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AI Breakfast

@AiBreakfast英文区其他X 主页 数据 10月1日 00:40 — 11月3日 05:34
日均 1.2 条活跃 23 天带媒体 40.5%

粉丝与涨粉

快照 11月3日 · 7 日榜 272 个账号可排
粉丝
24.3万
7 日涨粉
+223
+0.1% · 发帖 14 条
7 日名次
#163
中文区 —
30 日涨粉
—
快照还不够 30 天
10-09每日涨粉(16 次快照)11-03

本期浏览最高的帖子

  1. 1

    Watch how fast the Dessa speech model is 🤯

    浏览 1,871赞 2转 011月3日 05:04原帖
  2. 2

    The moat won't be the base model. It'll be the model you post-train on your own production data. Trajectory makes that self-serve, and NVIDIA, Harvey, Rogo, and Mercor are already using it.

    浏览 1,793赞 3转 011月3日 05:34原帖
  3. 3

    “You’re absolutely right to catch that — the cerebellum is indeed not considered disposable tissue.”

    浏览 1,670赞 7转 010月30日 03:19原帖
  4. 4

    Unpopular opinion: training a voice model to listen and not interrupt as soon as it has a response is as difficult as making it sound natural.

    浏览 1,586赞 4转 010月30日 03:24原帖
  5. 5

    Brain surgery on open source models. Neuron steering (or more specifically, MLP neuron steering/activation steering of individual MLP neurons) is an inference-time technique (weights don’t change) that injects a constant offset into the activation of one or more specific MLP neurons at the input to the down-projection on every token. Probably nothing.

    浏览 1,533赞 0转 010月29日 11:51原帖

本周涨粉观察

全部观察

每周一生成;中文区、7 日涨粉超过 1000 的账号会有条目。

帖子
42
活跃 23 天 / 跨度 35 天
中位浏览
1,359
P90 7,911 · 平均 2,457
互动率
0.3%
中位赞 4 · 最高 44

发布节奏

最近 12 周
09-28
10-05
10-12
10-19
10-26
11-02
柱状图,最大值 12 条

每周中位浏览

最近 12 周
09-2810-0510-1210-1910-2611-02
峰值 8,197

发布时间

颜色 = 中位浏览
最佳时段 21:00最佳星期 周三更优形式 纯文字
036912151821
一
二
三
四
五
六
日
少多

爆款 vs 日常

爆款 = 浏览前 10%
指标爆款日常
帖子537
带媒体20%43.2%
回复0%0%
平均字数105180
中位浏览8,2451,293

打法建议

只陈述相关性
  • 互动最好的时段是 21:00 前后(北京时间),中位浏览 7,296。
  • 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(1,284 vs 1,494)。
  • 爆款里带媒体占 20%、回复占 0%;日常分别是 43.2%、0%。
  • 当前节奏约 1.2 条/天,日均浏览 2,457。

内容结构

按本站分析的分类(近 120 天)

还没有分类分析。

形式与语言

帖子占比
  • 纯文字25
  • 视频10
  • 图片7
英文 · 40无语言 · 2

回复占比 0% · 引用 57.1% · 带媒体 40.5%

互动最高的帖子

按浏览排序
  1. 1

    This is endlessly fun! 100x better than just using an image editor for room remodels:

    浏览 9,225赞 21转 010月3日 02:09原帖
  2. 2

    Wow - OpenAI is testing image-based ads that will show up during ChatGPT image generation. https://openai.com/index/new-chatgpt-ads-format-and-measurement/

    浏览 8,755赞 14转 210月5日 21:14原帖
  3. 3

    People forget o1-pro launched in March 2025 at $150/$600 per million tokens. By the end of 2027, we’ll have leaderboard frontiers models in the $0.40-0.80 range.

    浏览 8,245赞 44转 210月4日 20:54原帖
  4. 4

    Internal benchmarking:

    浏览 8,149赞 22转 210月1日 05:00原帖
  5. 5

    It may have seemed dumb at first, but you have to admit Superintelligence has a nice ring to it.

    浏览 7,979赞 20转 010月1日 00:40原帖
  6. 6

    Why would you use any other @bot? Incredible

    浏览 7,296赞 17转 010月7日 21:07原帖
  7. 7

    This is how the “idea guys” get their app out of localhost Claude should have made this months ago!

    浏览 6,964赞 13转 110月7日 23:03原帖
  8. 8

    My worst deck problem: an old PowerPoint I can't bear to rebuild. Gamma 5 is out today from @MeetGamma, and it imports and exports PowerPoint with formatting intact. One import, one instruction, one export. Check out the Launch video 👇

    浏览 6,332赞 8转 210月6日 23:49原帖
  9. 9

    Nobody talks about this enough: You can reverse engineer most enterprise SaaS with frontier models for <$500 in compute

    浏览 2,193赞 10转 010月26日 02:37原帖
  10. 10

    Watch how fast the Dessa speech model is 🤯

    浏览 1,871赞 2转 011月3日 05:04原帖
  11. 11

    The moat won't be the base model. It'll be the model you post-train on your own production data. Trajectory makes that self-serve, and NVIDIA, Harvey, Rogo, and Mercor are already using it.

    浏览 1,793赞 3转 011月3日 05:34原帖
  12. 12

    Nearly one million hours of egocentric human data: hands, scenes, and how each interaction unfolds. Robotics has long been bottlenecked by teleoperation data. @naturewill takes a different approach by learning from how people use their hands to interact with the real world. Introducing Natura-1, a general foundation model for the physical world. By learning from real-world human interactions, Natura-1 aims to help robots develop more general-purpose capabilities.

    浏览 1,782赞 6转 110月13日 21:28原帖

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。