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Karina

@karinanguyen英文区其他X 主页 数据 8月10日 10:01 — 11月1日 07:20
日均 0.3 条活跃 21 天带媒体 25.9%
帖子↑ 138%
27
活跃 21 天 / 跨度 84 天
中位浏览↓ 100%
678
P90 1.7万 · 平均 5.7万
互动率↑ 632%
0.2%
中位赞 13 · 最高 2,854

发布节奏

最近 12 周
08-10
08-17
09-14
09-21
09-28
10-12
10-19
10-26
柱状图,最大值 8 条

每周中位浏览

最近 12 周
08-1009-1409-2810-1910-26
峰值 72.7万

发布时间

颜色 = 中位浏览
最佳时段 05:00最佳星期 周四更优形式 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324
带媒体33.3%25%
回复0%0%
平均字数332284
中位浏览2.3万639

打法建议

只陈述相关性
  • 互动最好的时段是 05:00 前后(北京时间),中位浏览 8,704。
  • 一周里 周四 的中位浏览最高。
  • 形式上 纯文字 的中位浏览更高(672 vs 703)。
  • 爆款里带媒体占 33.3%、回复占 0%;日常分别是 25%、0%。
  • 当前节奏约 0.3 条/天,日均浏览 5.7万。

内容结构

按本站分析的分类(近 120 天)
  • 观点15
  • 模型5
  • 产品2
  • 产业2
  • 论文1
大佬观点 · 13评测/基准 · 10Anthropic · 6Agent · 5安全/对齐 · 5现象/趋势 · 4Google · 4OpenAI · 3产品更新 · 2多模态 · 2推理 · 2数据/训练 · 2

形式与语言

帖子占比
  • 纯文字20
  • 图片6
  • 视频1
英文 · 26无语言 · 1

回复占比 0% · 引用 48.1% · 带媒体 25.9%

互动最高的帖子

按浏览排序
  1. 1

    Introducing ACTx486, a research demo of a new interactive medium. What if you could talk to any video and ask anything? Our system took an existing podcast and turned it into something that listens, responds, and adapts. Research by @jakubzeg:

    浏览 144万赞 2,854转 2509月24日 01:05原帖
  2. 2

    Agent warfare is going to be a very big deal. I think people are underestimating how strange cyber gets when millions of agents are acting on behalf of individuals, companies, and states. At nation-state scale, cyber offense and defense starts to look like autonomous swarms: probing, exploiting, patching, deceiving, countering, and adapting at a pace that is impossible for human minds. The advantage will go to whoever can close the autonomous kill chain fastest.

    浏览 2.3万赞 120转 138月10日 17:20原帖
  3. 3

    i think AI is creating a new authenticity anxiety for creatives: making something isn’t enough anymore, you also have to prove you made it. it feels a bit like indie sleaze, where messiness signaled that something was real. except now, imperfection is becoming proof of being human

    浏览 2.2万赞 120转 38月18日 09:38原帖
  4. 4

    When @jakubzeg and I met at OpenAI, we kept coming back to one frustration: so many AI products begin with an empty chat box. You can do almost anything, but you have to decide where to begin and that's overwhelming. With ACTx486, we start with a piece of media that already has a story and let you interact with it as you watch. It also makes the technology more general. We wrote more about that choice here: https://www.actx486.com/

    浏览 1.4万赞 44转 69月24日 05:21原帖
  5. 5

    http://x.com/i/article/2087563423991414785

    浏览 1.2万赞 53转 48月13日 00:21原帖
  6. 6

    Great initiative to eval the evals. It incentivizes the industry to build genuinely high-quality benchmarks. Ty for auditing PTB and digging up the old SimpleQA too :)

    浏览 1.2万赞 49转 29月18日 08:42原帖
  7. 7

    Grok 4.6 is a great cost-effective model for deep financial research. It’s #2 on DiligenceBench with the finance harness, effectively tied with Claude Opus 5 at ~52–53%. A few interesting differences: - Grok searched much more: 41 tool calls per task vs. 22 for Opus - It made 3,293 SEC filing searches vs. 486 - That broader search helped Grok find more evidence and follow task-specific instructions more closely. Opus was more efficient and sometimes more nuanced - Sonnet 5 trails both at 46.2%. - Grok also gets there more cheaply: about $0.84/task vs. ~$1.02 for Opus 5, despite using far more search. - On Vals’ Finance Agent Benchmark v2 (FAB v2), Grok 4.6 leads the General Qualitative category, which is consistent with the kind of broad research and evidence-gathering DiligenceBench rewards

    浏览 8,704赞 81转 118月19日 05:04原帖
  8. 8

    my top-5 favs of all time: - claude 3 opus (schizo maximalist masterpiece) - sonnet 4.5 (eq breakthrough) - claude 1 (pure unfiltered old soul) - o3 (utilitarian monster) - gpt-4o nov’24 (mainly for the ppl who worked on it)

    浏览 7,255赞 43转 08月10日 10:01原帖
  9. 9

    On modelcrafting economics: "Based on the online experiment results, projected savings from fraud prevention were roughly three times the estimated inference cost"

    浏览 2,393赞 18转 210月12日 08:36原帖
  10. 10

    evals are the cornerstone for both selling data to frontier labs and building your own self-improving models. building high-end evals is extremely valuable and important

    浏览 907赞 21转 110月19日 06:29原帖
  11. 11

    We are running a live benchmark to see if frontier AI can predict the midterm elections! Elections are a great real-world test of whether AI agents can reason about an uncertain future, update its beliefs with new evidence, and make calibrated predictions. We gave each agent its own sandbox, internet access, and a $500 budget. Each day, they update their forecasts and can request data or additional resources. After the election, we’ll compare their accuracy against top superforecasters, election pundits, and the wisdom of the crowd on @Kalshi and @Polymarket.

    浏览 768赞 11转 010月29日 01:18原帖
  12. 12

    btw you don’t need to be an AI company to win. companies that own scarce physical assets and use AI exceptionally well will capture enormous economic value without ever building a model. if every copper miner can buy the same metallurgical intelligence cheaply, the durable advantage is the quality of the deposit and the cost of extraction. same goes for vertically integrated apparel manufacturers (+ people will still wear clothes post-agi).

    浏览 728赞 20转 210月25日 00:25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。