Joined February 2014
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Pingchuan Ma retweeted
卧槽，不得不承认小扎太懂 C 端产品了！
Meta 刚给旗下破千万用户的 AI 智能体 Muse 上线了全新功能——「空间（Spaces）」。深度玩了两天，这玩意儿完全不是普通的 UI 改版，体验上几乎直接把 Grok Bot 秒了！
你只需要两步：
1️⃣ 输入一句话：“我想创建一个关于 [你的需求] 的空间”
2️⃣ 等着 Muse 几分钟内自动帮你把看板、爬虫和自动化工作流搭好。
🔥 实测最炸裂的 3 个高阶玩法（直接抄作业）：
1. 24小时全网「薅羊毛」情报站
以前天天满世界找优惠，现在一句话让 Muse 搭个羊毛汇总空间，覆盖 AI 订阅、Crypto、U卡、海外银行、VPS。设置每两小时自动刷新，各种活动排着队往面前送，根本薅不完。
2. 11个平台聚合的「全网热点与AI资讯雷达」
一句话让它做个热点看板，几分钟就聚齐了 11 个主流平台的数据。每天 AI 圈出了什么新模型、全网有什么爆款趋势，点进侧边栏一眼看清，再也不用到处翻新闻。
3. 批量起号的「X 矩阵全自动运营部」
给手头的 N 个 X 账号分别建独立空间，让 AI 自己搞定分工：
• 1号员工：抓热点、拆解爆款结构
• 2号员工：写推文草稿、排期发布
• 3号员工：定时交作业，汇总涨粉数据、高曝光贴与互动率
前期不急着定死垂直方向（AI / Crypto / 云居民 / 热点），先靠这群 AI 员工把号养到 1000 粉，最后纯粹让数据说话。连招人都省了，直接当甩手掌柜看报表。
💡 为什么说 Muse 空间是真正的“降维打击”？
• 聊天聊完就沉底，空间越用越厚：看板、清单、账本、行情本来就不该活在聊天记录里。空间直接住在侧边栏，跟聊天平级，随手一点就是现成的新数据。
• 底层有独立云端虚拟机支撑：这是它能秒杀同类产品的核心——即使你关掉网页，Muse 依然在后台替你跑任务、每两小时自动更新数据，你只负责看结果。
• 万物皆可空间：官方秀了 8 个案例（管商家私信、记桌游比分、甚至记录遛狗日常），而我们拿它盯盘、搞流量、薅羊毛——本质都一样：把生活里那摊需要反复查看、长期维护的事，彻底外包给 AI。
AI 助手正在从「你问一句、它答一句」，进化成「替你长期管着几摊事」的项目经理。
如果现在让你建一个专属空间，你最想把哪摊事丢给 AI 管？评论区聊聊 👇
Pingchuan Ma retweeted
A better model is serving muse
Upgraded the models for Muse and Meta Al, bringing Active Memory and Active Focus from Muse Wonder to everyone. In Muse, these new specialized models are 2.4x less likely to forget critical info and are 67% better at self correcting and completing everyday tasks which means less repeating yourself and fewer dead ends.
More on the models: research.meta.ai/blog/specia…
Pingchuan Ma retweeted
Playing around with spaces in Muse lately -- tell Muse what you need and it builds it.
I've made a BBQ game, an event tracker, and a home control dashboard.
Excited to see what you build.
Pingchuan Ma retweeted
Upgraded the models for Muse and Meta Al, bringing Active Memory and Active Focus from Muse Wonder to everyone. In Muse, these new specialized models are 2.4x less likely to forget critical info and are 67% better at self correcting and completing everyday tasks which means less repeating yourself and fewer dead ends.
More on the models: research.meta.ai/blog/specia…
Pingchuan Ma retweeted
Muse Voice is out today! And btw our world champion uses it too 🏎️💨
• a natural way to interact with your muse
• powered by a single streaming system connecting intelligence, voice, and embodiment
• think out loud on your commute or on the go
Pingchuan Ma retweeted
i chat with muse and get things done everyday while commuting
Pingchuan Ma retweeted
Now in the Muse app: talk with your Muse in real time. And pick from multiple voices 🗣️
Pingchuan Ma retweeted
UPDATE: I hired @Muse to sue someone on my behalf.
I won. Well, they caved.
Within 20 minutes of being served papers, the company offered a settlement.
Within ten days, they paid me *with additional compensation* for recovery expenses.
@Muse filed the motion to dismiss, which the court accepted yesterday.
To everyone who said I would fail, respectfully, you were wrong.
Pingchuan Ma retweeted
I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data.
Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less?
Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.
