ on reinforcement learning and ~47K GPU hours mean time between interruptions during Muse Wonder training. Take a look at the tech powering Prometheus: https://go.meta.me/4yXXApZ
点赞、转发、浏览是采集那一刻的快照,不是帖子最终数据;时间为北京时间。KPI 卡片里的同比只在账号有 8 周以上数据时显示, 否则只看近 4 周。“打法建议”只说明数据里的相关性,不代表因果。
